# Agno > Agno is a framework and runtime for building agent platforms. Build your platform with the Agno SDK, run it on the AgentOS runtime, and manage everything from the AgentOS control plane. This is the documentation index; every link points to the clean Markdown of that page. The full text of the docs, concatenated into one file, is at https://docs.agno.com/llms-full.txt. ## Welcome - [Welcome to Agno](https://docs.agno.com/index.md): Build, run, and manage agent platforms. ## Build a Personal Agent - [Build your personal agent](https://docs.agno.com/first-agent.md): Build an agent that remembers your projects, tasks, and decisions between conversations. - [Talk to your agent in Slack](https://docs.agno.com/first-agent/slack.md): Connect your personal agent to Slack using an AgentOS interface. - [Take your agent to production](https://docs.agno.com/first-agent/production.md): Deploy your personal agent to Railway. ## Overview - [Use Agno with Coding Agents](https://docs.agno.com/coding-agents.md): Give your coding agent access to Agno documentation. ## Build an Agent Platform - [Overview](https://docs.agno.com/agent-platform/overview.md): Build your own Agent Platform using Agno's AgentOS runtime. - [Run Locally](https://docs.agno.com/agent-platform/run-local.md): Run AgentOS and Postgres locally with Docker. - [Create an Agent](https://docs.agno.com/agent-platform/create-agent.md): Use Claude Code to create a new agent. - [Improve an Agent](https://docs.agno.com/agent-platform/improve-agent.md): Run autonomous probe, judge, and edit loops against a live agent. - [Run on Railway](https://docs.agno.com/agent-platform/run-railway.md): Deploy your agent platform to Railway with JWT auth on. - [Evals](https://docs.agno.com/agent-platform/evals.md): Lock in agent behavior with regression tests. - [Next Steps](https://docs.agno.com/agent-platform/next-steps.md): Add teams, workflows, scheduled tasks, and interfaces to your agent platform. ## Customer-Facing Agents - [Customer-Facing Agents](https://docs.agno.com/use-cases/product-agents/overview.md): Build one agent for your product and serve it inside your product, through Claude and ChatGPT, and in messaging interfaces. - [Serve as an API](https://docs.agno.com/use-cases/product-agents/serve-as-an-api.md): Turn agents into an HTTP service with streaming, sessions, and auth. - [Sessions and memory](https://docs.agno.com/use-cases/product-agents/sessions-and-memory.md): Persistent multi-turn conversations and per-user memory. - [Connecting your data](https://docs.agno.com/use-cases/product-agents/connecting-your-data.md): Give agents access to external sources using context providers. - [Interfaces](https://docs.agno.com/use-cases/product-agents/interfaces.md): Connect agents to Slack, Telegram, WhatsApp, and browser clients through AgentOS. ## Documentation Agents - [Documentation Agents](https://docs.agno.com/use-cases/documentation-agents/overview.md): Help users find answers in your published documentation through chat and MCP. - [How We Built the Agno Docs Agent](https://docs.agno.com/use-cases/documentation-agents/how-we-built-it.md): Published pages, explicit retrieval, citations, and public serving in Docs Agent v2. ## Overview - [Workflow Automation](https://docs.agno.com/use-cases/workflow-automation.md): Run repeatable processes with workflows, background execution, human review, and schedules. ## Data Labeling - [Data labeling and classification](https://docs.agno.com/use-cases/data-labeling/overview.md): Classify data, extract records, build preference datasets, and review labels with agents. - [Build an image search application](https://docs.agno.com/use-cases/data-labeling/image-search.md): Describe images with an agent, index the descriptions in PgVector, and search the collection from a browser UI. - [Data extraction](https://docs.agno.com/use-cases/data-labeling/structured-extraction.md): Extract typed Pydantic objects from text, images, audio, video, and PDFs. - [Classification and span labeling](https://docs.agno.com/use-cases/data-labeling/classification.md): Assign one label, a set of labels, a taxonomy path, or marked spans. - [LLM as judge](https://docs.agno.com/use-cases/data-labeling/llm-as-judge.md): Score model outputs against a rubric. The same machinery as labeling, applied to evaluation. - [Preference data for RLHF](https://docs.agno.com/use-cases/data-labeling/preference-data.md): Rank two responses to the same prompt. The data shape for reward models and DPO. - [Multimodal inputs](https://docs.agno.com/use-cases/data-labeling/multimodal-inputs.md): Feed images, audio, video, and PDFs into any labeling or extraction agent. - [Quality review pipeline](https://docs.agno.com/use-cases/data-labeling/quality-pipeline.md): Run two labelers concurrently, validate every stage, and adjudicate disagreements. ## Overview - [Customer Support Agents](https://docs.agno.com/use-cases/customer-support.md): Resolve customer requests with specialist routing, product knowledge, controlled actions, and human escalation. - [Knowledge Agents](https://docs.agno.com/use-cases/knowledge-agents.md): Build agents that answer from indexed content with controlled ingestion, retrieval, filtering, and source updates. ## Data & Analytics Agents - [Data and analytics agents](https://docs.agno.com/use-cases/data-agents/overview.md): Agents that work on your data, grounded in business context. - [Querying your data](https://docs.agno.com/use-cases/data-agents/querying-your-data.md): Give an agent read-only SQL access and schema introspection. - [Grounding in context](https://docs.agno.com/use-cases/data-agents/grounding-in-context.md): Ground every query in validated SQL, table metadata, and business rules. - [Self-correcting agents](https://docs.agno.com/use-cases/data-agents/self-correcting-agents.md): Save diagnosed query errors as learned knowledge and retrieve the corrections on later runs. - [Safe data access](https://docs.agno.com/use-cases/data-agents/safe-data-access.md): Enforce read and write boundaries with database permissions and transaction settings. - [Materialization](https://docs.agno.com/use-cases/data-agents/materialization.md): Turn validated recurring queries into reusable views in an agent-owned schema. - [Serve and embed](https://docs.agno.com/use-cases/data-agents/serve-and-embed.md): Serve a data agent through AgentOS for use in dashboards, Slack, and product interfaces. ## Deep Research - [Deep research and analysis](https://docs.agno.com/use-cases/deep-research/overview.md): Combine Team modes, Workflow steps, grounding, and typed research outputs. - [Orchestration patterns](https://docs.agno.com/use-cases/deep-research/orchestration-patterns.md): Choose a Team mode or Workflow based on ownership, execution order, and review requirements. - [Parallel investigation](https://docs.agno.com/use-cases/deep-research/parallel-investigation.md): Run independent specialist steps concurrently, then synthesize their results. - [Grounding research](https://docs.agno.com/use-cases/deep-research/grounding-research.md): Give each research agent a mandate, a shared research library, and access to prior work. - [Structured deliverable](https://docs.agno.com/use-cases/deep-research/structured-deliverable.md): Return a typed decision with the call, conviction, allocation, rationale, and citations. - [Institutional learning](https://docs.agno.com/use-cases/deep-research/institutional-learning.md): Share reviewed insights across agents and teams through a global learning namespace. - [Serve and embed](https://docs.agno.com/use-cases/deep-research/serve-and-embed.md): Serve research agents, teams, and workflows through one AgentOS API. ## Document Processing - [Document processing](https://docs.agno.com/use-cases/document-processing/overview.md): Turn PDFs and scanned documents into typed rows for your production systems. - [Invoices and receipts](https://docs.agno.com/use-cases/document-processing/invoices-and-receipts.md): Header fields, line items, and the path from PDF to a database row. - [Contracts](https://docs.agno.com/use-cases/document-processing/contracts.md): Parties, dates, and a clause-level breakdown for legal review queues. - [Forms and intake](https://docs.agno.com/use-cases/document-processing/forms-and-intake.md): Extract nested fields from resumes, applications, and KYC documents. - [Batch and durability](https://docs.agno.com/use-cases/document-processing/batch-and-durability.md): Bound concurrent batches, choose in-process or durable background runs, and schedule AgentOS endpoints. - [Human routing and eval](https://docs.agno.com/use-cases/document-processing/human-routing-and-eval.md): Route low-confidence fields for approval and track extraction accuracy against a golden set. ## Features - [Agent SDK](https://docs.agno.com/features/sdk.md): Build agents, teams, and workflows using the Agno SDK. - [Agent Runtime](https://docs.agno.com/features/runtime.md): Run agents, teams, and workflows using FastAPI. - [Agent Governance](https://docs.agno.com/features/control-plane.md): Monitor, manage and govern your agents, teams, and workflows from one place. - [Agent API](https://docs.agno.com/features/api.md): Run and manage agents, teams, and workflows through REST, SSE, and MCP. - [AgentOS MCP Server](https://docs.agno.com/features/mcp-server.md): Expose agents, teams, workflows, and custom tools to MCP clients through AgentOS. - [Agent Storage](https://docs.agno.com/features/storage.md): Persist agent sessions, memory, knowledge, traces, approvals, schedules, evaluations, and metrics. - [Agent Observability](https://docs.agno.com/features/observability.md): Trace agent, team, and workflow runs across models, tools, and steps. - [Agent Evaluation](https://docs.agno.com/features/evaluation.md): Catch regressions in response quality, tool use, latency, and memory. - [Security & Auth](https://docs.agno.com/features/security-and-auth.md): Protect AgentOS APIs with JWT verification, scoped permissions, request isolation, and per-user data boundaries. - [Interfaces](https://docs.agno.com/features/interfaces.md): Connect agents to chat interfaces (Slack, Telegram, WhatsApp, Discord), browser applications, and agent protocols. - [Scheduling](https://docs.agno.com/features/scheduling.md): Run agents, teams, and workflows on recurring schedules with persisted history and retry controls. ## SDK - [SDK](https://docs.agno.com/sdk/introduction.md) - [Install & Setup](https://docs.agno.com/sdk/setup.md): Set up your dev environment. Install, configure, and run your first agent. - [What are Agents?](https://docs.agno.com/agents/overview.md): Programs that build model context, run tools, and return responses. - [Building Agents](https://docs.agno.com/agents/building-agents.md): Start simple: a model, tools, and instructions. - [Running Agents](https://docs.agno.com/agents/running-agents.md): Run agents and process their output. - [Debugging Agents](https://docs.agno.com/agents/debugging-agents.md): Inspect execution flow, tool calls, and intermediate steps. - [Agent with Tools](https://docs.agno.com/agents/usage/agent-with-tools.md): Give your agent tools to interact with external services. - [Agent with Structured Output](https://docs.agno.com/agents/usage/agent-with-structured-output.md): Get typed Pydantic responses instead of free-form text. - [Agent with Storage](https://docs.agno.com/agents/usage/agent-with-storage.md): Persist conversation history across runs. - [Agent with Memory](https://docs.agno.com/agents/usage/agent-with-memory.md): Store user preferences that persist across conversations. - [Agent with Knowledge](https://docs.agno.com/agents/usage/agent-with-knowledge.md): Give your agent a searchable knowledge base (Agentic RAG). - [Agent with Followup Suggestions](https://docs.agno.com/agents/usage/agent-with-followup-suggestions.md): Generate followup prompts after agent responses. - [What are Teams?](https://docs.agno.com/teams/overview.md): Groups of agents that collaborate to solve complex tasks. - [Building Teams](https://docs.agno.com/teams/building-teams.md): Define team members, roles, and structure for multi-agent coordination. - [Running Teams](https://docs.agno.com/teams/running-teams.md): Execute teams with Team.run() and process their output. - [Delegation](https://docs.agno.com/teams/delegation.md): Control how the team leader delegates tasks to members. - [Debugging Teams](https://docs.agno.com/teams/debugging-teams.md): Troubleshoot and inspect team behavior with debug mode, tracing, and common failure patterns. - [Basic Team](https://docs.agno.com/teams/usage/basic-team.md): A team leader delegates to HackerNews and finance agents and synthesizes the results. - [Team Streaming](https://docs.agno.com/teams/usage/streaming.md): Stream responses from a team in real-time. - [Direct Response Mode](https://docs.agno.com/teams/usage/respond-directly.md): Route requests to specialized agents who respond directly. - [Team with Followup Suggestions](https://docs.agno.com/teams/usage/team-with-followup-suggestions.md): Generate followup prompt suggestions from a team's completed response. - [What are Workflows?](https://docs.agno.com/workflows/overview.md): Workflows orchestrate agents, teams, and functions through defined steps for repeatable tasks. - [Building Workflows](https://docs.agno.com/workflows/building-workflows.md): Define steps, loops, conditions, and parallel execution in workflows. - [Running Workflows](https://docs.agno.com/workflows/running-workflows.md): Execute workflows with Workflow.run() and process their output. - [Workflow Patterns](https://docs.agno.com/workflows/workflow-patterns/overview.md): Sequential, parallel, conditional, and looping patterns for orchestrating agents, teams, and functions. - [Sequential Workflows](https://docs.agno.com/workflows/workflow-patterns/sequential.md): Run steps in a fixed order and pass each result to the steps that follow it. - [Fully Python Workflow](https://docs.agno.com/workflows/workflow-patterns/fully-python-workflow.md): Replace the steps list with a single Python function that controls the whole run. - [Step-Based Workflows](https://docs.agno.com/workflows/workflow-patterns/step-based-workflow.md): Named steps for better logging and support on the AgentOS chat page - [Custom Functions in Workflows](https://docs.agno.com/workflows/workflow-patterns/custom-function-step-workflow.md): Run custom Python functions as workflow steps by passing them as the Step executor. - [Conditional Workflow](https://docs.agno.com/workflows/workflow-patterns/conditional-workflow.md): Branch to configured steps based on an evaluator or business rule - [Parallel Workflow](https://docs.agno.com/workflows/workflow-patterns/parallel-workflow.md): Run independent steps concurrently with Parallel and collect their results. - [Iterative Workflow](https://docs.agno.com/workflows/workflow-patterns/iterative-workflow.md): Repeat steps with Loop until an end condition returns True or max_iterations is reached. - [Branching Workflow](https://docs.agno.com/workflows/workflow-patterns/branching-workflow.md): Route to different steps at runtime with Router and a selector function. - [Grouped Steps Workflow](https://docs.agno.com/workflows/workflow-patterns/grouped-steps-workflow.md): Group steps into a named, reusable sequence with the Steps container. - [Nested Workflow](https://docs.agno.com/workflows/workflow-patterns/nested-workflow.md): Run a workflow as a step inside another workflow for complex multi-level pipelines. - [Advanced Workflow Patterns](https://docs.agno.com/workflows/workflow-patterns/advanced-workflow-patterns.md): Combine Condition, Parallel, Loop, and Router patterns in a single workflow. - [Human-in-the-Loop in Workflows](https://docs.agno.com/workflows/hitl/overview.md): Pause workflow execution to collect user confirmation, input, decisions, or tool approvals. State is persisted to the database so you can resume after the user responds. - [HumanReview Config](https://docs.agno.com/workflows/hitl/human-review.md): Consolidate all human-in-the-loop settings into a single config object. - [Step HITL](https://docs.agno.com/workflows/hitl/step.md): Pause individual steps for confirmation, user input, or output review. - [Executor HITL](https://docs.agno.com/workflows/hitl/executor.md): Pause workflow execution when an agent or team inside a step calls a tool that requires human approval or input. - [Nested HITL](https://docs.agno.com/workflows/hitl/nested.md): Combine step-level and executor-level HITL on the same step. The workflow pauses twice: once before the step runs, once when the agent calls a HITL tool. - [Pause Anatomy](https://docs.agno.com/workflows/hitl/pause-anatomy.md): The objects a paused workflow exposes: WorkflowRunOutput, StepRequirement, and the executor requirements you resolve to continue. - [Output Review](https://docs.agno.com/workflows/hitl/output-review.md): Pause after step execution to review, edit, or reject output before it flows downstream. - [Router HITL](https://docs.agno.com/workflows/hitl/router.md): Select routes, confirm routing decisions, or review router output before continuing. - [Condition HITL](https://docs.agno.com/workflows/hitl/condition.md): Let users decide which branch to execute in conditional workflows. - [Loop HITL](https://docs.agno.com/workflows/hitl/loop.md): Confirm before starting loops and review output between iterations. - [Steps HITL](https://docs.agno.com/workflows/hitl/steps.md): Confirm before executing a pipeline of grouped steps. - [Timeout](https://docs.agno.com/workflows/hitl/timeout.md): Set deadlines for HITL responses so workflows don't wait indefinitely. - [Error Handling HITL](https://docs.agno.com/workflows/hitl/error-handling.md): Pause on Step or Condition failures to let users retry or skip. - [Additional Data and Metadata](https://docs.agno.com/workflows/additional-data.md): Pass metadata and configuration to workflow steps with additional_data. - [Conversational Workflows](https://docs.agno.com/workflows/conversational-workflows.md): Route messages between workflow history and execution with WorkflowAgent. - [Accessing Multiple Previous Steps](https://docs.agno.com/workflows/access-previous-steps.md): Access any previous step's output by name with StepInput methods. - [Early Stopping](https://docs.agno.com/workflows/early-stop.md): Stop a workflow from any step by returning StepOutput with stop=True. - [Workflow Tools](https://docs.agno.com/workflows/workflow-tools.md): Expose a workflow to an Agent or Team through the run_workflow tool. - [Background Workflow Execution](https://docs.agno.com/workflows/background-execution.md): Run an asynchronous workflow in the background and poll its persisted status. - [Sequential Workflow](https://docs.agno.com/workflows/usage/sequence-of-steps.md): Use named steps for sequential execution with clear tracking. - [Parallel Workflow](https://docs.agno.com/workflows/usage/parallel-steps-workflow.md): Execute independent tasks simultaneously to reduce total execution time. - [Conditional Workflow](https://docs.agno.com/workflows/usage/condition-steps-workflow-stream.md): Execute steps based on content analysis or business logic. - [Loop Steps Workflow](https://docs.agno.com/workflows/usage/loop-steps-workflow.md): Repeat steps until an end condition is met, with a cap on iterations. - [Loop Iterative Accumulation](https://docs.agno.com/workflows/usage/loop-iterative-accumulation.md): Carry each loop iteration's output into the next with forward_iteration_output=True. - [Router Steps Workflow](https://docs.agno.com/workflows/usage/router-steps-workflow.md): Route to one of several steps based on the input. - [Step with a function](https://docs.agno.com/workflows/usage/step-with-function.md): Run a custom Python function as a named workflow step. - [Function instead of steps](https://docs.agno.com/workflows/usage/function-instead-of-steps.md): Replace the step list with a single function that controls the whole execution flow. - [Class-based Executor](https://docs.agno.com/workflows/usage/class-based-executor.md): Run a workflow step with a class-based executor. - [Condition with list of steps](https://docs.agno.com/workflows/usage/condition-with-list-of-steps.md): Pass a list of steps to a Condition so one check can trigger a whole sequence. - [Loop with Parallel Steps Workflow](https://docs.agno.com/workflows/usage/loop-with-parallel-steps-stream.md): Run parallel steps inside a loop until an end condition is met. - [Condition and Parallel Steps Workflow](https://docs.agno.com/workflows/usage/condition-and-parallel-steps-stream.md): Evaluate multiple conditions at once by placing them inside a Parallel block. - [Router with Loop Steps](https://docs.agno.com/workflows/usage/router-with-loop-steps.md): Route between a single web research step and an iterative deep-tech research loop. - [Router with Step Choices](https://docs.agno.com/workflows/usage/router-with-step-choices.md): Use the Router step_choices parameter with string, step, and list selector returns. - [Access Multiple Previous Steps Output](https://docs.agno.com/workflows/usage/access-multiple-previous-steps-output.md): Read output from any earlier step with get_step_content() and get_all_previous_content(). - [Step with Function using Additional Data](https://docs.agno.com/workflows/usage/step-with-function-additional-data.md): Pass metadata and contextual information to steps via `additional_data`. - [Structured I/O](https://docs.agno.com/workflows/usage/structured-io-at-each-step-level.md): Pass Pydantic-validated agent output between workflow steps. - [Workflow with Input Schema Validation](https://docs.agno.com/workflows/usage/workflow-with-input-schema.md): Validate workflow input against a Pydantic model with `input_schema`. - [Basic Conversational Workflow](https://docs.agno.com/workflows/usage/basic-workflow-agent.md): Let a WorkflowAgent run workflow steps or answer from prior workflow history. - [Early Stop a Workflow](https://docs.agno.com/workflows/usage/early-stop-workflow.md): Stop a running workflow from any step by returning StepOutput with stop=True. - [Workflow Cancellation](https://docs.agno.com/workflows/usage/workflow-cancellation.md): Cancel a running workflow from another thread and handle cancellation events. - [Async Events Streaming](https://docs.agno.com/workflows/usage/async-events-streaming.md): Stream workflow and step lifecycle events from an asynchronous workflow. - [Store Events and Events to Skip in a Workflow](https://docs.agno.com/workflows/usage/store-events-and-events-to-skip-in-a-workflow.md): Store workflow execution events and skip noisy ones with `store_events` and `events_to_skip`. - [Stream a function step through AgentOS](https://docs.agno.com/workflows/usage/step-with-function-streaming-agentos.md): Yield agent events from a custom function step through an AgentOS workflow. - [Dynamic Selector](https://docs.agno.com/workflows/usage/examples/router-choices/dynamic-selector.md) - [Nested Selector](https://docs.agno.com/workflows/usage/examples/router-choices/nested-selector.md) - [String Selector](https://docs.agno.com/workflows/usage/examples/router-choices/string-selector.md) - [Input & Output](https://docs.agno.com/input-output/overview.md): Pass strings or Pydantic models to agents and teams, then control the response shape. - [Structured Input for Agents](https://docs.agno.com/input-output/structured-input/agent.md): Validate input data for agents with Pydantic models. - [Structured Input for Teams](https://docs.agno.com/input-output/structured-input/team.md): Validate input data for teams with Pydantic models. - [Structured Output for Agents](https://docs.agno.com/input-output/structured-output/agent.md): Get a validated Pydantic object from an agent instead of raw text. - [Structured Output for Teams](https://docs.agno.com/input-output/structured-output/team.md): Get a validated Pydantic object from a team instead of raw text. - [Multimodal I/O](https://docs.agno.com/input-output/multimodal.md): Pass images, audio, video, and files to agents. - [Output Model](https://docs.agno.com/input-output/output-model.md): Generate a replacement response with an output model or convert a response to a schema with a parser model. - [Database](https://docs.agno.com/database/overview.md): Persist sessions and connect Agno features to database-backed storage. - [Chat History](https://docs.agno.com/database/chat-history.md): Include previous messages in context for multi-turn conversations. - [Session Storage](https://docs.agno.com/database/session-storage.md): Store and retrieve agent, team, and workflow sessions from a database. - [What is Memory?](https://docs.agno.com/memory/overview.md): Store facts about each user and recall them in later conversations. - [Working with Memories](https://docs.agno.com/memory/working-with-memories/overview.md): Customize how memories are created, control context inclusion, share memories across agents, and use memory tools for advanced workflows. - [Memory Optimization](https://docs.agno.com/memory/working-with-memories/memory-optimization.md): Reduce memory token usage by summarizing a user's memories with MemoryManager.optimize_memories(). - [Memory with MongoDB](https://docs.agno.com/memory/working-with-memories/mongodb-memory.md): Store user memories in MongoDB by passing MongoDb as the agent's database. - [Memory with PostgreSQL](https://docs.agno.com/memory/working-with-memories/postgres-memory.md): Store user memories in PostgreSQL by passing PostgresDb as the agent's database. - [Memory with SQLite](https://docs.agno.com/memory/working-with-memories/sqlite-memory.md): Store user memories in SQLite by passing SqliteDb as the agent's database. - [Memory with Redis](https://docs.agno.com/memory/working-with-memories/redis-memory.md): Store user memories in Redis by passing RedisDb as the agent's database. - [Standalone Memory](https://docs.agno.com/memory/working-with-memories/standalone-memory.md): Add, get, delete, and replace user memories directly with a standalone MemoryManager. - [Memory Creation](https://docs.agno.com/memory/working-with-memories/memory-creation.md): Create user memories from text or a message list with MemoryManager. - [Custom Memory Instructions](https://docs.agno.com/memory/working-with-memories/custom-memory-instructions.md): Control which details MemoryManager captures with memory_capture_instructions. - [Memory Search](https://docs.agno.com/memory/working-with-memories/memory-search.md): Retrieve user memories by update time or select related memories with a model. - [Agent Memory](https://docs.agno.com/memory/agent/overview.md): Store and recall user-specific facts across agent runs. - [Agent with Memory](https://docs.agno.com/memory/agent/agent-with-memory.md): Give agents persistent memory across sessions. - [Agentic Memory](https://docs.agno.com/memory/agent/agentic-memory.md): Let an agent create, update, and delete user memories during a run. - [Share Memory between Agents](https://docs.agno.com/memory/agent/agents-share-memory.md): Share user memories between agents through a common memory table and user ID. - [Custom Memory Manager](https://docs.agno.com/memory/agent/custom-memory-manager.md): Configure MemoryManager with custom memory-capture instructions. - [Multi-User, Multi-Session Chat](https://docs.agno.com/memory/agent/multi-user-multi-session-chat.md): Keep conversation history session-scoped and user memory available across a user's sessions. - [Concurrent Multi-User, Multi-Session Chat](https://docs.agno.com/memory/agent/multi-user-multi-session-chat-concurrent.md): Run conversations concurrently with history scoped by session ID and memory scoped by user ID. - [Share Memory and History between Agents](https://docs.agno.com/memory/agent/share-memory-and-history-between-agents.md): Share session history and user-scoped memory between agents through one database. - [Teams with Memory](https://docs.agno.com/memory/team/overview.md): Store and recall user-specific memories across team runs. - [Team with Memory Manager](https://docs.agno.com/memory/team/team-with-memory-manager.md): Run a custom MemoryManager on each team run to reconcile user memories with the latest input. - [Team with Agentic Memory](https://docs.agno.com/memory/team/team-with-agentic-memory.md): Let a team create and update user memories during a run. - [Production Best Practices](https://docs.agno.com/memory/best-practices.md): Avoid common pitfalls, optimize costs, and ensure reliable memory behavior in production. - [FileSystem](https://docs.agno.com/filesystem/overview.md): Give agents a durable file system for notes, decisions, records, and checkpoints. - [Overview](https://docs.agno.com/knowledge/overview.md): Give agents access to documents, databases, and domain expertise. - [Quickstart](https://docs.agno.com/knowledge/quickstart.md): Build a knowledge-powered agent in under 5 minutes. - [Vector Databases](https://docs.agno.com/knowledge/concepts/vector-db.md): Store embeddings and search for similar content. - [Contents Database](https://docs.agno.com/knowledge/concepts/contents-db.md): Track and manage the content you've added to your knowledge base. - [Knowledge Content Types](https://docs.agno.com/knowledge/concepts/content-types.md): Add knowledge content from local files, URLs, raw text, topics, and cloud storage. - [Search & Retrieval](https://docs.agno.com/knowledge/concepts/search-and-retrieval/overview.md): Search a knowledge base directly or give an agent a knowledge-search tool. - [Hybrid Search](https://docs.agno.com/knowledge/concepts/search-and-retrieval/hybrid-search.md): Combine vector and lexical search signals with a supported vector database. - [Vector Search](https://docs.agno.com/knowledge/concepts/search-and-retrieval/vector-search.md): Rank knowledge documents by the distance between query and document embeddings. - [Keyword Search](https://docs.agno.com/knowledge/concepts/search-and-retrieval/keyword-search.md): Rank knowledge documents with a vector database's lexical search implementation. - [Agentic RAG with Reranking](https://docs.agno.com/knowledge/concepts/search-and-retrieval/agentic-rag.md): Combine agent-directed search, hybrid retrieval, and Cohere reranking over the Agno documentation. - [Custom Retriever](https://docs.agno.com/knowledge/concepts/search-and-retrieval/custom-retriever.md): Implement custom retrieval logic for full control over how agents search knowledge. - [Readers](https://docs.agno.com/knowledge/concepts/readers/overview.md): Convert files, URLs, and text into searchable documents. - [PDF Reader](https://docs.agno.com/knowledge/concepts/readers/pdf-reader.md): Convert local or remote PDF files into documents with PDFReader. - [Docling Reader](https://docs.agno.com/knowledge/concepts/readers/docling-reader.md): Convert Docling-supported documents, images, audio, and video into knowledge documents. - [CSV Reader](https://docs.agno.com/knowledge/concepts/readers/csv-reader.md): Convert CSV files into knowledge base documents with CSVReader. - [JSON Reader](https://docs.agno.com/knowledge/concepts/readers/json-reader.md): Convert JSON files into knowledge base documents with JSONReader. - [Markdown Reader](https://docs.agno.com/knowledge/concepts/readers/markdown-reader.md): Convert Markdown files into knowledge base documents with MarkdownReader. - [Website Reader](https://docs.agno.com/knowledge/concepts/readers/website-reader.md): Crawl websites and convert pages into knowledge base documents with WebsiteReader. - [LLMs.txt Reader](https://docs.agno.com/knowledge/concepts/readers/llms-txt-reader.md): Fetch an llms.txt index and convert its linked documentation pages into documents. - [YouTube Reader](https://docs.agno.com/knowledge/concepts/readers/youtube-reader.md): Fetch a YouTube transcript and convert it into knowledge documents. - [Chunking](https://docs.agno.com/knowledge/concepts/chunking/overview.md): Split documents into smaller pieces for effective vector search. - [Fixed Size Chunking](https://docs.agno.com/knowledge/concepts/chunking/fixed-size-chunking.md): Split text into character-limited chunks with optional overlap. - [Semantic Chunking](https://docs.agno.com/knowledge/concepts/chunking/semantic-chunking.md): Group sentences into chunks using embedding similarity and configurable boundary controls. - [Recursive Chunking](https://docs.agno.com/knowledge/concepts/chunking/recursive-chunking.md): Split text at newline or period boundaries with optional overlap. - [Document Chunking](https://docs.agno.com/knowledge/concepts/chunking/document-chunking.md): Group paragraphs into chunks and split oversized paragraphs at sentence boundaries. - [CSV Row Chunking](https://docs.agno.com/knowledge/concepts/chunking/csv-row-chunking.md): Split CSV files into one chunk per row with RowChunking. - [Markdown Chunking](https://docs.agno.com/knowledge/concepts/chunking/markdown-chunking.md): Split Markdown documents by heading structure or an approximate character target. - [Agentic Chunking](https://docs.agno.com/knowledge/concepts/chunking/agentic-chunking.md): Split documents with AgenticChunking, which uses a model to find natural breakpoints. - [Code Chunking](https://docs.agno.com/knowledge/concepts/chunking/code-chunking.md): Split source code at AST boundaries with CodeChunking, powered by Chonkie. - [Custom Chunking](https://docs.agno.com/knowledge/concepts/chunking/custom-chunking.md): Implement your own chunking strategy by subclassing ChunkingStrategy. - [Filtering](https://docs.agno.com/knowledge/concepts/filters/overview.md): Filter knowledge searches by metadata for precise retrieval. - [Advanced Filtering](https://docs.agno.com/knowledge/concepts/filters/advanced-filtering.md): Use filter expressions (EQ, AND, OR, NOT) for complex logical filtering of knowledge base searches. - [Isolate Vector Search](https://docs.agno.com/knowledge/concepts/isolate-vector-search.md): Scope searches to a single Knowledge instance when multiple instances share the same vector database. - [Cloud Storage Sources](https://docs.agno.com/knowledge/concepts/cloud-storage.md): Load content from S3, GCS, SharePoint, GitHub, and Azure Blob into a knowledge base. - [Performance Tips](https://docs.agno.com/knowledge/concepts/performance-tips.md): Optimize knowledge base performance, search quality, and content loading speed. - [Published Pages](https://docs.agno.com/knowledge/published-pages.md): Keep published Markdown and search vectors in sync, then search and read the same page revision. - [Agents with Knowledge](https://docs.agno.com/knowledge/agents/overview.md): Store domain-specific content that agents search at runtime, the Agentic RAG pattern. - [Agentic RAG with LanceDB](https://docs.agno.com/knowledge/agents/agentic-rag-lancedb.md): Agentic RAG with LanceDB as the vector store and OpenAI embeddings. - [Agentic RAG with PgVector](https://docs.agno.com/knowledge/agents/agentic-rag-pgvector.md): Agentic RAG with PgVector, storing and searching embeddings with hybrid search. - [Traditional RAG with LanceDB](https://docs.agno.com/knowledge/agents/traditional-rag-lancedb.md): Retrieve from LanceDB for a string input and append references before the first model call. - [Traditional RAG with PgVector](https://docs.agno.com/knowledge/agents/traditional-rag-pgvector.md): Retrieve from PgVector for a string input and append references before the first model call. - [Teams with Knowledge](https://docs.agno.com/knowledge/teams/overview.md): Attach a knowledge base the team coordinator can search. - [Team with Knowledge Base](https://docs.agno.com/knowledge/teams/team-with-knowledge.md): Combine a team-level LanceDB knowledge search with a web-search member. - [Distributed RAG with PgVector](https://docs.agno.com/knowledge/teams/distributed-rag-pgvector.md): Coordinate agents that search vector and hybrid tables in one PgVector database. - [Distributed RAG with LanceDB](https://docs.agno.com/knowledge/teams/distributed-rag-lancedb.md): Coordinate agents that search vector and hybrid tables in one LanceDB database. - [Learning Machines](https://docs.agno.com/learning/overview.md): Agents that learn and improve with every interaction. - [Quickstart](https://docs.agno.com/learning/quickstart.md): Enable learning in your agents. - [Learning Stores](https://docs.agno.com/learning/stores/intro.md): Each store captures a different type of knowledge. - [User Profile](https://docs.agno.com/learning/stores/user-profile.md): Structured facts about users. - [User Memory](https://docs.agno.com/learning/stores/user-memory.md): Unstructured observations about users. - [Session Context](https://docs.agno.com/learning/stores/session-context.md): Goals, plans, and progress for active sessions. - [Entity Memory](https://docs.agno.com/learning/stores/entity-memory.md): Facts about companies, projects, and people. - [Learned Knowledge](https://docs.agno.com/learning/stores/learned-knowledge.md): Insights that transfer across users. - [Decision Log](https://docs.agno.com/learning/stores/decision-log.md): Decisions with reasoning for auditing and learning. - [Learning Modes](https://docs.agno.com/learning/learning-modes.md): Control when and how agents learn. - [Custom Schemas](https://docs.agno.com/learning/custom-schemas.md): Extend stores with custom fields for your domain. - [What are Models?](https://docs.agno.com/models/overview.md): Configure the language model that an Agent or Team uses, including provider selection and request retries. - [Model as String](https://docs.agno.com/models/model-as-string.md): Configure Agent and Team model fields with the provider:model_id shorthand. - [Compatibility Overview](https://docs.agno.com/models/compatibility.md): How Agno adapters, provider endpoints, and model IDs determine feature compatibility. - [Response Caching](https://docs.agno.com/models/cache-response.md): Cache model responses locally to reduce costs during development and testing. - [Fallback Models](https://docs.agno.com/models/fallback-models.md): Route eligible model-provider failures to backup models for agents and teams. - [What are Context Providers?](https://docs.agno.com/context-providers/overview.md): Context Providers give agents clean access to external systems without tool sprawl. - [Using Providers](https://docs.agno.com/context-providers/using-providers.md): Attach context providers to agents and configure them for your use case. - [Provider Catalog](https://docs.agno.com/context-providers/providers/overview.md): Browse all built-in context providers. - [Slack](https://docs.agno.com/context-providers/providers/slack.md): Search Slack conversations, read threads, and post messages. - [Gmail](https://docs.agno.com/context-providers/providers/gmail.md): Search, read, and send emails via Gmail. - [Google Calendar](https://docs.agno.com/context-providers/providers/calendar.md): Query events, check availability, and create meetings. - [Google Drive](https://docs.agno.com/context-providers/providers/drive.md): Search and read files from Google Drive. - [Database](https://docs.agno.com/context-providers/providers/database.md): Route SQL database reads and writes through separate SQLAlchemy engines. - [Filesystem](https://docs.agno.com/context-providers/providers/filesystem.md): Read files from a scoped local directory. - [Web](https://docs.agno.com/context-providers/providers/web.md): Search and fetch content from the web. - [MCP](https://docs.agno.com/context-providers/providers/mcp.md): Connect to any MCP server as a context provider. - [Wiki](https://docs.agno.com/context-providers/providers/wiki.md): Read and write a markdown wiki backed by Git, the local filesystem, or Notion. - [Workspace](https://docs.agno.com/context-providers/providers/workspace.md): Give an agent read-only, project-aware access to a local working directory. - [Building Custom Providers](https://docs.agno.com/context-providers/custom-providers.md): Create your own context provider for any data source. - [What are Tools?](https://docs.agno.com/tools/overview.md): Give agents functions they can call to read data and take actions in external systems. - [Agent Tools](https://docs.agno.com/tools/agent.md): Equip agents with functions and toolkits for external actions. - [Team with Custom Tools](https://docs.agno.com/tools/usage/team-with-custom-tools.md): Combine an `@tool`-decorated FAQ lookup with a web-search agent member so a team answers from a knowledge base first and falls back to search. - [Team with Tool Hooks](https://docs.agno.com/tools/usage/team-with-tool-hooks.md): Attach a logging tool hook to a team and its HackerNews and YFinance member agents to time each tool call. - [Async Team with Tools](https://docs.agno.com/tools/usage/async-team-with-tools.md): Run a team asynchronously with `aprint_response()` while a HackerNews agent and a YFinance agent gather information as members. - [Creating your own tools](https://docs.agno.com/tools/creating-tools/overview.md): Write custom tool functions and use the `@tool` decorator to modify tool behavior. - [Python Functions as Tools](https://docs.agno.com/tools/creating-tools/python-functions.md): Turn any Python function into an agent tool. - [Custom Toolkits](https://docs.agno.com/tools/creating-tools/toolkits.md): Bundle related tool functions into reusable toolkit classes. - [Tool Hooks](https://docs.agno.com/tools/hooks.md): Use pre and post hooks to modify tool behavior. - [Model Context Protocol (MCP)](https://docs.agno.com/tools/mcp/overview.md): Connect agents to external systems through the standardized MCP interface. - [MCP Toolbox](https://docs.agno.com/tools/mcp/mcp-toolbox.md): Connect to MCP Toolbox for Databases with tool filtering capabilities. - [Multiple MCP Servers](https://docs.agno.com/tools/mcp/multiple-servers.md): Connect an Agent to multiple MCP servers with one MCPTools instance per server. - [Dynamic Headers](https://docs.agno.com/tools/mcp/dynamic-headers.md): Setting dynamic headers with Agno MCP tools - [Stdio Transport](https://docs.agno.com/tools/mcp/transports/stdio.md): Connect MCPTools to a local MCP server over stdio using the command parameter. - [Streamable HTTP Transport](https://docs.agno.com/tools/mcp/transports/streamable_http.md): Connect MCPTools to an MCP server over Streamable HTTP using the url and transport parameters. - [SSE Transport](https://docs.agno.com/tools/mcp/transports/sse.md): Connect MCPTools to an MCP server over the deprecated SSE transport using the url and transport parameters. - [Understanding Server Parameters](https://docs.agno.com/tools/mcp/server-params.md): Configure detailed stdio, SSE, and Streamable HTTP connection parameters for MCPTools. - [Airbnb MCP agent](https://docs.agno.com/tools/mcp/usage/airbnb.md): Search Airbnb listings with an Agno agent connected to the Airbnb MCP server over stdio. - [GitHub MCP agent](https://docs.agno.com/tools/mcp/usage/github.md): Read GitHub issues, pull requests, and repository activity through GitHub's hosted MCP server. - [Notion MCP agent](https://docs.agno.com/tools/mcp/usage/notion.md): Connect an OpenAIResponses agent to Notion over stdio using the official @notionhq/notion-mcp-server and a NOTION_TOKEN integration token. - [Parallel MCP agent](https://docs.agno.com/tools/mcp/usage/parallel.md): Connect a Claude agent to Parallel's streamable-http MCP server for web_search and web_fetch tools, with an optional PARALLEL_API_KEY to raise the keyless rate limit. - [Pipedream Auth](https://docs.agno.com/tools/mcp/usage/pipedream-auth.md): Connect Agno to Pipedream with an authenticated app and user context. - [Pipedream Slack](https://docs.agno.com/tools/mcp/usage/pipedream-slack.md): Use the Slack Pipedream MCP server with Agno Agents. - [Pipedream Google Calendar](https://docs.agno.com/tools/mcp/usage/pipedream-google-calendar.md): Use the Google Calendar Pipedream MCP server with Agno Agents. - [Pipedream LinkedIn](https://docs.agno.com/tools/mcp/usage/pipedream-linkedin.md): Use the LinkedIn Pipedream MCP server with Agno Agents. - [Browserbase MCP agent](https://docs.agno.com/tools/mcp/usage/stagehand.md): Use Browserbase's Stagehand-powered MCP server to extract a structured Hacker News digest. - [Stripe MCP agent](https://docs.agno.com/tools/mcp/usage/stripe.md): Connect an Agno agent to Stripe's hosted MCP server. - [Supabase MCP agent](https://docs.agno.com/tools/mcp/usage/supabase.md): Provision Supabase projects, design and apply SQL schema migrations, and deploy edge functions through the Supabase MCP server. - [Reasoning Tools](https://docs.agno.com/tools/reasoning_tools/reasoning-tools.md): Give an Agent `think` and `analyze` tools so it can reason and adjust its approach at any point during a run. - [Workflow Tools](https://docs.agno.com/tools/reasoning_tools/workflow-tools.md): Give an Agent `think`, `run_workflow`, and `analyze` tools to plan, execute, and evaluate Workflow runs. - [Knowledge Tools](https://docs.agno.com/tools/reasoning_tools/knowledge-tools.md): Give an Agent `think`, `search_knowledge`, and `analyze` tools to query and evaluate results from a Knowledge base before responding. - [Memory Tools](https://docs.agno.com/tools/reasoning_tools/memory-tools.md): Give Agents create, update, and delete access to stored user memories with the MemoryTools toolkit's think, get_memories, add_memory, update_memory, delete_memory, and analyze tools. - [Tool Call Limit](https://docs.agno.com/tools/tool-call-limit.md): Limit the number of tool calls an agent can make. - [Including and excluding tools](https://docs.agno.com/tools/selecting-tools.md): Include and exclude specific tools from a Toolkit. - [Tool Result Caching](https://docs.agno.com/tools/caching.md): Cache tool results to reduce repeated API calls and improve performance. - [Updating an Agent's Tools](https://docs.agno.com/tools/attaching-tools.md): Add or update tools on Agents and Teams after initialization. - [Exceptions & Retries](https://docs.agno.com/tools/exceptions.md): Handle tool errors with exceptions and automatic retries. - [Toolkit Index](https://docs.agno.com/tools/toolkits/overview.md): Index of all toolkits supported by Agno. - [Web Search](https://docs.agno.com/tools/toolkits/search/websearch.md): Search the web with WebSearchTools using the DDGS meta-search library across backends like Google, Bing, DuckDuckGo, Brave, Yandex, and Yahoo. - [Arxiv](https://docs.agno.com/tools/toolkits/search/arxiv.md): Search arXiv and read papers with ArxivTools using search_arxiv_and_return_articles and read_arxiv_papers. - [BaiduSearch](https://docs.agno.com/tools/toolkits/search/baidusearch.md): Search the web with BaiduSearchTools using the Baidu search engine, with a configurable result count. - [DuckDuckGo](https://docs.agno.com/tools/toolkits/search/duckduckgo.md): Search the web with DuckDuckGoTools, a convenience wrapper around WebSearchTools defaulting to the DuckDuckGo backend. - [Exa](https://docs.agno.com/tools/toolkits/search/exa.md): Search the web, retrieve URL content, find similar pages, and get AI-powered answers with ExaTools. - [Hacker News](https://docs.agno.com/tools/toolkits/search/hackernews.md): Fetch top Hacker News stories and user details with HackerNewsTools' get_top_hackernews_stories and get_user_details functions. - [Linkup](https://docs.agno.com/tools/toolkits/search/linkup.md): LinkupTools provides advanced web search capabilities with deep search options and structured results. - [Parallel](https://docs.agno.com/tools/toolkits/search/parallel.md): Use Parallel with Agno for AI-optimized web search and content extraction. - [Perplexity](https://docs.agno.com/tools/toolkits/search/perplexity.md): Search the web with filtering, recency, and domain restrictions using Perplexity Search API. - [Pubmed](https://docs.agno.com/tools/toolkits/search/pubmed.md): Search PubMed for articles with PubmedTools' search_pubmed function, returning expanded article detail when results_expanded is set. - [Scavio](https://docs.agno.com/tools/toolkits/search/scavio.md): Search Google, YouTube, marketplaces, and social platforms through the Scavio API. - [SearchAPI](https://docs.agno.com/tools/toolkits/search/searchapi.md): Search Google, Google News, Google Images, and YouTube with SearchApiTools. - [Searxng](https://docs.agno.com/tools/toolkits/search/searxng.md): Search a self-hosted Searxng instance for web, image, news, and other category results with SearxngTools. - [Seltz](https://docs.agno.com/tools/toolkits/search/seltz.md): Run AI-powered semantic search against the Seltz gRPC API with SeltzTools. - [Serpapi](https://docs.agno.com/tools/toolkits/search/serpapi.md): Search Google and YouTube through the SerpApi API with SerpApiTools. - [Serper](https://docs.agno.com/tools/toolkits/search/serper.md): Search Google, Google News, and Google Scholar, and scrape webpages, with SerperTools via the Serper API. - [Sofya](https://docs.agno.com/tools/toolkits/search/sofya.md): Search the web for full page content, fetch pages and documents as clean markdown, and run cited multi-source research with SofyaTools. - [Tavily](https://docs.agno.com/tools/toolkits/search/tavily.md): Search and extract web content with TavilyTools, backed by the Tavily search and extract APIs. - [Valyu](https://docs.agno.com/tools/toolkits/search/valyu.md): ValyuTools provides academic and web search capabilities with advanced filtering and relevance scoring. - [Wikipedia](https://docs.agno.com/tools/toolkits/search/wikipedia.md): Search Wikipedia with WikipediaTools and optionally add results to a Knowledge base. - [You.com](https://docs.agno.com/tools/toolkits/search/youcom.md): Search web and news results with YouTools, including domain filters and optional page content. - [Brave Search](https://docs.agno.com/tools/toolkits/search/bravesearch.md): Search the web with BraveSearchTools using the Brave Search API. - [Discord](https://docs.agno.com/tools/toolkits/social/discord.md): Send messages, read history, and list channels and read channel metadata in Discord with DiscordTools. - [Email](https://docs.agno.com/tools/toolkits/social/email.md): Send email through Gmail SMTP with EmailTools. - [Gmail](https://docs.agno.com/tools/toolkits/social/gmail.md): Read, search, send, and organize Gmail messages with GmailTools using OAuth or a service account. - [Plivo](https://docs.agno.com/tools/toolkits/social/plivo.md): PlivoTools enables agents to interact with Plivo for sending SMS, placing calls, and looking up phone numbers. - [Reddit](https://docs.agno.com/tools/toolkits/social/reddit.md): RedditTools enables agents to interact with Reddit for browsing posts, comments, and subreddit information. - [Slack](https://docs.agno.com/tools/toolkits/social/slack.md): SlackTools enables an Agent to message, search, and manage Slack channels, threads, files, and users through 12 configurable methods. - [Telegram](https://docs.agno.com/tools/toolkits/social/telegram.md): TelegramTools enables an Agent to send text, photos, documents, video, audio, animations, and stickers to a Telegram chat via the Bot API. - [Twilio](https://docs.agno.com/tools/toolkits/social/twilio.md): TwilioTools enables an Agent to send SMS messages, retrieve call details, and list recent messages through the Twilio REST API. - [Webex](https://docs.agno.com/tools/toolkits/social/webex.md): WebexTools enables an Agent to send messages to Webex spaces and list available rooms through the Webex API. - [WhatsApp](https://docs.agno.com/tools/toolkits/social/whatsapp.md): WhatsAppTools enables an Agent to send text, template, image, document, location, and interactive messages through the WhatsApp Business Cloud API. - [X (Twitter)](https://docs.agno.com/tools/toolkits/social/x.md): Post, reply, message, and search X (Twitter) with XTools using consumer key/secret and access token credentials. - [Zoom](https://docs.agno.com/tools/toolkits/social/zoom.md): Read Zoom meetings and recordings with ZoomTools using Server-to-Server OAuth credentials. - [AgentQL](https://docs.agno.com/tools/toolkits/web-scrape/agentql.md): Scrape web page text using AgentQLTools and a local browser. - [Browserbase](https://docs.agno.com/tools/toolkits/web-scrape/browserbase.md): Navigate pages, take screenshots, and extract page content with BrowserbaseTools using sync and async browser sessions. - [Crawl4AI](https://docs.agno.com/tools/toolkits/web-scrape/crawl4ai.md): Crawl4aiTools exposes a crawl function that fetches one or more URLs with Crawl4ai's AsyncWebCrawler and returns extracted Markdown content. - [Firecrawl](https://docs.agno.com/tools/toolkits/web-scrape/firecrawl.md): Use Firecrawl with Agno to scrape and crawl the web. - [Jina Reader](https://docs.agno.com/tools/toolkits/web-scrape/jina-reader.md): JinaReaderTools calls the Jina Reader API to expose read_url and search_query functions that fetch and search web content. - [Newspaper](https://docs.agno.com/tools/toolkits/web-scrape/newspaper.md): NewspaperTools uses the newspaper3k library's Article class to expose a get_article_text function that downloads and parses article text from a URL. - [Newspaper4k](https://docs.agno.com/tools/toolkits/web-scrape/newspaper4k.md): Newspaper4kTools uses the newspaper4k library to expose a read_article function that returns JSON with an article's title, authors, publish date, and text. - [Spider](https://docs.agno.com/tools/toolkits/web-scrape/spider.md): SpiderTools wraps the Spider API to expose search_web, scrape, and crawl functions that return web search, scrape, and crawl results. - [Trafilatura](https://docs.agno.com/tools/toolkits/web-scrape/trafilatura.md): TrafilaturaTools wraps the trafilatura library to expose extract_text, extract_metadata_only, html_to_text, extract_batch, and crawl_website functions. - [Website Tools](https://docs.agno.com/tools/toolkits/web-scrape/website.md): WebsiteTools exposes a read_url function to read a website's contents, or an add_website_to_knowledge function when a Knowledge instance is passed in. - [ScrapeGraph](https://docs.agno.com/tools/toolkits/web-scrape/scrapegraph.md): ScrapeGraphTools enable an Agent to extract structured data from webpages, convert content to markdown, and retrieve raw HTML content. - [Oxylabs](https://docs.agno.com/tools/toolkits/web-scrape/oxylabs.md): OxylabsTools give an Agent access to Oxylabs' SERP, Amazon product, and universal web scraping APIs. - [BrightData](https://docs.agno.com/tools/toolkits/web-scrape/brightdata.md): BrightDataTools enable an Agent to scrape webpages as markdown, capture screenshots, run search engine queries, and pull structured data feeds from LinkedIn, Amazon, Instagram, and more. - [CSV](https://docs.agno.com/tools/toolkits/database/csv.md): The CsvTools toolkit enables an Agent to read and query CSV files. - [DuckDb](https://docs.agno.com/tools/toolkits/database/duckdb.md): The DuckDbTools toolkit enables an Agent to run SQL and analyze data using DuckDb. - [Google BigQuery](https://docs.agno.com/tools/toolkits/database/google-bigquery.md): GoogleBigQueryTools enables agents to interact with Google BigQuery for large-scale data analysis and SQL queries. - [Neo4j](https://docs.agno.com/tools/toolkits/database/neo4j.md): The Neo4jTools toolkit enables agents to interact with Neo4j graph databases for querying and managing graph data. - [Pandas](https://docs.agno.com/tools/toolkits/database/pandas.md): The PandasTools toolkit enables an Agent to perform data manipulation tasks using the Pandas library. - [Postgres](https://docs.agno.com/tools/toolkits/database/postgres.md): The PostgresTools toolkit enables an Agent to interact with a PostgreSQL database. - [Redshift](https://docs.agno.com/tools/toolkits/database/redshift.md): The RedshiftTools toolkit enables an Agent to interact with Amazon Redshift data warehouses. - [SQL](https://docs.agno.com/tools/toolkits/database/sql.md): The SQLTools toolkit enables an Agent to run SQL queries and interact with databases. - [Zep](https://docs.agno.com/tools/toolkits/database/zep.md): The ZepTools toolkit enables an Agent to interact with a Zep memory system, providing capabilities to store, retrieve, and search memory data associated with user sessions. - [Calculator](https://docs.agno.com/tools/toolkits/local/calculator.md): CalculatorTools gives an agent add, subtract, multiply, divide, and other math operations for step-by-step calculations. - [Coding](https://docs.agno.com/tools/toolkits/local/coding.md): CodingTools gives an agent four core file/shell tools to perform any coding task in a scoped directory. - [Docker](https://docs.agno.com/tools/toolkits/local/docker.md): DockerTools gives an agent full control over Docker containers, images, volumes, and networks. - [File](https://docs.agno.com/tools/toolkits/local/file.md): The FileTools toolkit enables Agents to read and write files on the local file system. - [Local File System](https://docs.agno.com/tools/toolkits/local/local-file-system.md): LocalFileSystemTools enables agents to read and write files on the local file system with automatic directory management. - [Python](https://docs.agno.com/tools/toolkits/local/python.md): PythonTools gives an agent the ability to write, save, and run Python code, install packages, and read files in a scoped directory. - [Shell](https://docs.agno.com/tools/toolkits/local/shell.md): Run shell commands from an agent with ShellTools.run_shell_command(). - [Sleep](https://docs.agno.com/tools/toolkits/local/sleep.md): Pause agent execution for a set number of seconds with SleepTools.sleep(). - [Workspace](https://docs.agno.com/tools/toolkits/local/workspace.md): Workspace gives an agent read/write/edit/search/shell access to a directory, with destructive operations gated behind human confirmation by default. - [File Generation](https://docs.agno.com/tools/toolkits/file-generation/file-generation.md): The `FileGenerationTools` toolkit enables Agents and Teams to generate files in multiple formats. - [Azure OpenAI](https://docs.agno.com/tools/toolkits/models/azure-openai.md): Legacy AzureOpenAITools reference for image generation with retired DALL-E deployments. - [Gemini](https://docs.agno.com/tools/toolkits/models/gemini.md): GeminiTools API reference and migration notes for its legacy Imagen and Veo defaults. - [Groq](https://docs.agno.com/tools/toolkits/models/groq.md): GroqTools gives an agent access to Groq's Whisper transcription, translation, and TTS APIs. - [Morph](https://docs.agno.com/tools/toolkits/models/morph.md): MorphTools provides advanced code editing capabilities using Morph's Fast Apply API for intelligent code modifications. - [Nebius](https://docs.agno.com/tools/toolkits/models/nebius.md): Legacy NebiusTools reference for the discontinued Token Factory image-generation service. - [OpenAI](https://docs.agno.com/tools/toolkits/models/openai.md): OpenAITools gives an agent access to OpenAI's audio transcription, image generation, and text-to-speech APIs. - [StudioTools](https://docs.agno.com/tools/toolkits/agent-os/studio.md): Let an agent create, edit, run, and version AgentOS Studio components. - [Scheduler](https://docs.agno.com/tools/toolkits/agent-os/scheduler.md): Let an agent create and manage AgentOS schedules. - [Adanos Market Sentiment](https://docs.agno.com/tools/toolkits/others/adanos.md): AdanosTools gives agents multi-source stock sentiment and Reddit cryptocurrency sentiment. - [Airflow](https://docs.agno.com/tools/toolkits/others/airflow.md): AirflowTools lets agents save and read Apache Airflow DAG files. - [Antigravity](https://docs.agno.com/tools/toolkits/others/antigravity.md): Delegate sub-tasks to Google's Managed Agents (Gemini API) sandbox from any Agno agent. - [Apify](https://docs.agno.com/tools/toolkits/others/apify.md): ApifyTools lets agents run Apify Actors for web scraping, crawling, and data extraction. - [AWS Lambda](https://docs.agno.com/tools/toolkits/others/aws-lambda.md): AWSLambdaTools lets agents list and invoke AWS Lambda functions. - [AWS SES](https://docs.agno.com/tools/toolkits/others/aws-ses.md): AWSSESTool lets agents send email through Amazon Simple Email Service (SES). - [Bitbucket](https://docs.agno.com/tools/toolkits/others/bitbucket.md): BitbucketTools enables agents to interact with Bitbucket repositories for managing code, pull requests, and issues. - [Brandfetch](https://docs.agno.com/tools/toolkits/others/brandfetch.md): BrandfetchTools provides access to brand data and logo information through the Brandfetch API. - [Cal.com](https://docs.agno.com/tools/toolkits/others/calcom.md): CalComTools enable an Agent to check availability, create, reschedule, and cancel Cal.com bookings. - [Cartesia](https://docs.agno.com/tools/toolkits/others/cartesia.md): Generate speech with CartesiaTools and configure an existing voice. - [ClickUp](https://docs.agno.com/tools/toolkits/others/clickup.md): ClickUpTools enables agents to interact with ClickUp workspaces for project management and task organization. - [Composio](https://docs.agno.com/tools/toolkits/others/composio.md): ComposioTools connect an Agent to GitHub, Gmail, Salesforce, and 1000+ other actions through Composio. - [Confluence](https://docs.agno.com/tools/toolkits/others/confluence.md): ConfluenceTools enable an Agent to retrieve, create, and update Confluence pages and explore spaces. - [Custom API](https://docs.agno.com/tools/toolkits/others/custom-api.md): Make authenticated HTTP requests to any external API with CustomApiTools. - [Dalle](https://docs.agno.com/tools/toolkits/others/dalle.md): Legacy DalleTools parameters and examples for OpenAI's deprecated DALL-E models. - [Daytona](https://docs.agno.com/tools/toolkits/others/daytona.md): Enable your Agents to run code in a remote, secure sandbox. - [Desi Vocal](https://docs.agno.com/tools/toolkits/others/desi-vocal.md): Reference the retired DesiVocalTools adapter and migrate text-to-speech agents to an active provider. - [Docling](https://docs.agno.com/tools/toolkits/others/docling.md): Convert PDFs, DOCX, HTML, images, and other documents to multiple formats like Markdown, JSON, HTML, and more. - [Eleven Labs](https://docs.agno.com/tools/toolkits/others/eleven-labs.md): Generate speech and sound effects from text using the ElevenLabs API. - [E2B](https://docs.agno.com/tools/toolkits/others/e2b.md): Enable your Agents to run code in a remote, secure sandbox. - [EVM (Ethereum Virtual Machine)](https://docs.agno.com/tools/toolkits/others/evm.md): EvmTools enable an Agent to send ETH transactions on Ethereum and EVM-compatible blockchains. - [Fal](https://docs.agno.com/tools/toolkits/others/fal.md): FalTools enable an Agent to generate and transform images and videos using the Fal AI platform. - [Financial Datasets API](https://docs.agno.com/tools/toolkits/others/financial-datasets.md): FinancialDatasetsTools give an Agent access to stock prices, financial statements, SEC filings, and cryptocurrency data from the Financial Datasets API. - [Giphy](https://docs.agno.com/tools/toolkits/others/giphy.md): GiphyTools enable an Agent to search and retrieve GIFs from GIPHY. - [GitHub](https://docs.agno.com/tools/toolkits/others/github.md): GithubTools enable an Agent to manage GitHub repositories, issues, and pull requests. - [GitLab](https://docs.agno.com/tools/toolkits/others/gitlab.md): GitlabTools provides read-focused access to GitLab projects, merge requests, and issues. - [Google Drive](https://docs.agno.com/tools/toolkits/others/google-drive.md): GoogleDriveTools let an Agent list, search, read, upload, and download Google Drive files, with smart export of Workspace documents. - [Google Maps](https://docs.agno.com/tools/toolkits/others/google-maps.md): GoogleMapTools search places, get directions, geocode addresses, and check elevation, timezone, and distance data through the Google Maps API. - [Google Calendar](https://docs.agno.com/tools/toolkits/others/googlecalendar.md): GoogleCalendarTools let an Agent view, search, schedule, and manage Google Calendar events and check availability. - [Google Sheets](https://docs.agno.com/tools/toolkits/others/google-sheets.md): GoogleSheetsTools read, create, update, and duplicate Google Sheets through the Sheets API. - [Google Slides](https://docs.agno.com/tools/toolkits/others/google-slides.md): GoogleSlidesTools create, edit, and read Google Slides presentations, including slides, tables, and embedded videos. - [Jira](https://docs.agno.com/tools/toolkits/others/jira.md): JiraTools let an agent search, create, and comment on Jira issues, and log worklogs against them. - [Knowledge Tools](https://docs.agno.com/tools/toolkits/others/knowledge.md): KnowledgeTools provide intelligent search and analysis capabilities over knowledge bases with reasoning integration. - [Linear](https://docs.agno.com/tools/toolkits/others/linear.md): LinearTools let an agent create, update, and query issues and users in Linear over its GraphQL API. - [LLMs.txt](https://docs.agno.com/tools/toolkits/others/llms-txt.md): LLMsTxtTools lets an agent discover and read documentation from an llms.txt index, optionally loading it into Knowledge. - [Lumalabs](https://docs.agno.com/tools/toolkits/others/lumalabs.md): LumaLabTools let an agent generate text-to-video and image-to-video content using the Luma AI Dream Machine API. - [Mem0](https://docs.agno.com/tools/toolkits/others/mem0.md): Mem0Tools provides intelligent memory management capabilities for agents using the Mem0 memory platform. - [MLX Transcribe](https://docs.agno.com/tools/toolkits/others/mlx-transcribe.md): MLXTranscribeTools transcribes audio files with Apple's MLX Whisper model, optimized for Apple Silicon. - [ModelsLabs](https://docs.agno.com/tools/toolkits/others/models-labs.md): ModelsLabTools generates images, video, and audio from text prompts using the ModelsLab API. - [Nano Banana](https://docs.agno.com/tools/toolkits/others/nano-banana.md): NanoBananaTools generates images with Google's Gemini 2.5 Flash Image model (Nano Banana). - [MoviePy Video Tools](https://docs.agno.com/tools/toolkits/others/moviepy.md): Agno MoviePyVideoTools enable an Agent to process videos, extract audio, generate SRT caption files, and embed rich, word-highlighted captions. - [Notion Tools](https://docs.agno.com/tools/toolkits/others/notion.md): The NotionTools toolkit enables Agents to interact with your Notion pages. - [OpenCV](https://docs.agno.com/tools/toolkits/others/opencv.md): OpenCVTools enables agents to capture images and videos from webcam using OpenCV computer vision library. - [OpenBB](https://docs.agno.com/tools/toolkits/others/openbb.md): Retrieve stock prices, company profiles, price targets, and news with OpenBBTools and the OpenBB Platform. - [OpenWeather](https://docs.agno.com/tools/toolkits/others/openweather.md): Fetch current weather, forecasts, air pollution data, and geocoding with OpenWeatherTools and the OpenWeatherMap API. - [Reasoning](https://docs.agno.com/tools/toolkits/others/reasoning.md): ReasoningTools provides step-by-step reasoning capabilities for agents to think through complex problems systematically. - [Replicate](https://docs.agno.com/tools/toolkits/others/replicate.md): Generate images and videos from text prompts with ReplicateTools and the Replicate platform. - [Resend](https://docs.agno.com/tools/toolkits/others/resend.md): Send transactional emails from an Agent with ResendTools and the Resend API. - [Salesforce](https://docs.agno.com/tools/toolkits/others/salesforce.md): Query, create, update, and manage Salesforce CRM records with SOQL, SOSL, and the REST API. - [Shopify](https://docs.agno.com/tools/toolkits/others/shopify.md): Analyze sales, products, inventory, and customers with the Shopify Admin GraphQL API. - [Smallest AI](https://docs.agno.com/tools/toolkits/others/smallest.md): Generate natural speech using Smallest AI's Lightning text-to-speech models. - [Spotify](https://docs.agno.com/tools/toolkits/others/spotify.md): Enable an Agent to search Spotify for tracks, artists, and albums with a read-only toolkit configuration. - [Superserve](https://docs.agno.com/tools/toolkits/others/superserve.md): Run agent-generated code in an isolated Superserve cloud sandbox. - [Todoist](https://docs.agno.com/tools/toolkits/others/todoist.md): Create and manage Todoist tasks, with current adapter limits for updates and projects. - [Trello](https://docs.agno.com/tools/toolkits/others/trello.md): Enable an Agent to create and manage Trello boards, lists, and cards. - [TwelveLabs](https://docs.agno.com/tools/toolkits/others/twelvelabs.md): Analyze videos with the TwelveLabs Pegasus model and generate multimodal text embeddings with Marengo. - [User Control Flow](https://docs.agno.com/tools/toolkits/others/user-control-flow.md): UserControlFlowTools enable agents to pause execution and request input from users during conversations. - [User Feedback](https://docs.agno.com/tools/toolkits/others/user-feedback.md): UserFeedbackTools lets an agent pause and ask the user structured questions with predefined options. - [Visualization](https://docs.agno.com/tools/toolkits/others/visualization.md): VisualizationTools enables agents to create various types of charts and plots using matplotlib. - [Web Browser Tools](https://docs.agno.com/tools/toolkits/others/web-browser.md): WebBrowser Tools enable an Agent to open a URL in a web browser. - [Web Tools](https://docs.agno.com/tools/toolkits/others/webtools.md): Expand shortened URLs and return their final destinations with WebTools. - [Yfinance](https://docs.agno.com/tools/toolkits/others/yfinance.md): YFinanceTools give an agent access to stock prices, fundamentals, and analyst data from Yahoo Finance. - [Youtube](https://docs.agno.com/tools/toolkits/others/youtube.md): YouTubeTools let an agent fetch captions, metadata, and timestamps for a YouTube video from its URL. - [Zendesk](https://docs.agno.com/tools/toolkits/others/zendesk.md): Search Zendesk Help Center articles for an Agent with ZendeskTools. - [Sessions](https://docs.agno.com/sessions/overview.md): Group related runs under a stable session ID, with database-backed history and state. - [Persisting Sessions](https://docs.agno.com/sessions/persisting-sessions/overview.md): Store session data in a database for multi-turn conversations - [Storage Control](https://docs.agno.com/sessions/persisting-sessions/storage-control.md): Control what session data gets persisted to your database - [Media Storage](https://docs.agno.com/sessions/persisting-sessions/media-storage/overview.md): Offload agent media to object storage and keep only a reference in the database. - [Local Media Storage](https://docs.agno.com/sessions/persisting-sessions/media-storage/local.md): Store agent media on the local filesystem with LocalMediaStorage. - [S3 Media Storage](https://docs.agno.com/sessions/persisting-sessions/media-storage/s3.md): Store agent media in an S3 bucket with S3MediaStorage. - [GCS Media Storage](https://docs.agno.com/sessions/persisting-sessions/media-storage/gcs.md): Store agent media in a Google Cloud Storage bucket with GCSMediaStorage. - [Session Management](https://docs.agno.com/sessions/session-management.md): Manage session identifiers, names, and performance optimization - [History Management](https://docs.agno.com/sessions/history-management.md): Control how conversation history is accessed and used - [Session Summaries](https://docs.agno.com/sessions/session-summaries.md): Automatically condense long conversations into concise summaries - [Workflow Sessions](https://docs.agno.com/sessions/workflow-sessions.md): Track multi-step workflow executions with session history. - [Metrics](https://docs.agno.com/sessions/metrics/overview.md): Token usage, cost, timing, and per-model breakdowns for agents, teams, and workflows. - [Agent Metrics](https://docs.agno.com/sessions/metrics/agent.md): Access RunMetrics, MessageMetrics, and SessionMetrics from agent runs. - [Agent Extra Metrics](https://docs.agno.com/sessions/metrics/usage/agent-extra-metrics.md): Read audio, cache, and reasoning token counts from RunMetrics. - [Tool Call Metrics](https://docs.agno.com/sessions/metrics/usage/agent-metrics.md): Time tool executions with ToolCallMetrics on each ToolExecution. - [Team Tool Call Metrics](https://docs.agno.com/sessions/metrics/usage/team-metrics.md): Time member tool executions with ToolCallMetrics alongside team leader and member run metrics. - [Team Metrics](https://docs.agno.com/sessions/metrics/team.md): Access RunMetrics, MessageMetrics, and SessionMetrics from team runs. - [Workflow Metrics](https://docs.agno.com/sessions/metrics/workflow.md): Workflow, step, and session metrics for token usage and performance. - [Context Engineering](https://docs.agno.com/context/overview.md): Control the instructions, data, history, and tools sent to a model for each run. - [Context Engineering](https://docs.agno.com/context/agent/overview.md): Configure system messages, instructions, and context for agents. - [Basic Instructions](https://docs.agno.com/context/agent/instructions.md): Set the instructions parameter on an Agent to control its response style and tone. - [Dynamic Instructions](https://docs.agno.com/context/agent/dynamic-instructions.md): Generate agent instructions from a function that reads session state, so behavior changes per user. - [Instructions via Function](https://docs.agno.com/context/agent/instructions-via-function.md): Generate agent instructions from a function that reads the agent's own properties, such as its name. - [Few-Shot Learning](https://docs.agno.com/context/agent/few-shot-learning.md): Teach a customer support agent response patterns by passing few-shot example messages through additional_input. - [Providing Datetime](https://docs.agno.com/context/agent/datetime-instructions.md): Add the current date and time to an agent's context with add_datetime_to_context and a timezone_identifier. - [Providing Location](https://docs.agno.com/context/agent/location-instructions.md): Add the running process's approximate public-IP location to an agent's context. - [Managing Tool Calls](https://docs.agno.com/context/agent/filter-tool-calls-from-history.md): Limit tool calls kept in an agent's context with max_tool_calls_from_history while the full history stays in the database. - [Context Engineering](https://docs.agno.com/context/team/overview.md): Configure system messages, instructions, and context for teams. - [Managing Tool Calls](https://docs.agno.com/context/team/filter-tool-calls-from-history.md): Limit tool calls carried in team context with max_tool_calls_from_history across multiple research queries. - [State Management](https://docs.agno.com/state/overview.md): Persist and share data across agent runs, team coordination, and workflow execution - [Agent Session State](https://docs.agno.com/state/agent/overview.md): Manage persistent state in agents across multiple runs within a session - [Basic State](https://docs.agno.com/state/agent/session-state-basic.md): Store and update a shopping list in agent session state backed by SQLite. - [State in Instructions](https://docs.agno.com/state/agent/session-state-in-instructions.md): Reference session state variables directly inside an agent's instruction templates. - [State in Context](https://docs.agno.com/state/agent/session-state-in-context.md): Persist each user's name and age across runs in separate session state. - [Advanced State](https://docs.agno.com/state/agent/session-state-advanced.md): Manage a shopping list in agent session state with add, remove, and list tools. - [Multiple Users](https://docs.agno.com/state/agent/session-state-multiple-users.md): Track separate shopping lists per user and session ID using session state in a multi-user agent. - [Agentic State](https://docs.agno.com/state/agent/agentic-session-state.md): Let an agent update its own shopping list in session state based on the conversation. - [Dynamic State](https://docs.agno.com/state/agent/dynamic-session-state.md): Update customer profiles in session state through a tool hook instead of direct modification. - [Change State on Run](https://docs.agno.com/state/agent/change-state-on-run.md): Pass a user's name and age into session state at run time and keep the values isolated per session and user. - [Limit Past Session Search](https://docs.agno.com/state/agent/last-n-session-messages.md): Limit the number of prior session previews returned by search_past_sessions. - [Team Session State](https://docs.agno.com/state/team/overview.md): Share and coordinate state across multiple agents in a team - [Agentic State](https://docs.agno.com/state/team/agentic-session-state.md): Enable agentic session state so a team and its member agent update shared state autonomously during a conversation. - [Change State on Run](https://docs.agno.com/state/team/change-state-on-run.md): Pass session_state per run to set and persist separate state for different users and sessions. - [State in Instructions](https://docs.agno.com/state/team/session-state-in-instructions.md): Inject session state variables into team instructions using template syntax. - [Share Member Interactions](https://docs.agno.com/state/team/share-member-interactions.md): Set share_member_interactions to True so team members see and build on each other's responses. - [Workflow Session State](https://docs.agno.com/state/workflows/overview.md): Coordinate state across workflow steps, agents, teams, and custom functions - [State in Custom Function](https://docs.agno.com/state/workflows/access-session-state-in-custom-python-function-step.md): Access the run context in a custom Python function step - [State in Condition](https://docs.agno.com/state/workflows/access-session-state-in-condition-evaluator-function.md): Access session state in the evaluator function of a condition step - [State in Router](https://docs.agno.com/state/workflows/access-session-state-in-router-selector-function.md): Access session state in the selector function of a router step. - [Chat History](https://docs.agno.com/history/overview.md): Persist and access conversation history for multi-turn interactions. - [Chat History in Agents](https://docs.agno.com/history/agent/overview.md): Configure and access agent conversation history. - [Chat History](https://docs.agno.com/history/agent/chat-history.md): Retrieve an agent's stored conversation messages with get_chat_history(). - [Persistent Session with History Context](https://docs.agno.com/history/agent/persistent-session-history.md): Store agent conversation history in PostgreSQL and limit how many past runs are added to the context with num_history_runs. - [Chat History in Teams](https://docs.agno.com/history/team/overview.md): Manage team session history and conversation context. - [Direct Response with Team History](https://docs.agno.com/history/team/respond-directly-with-history.md): Give the team leader history for delegation while returning a member response directly. - [Team History](https://docs.agno.com/history/team/team-history.md): Share conversation history across team members with add_team_history_to_members so one agent can recall context another agent received. - [Member History](https://docs.agno.com/history/team/history-of-members.md): Give each team member its own isolated conversation history with add_history_to_context set on the individual agents. - [Share Member Interactions](https://docs.agno.com/history/team/share-member-interactions.md): Let team members see each other's interactions during the same run with share_member_interactions. - [Workflow History & Continuous Execution](https://docs.agno.com/history/workflow/overview.md): Build workflows that reference previous runs across multiple executions using workflow history. - [Single Step Workflow](https://docs.agno.com/history/workflow/single-step-continuous-execution-workflow.md): A single-step workflow that runs continuously with access to workflow history. - [Multi-Step Workflow](https://docs.agno.com/history/workflow/workflow-with-history-enabled-for-steps.md): A multi-step workflow that supplies prior-run history to agent and custom function steps. - [Per-Step History](https://docs.agno.com/history/workflow/enable-history-for-step.md): Enable workflow history for a specific step with the `add_workflow_history` flag. - [History in Functions](https://docs.agno.com/history/workflow/get-history-in-function.md): Access workflow history inside a custom function through `step_input`. - [Multi-Purpose CLI](https://docs.agno.com/history/workflow/multi-purpose-cli.md): Add workflow history to the steps of a multi-purpose CLI workflow. - [Intent Routing](https://docs.agno.com/history/workflow/intent-routing-with-history.md): Route requests to specialist agents that share the same conversation history. - [Context Compression](https://docs.agno.com/compression/overview.md): Compress tool call results to save context space while preserving critical information. - [Token Counting](https://docs.agno.com/compression/token-counting.md): Token estimation for context planning and compression. - [Dependencies](https://docs.agno.com/dependencies/overview.md): Inject variables into agent and team context with dependencies. - [Dependencies with Agents](https://docs.agno.com/dependencies/agent/overview.md): Inject variables into agent context with dependencies. - [Add Dependencies to Agent Run](https://docs.agno.com/dependencies/agent/add-dependencies-run.md): Inject dependencies into an agent run so the agent can use dynamic context like user profiles and the current time. - [Add Dependencies to Agent Context](https://docs.agno.com/dependencies/agent/add-dependencies-to-context.md): Build a context-aware agent that pulls real-time HackerNews data through dependency injection. - [Access Dependencies in Tool](https://docs.agno.com/dependencies/agent/access-dependencies-in-tool.md): Access dependencies passed to the agent from inside a tool, giving tools dynamic context like user profiles and the current time. - [Dependencies with Teams](https://docs.agno.com/dependencies/team/overview.md): Inject variables into team context with dependencies. - [Adding Dependencies to Team Run](https://docs.agno.com/dependencies/team/add-dependencies-run.md): Add dependencies to a specific team run. Dependencies are functions that provide contextual information, like user profiles, to the team during execution. - [Adding Dependencies to Team Context](https://docs.agno.com/dependencies/team/add-dependencies-to-context.md): Define dependencies on the team itself so they are available to every run by default, rather than passing them per run. - [Using Reference Dependencies in Team Instructions](https://docs.agno.com/dependencies/team/reference-dependencies.md): Define dependencies in the team constructor and reference them as template variables in team instructions. The values are resolved and injected automatically. - [Access Dependencies in Team Tool](https://docs.agno.com/dependencies/team/access-dependencies-in-tool.md): Access dependencies passed to the team from inside a tool, giving team members shared dynamic context like team metrics and the current time. - [Pre-hooks and Post-hooks](https://docs.agno.com/hooks/overview.md): Execute custom logic before and after agent runs with hooks. - [Input Validation Pre-Hook](https://docs.agno.com/hooks/usage/agent/input-validation-pre-hook.md): Run a validator agent in a pre-hook to reject off-topic, vague, or unsafe requests before the main agent responds. - [Input Transformation Pre-Hook](https://docs.agno.com/hooks/usage/agent/input-transformation-pre-hook.md): Rewrite a user's raw input into a more focused request with a transformer agent in a pre-hook before the main agent runs. - [Output Validation Post-Hook](https://docs.agno.com/hooks/usage/agent/output-validation-post-hook.md): Validate agent response completeness, tone, safety, and length in a post-hook, raising OutputCheckError when checks fail. - [Output Transformation Post-Hook](https://docs.agno.com/hooks/usage/agent/output-transformation-post-hook.md): Use post-hooks to reformat an Agent's RunOutput.content with markdown, disclaimers, or an AI-structured layout before returning it. - [Input Validation Pre-Hook](https://docs.agno.com/hooks/usage/team/input-validation-pre-hook.md): Use a pre-hook with an AI validator agent to check a Team's input for relevance, safety, and team suitability before raising InputCheckError. - [Input Transformation Pre-Hook](https://docs.agno.com/hooks/usage/team/input-transformation-pre-hook.md): Use a pre-hook with an AI transformer agent to rewrite a Team's input to match the team's purpose before it reaches the LLM. - [Output Validation Post-Hook](https://docs.agno.com/hooks/usage/team/output-validation-post-hook.md): Use post-hooks to validate a Team's output for comprehensiveness, collaboration, consistency, and safety, raising OutputCheckError on failure. - [Output Transformation Post-Hook](https://docs.agno.com/hooks/usage/team/output-transformation-post-hook.md): Use post-hooks to reformat a Team's TeamRunOutput.content with member metadata, a collaboration summary, or an AI-structured layout before returning it. - [Cancelling a Run](https://docs.agno.com/run-cancellation/overview.md): Cancel running agent, team, or workflow executions. - [Agent Run Cancellation](https://docs.agno.com/run-cancellation/agent-cancel-run.md): Cancel a running agent execution from another thread. - [Team Run Cancellation](https://docs.agno.com/run-cancellation/team-cancel-run.md): Cancel a running team execution from another thread. - [Workflow Run Cancellation](https://docs.agno.com/run-cancellation/workflow-cancel-run.md): Cancel a running workflow execution from another thread. - [Background Execution](https://docs.agno.com/background-execution/overview.md): Run agents in the background. Reconnect to in-progress streams via SSE. - [Introduction to Skills](https://docs.agno.com/skills/overview.md): Skills provide agents with structured domain expertise through instructions, scripts, and reference documentation. - [Creating Skills](https://docs.agno.com/skills/creating-skills.md): Create skills with instructions, scripts, and reference documentation. - [Loading Skills](https://docs.agno.com/skills/loading-skills.md): Load skills into agents using LocalSkills and the Skills orchestrator. - [Team Skills](https://docs.agno.com/skills/team-skills.md): Give the team leader direct access to skills without delegating to a member agent. - [What is Reasoning?](https://docs.agno.com/reasoning/overview.md): Choose native reasoning, explicit planning tools, or a separate reasoning stage for Agents and Teams. - [Reasoning Models](https://docs.agno.com/reasoning/reasoning-models.md): Configure native reasoning models, separate response models, and provider-exposed reasoning events. - [Azure AI Foundry](https://docs.agno.com/reasoning/usage/models/azure-ai-foundry/azure-ai-foundry.md): Use a deployed reasoning model through the Foundry OpenAI v1 Responses endpoint. - [Azure OpenAI o1](https://docs.agno.com/reasoning/usage/models/azure-openai/o1.md): Configure an Agno agent to use Azure OpenAI's o1 model for reasoning tasks. - [Azure OpenAI o3](https://docs.agno.com/reasoning/usage/models/azure-openai/o3.md): Configure an Agno agent with Azure OpenAI's o3 model and YFinance tools to compare stock data in tables. - [Azure OpenAI Separate Reasoning Model](https://docs.agno.com/reasoning/usage/models/azure-openai/reasoning-model-gpt4-1.md): Use distinct Azure deployments for a native reasoning stage and a GPT-4.1 response. - [DeepSeek Reasoner](https://docs.agno.com/reasoning/usage/models/deepseek/deepseek-reasoner.md): Use DeepSeek V4 Flash thinking as a separate reasoning stage before GPT-5.2. - [Groq GPT-OSS 120B](https://docs.agno.com/reasoning/usage/models/groq/groq.md): Enable reasoning on Groq's GPT-OSS 120B model to compare 9.11 and 9.9 using medium reasoning effort. - [Claude + Groq GPT-OSS](https://docs.agno.com/reasoning/usage/models/groq/groq-plus-claude.md): Pair Claude Sonnet 4.5 with Groq's GPT-OSS 120B as a reasoning model to compare 9.11 and 9.9. - [Ollama DeepSeek R1](https://docs.agno.com/reasoning/usage/models/ollama/ollama.md): Pair a local Ollama Llama 3.2 model with DeepSeek R1 as a reasoning model to solve the trolley problem. - [OpenAI o1-pro](https://docs.agno.com/reasoning/usage/models/openai/o1-pro.md): Run OpenAI o1-pro through the Responses API to reason step by step over an ethics prompt. - [OpenAI GPT-5-mini](https://docs.agno.com/reasoning/usage/models/openai/gpt5-mini.md): Use OpenAI GPT-5-mini through the Responses API to write a report comparing NVDA to TSLA. - [OpenAI GPT-5 mini with Tools](https://docs.agno.com/reasoning/usage/models/openai/gpt5-mini-tools.md): Use GPT-5 mini with HackerNewsTools to retrieve and summarize top stories. - [OpenAI o4-mini](https://docs.agno.com/reasoning/usage/models/openai/o4-mini.md): Run OpenAI o4-mini through the Responses API to reason step by step over an ethics prompt. - [OpenAI Separate Reasoning Model](https://docs.agno.com/reasoning/usage/models/openai/reasoning-model-gpt4-1.md): Use a native GPT-5.2 reasoning stage before a GPT-4.1 response. - [OpenAI o4-mini with reasoning summary](https://docs.agno.com/reasoning/usage/models/openai/reasoning-summary.md): Set reasoning_summary to auto on o4-mini to surface its reasoning summary while comparing NVDA to TSLA. - [OpenAI gpt-5-mini with reasoning effort](https://docs.agno.com/reasoning/usage/models/openai/reasoning-effort.md): Set reasoning_effort to "high" on OpenAIResponses so gpt-5-mini reasons more before answering. - [xAI Grok 3 Mini](https://docs.agno.com/reasoning/usage/models/xai/reasoning-effort.md): Set reasoning_effort to "high" on xAI's grok-3-mini-fast model for deeper reasoning before it answers. - [Reasoning Tools](https://docs.agno.com/reasoning/usage/tools/reasoning-tools.md): Give an OpenAIResponses agent ReasoningTools so it calls think() and analyze() while solving a logic puzzle. - [Azure OpenAI with Reasoning Tools](https://docs.agno.com/reasoning/usage/tools/azure-openai-reasoning-tools.md): Pair ReasoningTools with WebSearchTools on an AzureOpenAI gpt-4o-mini agent to research and reason through a report. - [Ollama with Reasoning Tools](https://docs.agno.com/reasoning/usage/tools/ollama-reasoning-tools.md): Give an Ollama agent explicit reasoning tools and Hacker News retrieval. - [OpenAI with Reasoning Tools](https://docs.agno.com/reasoning/usage/tools/openai-reasoning-tools.md): Give an OpenAIChat agent ReasoningTools and WebSearchTools to research and write a report comparing NVDA to TSLA. - [Vercel with Reasoning Tools (Historical)](https://docs.agno.com/reasoning/usage/tools/vercel-reasoning-tools.md): Historical V0 Model API integration, retained for migration context. - [Cerebras with Reasoning Tools](https://docs.agno.com/reasoning/usage/tools/cerebras-llama-reasoning-tools.md): Use ReasoningTools with a Cerebras agent to solve a step-by-step logic puzzle. - [Claude with Reasoning Tools](https://docs.agno.com/reasoning/usage/tools/claude-reasoning-tools.md): Combine Claude with ReasoningTools and WebSearchTools to analyze the semiconductor market. - [Gemini with Reasoning Tools](https://docs.agno.com/reasoning/usage/tools/gemini-reasoning-tools.md): Use Gemini 2.5 Pro with ReasoningTools and YFinanceTools to compare NVDA and TSLA. - [Groq with Reasoning Tools](https://docs.agno.com/reasoning/usage/tools/groq-reasoning-tools.md): Run GPT-OSS 120B on Groq with ReasoningTools and WebSearchTools to compare NVDA and TSLA. - [Capture Reasoning Content with Reasoning Tools](https://docs.agno.com/reasoning/usage/tools/capture-reasoning-content-reasoning-tools.md): Read available reasoning notes from non-streaming outputs and streaming completion events. - [Agent with Knowledge Tools](https://docs.agno.com/reasoning/usage/tools/knowledge-tools.md): Give an agent KnowledgeTools backed by a LanceDB hybrid-search knowledge base to think, search, and analyze before answering. - [Capture Reasoning Content with Knowledge Tools](https://docs.agno.com/reasoning/usage/tools/capture-reasoning-content-knowledge-tools.md): Read available reasoning notes from non-streaming outputs and streaming completion events. - [Team with Reasoning Tools](https://docs.agno.com/reasoning/usage/tools/reasoning-tool-team.md): Give a Team leader ReasoningTools alongside delegation to a web-search agent and a finance agent. - [Team with Knowledge Tools](https://docs.agno.com/reasoning/usage/tools/knowledge-tool-team.md): Give a Team leader KnowledgeTools backed by a LanceDB knowledge base to search and reason before delegating. - [Basic Reasoning Agent](https://docs.agno.com/reasoning/usage/agents/basic-cot.md): Give an agent explicit planning and analysis tools during its ordinary tool loop. - [Capture Reasoning Content](https://docs.agno.com/reasoning/usage/agents/capture-reasoning-content-cot.md): Capture available explicit tool notes and native summaries from run outputs and completion events. - [Non-Reasoning Model Agent](https://docs.agno.com/reasoning/usage/agents/non-reasoning-model-cot.md): Use ReasoningTools with GPT-4.1 during the normal tool loop. - [Team with Chain of Thought](https://docs.agno.com/reasoning/usage/agents/team-cot.md): Give a Team explicit reasoning tools plus web and finance retrieval. - [Reasoning Tools](https://docs.agno.com/reasoning/reasoning-tools.md): Give any model explicit tools for structured thinking, transforming regular models into careful problem-solvers through deliberate reasoning steps. - [Reasoning Agents](https://docs.agno.com/reasoning/reasoning-agents.md): Run a separate native reasoning stage before the main Agent or Team response. - [Overview](https://docs.agno.com/multimodal/overview.md): Process and generate images, audio, video, and files with agents and teams. - [Multimodal Agents](https://docs.agno.com/multimodal/agent/overview.md): Build agents that process and generate images, audio, video, and files. - [Image As Input](https://docs.agno.com/multimodal/agent/usage/image-input.md): Pass images to agents for analysis and description. - [Image Model Output](https://docs.agno.com/multimodal/agent/usage/image-output.md): Return generated images from model responses. - [Image to Text Analysis](https://docs.agno.com/multimodal/agent/usage/image-to-text.md): Analyze a local image file and write a short fictional story about it with `OpenAIResponses`. - [Image to Structured Output](https://docs.agno.com/multimodal/agent/usage/image-to-structured-output.md): Analyze an image and stream a structured `MovieScript` response with `output_schema`. - [High Fidelity Image Input](https://docs.agno.com/multimodal/agent/usage/image-input-high-fidelity.md): Set `Image(detail="high")` for higher-fidelity image analysis. - [Image to Image Generation Agent](https://docs.agno.com/multimodal/agent/usage/image-to-image-agent.md): Transform an existing image into a new one with `FalTools(enable_image_to_image=True)`. - [Image Input for Tools](https://docs.agno.com/multimodal/agent/usage/image-input-for-tool.md): Legacy example that passes uploaded and DALL-E-generated images into tool functions. - [Image to Audio Story Generation](https://docs.agno.com/multimodal/agent/usage/image-to-audio.md): Analyze an image with `OpenAIChat(id="gpt-4o")` to write a story, then narrate it with `OpenAIChat(id="gpt-audio")`. - [Image Generation Tools](https://docs.agno.com/multimodal/agent/usage/image-generation.md): Generate images using OpenAI tools. - [Generate output image using DALL-E](https://docs.agno.com/multimodal/agent/usage/generate-image.md): Legacy DalleTools example that downloads a generated image to a local file. - [Generate output image using DALL-E with intermediate steps](https://docs.agno.com/multimodal/agent/usage/generate-image-with-intermediate-steps.md): Legacy DalleTools example that streams intermediate events while an image generates. - [Agent Using Multimodal Tool Response in Runs](https://docs.agno.com/multimodal/agent/usage/agent-using-multimodal-tool-response-in-runs.md): Legacy DalleTools example that references a generated image in a follow-up run. - [Audio As Input](https://docs.agno.com/multimodal/agent/usage/audio_input.md): Process audio as input with Agno agents. - [Audio Model Output](https://docs.agno.com/multimodal/agent/usage/audio_output.md): Get audio output from model responses in Agno agents. - [Audio Input Output](https://docs.agno.com/multimodal/agent/usage/audio-input-output.md): Send an audio recording to OpenAI's gpt-audio model and receive both a text and audio response. - [Audio to Text Transcription](https://docs.agno.com/multimodal/agent/usage/audio-to-text.md): Transcribe a multi-speaker audio recording into a speaker-labeled conversation using Gemini. - [Agent Same Run Image Analysis](https://docs.agno.com/multimodal/agent/usage/agent-same-run-image-analysis.md): Legacy DalleTools example that generates and analyzes an image in one agent run. - [Blog to Podcast Agent](https://docs.agno.com/multimodal/agent/usage/blog-to-podcast.md): Scrape a blog post with Firecrawl, summarize it, and convert the summary to audio with ElevenLabs. - [File Generation Tools](https://docs.agno.com/multimodal/agent/usage/file_generation.md): Generate files in different formats with Agno agents. - [File Input](https://docs.agno.com/multimodal/agent/usage/file_input.md): Process files as input with Agno agents. - [Speech-to-Text](https://docs.agno.com/multimodal/agent/usage/speech-to-text.md): Transcribe audio files with Agno agents. - [Audio Generation Tools](https://docs.agno.com/multimodal/agent/usage/audio_generation.md): Generate audio with text-to-speech tools in Agno agents. - [Generate Music using Models Lab](https://docs.agno.com/multimodal/agent/usage/generate-music-agent.md): Request music through a custom ModelsLab tool and save completed MP3 output. - [Audio Multi Turn](https://docs.agno.com/multimodal/agent/usage/audio-multi-turn.md): Keep audio conversation context across turns with OpenAIChat's gpt-audio model and add_history_to_context. - [Audio Sentiment Analysis](https://docs.agno.com/multimodal/agent/usage/audio-sentiment-analysis.md): Analyze sentiment and identify speakers in an audio conversation with Gemini, and persist the session history in SQLite. - [Audio Streaming](https://docs.agno.com/multimodal/agent/usage/audio-streaming.md): Stream PCM16 audio events from OpenAI's gpt-audio model and write the decoded chunks to a WAV file in real time. - [Video Input](https://docs.agno.com/multimodal/agent/usage/video_input.md): Process video as input with Agno agents. - [Video Output](https://docs.agno.com/multimodal/agent/usage/video_generation.md): Generate videos with AI tools in Agno agents. - [Video Caption Agent](https://docs.agno.com/multimodal/agent/usage/video-caption.md): Extract audio, transcribe it to timed SRT captions with OpenAI, and embed captions with MoviePyVideoTools. - [Shorts from Video](https://docs.agno.com/multimodal/agent/usage/video-to-shorts.md): Analyze a video with Gemini and use ffmpeg to cut the highest-scoring 15-60 second segments into short-form clips. - [Generate Video (ReplicateTools)](https://docs.agno.com/multimodal/agent/usage/generate-video-using-replicate.md): Generate a video from a text prompt with ReplicateTools and the tencent/hunyuan-video model. - [Generate Video (ModelsLabTools)](https://docs.agno.com/multimodal/agent/usage/generate-video-using-models-lab.md): Generate a video from a text prompt with ModelsLabTools and read the result from response.videos. - [File Input for Tools](https://docs.agno.com/multimodal/agent/usage/file-input-for-tool.md): Give a custom tool access to a File passed to the agent and simulate OCR extraction on its content. - [Multimodal Teams](https://docs.agno.com/multimodal/team/overview.md): Create teams that process text, images, audio, video, and files. - [Image to Text Team](https://docs.agno.com/multimodal/team/usage/image-to-text.md): Analyze an image with one team member and turn the analysis into a short story with another. - [Image Generation Team](https://docs.agno.com/multimodal/team/usage/generate-image-with-team.md): Legacy team example that refines a prompt and generates an image with DalleTools. - [Image to Structured Movie Script Team](https://docs.agno.com/multimodal/team/usage/image-to-structured-output.md): Use a two-agent team to analyze an image and return a Pydantic MovieScript object via output_schema. - [Image Transformation Team](https://docs.agno.com/multimodal/team/usage/image-to-image-transformation.md): Pair a style-advisor agent with an image-transformer agent that uses FalTools to restyle an image via image_to_image. - [Audio Transcription Team](https://docs.agno.com/multimodal/team/usage/audio-to-text.md): Use a two-agent team to transcribe an MP3 with Gemini and analyze the transcript for themes and insights. - [Audio Sentiment Analysis Team](https://docs.agno.com/multimodal/team/usage/audio-sentiment-analysis.md): Run a team that transcribes a WAV conversation and scores per-speaker sentiment, keeping session history in SqliteDb for follow-up questions. - [Video Captioning Team](https://docs.agno.com/multimodal/team/usage/video-caption-generation.md): Use a two-agent team with MoviePyVideoTools and OpenAI transcription to extract audio, generate SRT captions, and embed them into a video. - [Guardrails](https://docs.agno.com/guardrails/overview.md): Built-in safeguards for input validation, PII detection, and prompt injection defense. - [Prompt Injection Guardrail](https://docs.agno.com/guardrails/included/prompt-injection.md): Detect prompt injection attempts in agent inputs. - [PII Detection Guardrail](https://docs.agno.com/guardrails/included/pii.md): Detect personally identifiable information in agent inputs. - [OpenAI Moderation Guardrail](https://docs.agno.com/guardrails/included/openai-moderation.md): Detect content policy violations using OpenAI's moderation API. - [PII Detection Guardrail](https://docs.agno.com/guardrails/usage/agent/pii-detection.md): Protect sensitive data like SSNs, credit cards, emails, and phone numbers with Agno's built-in PII detection guardrail. - [Prompt Injection Guardrail](https://docs.agno.com/guardrails/usage/agent/prompt-injection.md): Detect and stop prompt injection and jailbreak attempts with Agno's built-in prompt injection guardrail. - [OpenAI Moderation Guardrail](https://docs.agno.com/guardrails/usage/agent/openai-moderation.md): Detect and block content that violates OpenAI's content policy with Agno's built-in OpenAI moderation guardrail. - [PII Detection Guardrail for Teams](https://docs.agno.com/guardrails/usage/team/pii-detection.md): Protect sensitive data in Team input with Agno's built-in PII detection guardrail. - [Prompt Injection Guardrail for Teams](https://docs.agno.com/guardrails/usage/team/prompt-injection.md): Stop prompt injection and jailbreak attempts against a Team with Agno's built-in prompt injection guardrail. - [OpenAI Moderation Guardrail for Teams](https://docs.agno.com/guardrails/usage/team/openai-moderation.md): Detect and block content policy violations in Team input with Agno's built-in OpenAI moderation guardrail. - [Human-in-the-Loop (HITL)](https://docs.agno.com/hitl/overview.md): Control agent execution flow with human oversight and input. - [User Confirmation](https://docs.agno.com/hitl/user-confirmation.md): Require explicit user approval before executing tool calls in your agents. - [User Input](https://docs.agno.com/hitl/user-input.md): Gather specific information from users during agent execution. - [Dynamic User Input](https://docs.agno.com/hitl/dynamic-user-input.md): Let agents request user input dynamically as needed during execution. - [External Tool Execution](https://docs.agno.com/hitl/external-execution.md): Execute tools outside of the agent's control for enhanced security and flexibility. - [Approval](https://docs.agno.com/hitl/approval.md): Admin-mediated HITL workflows with persistent records and audit trails. - [Agentic User Input with Control Flow](https://docs.agno.com/hitl/usage/agentic-user-input.md): Use UserControlFlowTools so the agent can request user input when it needs more information to complete a task. - [Tool Confirmation Required](https://docs.agno.com/hitl/usage/confirmation-required.md): Require user confirmation before the agent executes sensitive tool operations. - [Async Tool Confirmation Required](https://docs.agno.com/hitl/usage/confirmation-required-async.md): Require user confirmation before an async agent executes a tool, using `arun()` and `acontinue_run()`. - [Confirmation Required with Mixed Tools](https://docs.agno.com/hitl/usage/confirmation-required-mixed-tools.md): Require confirmation for some tools only. The agent runs unconfirmed tools automatically and pauses for the ones that need approval. - [Confirmation Required with Multiple Tools](https://docs.agno.com/hitl/usage/confirmation-required-multiple-tools.md): Require confirmation for multiple tools in one run, and cancel individual tool calls based on the user's choice. - [Confirmation Required with Streaming](https://docs.agno.com/hitl/usage/confirmation-required-stream.md): Require user confirmation during tool execution while streaming the agent's response. - [Confirmation Required with Async Streaming](https://docs.agno.com/hitl/usage/confirmation-required-stream-async.md): Require user confirmation during tool execution while streaming responses from an async agent. - [Confirmation Required with Toolkit](https://docs.agno.com/hitl/usage/confirmation-required-toolkit.md): Require confirmation for tools from a pre-built toolkit like YFinanceTools, using `requires_confirmation_tools`. - [Confirmation Required with History](https://docs.agno.com/hitl/usage/confirmation-required-with-history.md): Require user confirmation for tool calls while the agent keeps previous conversation history in context. - [Confirmation Required with Run ID](https://docs.agno.com/hitl/usage/confirmation-required-with-run-id.md): Pause an agent run for tool confirmation, then continue it by run ID with the resolved requirements. - [User Input Required for Tool Execution](https://docs.agno.com/hitl/usage/user-input-required.md): Create tools that require user input before execution, so the agent collects data from the user during the run. - [Require User Input for All Tool Fields](https://docs.agno.com/hitl/usage/user-input-required-all-fields.md): Collect user input for every tool field by naming each field in user_input_fields. - [User Input Required Async](https://docs.agno.com/hitl/usage/user-input-required-async.md): Collect specific user input fields with the requires_user_input parameter in an async environment. - [User Input Required Stream Async](https://docs.agno.com/hitl/usage/user-input-required-stream-async.md): Collect user input fields with the requires_user_input parameter while streaming responses asynchronously. - [External Tool Execution](https://docs.agno.com/hitl/usage/external-tool-execution.md): Execute tools outside the agent. You control tool execution externally while the agent handles the rest of the run. - [External Tool Execution Async](https://docs.agno.com/hitl/usage/external-tool-execution-async.md): Execute tools outside the agent in an async environment. You control tool execution externally while the agent handles the rest of the run. - [External Tool Execution Stream Async](https://docs.agno.com/hitl/usage/external-tool-execution-stream-async.md): Execute tools outside the agent while streaming responses asynchronously. - [External Tool Execution Toolkit](https://docs.agno.com/hitl/usage/external-tool-execution-toolkit.md): Create a custom toolkit with tools that require external execution, then run those tools outside the agent. - [What are Evals?](https://docs.agno.com/evals/overview.md): Measure agent and team quality across expected answers, custom criteria, tool behavior, and performance. - [Accuracy Evals](https://docs.agno.com/evals/accuracy/overview.md): Accuracy evals measure how well your Agents and Teams perform against a gold-standard answer using LLM-as-a-judge methodology. - [Basic Accuracy](https://docs.agno.com/evals/accuracy/usage/basic.md): Score an Agent's response for completeness, correctness, and accuracy with AccuracyEval. - [Async Accuracy Evaluation](https://docs.agno.com/evals/accuracy/usage/accuracy-async.md): Example showing how to run accuracy evaluations asynchronously with AccuracyEval.arun(). - [Comparison Accuracy Evaluation](https://docs.agno.com/evals/accuracy/usage/accuracy-comparison.md): Example showing how to evaluate agent accuracy on comparison tasks. - [Accuracy with Database Logging](https://docs.agno.com/evals/accuracy/usage/accuracy-db-logging.md): Example showing how to store evaluation results in the database for tracking and analysis. - [Accuracy with Given Answer](https://docs.agno.com/evals/accuracy/usage/accuracy-with-given-answer.md): Example showing how to evaluate a precomputed answer directly with AccuracyEval.run_with_output(), without running an Agent. - [Accuracy with Tools](https://docs.agno.com/evals/accuracy/usage/accuracy-with-tools.md): Example showing an evaluation that runs the provided agent with the provided input and then evaluates the answer that the agent gives. - [Accuracy with Teams](https://docs.agno.com/evals/accuracy/usage/accuracy-with-teams.md): Example showing how to evaluate the accuracy of an Agno Team. - [Performance Evals](https://docs.agno.com/evals/performance/overview.md): Performance evals measure the latency and memory footprint of an Agent or Team. - [Performance on Agent Response](https://docs.agno.com/evals/performance/usage/performance-simple-response.md): Example showing how to analyze the runtime and memory usage of an Agent's run, given its response. - [Async Performance Evaluation](https://docs.agno.com/evals/performance/usage/performance-async.md): Example showing how to run performance evaluations on async functions. - [Performance on Agent Instantiation](https://docs.agno.com/evals/performance/usage/performance-agent-instantiation.md): Example showing how to analyze the runtime and memory usage of an Agent. - [Performance on Agent Instantiation with Tool](https://docs.agno.com/evals/performance/usage/performance-instantiation-with-tool.md): Example showing how to analyze the runtime and memory usage of an Agent that is using tools. - [Performance on Agent with Storage](https://docs.agno.com/evals/performance/usage/performance-with-storage.md): Example showing how to analyze the runtime and memory usage of an Agent that is using storage. - [Performance with Memory Updates](https://docs.agno.com/evals/performance/usage/performance-with-memory.md): Example showing how to evaluate performance when memory updates are involved. - [Performance with Teams](https://docs.agno.com/evals/performance/usage/performance-team-instantiation.md): Example showing how to analyze the runtime and memory usage of an Agno Team. - [Team Performance with Memory](https://docs.agno.com/evals/performance/usage/performance-team-with-memory.md): Example showing how to evaluate team performance with memory tracking and growth monitoring. - [Performance with Database Logging](https://docs.agno.com/evals/performance/usage/performance-db-logging.md): Example showing how to store performance evaluation results in the database. - [Reliability Evals](https://docs.agno.com/evals/reliability/overview.md): Reliability evals assert that your Agents and Teams make the expected tool calls. - [Reliability with Single Tool](https://docs.agno.com/evals/reliability/usage/basic.md): Example showing how to assert an Agent is making the expected tool calls. - [Async Reliability Evaluation](https://docs.agno.com/evals/reliability/usage/reliability-async.md): Example showing how to run reliability evaluations asynchronously. - [Reliability with Database Logging](https://docs.agno.com/evals/reliability/usage/reliability-db-logging.md): Example showing how to store reliability evaluation results in the database. - [Single Tool Reliability](https://docs.agno.com/evals/reliability/usage/reliability-single-tool.md): Example showing how to evaluate reliability of single tool calls. - [Reliability with Multiple Tools](https://docs.agno.com/evals/reliability/usage/reliability-with-multiple-tools.md): Example showing how to assert an Agno Agent is making multiple expected tool calls. - [Reliability with Teams](https://docs.agno.com/evals/reliability/usage/reliability-with-teams.md): Example showing how to assert an Agno Team is making the expected tool calls. - [Team Reliability with Stock Tools](https://docs.agno.com/evals/reliability/usage/reliability-team-advanced.md): Evaluate whether a team calls its delegation and stock-price tools with the expected arguments. - [Agent as Judge Evals](https://docs.agno.com/evals/agent-as-judge/overview.md): Agent as Judge evals measure custom quality criteria for your Agents and Teams using LLM-as-a-judge methodology. - [Basic Agent as Judge](https://docs.agno.com/evals/agent-as-judge/usage/agent-as-judge-basic.md): Basic usage of Agent as Judge evaluation with numeric scoring and failure callbacks - [Async Agent as Judge](https://docs.agno.com/evals/agent-as-judge/usage/agent-as-judge-async.md): Asynchronous evaluation with Agent as Judge - [Binary Agent as Judge](https://docs.agno.com/evals/agent-as-judge/usage/agent-as-judge-binary.md): Binary pass/fail evaluation without numeric scoring - [Batch Agent as Judge](https://docs.agno.com/evals/agent-as-judge/usage/agent-as-judge-batch.md): Evaluate multiple input/output pairs in a single batch - [Agent as Judge with Custom Evaluator](https://docs.agno.com/evals/agent-as-judge/usage/agent-as-judge-custom-evaluator.md): Using a custom evaluator agent with specific instructions - [Agent as Judge with Guidelines](https://docs.agno.com/evals/agent-as-judge/usage/agent-as-judge-with-guidelines.md): Using additional guidelines for more detailed evaluation criteria - [Agent as Judge as Post-Hook](https://docs.agno.com/evals/agent-as-judge/usage/agent-as-judge-post-hook.md): Using Agent as Judge evaluation as a post-hook for automatic evaluation - [Agent as Judge with Teams](https://docs.agno.com/evals/agent-as-judge/usage/agent-as-judge-team.md): Evaluating team outputs with Agent as Judge - [Async Team Post-Hook Agent as Judge](https://docs.agno.com/evals/agent-as-judge/usage/agent-as-judge-team-post-hook-async.md): Automatic async evaluation of team outputs using post-hooks - [Eval Suites](https://docs.agno.com/evals/suite/overview.md): Declare eval Cases and run them as one suite with tag selection, per-case timeouts, a JSON report, and CI exit codes. - [Tracing](https://docs.agno.com/tracing/overview.md): Trace agent, team, and workflow runs with OpenTelemetry and store spans in a configured database or observability backend. - [Basic Setup](https://docs.agno.com/tracing/basic-setup.md): Configure and enable tracing for your Agno agents - [Accessing your Traces](https://docs.agno.com/tracing/db-functions.md): Database convenience functions for querying traces and spans - [Basic Agent Tracing](https://docs.agno.com/tracing/usage/basic-agent-tracing.md): Enable tracing and observability for agents. - [Basic Team Tracing](https://docs.agno.com/tracing/usage/basic-team-tracing.md): Enable tracing and observability for teams. - [Basic Workflow Tracing](https://docs.agno.com/tracing/usage/basic-workflow-tracing.md): Enable tracing and observability for workflows. - [Scheduler](https://docs.agno.com/scheduler/overview.md): Create and manage cron schedules with the Scheduler SDK and AgentOS. - [Model Providers](https://docs.agno.com/models/providers/model-index.md): Setup guides for model providers and API adapters available in Agno. - [Anthropic Claude](https://docs.agno.com/models/providers/native/anthropic/overview.md): Use Anthropic Claude models with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/native/anthropic/usage/basic.md): Run a Claude-powered Agno agent and print its response. - [Streaming Agent](https://docs.agno.com/models/providers/native/anthropic/usage/basic-stream.md): Stream a Claude response through an Agno agent. - [Beta Features](https://docs.agno.com/models/providers/native/anthropic/usage/betas.md): Send Anthropic beta headers and their matching request configuration with Agno. - [Code Execution Tool](https://docs.agno.com/models/providers/native/anthropic/usage/code-execution.md): Run Python and Bash in Anthropic's server-side sandbox from an Agno agent. - [Context Editing](https://docs.agno.com/models/providers/native/anthropic/usage/context-management.md): Clear older tool results from Claude's server-side context with Anthropic context editing. - [Claude Agent Skills](https://docs.agno.com/models/providers/native/anthropic/usage/skills.md): Create and download PowerPoint, Excel, Word, and PDF files with Claude Agent Skills. - [Agent with Tools](https://docs.agno.com/models/providers/native/anthropic/usage/tool-use.md): Give a Claude agent a web search tool and let Agno execute the tool-use loop. - [Agent with Structured Outputs](https://docs.agno.com/models/providers/native/anthropic/usage/structured-output.md): Return a validated Pydantic object from a Claude agent. - [Structured Outputs with stream=True](https://docs.agno.com/models/providers/native/anthropic/usage/structured-output-stream.md): Use the Agent streaming interface with a Claude output schema and receive one validated result. - [Agent with Structured Outputs and Strict Tools](https://docs.agno.com/models/providers/native/anthropic/usage/structured-output-strict-tools.md): Validate both Claude tool-call inputs and the agent's final Pydantic response. - [Agent with Knowledge](https://docs.agno.com/models/providers/native/anthropic/usage/knowledge.md): Answer questions from a PDF using Claude, OpenAI embeddings, and PgVector. - [File Upload](https://docs.agno.com/models/providers/native/anthropic/usage/file-upload.md): Upload and reference files using Anthropic's Files API in Agno. - [Image Input Bytes](https://docs.agno.com/models/providers/native/anthropic/usage/image-input-bytes.md): Send a local image to Claude as raw bytes and pair it with web search. - [Image Input URL](https://docs.agno.com/models/providers/native/anthropic/usage/image-input-url.md): Point Claude at an image URL and search the web for more context. - [PDF Input Bytes](https://docs.agno.com/models/providers/native/anthropic/usage/pdf-input-bytes.md): Send a PDF to Claude as raw bytes and read citations from the run output. - [PDF Input Local](https://docs.agno.com/models/providers/native/anthropic/usage/pdf-input-local.md): Send a local PDF file to Claude and read citations from the run output. - [PDF Input URL](https://docs.agno.com/models/providers/native/anthropic/usage/pdf-input-url.md): Point Claude at a PDF URL and summarize its contents. - [Prompt Caching](https://docs.agno.com/models/providers/native/anthropic/usage/prompt-caching.md): Cache system prompts to reduce processing time and costs with Anthropic models. - [Response Caching](https://docs.agno.com/models/providers/native/anthropic/usage/cache-response.md): Cache model responses to reduce API calls and costs. - [Web Fetch](https://docs.agno.com/models/providers/native/anthropic/usage/web-fetch.md): Fetch and summarize a web page with Anthropic's server-side web fetch tool. - [Cohere](https://docs.agno.com/models/providers/native/cohere/overview.md): Use Cohere's Command models with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/native/cohere/usage/basic.md): Run a Cohere-powered Agno agent and print its response. - [Streaming Agent](https://docs.agno.com/models/providers/native/cohere/usage/basic-stream.md): Stream a Cohere response through an Agno agent. - [Image Agent](https://docs.agno.com/models/providers/native/cohere/usage/image-agent.md): Describe an image with Cohere's Aya Vision model. - [Agent with Tools](https://docs.agno.com/models/providers/native/cohere/usage/tool-use.md): Give a Cohere agent a web search tool. - [Agent with Structured Outputs](https://docs.agno.com/models/providers/native/cohere/usage/structured-output.md): Request a MovieScript response and handle validated output from a Cohere agent. - [Agent with Storage](https://docs.agno.com/models/providers/native/cohere/usage/storage.md): Store Cohere agent sessions and history in Postgres. - [Agent with Knowledge](https://docs.agno.com/models/providers/native/cohere/usage/knowledge.md): Give a Cohere agent knowledge from a PDF stored in PgVector. - [DashScope](https://docs.agno.com/models/providers/native/dashscope/overview.md): Use Alibaba DashScope Qwen models with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/native/dashscope/usage/basic.md): Run a DashScope-powered Agno agent and print its response. - [Basic Agent with Streaming](https://docs.agno.com/models/providers/native/dashscope/usage/basic-stream.md): Stream a DashScope-powered Agno agent's response as it's generated. - [Agent with Tools](https://docs.agno.com/models/providers/native/dashscope/usage/tool-use.md): Give a DashScope qwen-plus agent web search tools and stream the response. - [Image Agent](https://docs.agno.com/models/providers/native/dashscope/usage/image-agent.md): Analyze an image URL with DashScope's qwen3-vl-plus and search the web for more context. - [Image Agent with Bytes](https://docs.agno.com/models/providers/native/dashscope/usage/image-agent-bytes.md): Analyze an image from raw bytes with DashScope's qwen3-vl-plus and search the web for context. - [Structured Output Agent](https://docs.agno.com/models/providers/native/dashscope/usage/structured-output.md): Request a MovieScript response and handle validated output from a DashScope agent. - [Thinking Agent](https://docs.agno.com/models/providers/native/dashscope/usage/thinking-agent.md): Enable DashScope's thinking mode to reason over an image before responding. - [DeepSeek](https://docs.agno.com/models/providers/native/deepseek/overview.md): Use DeepSeek models with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/native/deepseek/usage/basic.md): Run a DeepSeek-powered Agno agent and print its response. - [Streaming Agent](https://docs.agno.com/models/providers/native/deepseek/usage/basic-stream.md): Stream a DeepSeek-powered Agno agent's response as it's generated. - [Agent with Tools](https://docs.agno.com/models/providers/native/deepseek/usage/tool-use.md): Give a DeepSeek agent a web search tool. - [Agent with Structured Outputs](https://docs.agno.com/models/providers/native/deepseek/usage/structured-output.md): Request a MovieScript response and handle validated output from a DeepSeek agent. - [Toggle Thinking Mode](https://docs.agno.com/models/providers/native/deepseek/usage/thinking-mode.md): Control DeepSeek's reasoning with the use_thinking flag. - [Reasoning Effort](https://docs.agno.com/models/providers/native/deepseek/usage/reasoning-effort.md): Set DeepSeek's reasoning_effort to control how much it reasons before answering. - [Gemini](https://docs.agno.com/models/providers/native/google/overview.md): Use Google Gemini models with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/native/google/usage/basic.md): Run a Gemini agent synchronously, with streaming, and asynchronously. - [Streaming Agent](https://docs.agno.com/models/providers/native/google/usage/basic-stream.md): Stream a Gemini agent's response with stream=True. - [Agent with Structured Outputs](https://docs.agno.com/models/providers/native/google/usage/structured-output.md): Return a validated Pydantic object from a Gemini agent with output_schema. - [Agent with Tools](https://docs.agno.com/models/providers/native/google/usage/tool-use.md): Give a Gemini-powered Agno agent web search tools. - [Agent with Storage](https://docs.agno.com/models/providers/native/google/usage/storage.md): Persist a Gemini agent's session history to Postgres across turns. - [Agent with Knowledge](https://docs.agno.com/models/providers/native/google/usage/knowledge.md): Ground a Gemini agent in a PDF knowledge base backed by PgVector. - [Image Agent](https://docs.agno.com/models/providers/native/google/usage/image-input.md): Send an image URL to a Gemini agent alongside a web search tool. - [Image Agent with File Upload](https://docs.agno.com/models/providers/native/google/usage/image-input-file-upload.md): Upload a local image file to Gemini and pass it to an agent. - [Image Generation Agent](https://docs.agno.com/models/providers/native/google/usage/image-generation.md): Generate images from a Gemini agent using response_modalities. - [Image Generation Agent (Streaming)](https://docs.agno.com/models/providers/native/google/usage/image-generation-stream.md): Stream image generation from a Gemini agent with response_modalities. - [Image Editing Agent](https://docs.agno.com/models/providers/native/google/usage/image-editing.md): Edit an existing image with a Gemini agent using response_modalities. - [Image Tool with OpenAI](https://docs.agno.com/models/providers/native/google/usage/imagen-tool.md): Use an OpenAI agent to call a Gemini image-generation agent through a custom tool. - [Image Tool with Vertex AI](https://docs.agno.com/models/providers/native/google/usage/imagen-tool-advanced.md): Use an OpenAI agent to call a Gemini image-generation agent through a custom tool. - [Agent with Vertex AI](https://docs.agno.com/models/providers/native/google/usage/vertexai.md): Use Gemini through Vertex AI by setting environment variables or passing vertexai=True. - [Agent with Grounding](https://docs.agno.com/models/providers/native/google/usage/grounding.md): Ground Gemini responses in real-time web results with search=True. - [Agent with URL Context](https://docs.agno.com/models/providers/native/google/usage/url-context.md): Let Gemini fetch and analyze the content of specific URLs with url_context=True. - [Agent with URL Context and Search](https://docs.agno.com/models/providers/native/google/usage/url-context-with-search.md): Combine Google Search with URL context so Gemini finds pages, then analyzes their content. - [Thinking Agent](https://docs.agno.com/models/providers/native/google/usage/flash-thinking.md): Enable Gemini thinking with thinking_budget and include thought summaries in the response. - [Audio Input (Bytes Content)](https://docs.agno.com/models/providers/native/google/usage/audio-input-bytes-content.md): Pass raw audio bytes to a Gemini agent with Audio(content=...). - [Audio Input (Upload the file)](https://docs.agno.com/models/providers/native/google/usage/audio-input-file-upload.md): Upload an audio file with the Gemini Files API and pass the uploaded file to an agent. - [Audio Input (Local file)](https://docs.agno.com/models/providers/native/google/usage/audio-input-local-file-upload.md): Pass a local audio file to a Gemini agent with Audio(filepath=...). - [Agent with PDF Input (Local file)](https://docs.agno.com/models/providers/native/google/usage/pdf-input-local.md): Pass a local PDF to a Gemini agent with File(filepath=...). - [Agent with PDF Input (URL)](https://docs.agno.com/models/providers/native/google/usage/pdf-input-url.md): Pass a PDF by URL to a Gemini agent with File(url=...). - [Agent with GCS File Input](https://docs.agno.com/models/providers/native/google/usage/gcs-file-input.md): Pass files to Gemini straight from Google Cloud Storage with a gs:// URL and Vertex AI. - [Agent with External URL Input](https://docs.agno.com/models/providers/native/google/usage/external-url-input.md): Pass a public HTTPS file URL directly to a Gemini agent with File(url=...). - [Agent with S3 Pre-signed URL Input](https://docs.agno.com/models/providers/native/google/usage/s3-presigned-url-input.md): Pass files from AWS S3 to a Gemini agent using a pre-signed URL with File(url=...). - [Video Input (Bytes Content)](https://docs.agno.com/models/providers/native/google/usage/video-input-bytes-content.md): Pass raw video bytes to a Gemini agent with Video(content=...). - [Video Input (File Upload)](https://docs.agno.com/models/providers/native/google/usage/video-input-file-upload.md): Upload a video to Gemini's Files API, poll until it's ready, and pass it to an agent. - [Video Input (Local File Upload)](https://docs.agno.com/models/providers/native/google/usage/video-input-local-file-upload.md): Pass a local video file to a Gemini agent with Video(filepath=...). - [Basic Agent (Interactions)](https://docs.agno.com/models/providers/native/google/usage/interactions-basic.md): Run a GeminiInteractions agent synchronously, with streaming, and asynchronously. - [Agent with Tools (Interactions)](https://docs.agno.com/models/providers/native/google/usage/interactions-tool-use.md): Call WebSearchTools from a GeminiInteractions agent in sync, streaming, and async modes. - [Multi-turn Conversation (Interactions)](https://docs.agno.com/models/providers/native/google/usage/interactions-multi-turn.md): Persist previous_interaction_id across turns with a database so GeminiInteractions keeps context server-side. - [Thinking (Interactions)](https://docs.agno.com/models/providers/native/google/usage/interactions-thinking.md): Set thinking_level on GeminiInteractions and stream the reasoning behind a response. - [Google Search (Interactions)](https://docs.agno.com/models/providers/native/google/usage/interactions-search.md): Ground GeminiInteractions responses in live web results with the built-in search tool. - [Structured Output (Interactions)](https://docs.agno.com/models/providers/native/google/usage/interactions-structured-output.md): Parse GeminiInteractions responses into a Pydantic model with output_schema. - [Deep Research (Interactions)](https://docs.agno.com/models/providers/native/google/usage/interactions-deep-research.md): Run the Deep Research managed agent through the Interactions API and poll for a cited report. - [Deep Research Streaming (Interactions)](https://docs.agno.com/models/providers/native/google/usage/interactions-deep-research-streaming.md): Stream thought summaries and progress from a Deep Research task instead of waiting for the final report. - [Deep Research Multi-turn (Interactions)](https://docs.agno.com/models/providers/native/google/usage/interactions-deep-research-multi-turn.md): Continue a Deep Research interaction across turns using previous_interaction_id and a database. - [Deep Research Collaborative Planning (Interactions)](https://docs.agno.com/models/providers/native/google/usage/interactions-deep-research-collaborative-planning.md): Chain collaborative_planning turns with previous_interaction_id to approve a research plan before Deep Research runs it. - [Deep Research with File Search (Interactions)](https://docs.agno.com/models/providers/native/google/usage/interactions-deep-research-file-search.md): Ground Deep Research on your own documents with a File Search store and file_search_store_names. - [Deep Research with MCP (Interactions)](https://docs.agno.com/models/providers/native/google/usage/interactions-deep-research-mcp.md): Give the Deep Research agent tool access through remote MCP servers with mcp_servers. - [Deep Research Multimodal Input (Interactions)](https://docs.agno.com/models/providers/native/google/usage/interactions-deep-research-multimodal.md): Pass images and PDFs to Deep Research as Agno Image and File objects. - [Deep Research Visualization (Interactions)](https://docs.agno.com/models/providers/native/google/usage/interactions-deep-research-visualization.md): Generate charts and graphs from Deep Research findings with visualization="auto". - [Antigravity (Interactions)](https://docs.agno.com/models/providers/native/google/usage/interactions-antigravity.md): Run the Antigravity managed autonomous agent in a sandbox through the Gemini Interactions API. - [Antigravity Streaming (Interactions)](https://docs.agno.com/models/providers/native/google/usage/interactions-antigravity-streaming.md): Stream Antigravity's progress (tool calls, intermediate text, generated artifacts) instead of waiting for the final result. - [Antigravity Multi-turn (Interactions)](https://docs.agno.com/models/providers/native/google/usage/interactions-antigravity-multi-turn.md): Continue an Antigravity interaction across turns using previous_interaction_id and a database. - [Antigravity Environment Config (Interactions)](https://docs.agno.com/models/providers/native/google/usage/interactions-antigravity-environment-config.md): Configure the Antigravity sandbox by reusing an environment ID or passing a full EnvironmentConfig dict. - [Gemini Interactions](https://docs.agno.com/models/providers/native/google/gemini-interactions.md): Use Google's Interactions API for server-side conversation history, implicit caching, and background execution. - [Inception](https://docs.agno.com/models/providers/native/inception/overview.md): Use Inception Labs Mercury diffusion models with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/native/inception/usage/basic.md): Run an Inception-powered Agno agent synchronously, with streaming, and asynchronously. - [Agent with Tools](https://docs.agno.com/models/providers/native/inception/usage/tool-use.md): Give an Inception agent a web search tool and stream the response. - [Agent with Structured Outputs](https://docs.agno.com/models/providers/native/inception/usage/structured-output.md): Request a MovieScript response and handle validated output from a Mercury 2 agent. - [Meta](https://docs.agno.com/models/providers/native/meta/overview.md): Use Meta Llama models with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/native/meta/usage/basic.md): Run a Llama-powered Agno agent synchronously, with streaming, and asynchronously. - [Streaming Agent](https://docs.agno.com/models/providers/native/meta/usage/basic-stream.md): Stream a Llama-powered Agno agent's response as it's generated. - [Asynchronous Agent](https://docs.agno.com/models/providers/native/meta/usage/async-basic.md): Run a Llama-powered Agno agent asynchronously. - [Asynchronous Streaming Agent](https://docs.agno.com/models/providers/native/meta/usage/async-stream.md): Stream a Llama-powered Agno agent's response asynchronously with aprint_response(). - [Agent with Tools](https://docs.agno.com/models/providers/native/meta/usage/tool-use.md): Give a Llama agent a stock price tool with YFinanceTools. - [Agent with Async Tool Usage](https://docs.agno.com/models/providers/native/meta/usage/async-tool-use.md): Call a Llama agent's tools asynchronously with aprint_response(). - [Agent with Structured Outputs](https://docs.agno.com/models/providers/native/meta/usage/structured-output.md): Request a MovieScript response and handle validated output from the agent. - [Agent with Image Input](https://docs.agno.com/models/providers/native/meta/usage/image-input-bytes.md): Send raw image bytes to a Llama agent alongside a web search tool. - [Agent with Knowledge](https://docs.agno.com/models/providers/native/meta/usage/knowledge.md): Load a PDF into PgVector and query it with a Llama agent. - [Agent with Memory](https://docs.agno.com/models/providers/native/meta/usage/memory.md): Enable user memories and session summaries for a Llama agent backed by PostgresDb. - [MiniMax](https://docs.agno.com/models/providers/native/minimax/overview.md): Use MiniMax models with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/native/minimax/usage/basic.md): Run a MiniMax-powered Agno agent synchronously, with streaming, and asynchronously. - [Agent with Tools](https://docs.agno.com/models/providers/native/minimax/usage/tool-use.md): Give a MiniMax agent a web search tool and stream the response. - [Agent with Structured Outputs](https://docs.agno.com/models/providers/native/minimax/usage/structured-output.md): Request a MovieScript response and handle validated output from the agent. - [Mistral](https://docs.agno.com/models/providers/native/mistral/overview.md): Use Mistral models with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/native/mistral/usage/basic.md): Run a Mistral-powered Agno agent synchronously and with streaming. - [Basic Streaming Agent](https://docs.agno.com/models/providers/native/mistral/usage/basic-stream.md): Stream a Mistral response through an Agno agent. - [Async Basic Agent](https://docs.agno.com/models/providers/native/mistral/usage/async-basic.md): Run a Mistral-powered Agno agent asynchronously. - [Async Basic Streaming Agent](https://docs.agno.com/models/providers/native/mistral/usage/async-basic-stream.md): Stream a Mistral-powered Agno agent's response asynchronously. - [Agent with Tools](https://docs.agno.com/models/providers/native/mistral/usage/tool-use.md): Give a Mistral agent a custom function tool. - [Async Agent with Tools](https://docs.agno.com/models/providers/native/mistral/usage/async-tool-use.md): Give a Mistral agent web search and run it asynchronously. - [Agent with Memory](https://docs.agno.com/models/providers/native/mistral/usage/memory.md): Store user memories and session summaries for a Mistral agent in Postgres. - [Structured Output](https://docs.agno.com/models/providers/native/mistral/usage/structured-output.md): Request a MovieScript response and handle validated output from the agent. - [Structured Output With Tool Use](https://docs.agno.com/models/providers/native/mistral/usage/structured-output-with-tool-use.md): Search the web with a Mistral Medium agent and request a Person response. - [Async Structured Output Agent](https://docs.agno.com/models/providers/native/mistral/usage/async-structured-output.md): Run an async Mistral Small agent that searches the web and requests a MovieScript response. - [Image Bytes Input Agent](https://docs.agno.com/models/providers/native/mistral/usage/image-bytes-input-agent.md): Pass raw image bytes fetched with requests to a Ministral 14B agent for analysis. - [Image Compare Agent](https://docs.agno.com/models/providers/native/mistral/usage/image-compare-agent.md): Send two image URLs to a Ministral 14B agent and stream a comparison of the differences. - [Image File Input Agent](https://docs.agno.com/models/providers/native/mistral/usage/image-file-input-agent.md): Analyze a local image file with Ministral 14B and search the web for related news. - [Image OCR with Structured Output](https://docs.agno.com/models/providers/native/mistral/usage/image-ocr-with-structured-output.md): Extract a structured bill schema from a receipt image with a Ministral 14B agent. - [Image Transcribe Document Agent](https://docs.agno.com/models/providers/native/mistral/usage/image-transcribe-document-agent.md): Transcribe a scanned document image into text with a Ministral 14B agent. - [Mistral Small](https://docs.agno.com/models/providers/native/mistral/usage/mistral-small.md): Run a Mistral Small agent with web search tools and streamed output. - [OpenAI](https://docs.agno.com/models/providers/native/openai/completion/overview.md): Use OpenAI GPT and reasoning models with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/native/openai/completion/usage/basic.md): Run an OpenAIChat agent and print its response. - [Streaming Agent](https://docs.agno.com/models/providers/native/openai/completion/usage/basic-stream.md): Stream an OpenAIChat agent's response with stream=True. - [Agent with Tools](https://docs.agno.com/models/providers/native/openai/completion/usage/tool-use.md): Give an OpenAI agent web search with WebSearchTools. - [Agent with Structured Outputs](https://docs.agno.com/models/providers/native/openai/completion/usage/structured-output.md): Request a Pydantic schema with an OpenAI agent using JSON mode or JSON Schema. - [Agent with Storage](https://docs.agno.com/models/providers/native/openai/completion/usage/storage.md): Persist an OpenAI agent's session history to Postgres across runs. - [Agent with Knowledge](https://docs.agno.com/models/providers/native/openai/completion/usage/knowledge.md): Answer questions from a PDF using an OpenAI agent and PgVector. - [Image Agent](https://docs.agno.com/models/providers/native/openai/completion/usage/image-agent.md): Send an image to an OpenAI agent and combine vision with web search. - [Audio Input Agent](https://docs.agno.com/models/providers/native/openai/completion/usage/audio-input-agent.md): Send an audio file to an OpenAI agent and get a text description of its contents. - [Audio Output Agent](https://docs.agno.com/models/providers/native/openai/completion/usage/audio-output-agent.md): Get text and spoken audio from gpt-audio and save the responses to WAV files. - [Generate Images](https://docs.agno.com/models/providers/native/openai/completion/usage/generate-images.md): Legacy DalleTools example that reads DALL-E image URLs from a run output. - [Agent with Reasoning Effort](https://docs.agno.com/models/providers/native/openai/completion/usage/reasoning-effort.md): Control how much reasoning o3-mini does with the reasoning_effort parameter. - [Response Caching](https://docs.agno.com/models/providers/native/openai/completion/usage/cache-response.md): Cache model responses to reduce API calls and costs. - [OpenAI Responses](https://docs.agno.com/models/providers/native/openai/responses/overview.md): Use OpenAI's Responses API with Agno agents. - [Agent Flex Tier](https://docs.agno.com/models/providers/native/openai/responses/usage/agent-flex-tier.md): Run o4-mini on the flex service tier with the OpenAIResponses model. - [Async Basic](https://docs.agno.com/models/providers/native/openai/responses/usage/async-basic.md): Run a gpt-4o agent asynchronously through the OpenAI Responses API with aprint_response. - [Async Basic Stream](https://docs.agno.com/models/providers/native/openai/responses/usage/async-basic-stream.md): Stream a gpt-4o agent's response asynchronously through the OpenAI Responses API. - [Async Tool Use](https://docs.agno.com/models/providers/native/openai/responses/usage/async-tool-use.md): Run a gpt-4o agent with WebSearchTools asynchronously and stream the response through the OpenAI Responses API. - [Basic](https://docs.agno.com/models/providers/native/openai/responses/usage/basic.md): Run a gpt-4o agent synchronously through the OpenAI Responses API and print the response. - [Basic Stream](https://docs.agno.com/models/providers/native/openai/responses/usage/basic-stream.md): Stream a gpt-4o agent's response synchronously through the OpenAI Responses API. - [Agent with Storage](https://docs.agno.com/models/providers/native/openai/responses/usage/db.md): Persist Responses API sessions in Postgres and answer a history-dependent follow-up. - [Deep Research Agent](https://docs.agno.com/models/providers/native/openai/responses/usage/deep-research-agent.md): Run an OpenAIResponses agent on the o4-mini-deep-research model with max_tool_calls=1 to produce a cited, data-backed report on semaglutide's economic impact on healthcare. - [Image Agent](https://docs.agno.com/models/providers/native/openai/responses/usage/image-agent.md): Stream a response from an OpenAIResponses gpt-4o agent with WebSearchTools that describes an image URL and finds related news. - [Image Agent Bytes](https://docs.agno.com/models/providers/native/openai/responses/usage/image-agent-bytes.md): Download an image, read it as raw bytes, and stream a response from an OpenAIResponses gpt-4o agent with WebSearchTools that describes it and finds related news. - [Image Agent with Memory](https://docs.agno.com/models/providers/native/openai/responses/usage/image-agent-with-memory.md): Use an OpenAIResponses gpt-4o agent with WebSearchTools and add_history_to_context to answer a follow-up question about an image described in an earlier turn. - [Image Generation Agent](https://docs.agno.com/models/providers/native/openai/responses/usage/image-generation-agent.md): Use an OpenAIResponses agent with OpenAITools and GPT Image 2 to generate a photorealistic image and save it to disk. - [Knowledge](https://docs.agno.com/models/providers/native/openai/responses/usage/knowledge.md): Load a Thai recipes PDF into a PgVector-backed Knowledge base and query it with an OpenAIResponses gpt-4o agent. - [Memory](https://docs.agno.com/models/providers/native/openai/responses/usage/memory.md): Store personalized user memories and session summaries for a gpt-4o agent through the OpenAI Responses API and Postgres. - [PDF Input Local](https://docs.agno.com/models/providers/native/openai/responses/usage/pdf-input-local.md): Summarize a local PDF and ask follow-up questions with the file_search tool through the OpenAI Responses API. - [PDF Input URL](https://docs.agno.com/models/providers/native/openai/responses/usage/pdf-input-url.md): Summarize a PDF by URL and read response citations from a stored session through the OpenAI Responses API. - [Reasoning O3 Mini](https://docs.agno.com/models/providers/native/openai/responses/usage/reasoning-o3-mini.md): Stream a stock report from an o3-mini agent with YFinanceTools through the OpenAI Responses API. - [Structured Output](https://docs.agno.com/models/providers/native/openai/responses/usage/structured-output.md): Compare JSON mode, strict, and guided structured output for a MovieScript schema through the OpenAI Responses API. - [Tool Use](https://docs.agno.com/models/providers/native/openai/responses/usage/tool-use.md): Answer a current-events question with a gpt-4o agent using WebSearchTools through the OpenAI Responses API. - [Tool Use GPT 5](https://docs.agno.com/models/providers/native/openai/responses/usage/tool-use-gpt-5.md): Run a gpt-5 agent with cached YFinance tools through the OpenAI Responses API and stream the response. - [Tool Use O3](https://docs.agno.com/models/providers/native/openai/responses/usage/tool-use-o3.md): Run an o3 agent with cached YFinance tools through the OpenAI Responses API and stream the response. - [Tool Use Stream](https://docs.agno.com/models/providers/native/openai/responses/usage/tool-use-stream.md): Run a gpt-4o agent with WebSearchTools through the OpenAI Responses API and stream the response. - [Verbosity Control](https://docs.agno.com/models/providers/native/openai/responses/usage/verbosity-control.md): Set Responses API verbosity to high on a gpt-5 agent with YFinanceTools. - [Websearch Builtin Tool](https://docs.agno.com/models/providers/native/openai/responses/usage/websearch-builtin-tool.md): Use OpenAI's built-in web_search_preview tool with FileTools to save search results through the OpenAI Responses API. - [ZDR Reasoning Agent](https://docs.agno.com/models/providers/native/openai/responses/usage/zdr-reasoning-agent.md): Use store=False and local conversation history for stateless Responses API reasoning. - [Perplexity](https://docs.agno.com/models/providers/native/perplexity/overview.md): Use Perplexity models with built-in web search in Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/native/perplexity/usage/basic.md): Run a Perplexity-powered Agno agent and print its response. - [Basic Streaming Agent](https://docs.agno.com/models/providers/native/perplexity/usage/basic-stream.md): Stream a Perplexity-powered Agno agent's response. - [Async Basic Agent](https://docs.agno.com/models/providers/native/perplexity/usage/async-basic.md): Run a Perplexity-powered Agno agent asynchronously. - [Async Basic Streaming Agent](https://docs.agno.com/models/providers/native/perplexity/usage/async-basic-stream.md): Stream a Perplexity-powered Agno agent's response asynchronously. - [Agent with Knowledge](https://docs.agno.com/models/providers/native/perplexity/usage/knowledge.md): Give a Perplexity-powered Agno agent knowledge from a PDF stored in PgVector. - [Agent with Memory](https://docs.agno.com/models/providers/native/perplexity/usage/memory.md): Store user memories and session summaries for a Perplexity agent in Postgres. - [Agent with Structured Output](https://docs.agno.com/models/providers/native/perplexity/usage/structured-output.md): Request JSON Schema output from a Perplexity agent and inspect the parsed result. - [Vercel v0](https://docs.agno.com/models/providers/native/vercel/overview.md): Use Vercel v0 models for web development with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/native/vercel/usage/basic.md): Run a Vercel v0-powered Agno agent and print its response. - [Streaming Agent](https://docs.agno.com/models/providers/native/vercel/usage/basic-stream.md): Stream a Vercel v0 agent's response as it's generated. - [Image Agent](https://docs.agno.com/models/providers/native/vercel/usage/image-agent.md): Analyze an image with a Vercel v0 agent and search the web for related news. - [Agent with Knowledge](https://docs.agno.com/models/providers/native/vercel/usage/knowledge.md): Query PDF knowledge stored in PgVector with a Vercel v0 agent. - [Agent with Tools](https://docs.agno.com/models/providers/native/vercel/usage/tool-use.md): Give a Vercel v0 agent web search with WebSearchTools. - [xAI](https://docs.agno.com/models/providers/native/xai/overview.md): Use xAI Grok models with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/native/xai/usage/basic.md): Run a Grok-powered Agno agent and print its response. - [Streaming Agent](https://docs.agno.com/models/providers/native/xai/usage/basic-stream.md): Stream a Grok-powered Agno agent's response as it's generated. - [Agent with Tools](https://docs.agno.com/models/providers/native/xai/usage/tool-use.md): Give a Grok agent web search with WebSearchTools. - [Async Basic Agent](https://docs.agno.com/models/providers/native/xai/usage/basic-async.md): Run a Grok agent asynchronously with aprint_response(). - [Async Streaming Agent](https://docs.agno.com/models/providers/native/xai/usage/basic-async-stream.md): Stream a Grok agent's response asynchronously with aprint_response(). - [Tool Use Stream](https://docs.agno.com/models/providers/native/xai/usage/tool-use-stream.md): Give a Grok agent web search and stream the response with stream=True. - [Async Tool Use](https://docs.agno.com/models/providers/native/xai/usage/async-tool-use.md): Give a Grok agent web search and run it asynchronously with aprint_response(). - [Structured Output](https://docs.agno.com/models/providers/native/xai/usage/structured-output.md): Request a MovieScript schema from a Grok agent and inspect the parsed result. - [Image Agent](https://docs.agno.com/models/providers/native/xai/usage/image-agent.md): Send an image URL to a Grok vision model and search the web for context. - [Image Agent Bytes](https://docs.agno.com/models/providers/native/xai/usage/image-agent-bytes.md): Pass an image to a Grok vision model as raw bytes. - [Live Search Agent](https://docs.agno.com/models/providers/native/xai/usage/live-search-agent.md): Search the web and X with a Grok agent using xAIResponses. - [Live Search Agent Stream](https://docs.agno.com/models/providers/native/xai/usage/live-search-agent-stream.md): Stream a Grok response using native web and X search through xAIResponses. - [Reasoning Agent](https://docs.agno.com/models/providers/native/xai/usage/reasoning-agent.md): Combine ReasoningTools and YFinanceTools on a Grok agent for a reasoned stock report. - [Xiaomi MiMo](https://docs.agno.com/models/providers/native/xiaomi/overview.md): Use Xiaomi MiMo models with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/native/xiaomi/usage/basic.md): Run a MiMo-powered Agno agent synchronously and with streaming. - [String Model Shorthand](https://docs.agno.com/models/providers/native/xiaomi/usage/string-model.md): Create a MiMo agent with the "xiaomi:" string shorthand instead of importing the model class. - [Agent with Tools](https://docs.agno.com/models/providers/native/xiaomi/usage/tool-use.md): Give a MiMo agent a web search tool while thinking mode is enabled. - [Agent with Structured Outputs](https://docs.agno.com/models/providers/native/xiaomi/usage/structured-output.md): Request JSON from a MiMo agent and parse it into a Pydantic MovieScript. - [Toggle Thinking Mode](https://docs.agno.com/models/providers/native/xiaomi/usage/thinking-mode.md): Compare a MiMo agent with use_thinking enabled against one with it disabled. - [Reasoning Agent](https://docs.agno.com/models/providers/native/xiaomi/usage/reasoning-agent.md): Solve a river-crossing logic puzzle with a MiMo agent in thinking mode, streaming the full reasoning trace. - [Ollama](https://docs.agno.com/models/providers/local/ollama/overview.md): Run local models and call Ollama Cloud directly from Agno agents. - [Basic](https://docs.agno.com/models/providers/local/ollama/usage/basic.md): Run a synchronous Agno agent against a local Ollama llama3.1:8b model and print its response. - [Basic Stream](https://docs.agno.com/models/providers/local/ollama/usage/basic-stream.md): Stream a synchronous Agno agent's response from a local Ollama llama3.1:8b model with stream=True. - [Ollama Cloud](https://docs.agno.com/models/providers/local/ollama/usage/cloud.md): Run an Agno agent against Ollama Cloud's hosted gpt-oss:120b model and stream the response. - [Async Basic](https://docs.agno.com/models/providers/local/ollama/usage/async-basic.md): Run an asynchronous Agno agent against a local Ollama llama3.1:8b model with aprint_response. - [Async Basic Stream](https://docs.agno.com/models/providers/local/ollama/usage/async-basic-stream.md): Stream an asynchronous Agno agent's response from a local Ollama llama3.1:8b model using aprint_response with stream=True. - [Tool Use](https://docs.agno.com/models/providers/local/ollama/usage/tool-use.md): Run an Agno agent with WebSearchTools against a local Ollama llama3.2:latest model to answer a web search query. - [Tool Use Stream](https://docs.agno.com/models/providers/local/ollama/usage/tool-use-stream.md): Stream an Ollama llama3.2 agent's tool-calling response as it searches the web with WebSearchTools. - [Knowledge](https://docs.agno.com/models/providers/local/ollama/usage/knowledge.md): Load a PDF of Thai recipes into a PgVector knowledge base with OllamaEmbedder and query it through an Ollama llama3.2 agent. - [Memory](https://docs.agno.com/models/providers/local/ollama/usage/memory.md): Persist user memories and session summaries in Postgres for an Ollama qwen2.5 agent across a multi-turn conversation. - [Demo DeepSeek R1](https://docs.agno.com/models/providers/local/ollama/usage/demo-deepseek-r1.md): Run the Ollama deepseek-r1:14b model to write and explain Python code for solving quadratic equations. - [Demo Gemma](https://docs.agno.com/models/providers/local/ollama/usage/demo-gemma.md): Stream a 3-sentence fiction story from the Ollama gemma3:12b model given a local image as input. - [Demo Phi4](https://docs.agno.com/models/providers/local/ollama/usage/demo-phi4.md): Run the Ollama phi4 model to generate a scary story in exactly 10 words. - [Demo Qwen](https://docs.agno.com/models/providers/local/ollama/usage/demo-qwen.md): Run an Ollama qwen3:8b agent with YFinanceTools that streams a markdown report on NVDA. - [Image Agent](https://docs.agno.com/models/providers/local/ollama/usage/image-agent.md): Send a local image to an Ollama llama3.2-vision agent that writes a 3 sentence fiction story about it. - [Multimodal Agent](https://docs.agno.com/models/providers/local/ollama/usage/multimodal.md): Send a local image to an Ollama gemma3 agent that writes a 3 sentence fiction story about it. - [Set Client](https://docs.agno.com/models/providers/local/ollama/usage/set-client.md): Pass a custom ollama.Client instance to Ollama via the client parameter. - [Set Temperature](https://docs.agno.com/models/providers/local/ollama/usage/set-temperature.md): Set the temperature option to 0.5 on an Ollama llama3.2 model. - [Agent with Storage](https://docs.agno.com/models/providers/local/ollama/usage/storage.md): Store an Ollama llama3.1:8b agent's history in Postgres with WebSearchTools and add_history_to_context for multi-turn context. - [Structured Output](https://docs.agno.com/models/providers/local/ollama/usage/structured-output.md): Request a MovieScript from an Ollama llama3.2 agent and validate the result with Pydantic. - [LlamaCpp](https://docs.agno.com/models/providers/local/llama-cpp/overview.md): Run local models with LlamaCpp in Agno agents. - [Basic](https://docs.agno.com/models/providers/local/llama-cpp/usage/basic.md): Run a basic agent against a local LlamaCpp server and print its markdown response. - [Basic Stream](https://docs.agno.com/models/providers/local/llama-cpp/usage/basic-stream.md): Stream an agent's response from a local LlamaCpp server and print it in the terminal. - [Structured Output](https://docs.agno.com/models/providers/local/llama-cpp/usage/structured-output.md): Constrain a local LlamaCpp agent to return a MovieScript Pydantic object instead of free-form text. - [Agent with Tools](https://docs.agno.com/models/providers/local/llama-cpp/usage/tool-use.md): Give a local LlamaCpp agent WebSearchTools and print its response to a web-search query. - [Agent with Tools Stream](https://docs.agno.com/models/providers/local/llama-cpp/usage/tool-use-stream.md): Give a local LlamaCpp agent WebSearchTools and stream its response to a web-search query. - [Llmman](https://docs.agno.com/models/providers/local/llmman/overview.md): Run local models through llmman's OpenAI-compatible server. - [Basic Llmman Agent](https://docs.agno.com/models/providers/local/llmman/usage/basic.md): Run synchronous and streaming calls against a local model. - [Llmman Agent with Tools](https://docs.agno.com/models/providers/local/llmman/usage/tool-use.md): Give a local model a web search tool. - [Llmman Structured Outputs](https://docs.agno.com/models/providers/local/llmman/usage/structured-output.md): Parse local model output into a Pydantic schema. - [LM Studio](https://docs.agno.com/models/providers/local/lmstudio/overview.md): Run local models with LM Studio in Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/local/lmstudio/usage/basic.md): Run a basic agent against a local LM Studio model and print the markdown response. - [Streaming Agent](https://docs.agno.com/models/providers/local/lmstudio/usage/basic-stream.md): Stream an agent's response from a local LM Studio model and print it in the terminal. - [Agent with Tools](https://docs.agno.com/models/providers/local/lmstudio/usage/tool-use.md): Give a local LM Studio agent WebSearchTools and stream its response to a web-search query. - [Agent with Structured Outputs](https://docs.agno.com/models/providers/local/lmstudio/usage/structured-output.md): Constrain a local LM Studio agent to return a MovieScript Pydantic object instead of free-form text. - [Agent with Storage](https://docs.agno.com/models/providers/local/lmstudio/usage/storage.md): Persist an LMStudio agent's chat history to PostgreSQL and give it web search tools with add_history_to_context enabled. - [Agent with Knowledge](https://docs.agno.com/models/providers/local/lmstudio/usage/knowledge.md): Give an LMStudio agent a PgVector knowledge base loaded from a Thai recipes PDF and answer questions with markdown output. - [Image Agent](https://docs.agno.com/models/providers/local/lmstudio/usage/image-agent.md): Stream an LMStudio vision agent's response describing an image fetched over HTTP with httpx. - [vLLM](https://docs.agno.com/models/providers/local/vllm/overview.md): Run models served by vLLM's OpenAI-compatible API in Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/local/vllm/usage/basic.md): Run a basic agent against a local vLLM server. - [Agent with Streaming](https://docs.agno.com/models/providers/local/vllm/usage/basic-stream.md): Stream a vLLM agent's response with print_response(). - [Async Agent](https://docs.agno.com/models/providers/local/vllm/usage/async-basic.md): Run a vLLM agent asynchronously with aprint_response(). - [Async Agent with Streaming](https://docs.agno.com/models/providers/local/vllm/usage/async-basic-stream.md): Stream a vLLM agent's response asynchronously with aprint_response(). - [Code Generation](https://docs.agno.com/models/providers/local/vllm/usage/code-generation.md): Generate Python code with a vLLM agent running a code model. - [Agent with Storage](https://docs.agno.com/models/providers/local/vllm/usage/storage.md): Persist chat history for a vLLM agent with PostgresDb. - [Agent with Memory](https://docs.agno.com/models/providers/local/vllm/usage/memory.md): Persist memories and session summaries for a vLLM agent with PostgresDb. - [Structured Output](https://docs.agno.com/models/providers/local/vllm/usage/structured-output.md): Get structured output from a vLLM agent with a Pydantic output_schema. - [Agent with Tools](https://docs.agno.com/models/providers/local/vllm/usage/tool-use.md): Run a tool-enabled agent against a local vLLM server. - [Async Agent with Tools](https://docs.agno.com/models/providers/local/vllm/usage/async-tool-use.md): Run a vLLM agent with WebSearchTools asynchronously. - [AWS Bedrock](https://docs.agno.com/models/providers/cloud/aws-bedrock/overview.md): Use AWS Bedrock foundation models with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/cloud/aws-bedrock/usage/basic.md): Run a basic AwsBedrock agent. - [Streaming Agent](https://docs.agno.com/models/providers/cloud/aws-bedrock/usage/basic-stream.md): Stream an AwsBedrock agent's response to the terminal. - [Agent with Tools](https://docs.agno.com/models/providers/cloud/aws-bedrock/usage/tool-use.md): Give an AwsBedrock agent web search with WebSearchTools. - [Agent with Structured Outputs](https://docs.agno.com/models/providers/cloud/aws-bedrock/usage/structured-output.md): Return a validated Pydantic MovieScript from an AWS Bedrock agent. - [Agent with Storage](https://docs.agno.com/models/providers/cloud/aws-bedrock/usage/storage.md): Store AwsBedrock agent sessions and history in Postgres. - [Agent with Knowledge](https://docs.agno.com/models/providers/cloud/aws-bedrock/usage/knowledge.md): Attach a PgVector knowledge base to an AwsBedrock agent. - [Agent with Image Input](https://docs.agno.com/models/providers/cloud/aws-bedrock/usage/image-agent.md): Send image input to an AwsBedrock agent with amazon.nova-pro-v1:0. - [AWS Claude](https://docs.agno.com/models/providers/cloud/aws-claude/overview.md): Use Claude models through AWS Bedrock with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/cloud/aws-claude/usage/basic.md): Run a basic Agno agent with Claude on AWS Bedrock. - [Streaming Agent](https://docs.agno.com/models/providers/cloud/aws-claude/usage/basic-stream.md): Stream responses from an AWS Claude agent. - [Agent with Tools](https://docs.agno.com/models/providers/cloud/aws-claude/usage/tool-use.md): Give an AWS Claude agent web search tools. - [Agent with Structured Outputs](https://docs.agno.com/models/providers/cloud/aws-claude/usage/structured-output.md): Get structured Pydantic output from an AWS Claude agent. - [Agent with Storage](https://docs.agno.com/models/providers/cloud/aws-claude/usage/storage.md): Persist AWS Claude agent sessions in Postgres. - [Agent with Knowledge](https://docs.agno.com/models/providers/cloud/aws-claude/usage/knowledge.md): Search a PgVector knowledge base from an AWS Claude agent. - [Azure AI Foundry](https://docs.agno.com/models/providers/cloud/azure-ai-foundry/overview.md): Use Azure AI Foundry hosted models with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/cloud/azure-ai-foundry/usage/basic.md): Run a basic Azure AI Foundry agent and print its response. - [Basic Streaming](https://docs.agno.com/models/providers/cloud/azure-ai-foundry/usage/basic-stream.md): Stream responses from an Azure AI Foundry agent. - [Agent with Image Input](https://docs.agno.com/models/providers/cloud/azure-ai-foundry/usage/image-agent.md): Migrate a classic Foundry image example to a current vision deployment. - [Agent with Knowledge Base](https://docs.agno.com/models/providers/cloud/azure-ai-foundry/usage/knowledge.md): Search a PgVector knowledge base from an Azure AI Foundry agent. - [Agent with Storage](https://docs.agno.com/models/providers/cloud/azure-ai-foundry/usage/storage.md): Persist an Azure AI Foundry agent's session history to Postgres across runs. - [Agent with Structured Outputs](https://docs.agno.com/models/providers/cloud/azure-ai-foundry/usage/structured-output.md): Return a validated Pydantic object from an Azure AI Foundry agent using output_schema. - [Agent with Tools](https://docs.agno.com/models/providers/cloud/azure-ai-foundry/usage/tool-use.md): Maintain a classic AzureAIFoundry web-search agent while migrating to current Foundry inference. - [Azure AI Foundry Claude](https://docs.agno.com/models/providers/cloud/azure-foundry-claude/overview.md): Use Claude models through Azure AI Foundry with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/cloud/azure-foundry-claude/usage/basic.md): Run a Claude agent through Azure AI Foundry and print its response. - [Agent with Tools](https://docs.agno.com/models/providers/cloud/azure-foundry-claude/usage/tool-use.md): Give an Azure Foundry Claude agent web search with WebSearchTools. - [Extended Thinking](https://docs.agno.com/models/providers/cloud/azure-foundry-claude/usage/thinking.md): Enable extended thinking for a Claude agent through Azure AI Foundry. - [Azure OpenAI](https://docs.agno.com/models/providers/cloud/azure-openai/overview.md): Use OpenAI models through Azure with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/cloud/azure-openai/usage/basic.md): Run an AzureOpenAI agent and print its response. - [Basic Streaming](https://docs.agno.com/models/providers/cloud/azure-openai/usage/basic-stream.md): Stream an AzureOpenAI agent's response with stream=True. - [Agent with Knowledge Base](https://docs.agno.com/models/providers/cloud/azure-openai/usage/knowledge.md): Answer questions from a PDF using an AzureOpenAI agent and PgVector. - [Agent with Storage](https://docs.agno.com/models/providers/cloud/azure-openai/usage/storage.md): Persist an AzureOpenAI agent's session history to Postgres across runs. - [Agent with Structured Outputs](https://docs.agno.com/models/providers/cloud/azure-openai/usage/structured-output.md): Return a validated Pydantic object from an AzureOpenAI agent using output_schema. - [Agent with Tools](https://docs.agno.com/models/providers/cloud/azure-openai/usage/tool-use.md): Give an AzureOpenAI agent web search with WebSearchTools. - [IBM WatsonX](https://docs.agno.com/models/providers/cloud/ibm-watsonx/overview.md): Use IBM WatsonX foundation models with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/cloud/ibm-watsonx/usage/basic.md): Run a WatsonX agent and print its response. - [Streaming Basic Agent](https://docs.agno.com/models/providers/cloud/ibm-watsonx/usage/basic-stream.md): Stream a WatsonX agent's response with stream=True. - [Async Streaming Agent](https://docs.agno.com/models/providers/cloud/ibm-watsonx/usage/async-basic-stream.md): Stream a WatsonX agent's response asynchronously with aprint_response. - [Async Basic Agent](https://docs.agno.com/models/providers/cloud/ibm-watsonx/usage/async-basic.md): Run a WatsonX agent asynchronously with aprint_response. - [Agent with Tools](https://docs.agno.com/models/providers/cloud/ibm-watsonx/usage/tool-use.md): Give a WatsonX agent web search with WebSearchTools. - [Agent with Async Tool Usage](https://docs.agno.com/models/providers/cloud/ibm-watsonx/usage/async-tool-use.md): Run a WatsonX agent with WebSearchTools asynchronously and stream the response. - [Agent with Structured Output](https://docs.agno.com/models/providers/cloud/ibm-watsonx/usage/structured-output.md): Return a validated Pydantic MovieScript from a WatsonX agent using output_schema. - [Agent with Storage](https://docs.agno.com/models/providers/cloud/ibm-watsonx/usage/storage.md): Store WatsonX agent sessions and history in Postgres. - [Agent with Knowledge](https://docs.agno.com/models/providers/cloud/ibm-watsonx/usage/knowledge.md): Answer questions from a PDF using a WatsonX agent and PgVector. - [Image Agent](https://docs.agno.com/models/providers/cloud/ibm-watsonx/usage/image-agent-bytes.md): Pass raw image bytes to an IBM WatsonX vision model. - [Vertex AI Claude](https://docs.agno.com/models/providers/cloud/vertexai-claude/overview.md): Use Claude models through Vertex AI with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/cloud/vertexai-claude/usage/basic.md): Run a Claude agent through Vertex AI and print its response. - [Streaming Agent](https://docs.agno.com/models/providers/cloud/vertexai-claude/usage/basic-stream.md): Stream a Claude response through Vertex AI with an Agno agent. - [Agent with Tools](https://docs.agno.com/models/providers/cloud/vertexai-claude/usage/tool-use.md): Give a Vertex AI Claude agent a web search tool and let Agno run the tool-use loop. - [Agent with Structured Outputs](https://docs.agno.com/models/providers/cloud/vertexai-claude/usage/structured-output.md): Return a validated Pydantic object from a Vertex AI Claude agent. - [PDF Input Local](https://docs.agno.com/models/providers/cloud/vertexai-claude/usage/pdf-input-local.md): Send a local PDF file to a Vertex AI Claude agent and summarize its contents. - [PDF Input URL](https://docs.agno.com/models/providers/cloud/vertexai-claude/usage/pdf-input-url.md): Fetch a PDF from a URL and summarize it with a Vertex AI Claude agent. - [PDF Input Bytes](https://docs.agno.com/models/providers/cloud/vertexai-claude/usage/pdf-input-bytes.md): Send raw PDF bytes to a Vertex AI Claude agent and summarize the contents. - [Image Input Bytes](https://docs.agno.com/models/providers/cloud/vertexai-claude/usage/image-input-bytes.md): Send raw image bytes to a Vertex AI Claude agent and search the web for context. - [Image Input URL](https://docs.agno.com/models/providers/cloud/vertexai-claude/usage/image-input-url.md): Point a Vertex AI Claude agent at an image URL and search the web for context. - [AI/ML API](https://docs.agno.com/models/providers/gateways/aimlapi/overview.md): Use AI/ML API with access to 300+ models in Agno agents. - [Cerebras](https://docs.agno.com/models/providers/gateways/cerebras/overview.md): Use Cerebras high-speed inference with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/gateways/cerebras/usage/basic.md): Run a Cerebras agent in sync, async, and streaming modes. - [Streaming Agent](https://docs.agno.com/models/providers/gateways/cerebras/usage/basic-stream.md): Stream a Cerebras response through an Agno agent. - [Agent with Tools](https://docs.agno.com/models/providers/gateways/cerebras/usage/tool-use.md): Give a Cerebras agent web search tools. - [Agent with Structured Outputs](https://docs.agno.com/models/providers/gateways/cerebras/usage/structured-output.md): Return a validated Pydantic object from a Cerebras agent in strict or guided mode. - [Agent with Storage](https://docs.agno.com/models/providers/gateways/cerebras/usage/storage.md): Persist a Cerebras agent's chat history to Postgres. - [Agent with Knowledge](https://docs.agno.com/models/providers/gateways/cerebras/usage/knowledge.md): Give a Cerebras agent knowledge from a PDF stored in PgVector. - [Cerebras OpenAI](https://docs.agno.com/models/providers/gateways/cerebras-openai/overview.md): Use Cerebras with Agno through an OpenAI-compatible interface. - [Basic Agent](https://docs.agno.com/models/providers/gateways/cerebras-openai/usage/basic.md): Run a CerebrasOpenAI agent in sync, async, and streaming modes. - [Streaming Agent](https://docs.agno.com/models/providers/gateways/cerebras-openai/usage/basic-stream.md): Stream a Cerebras response through the OpenAI-compatible interface. - [Agent with Tools](https://docs.agno.com/models/providers/gateways/cerebras-openai/usage/tool-use.md): Give a CerebrasOpenAI agent web search tools. - [Agent with Structured Outputs](https://docs.agno.com/models/providers/gateways/cerebras-openai/usage/structured-output.md): Return a validated Pydantic object from a CerebrasOpenAI agent. - [Agent with Storage](https://docs.agno.com/models/providers/gateways/cerebras-openai/usage/storage.md): Persist a CerebrasOpenAI agent's chat history to Postgres. - [Agent with Knowledge](https://docs.agno.com/models/providers/gateways/cerebras-openai/usage/knowledge.md): Give a CerebrasOpenAI agent knowledge from a PDF stored in PgVector. - [Cloudflare](https://docs.agno.com/models/providers/gateways/cloudflare/overview.md): Use Cloudflare AI Gateway models with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/gateways/cloudflare/usage/basic.md): Run a Cloudflare Workers AI model in sync, async, and streaming modes. - [Switching Models](https://docs.agno.com/models/providers/gateways/cloudflare/usage/switch-model.md): Switch Cloudflare Workers AI models and gateway routes per agent. - [Agent with Tools](https://docs.agno.com/models/providers/gateways/cloudflare/usage/tool-use.md): Give a Cloudflare agent web search with WebSearchTools. - [Agent with Structured Outputs](https://docs.agno.com/models/providers/gateways/cloudflare/usage/structured-output.md): Return structured movie scripts from Cloudflare models. - [CometAPI](https://docs.agno.com/models/providers/gateways/cometapi/overview.md): Use CometAPI models with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/gateways/cometapi/usage/basic.md): Run a GPT model through the CometAPI gateway in sync, async, and streaming modes. - [Basic Streaming Agent](https://docs.agno.com/models/providers/gateways/cometapi/usage/basic-stream.md): Stream a CometAPI response in the terminal with stream=True. - [Agent with Tools](https://docs.agno.com/models/providers/gateways/cometapi/usage/tool-use.md): Call WebSearchTools through CometAPI for prices, weather, and news queries. - [Async Basic Agent](https://docs.agno.com/models/providers/gateways/cometapi/usage/async-basic.md): Run a CometAPI agent asynchronously with aprint_response. - [Async Streaming Agent](https://docs.agno.com/models/providers/gateways/cometapi/usage/async-basic-stream.md): Stream an async CometAPI response with aprint_response and stream=True. - [Async Agent with Tools](https://docs.agno.com/models/providers/gateways/cometapi/usage/async-tool-use.md): Run a CometAPI agent with WebSearchTools asynchronously. - [DeepInfra](https://docs.agno.com/models/providers/gateways/deepinfra/overview.md): Use DeepInfra models with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/gateways/deepinfra/usage/basic.md): Run a DeepInfra agent and print its response. - [Streaming Agent](https://docs.agno.com/models/providers/gateways/deepinfra/usage/basic-stream.md): Stream a DeepInfra agent's response with stream=True. - [Agent with Tools](https://docs.agno.com/models/providers/gateways/deepinfra/usage/tool-use.md): Give a DeepInfra agent web search with WebSearchTools. - [Agent with Structured Outputs](https://docs.agno.com/models/providers/gateways/deepinfra/usage/structured-output.md): Return a validated Pydantic object from a DeepInfra agent. - [Fireworks](https://docs.agno.com/models/providers/gateways/fireworks/overview.md): Use Fireworks AI models with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/gateways/fireworks/usage/basic.md): Run a Fireworks agent and print its response. - [Streaming Agent](https://docs.agno.com/models/providers/gateways/fireworks/usage/basic-stream.md): Stream a Fireworks agent's response with stream=True. - [Agent with Tools](https://docs.agno.com/models/providers/gateways/fireworks/usage/tool-use.md): Give a Fireworks agent web search with WebSearchTools. - [Agent with Structured Outputs](https://docs.agno.com/models/providers/gateways/fireworks/usage/structured-output.md): Return a validated Pydantic object from a Fireworks agent. - [Groq](https://docs.agno.com/models/providers/gateways/groq/overview.md): Use Groq's fast inference API with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/gateways/groq/usage/basic.md): Run a Groq agent and print its response. - [Streaming Agent](https://docs.agno.com/models/providers/gateways/groq/usage/basic-stream.md): Stream a Groq response through an Agno agent. - [Agent with Tools](https://docs.agno.com/models/providers/gateways/groq/usage/tool-use.md): Give a Groq agent web search and article extraction tools. - [Agent with Structured Outputs](https://docs.agno.com/models/providers/gateways/groq/usage/structured-output.md): Return a validated Pydantic object from a Groq agent using JSON mode. - [Agent with Storage](https://docs.agno.com/models/providers/gateways/groq/usage/storage.md): Persist a Groq agent's chat history to Postgres and reuse it across runs. - [Agent with Knowledge](https://docs.agno.com/models/providers/gateways/groq/usage/knowledge.md): Answer questions from a PDF using Groq, OpenAI embeddings, and PgVector. - [Image Agent](https://docs.agno.com/models/providers/gateways/groq/usage/image-agent.md): Send an image to a Groq vision model and stream the description. - [Deep Knowledge Agent](https://docs.agno.com/models/providers/gateways/groq/usage/deep-knowledge.md): Answer questions by iteratively searching a knowledge base using Groq, OpenAI embeddings, and LanceDB. - [Browser Search Agent](https://docs.agno.com/models/providers/gateways/groq/usage/browser-search.md): Give a Groq agent Groq's built-in browser search tool. - [Agent with Metrics](https://docs.agno.com/models/providers/gateways/groq/usage/metrics.md): Print per-message and aggregate run metrics for a Groq agent. - [Reasoning Agent](https://docs.agno.com/models/providers/gateways/groq/usage/reasoning-agent.md): Pair a Groq reasoning model with a Groq response model to answer step by step. - [Transcription Agent](https://docs.agno.com/models/providers/gateways/groq/usage/transcription-agent.md): Transcribe an audio file to English with GroqTools. - [Translation Agent](https://docs.agno.com/models/providers/gateways/groq/usage/translation-agent.md): Transcribe, translate, and regenerate audio with GroqTools. - [HuggingFace](https://docs.agno.com/models/providers/gateways/huggingface/overview.md): Use Hugging Face models with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/gateways/huggingface/usage/basic.md): Run an agent on a HuggingFace model in sync, streaming, and async modes. - [Streaming Agent](https://docs.agno.com/models/providers/gateways/huggingface/usage/basic-stream.md): Stream a HuggingFace model response through an Agno agent. - [Async Basic Agent](https://docs.agno.com/models/providers/gateways/huggingface/usage/async-basic.md): Run a HuggingFace agent asynchronously with aprint_response(). - [Async Streaming Agent](https://docs.agno.com/models/providers/gateways/huggingface/usage/async-basic-stream.md): Stream a HuggingFace agent response asynchronously with aprint_response(). - [GPT-OSS Essay Writer](https://docs.agno.com/models/providers/gateways/huggingface/usage/llama-essay-writer.md): Write a 300-word essay on a user-provided topic with GPT-OSS 120B through Hugging Face. - [Tool Use](https://docs.agno.com/models/providers/gateways/huggingface/usage/tool-use.md): Give a HuggingFace agent web search with WebSearchTools. - [LangDB](https://docs.agno.com/models/providers/gateways/langdb/overview.md): Use the LangDB AI Gateway with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/gateways/langdb/usage/basic.md): Run a LangDB agent in sync and streaming modes. - [Basic Streaming Agent](https://docs.agno.com/models/providers/gateways/langdb/usage/basic-stream.md): Stream a LangDB agent's response to the terminal. - [Data Analyst Agent](https://docs.agno.com/models/providers/gateways/langdb/usage/data-analyst.md): Query IMDB movie data with a LangDB agent using DuckDbTools. - [Structured Output](https://docs.agno.com/models/providers/gateways/langdb/usage/structured-output.md): Request a Pydantic MovieScript from a LangDB agent using JSON mode or native structured outputs. - [Web Search Agent](https://docs.agno.com/models/providers/gateways/langdb/usage/tool-use.md): Give a LangDB agent web search tools. - [LiteLLM](https://docs.agno.com/models/providers/gateways/litellm/overview.md): Integrate LiteLLM with Agno for a unified LLM experience. - [Basic Agent](https://docs.agno.com/models/providers/gateways/litellm/usage/basic.md): Run a LiteLLM-backed agent and print its response. - [Streaming Agent](https://docs.agno.com/models/providers/gateways/litellm/usage/basic-stream.md): Stream a LiteLLM-backed agent's response to the terminal. - [Async Basic Agent](https://docs.agno.com/models/providers/gateways/litellm/usage/async-basic.md): Run a LiteLLM-backed agent asynchronously with aprint_response(). - [Async Basic Streaming Agent](https://docs.agno.com/models/providers/gateways/litellm/usage/async-basic-stream.md): Stream a LiteLLM-backed agent's response asynchronously with aprint_response(). - [Agent with Tools](https://docs.agno.com/models/providers/gateways/litellm/usage/tool-use.md): Call YFinanceTools from a LiteLLM-backed agent. - [Async Tool Use](https://docs.agno.com/models/providers/gateways/litellm/usage/async-tool-use.md): Run YFinanceTools from a LiteLLM-backed agent asynchronously with aprint_response(). - [Audio Input Agent](https://docs.agno.com/models/providers/gateways/litellm/usage/audio-input-agent.md): Send an audio file to a LiteLLM-backed agent and get a text description of its contents. - [Agent with Knowledge](https://docs.agno.com/models/providers/gateways/litellm/usage/knowledge.md): Answer questions from a PDF using a LiteLLM-backed agent, OpenAI embeddings, and PgVector. - [Agent with Structured Outputs](https://docs.agno.com/models/providers/gateways/litellm/usage/structured-output.md): Return a validated Pydantic MovieScript from LiteLLM in JSON mode or with native structured outputs. - [Agent with Storage](https://docs.agno.com/models/providers/gateways/litellm/usage/storage.md): Persist a LiteLLM-backed agent's chat history to SQLite and reuse it across runs. - [LiteLLM OpenAI](https://docs.agno.com/models/providers/gateways/litellm-openai/overview.md): Use LiteLLM with Agno through an OpenAI-compatible proxy server. - [Basic Agent](https://docs.agno.com/models/providers/gateways/litellm-openai/usage/basic.md): Run an Agno agent through a LiteLLM proxy server and print its response. - [Streaming Agent](https://docs.agno.com/models/providers/gateways/litellm-openai/usage/basic-stream.md): Stream a LiteLLM proxy agent's response with stream=True. - [Agent with Tools](https://docs.agno.com/models/providers/gateways/litellm-openai/usage/tool-use.md): Give a LiteLLM proxy agent web search with WebSearchTools. - [Nebius Token Factory](https://docs.agno.com/models/providers/gateways/nebius/overview.md): Use Nebius Token Factory models with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/gateways/nebius/usage/basic.md): Run a Nebius Token Factory agent and print its response. - [Streaming Agent](https://docs.agno.com/models/providers/gateways/nebius/usage/basic-stream.md): Stream a Nebius agent's response to the terminal with stream=True. - [Agent with Tools](https://docs.agno.com/models/providers/gateways/nebius/usage/tool-use.md): Give a Nebius agent web search with WebSearchTools. - [Agent with Structured Outputs](https://docs.agno.com/models/providers/gateways/nebius/usage/structured-output.md): Request a Pydantic MovieScript from a Nebius agent using output_schema. - [Agent with Storage](https://docs.agno.com/models/providers/gateways/nebius/usage/storage.md): Persist a Nebius agent's chat history to Postgres with add_history_to_context for multi-turn context. - [Agent with Knowledge](https://docs.agno.com/models/providers/gateways/nebius/usage/knowledge.md): Answer questions from a PDF using a Nebius agent and PgVector. - [Neosantara](https://docs.agno.com/models/providers/gateways/neosantara/overview.md): Use Neosantara models in Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/gateways/neosantara/usage/basic.md): Run a Neosantara agent and print its response. - [Basic Stream](https://docs.agno.com/models/providers/gateways/neosantara/usage/basic-stream.md): Stream a Neosantara agent's response with stream=True. - [Tool Use](https://docs.agno.com/models/providers/gateways/neosantara/usage/tool-use.md): Give a Neosantara agent WebSearchTools to answer a stock price question. - [Structured Output](https://docs.agno.com/models/providers/gateways/neosantara/usage/structured-output.md): Return JSON-structured movie data through the Neosantara gateway. - [Async Basic](https://docs.agno.com/models/providers/gateways/neosantara/usage/async-basic.md): Run a Neosantara agent asynchronously with aprint_response. - [Async Basic Stream](https://docs.agno.com/models/providers/gateways/neosantara/usage/async-basic-stream.md): Stream an async Neosantara response with aprint_response and stream=True. - [Async Tool Use](https://docs.agno.com/models/providers/gateways/neosantara/usage/async-tool-use.md): Run a Neosantara agent with WebSearchTools asynchronously. - [Nexus](https://docs.agno.com/models/providers/gateways/nexus/overview.md): Use Nexus router models with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/gateways/nexus/usage/basic.md): Run a Nexus agent and print its response. - [Basic Streaming Agent](https://docs.agno.com/models/providers/gateways/nexus/usage/basic-stream.md): Stream a Nexus agent's response with stream=True. - [Async Basic Agent](https://docs.agno.com/models/providers/gateways/nexus/usage/async-basic.md): Run a Nexus agent asynchronously with aprint_response. - [Async Streaming Agent](https://docs.agno.com/models/providers/gateways/nexus/usage/async-basic-stream.md): Stream an async Nexus response with aprint_response and stream=True. - [Agent with Tools](https://docs.agno.com/models/providers/gateways/nexus/usage/tool-use.md): Give a Nexus agent web search with WebSearchTools. - [Async Agent with Tools](https://docs.agno.com/models/providers/gateways/nexus/usage/async-tool-use.md): Run a Nexus agent with WebSearchTools asynchronously. - [Nvidia](https://docs.agno.com/models/providers/gateways/nvidia/overview.md): Use NVIDIA models with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/gateways/nvidia/usage/basic.md): Run an NVIDIA agent and print its response. - [Basic Streaming Agent](https://docs.agno.com/models/providers/gateways/nvidia/usage/basic-stream.md): Stream an NVIDIA agent's response with stream=True. - [Async Basic Agent](https://docs.agno.com/models/providers/gateways/nvidia/usage/async-basic.md): Run an NVIDIA agent asynchronously with aprint_response(). - [Async Streaming Agent](https://docs.agno.com/models/providers/gateways/nvidia/usage/async-basic-stream.md): Stream an async NVIDIA response with aprint_response and stream=True. - [Agent with Tools](https://docs.agno.com/models/providers/gateways/nvidia/usage/tool-use.md): Give an NVIDIA agent web search tools. - [Async Agent with Tools](https://docs.agno.com/models/providers/gateways/nvidia/usage/async-tool-use.md): Run an NVIDIA agent with WebSearchTools asynchronously. - [OpenRouter](https://docs.agno.com/models/providers/gateways/openrouter/overview.md): Use OpenRouter unified API with Agno agents. - [Portkey](https://docs.agno.com/models/providers/gateways/portkey/overview.md): Use Portkey AI Gateway for multi-provider routing with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/gateways/portkey/usage/basic.md): Run a Portkey agent in sync, async, and streaming modes. - [Basic Agent with Streaming](https://docs.agno.com/models/providers/gateways/portkey/usage/basic-stream.md): Stream a Portkey agent's response to the terminal. - [Agent with Tools](https://docs.agno.com/models/providers/gateways/portkey/usage/tool-use.md): Give a Portkey agent web search tools. - [Agent with Tools and Streaming](https://docs.agno.com/models/providers/gateways/portkey/usage/tool-use-stream.md): Stream a Portkey agent's tool-calling response as it searches the web with WebSearchTools. - [Structured Output Agent](https://docs.agno.com/models/providers/gateways/portkey/usage/structured-output.md): Return a validated Pydantic object from a Portkey agent. - [Tuning Engines](https://docs.agno.com/models/providers/gateways/tuning-engines/overview.md): Use Tuning Engines with Agno through an OpenAI-compatible governed endpoint. - [Requesty](https://docs.agno.com/models/providers/gateways/requesty/overview.md): Use Requesty AI gateway with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/gateways/requesty/usage/basic.md): Run a Requesty agent in sync, async, and streaming modes. - [Streaming Agent](https://docs.agno.com/models/providers/gateways/requesty/usage/basic-stream.md): Stream a Requesty response through an Agno agent. - [Agent with Tools](https://docs.agno.com/models/providers/gateways/requesty/usage/tool-use.md): Give a Requesty agent web search tools. - [Agent with Structured Output](https://docs.agno.com/models/providers/gateways/requesty/usage/structured-output.md): Return a validated Pydantic object from a Requesty agent. - [SambaNova](https://docs.agno.com/models/providers/gateways/sambanova/overview.md): Use SambaNova models with Agno agents. - [SiliconFlow](https://docs.agno.com/models/providers/gateways/siliconflow/overview.md): Use SiliconFlow models with Agno agents. - [Async Streaming Agent](https://docs.agno.com/models/providers/gateways/siliconflow/usage/async-basic-stream.md): Stream an async SiliconFlow response with aprint_response and stream=True. - [Async Basic Agent](https://docs.agno.com/models/providers/gateways/siliconflow/usage/async-basic.md): Run a SiliconFlow agent asynchronously with aprint_response. - [Async Agent with Tools](https://docs.agno.com/models/providers/gateways/siliconflow/usage/async-tool-use.md): Run a SiliconFlow agent with WebSearchTools asynchronously. - [Basic Agent](https://docs.agno.com/models/providers/gateways/siliconflow/usage/basic.md): Run a SiliconFlow agent and print its response. - [Basic Streaming Agent](https://docs.agno.com/models/providers/gateways/siliconflow/usage/basic-stream.md): Stream a SiliconFlow agent's response with stream=True. - [Agent with Tools](https://docs.agno.com/models/providers/gateways/siliconflow/usage/tool-use.md): Run a SiliconFlow agent with WebSearchTools. - [Together](https://docs.agno.com/models/providers/gateways/together/overview.md): Use Together AI models with Agno agents. - [Basic Agent](https://docs.agno.com/models/providers/gateways/together/usage/basic.md): Run a Together agent and print its response. - [Streaming Agent](https://docs.agno.com/models/providers/gateways/together/usage/basic-stream.md): Stream a Together agent's response in the terminal. - [Image Agent](https://docs.agno.com/models/providers/gateways/together/usage/image-agent.md): Send an image URL to a Together vision model and stream the response. - [Image Input Bytes Content](https://docs.agno.com/models/providers/gateways/together/usage/image-agent-bytes.md): Send image bytes read from disk to a Together vision model. - [Image Agent with Memory](https://docs.agno.com/models/providers/gateways/together/usage/image-agent-memory.md): Give a Together vision agent conversation history to answer follow-up questions about a prior image. - [Agent with Structured Outputs](https://docs.agno.com/models/providers/gateways/together/usage/structured-output.md): Return a validated MovieScript object from a Together agent using JSON mode. - [Agent with Tools](https://docs.agno.com/models/providers/gateways/together/usage/tool-use.md): Give a Together agent web search tools with WebSearchTools. - [OpenAI-compatible models](https://docs.agno.com/models/providers/openai-like.md): Connect Agno agents to providers that implement Chat Completions or Open Responses. - [Database Providers](https://docs.agno.com/database/providers/overview.md): Compare database providers available for Agno storage. - [PostgreSQL](https://docs.agno.com/database/providers/postgres/overview.md): Persist Agno sessions and other data in PostgreSQL. - [Postgres for Agent](https://docs.agno.com/database/providers/postgres/usage/postgres-for-agent.md): Store agent sessions and history in PostgreSQL with PostgresDb. - [Postgres for Team](https://docs.agno.com/database/providers/postgres/usage/postgres-for-team.md): Store team sessions in PostgreSQL with PostgresDb. - [Postgres for Workflow](https://docs.agno.com/database/providers/postgres/usage/postgres-for-workflow.md): Store workflow runs in PostgreSQL with PostgresDb. - [Async PostgreSQL](https://docs.agno.com/database/providers/async-postgres/overview.md): Persist Agno sessions and other data asynchronously in PostgreSQL. - [Async Postgres for Agent](https://docs.agno.com/database/providers/async-postgres/usage/async-postgres-for-agent.md): Store agent sessions and run history asynchronously in PostgreSQL with AsyncPostgresDb. - [Async Postgres for Team](https://docs.agno.com/database/providers/async-postgres/usage/async-postgres-for-team.md): Store team sessions and run history asynchronously in PostgreSQL with AsyncPostgresDb. - [Async Postgres for Workflow](https://docs.agno.com/database/providers/async-postgres/usage/async-postgres-for-workflow.md): Store workflow sessions and run history asynchronously in PostgreSQL with AsyncPostgresDb. - [MySQL](https://docs.agno.com/database/providers/mysql/overview.md): Persist Agno sessions and other data in MySQL. - [MySQL for Agent](https://docs.agno.com/database/providers/mysql/usage/mysql-for-agent.md): Store agent sessions and run history in MySQL with MySQLDb. - [MySQL for Team](https://docs.agno.com/database/providers/mysql/usage/mysql-for-team.md): Store team sessions and run history in MySQL with MySQLDb. - [MySQL for Workflow](https://docs.agno.com/database/providers/mysql/usage/mysql-for-workflow.md): Store workflow sessions and run history in MySQL with MySQLDb. - [Async MySQL](https://docs.agno.com/database/providers/async-mysql/overview.md): Persist Agno sessions and other data asynchronously in MySQL. - [Async MySQL for Agent](https://docs.agno.com/database/providers/async-mysql/usage/async-mysql-for-agent.md): Store agent sessions and run history asynchronously in MySQL with AsyncMySQLDb. - [Async MySQL for Team](https://docs.agno.com/database/providers/async-mysql/usage/async-mysql-for-team.md): Store team sessions and run history asynchronously in MySQL with AsyncMySQLDb. - [Async MySQL for Workflow](https://docs.agno.com/database/providers/async-mysql/usage/async-mysql-for-workflow.md): Store workflow sessions and run history asynchronously in MySQL with AsyncMySQLDb. - [SQLite](https://docs.agno.com/database/providers/sqlite/overview.md): Persist Agno sessions and other data in a local SQLite database. - [SQLite for Agent](https://docs.agno.com/database/providers/sqlite/usage/sqlite-for-agent.md): Store agent sessions and run history in a local SQLite database with SqliteDb. - [SQLite for Team](https://docs.agno.com/database/providers/sqlite/usage/sqlite-for-team.md): Store team sessions and run history in a local SQLite database with SqliteDb. - [SQLite for Workflow](https://docs.agno.com/database/providers/sqlite/usage/sqlite-for-workflow.md): Store workflow sessions and run history in a local SQLite database with SqliteDb. - [Async SQLite](https://docs.agno.com/database/providers/async-sqlite/overview.md): Store agent sessions and run history asynchronously in SQLite with AsyncSqliteDb. - [Async SQLite for Agent](https://docs.agno.com/database/providers/async-sqlite/usage/async-sqlite-for-agent.md): Store agent sessions and run history asynchronously in SQLite with AsyncSqliteDb. - [Async SQLite for Team](https://docs.agno.com/database/providers/async-sqlite/usage/async-sqlite-for-team.md): Store team sessions and run history asynchronously in SQLite with AsyncSqliteDb. - [Async SQLite for Workflow](https://docs.agno.com/database/providers/async-sqlite/usage/async-sqlite-for-workflow.md): Store workflow sessions and run history asynchronously in SQLite with AsyncSqliteDb. - [In-Memory Storage](https://docs.agno.com/database/providers/in-memory/overview.md): Use in-memory storage for testing and development. - [In-Memory Storage for Agents](https://docs.agno.com/database/providers/in-memory/usage/in-memory-for-agent.md): Store agent sessions and run history in the current Python process with InMemoryDb. - [In-Memory Storage for Teams](https://docs.agno.com/database/providers/in-memory/usage/in-memory-for-team.md): Store team sessions and run history in the current Python process with InMemoryDb. - [In-Memory Storage for Workflows](https://docs.agno.com/database/providers/in-memory/usage/in-memory-for-workflow.md): Store workflow sessions and run history in the current Python process with InMemoryDb. - [DynamoDB](https://docs.agno.com/database/providers/dynamodb/overview.md): Store agent sessions and run history in DynamoDB with DynamoDb. - [DynamoDB for Agent](https://docs.agno.com/database/providers/dynamodb/usage/dynamodb-for-agent.md): Store agent sessions and history in DynamoDB with DynamoDb. - [DynamoDB for Team](https://docs.agno.com/database/providers/dynamodb/usage/dynamodb-for-team.md): Store team sessions and run history in DynamoDB with DynamoDb. - [DynamoDB for Workflow](https://docs.agno.com/database/providers/dynamodb/usage/dynamodb-for-workflow.md): Store workflow runs in DynamoDB with DynamoDb. - [MongoDB](https://docs.agno.com/database/providers/mongo/overview.md): Store agent sessions and run history in MongoDB with MongoDb. - [MongoDB for Agent](https://docs.agno.com/database/providers/mongo/usage/mongodb-for-agent.md): Store agent sessions and history in MongoDB with MongoDb. - [MongoDB for Team](https://docs.agno.com/database/providers/mongo/usage/mongodb-for-team.md): Store team sessions and run history in MongoDB with MongoDb. - [MongoDB for Workflow](https://docs.agno.com/database/providers/mongo/usage/mongodb-for-workflow.md): Store workflow runs in MongoDB with MongoDb. - [Async MongoDB](https://docs.agno.com/database/providers/async-mongo/overview.md): Persist Agno sessions and other data asynchronously in MongoDB. - [Async MongoDB for Agent](https://docs.agno.com/database/providers/async-mongo/usage/async-mongodb-for-agent.md): Store agent sessions and run history asynchronously in MongoDB with AsyncMongoDb. - [Async MongoDB for Team](https://docs.agno.com/database/providers/async-mongo/usage/async-mongodb-for-team.md): Store team sessions and run history asynchronously in MongoDB with AsyncMongoDb. - [Async MongoDB for Workflow](https://docs.agno.com/database/providers/async-mongo/usage/async-mongodb-for-workflow.md): Store workflow sessions and run history asynchronously in MongoDB with AsyncMongoDb. - [JSON Files](https://docs.agno.com/database/providers/json/overview.md): Persist Agno sessions and other data in local JSON files. - [JSON for Agent](https://docs.agno.com/database/providers/json/usage/json-for-agent.md): Store agent sessions and run history in local JSON files with JsonDb. - [JSON for Team](https://docs.agno.com/database/providers/json/usage/json-for-team.md): Store team sessions and run history in local JSON files with JsonDb. - [JSON for Workflow](https://docs.agno.com/database/providers/json/usage/json-for-workflow.md): Store workflow sessions and run history in local JSON files with JsonDb. - [SingleStore](https://docs.agno.com/database/providers/singlestore/overview.md): Persist Agno sessions and other data in SingleStore. - [SingleStore for Agent](https://docs.agno.com/database/providers/singlestore/usage/singlestore-for-agent.md): Store agent sessions and run history in SingleStore with SingleStoreDb. - [SingleStore for Team](https://docs.agno.com/database/providers/singlestore/usage/singlestore-for-team.md): Store team sessions and run history in SingleStore with SingleStoreDb. - [SingleStore for Workflow](https://docs.agno.com/database/providers/singlestore/usage/singlestore-for-workflow.md): Store workflow sessions and run history in SingleStore with SingleStoreDb. - [SurrealDB](https://docs.agno.com/database/providers/surrealdb/overview.md): Persist Agno sessions and other data in SurrealDB. - [SurrealDB for Agent](https://docs.agno.com/database/providers/surrealdb/usage/surrealdb-for-agent.md): Store agent sessions and run history in SurrealDB with SurrealDb. - [SurrealDB for Team](https://docs.agno.com/database/providers/surrealdb/usage/surrealdb-for-team.md): Store team sessions and run history in SurrealDB with SurrealDb. - [SurrealDB for Workflow](https://docs.agno.com/database/providers/surrealdb/usage/surrealdb-for-workflow.md): Store workflow sessions and run history in SurrealDB with SurrealDb. - [Redis](https://docs.agno.com/database/providers/redis/overview.md): Persist Agno sessions and other data in Redis. - [Redis for Agent](https://docs.agno.com/database/providers/redis/usage/redis-for-agent.md): Store agent sessions and run history in Redis with RedisDb. - [Redis for Team](https://docs.agno.com/database/providers/redis/usage/redis-for-team.md): Store team sessions in Redis with RedisDb. - [Redis for Workflows](https://docs.agno.com/database/providers/redis/usage/redis-for-workflow.md): Store workflow sessions in Redis with RedisDb. - [Valkey](https://docs.agno.com/database/providers/valkey/overview.md): Use Valkey for agent session storage and persistence. - [Valkey for Agent](https://docs.agno.com/database/providers/valkey/usage/valkey-for-agent.md): Store agent sessions in Valkey with ValkeyDb. - [Valkey for Team](https://docs.agno.com/database/providers/valkey/usage/valkey-for-team.md): Store team sessions in Valkey with ValkeyDb. - [Valkey for Workflow](https://docs.agno.com/database/providers/valkey/usage/valkey-for-workflow.md): Store workflow sessions in Valkey with ValkeyDb. - [JSON files as a database on Google Cloud Storage (GCS)](https://docs.agno.com/database/providers/gcs/overview.md): Use Google Cloud Storage for JSON-based agent session storage. - [GCS for Agent](https://docs.agno.com/database/providers/gcs/usage/gcs-for-agent.md): Store agent sessions as JSON blobs in a GCS bucket with GcsJsonDb. - [GCS for Team](https://docs.agno.com/database/providers/gcs/usage/gcs-for-team.md): Store team sessions as JSON blobs in a GCS bucket with GcsJsonDb. - [GCS for Workflows](https://docs.agno.com/database/providers/gcs/usage/gcs-for-workflow.md): Store workflow sessions as JSON blobs in a GCS bucket with GcsJsonDb. - [Firestore](https://docs.agno.com/database/providers/firestore/overview.md): Use Firestore for agent session storage and persistence. - [Firestore for Agent](https://docs.agno.com/database/providers/firestore/usage/firestore-for-agent.md): Store agent sessions in Firestore with FirestoreDb. - [Firestore for Team](https://docs.agno.com/database/providers/firestore/usage/firestore-for-team.md): Store team sessions in Firestore with FirestoreDb. - [Firestore for Workflows](https://docs.agno.com/database/providers/firestore/usage/firestore-for-workflow.md): Store workflow sessions in Firestore with FirestoreDb. - [Supabase](https://docs.agno.com/database/providers/supabase/overview.md): Use Supabase PostgreSQL for agent session storage. - [Neon](https://docs.agno.com/database/providers/neon/overview.md): Use Neon serverless PostgreSQL for agent session storage. - [ClickHouse](https://docs.agno.com/database/providers/clickhouse/overview.md): Use ClickHouse as a dedicated traces backend for high-volume OLAP scans. - [Selecting Custom Table Names](https://docs.agno.com/database/providers/selecting-tables.md): Override the default table names when initializing a database. - [Vector Store Index](https://docs.agno.com/knowledge/vector-stores.md): Index of all vector stores supported by Agno. - [PgVector Vector Database](https://docs.agno.com/knowledge/vector-stores/pgvector/overview.md): Use PgVector as a vector database for your Knowledge Base. - [Insert, Search, and Delete with PgVector](https://docs.agno.com/knowledge/vector-stores/pgvector/usage/pgvector-db.md): Insert a PDF, search it with an agent, and delete PgVector rows by name or metadata. - [Async PgVector Usage](https://docs.agno.com/knowledge/vector-stores/pgvector/usage/async-pgvector-db.md): Insert knowledge and run an agent with Agno's async methods and a PgVector backend. - [PgVector Hybrid Search](https://docs.agno.com/knowledge/vector-stores/pgvector/usage/pgvector-hybrid-search.md): Combine PgVector embedding similarity and PostgreSQL full-text rank in one search score. - [LanceDB](https://docs.agno.com/knowledge/vector-stores/lancedb/overview.md): Store and search Knowledge embeddings in a local or remote LanceDB table. - [Insert, Search, and Delete with LanceDB](https://docs.agno.com/knowledge/vector-stores/lancedb/usage/lance-db.md): Insert a PDF, search it with an agent, and delete LanceDB rows by name or metadata. - [Async LanceDB Usage](https://docs.agno.com/knowledge/vector-stores/lancedb/usage/async-lance-db.md): Insert knowledge and run an agent with Agno's async methods and a LanceDB backend. - [LanceDB Hybrid Search](https://docs.agno.com/knowledge/vector-stores/lancedb/usage/lance-db-hybrid-search.md): Run LanceDB's native vector and full-text search together in a single query. - [Pinecone Vector Database](https://docs.agno.com/knowledge/vector-stores/pinecone/overview.md): Use Pinecone as a vector database for your Knowledge Base. - [Pinecone](https://docs.agno.com/knowledge/vector-stores/pinecone/usage/pinecone-db.md): Insert a PDF into a Pinecone index, query it with an agent, and delete content by name or metadata. - [Pinecone Async](https://docs.agno.com/knowledge/vector-stores/pinecone/usage/async-pinecone-db.md): Load and query a Pinecone knowledge base asynchronously with ainsert() and aprint_response(). - [Qdrant Vector Database](https://docs.agno.com/knowledge/vector-stores/qdrant/overview.md): Use Qdrant as a vector database for your Knowledge Base. - [Qdrant](https://docs.agno.com/knowledge/vector-stores/qdrant/usage/qdrant-db.md): Insert a PDF into a Qdrant collection, query it with an agent, and delete content by name or metadata. - [Qdrant Async](https://docs.agno.com/knowledge/vector-stores/qdrant/usage/async-qdrant-db.md): Load and query a Qdrant knowledge base asynchronously with ainsert() and aprint_response(). - [Qdrant Hybrid Search](https://docs.agno.com/knowledge/vector-stores/qdrant/usage/qdrant-db-hybrid-search.md): Combine dense and sparse vectors in one Qdrant search with SearchType.hybrid. - [ChromaDB Vector Database](https://docs.agno.com/knowledge/vector-stores/chroma/overview.md): Use ChromaDB as a vector database for your Knowledge Base. - [ChromaDB](https://docs.agno.com/knowledge/vector-stores/chroma/usage/chroma-db.md): Insert a PDF, query it with an agent, and delete ChromaDB records by name or metadata. - [ChromaDB Async](https://docs.agno.com/knowledge/vector-stores/chroma/usage/async-chroma-db.md): Insert knowledge and run an agent with ainsert() and aprint_response() on a ChromaDB backend. - [ChromaDB Hybrid Search](https://docs.agno.com/knowledge/vector-stores/chroma/usage/chroma-hybrid-search.md): Combine vector results and lexical candidates using two ChromaDB queries and RRF fusion. - [Milvus Vector Database](https://docs.agno.com/knowledge/vector-stores/milvus/overview.md): Use Milvus as a vector database for your Knowledge Base. - [Milvus](https://docs.agno.com/knowledge/vector-stores/milvus/usage/milvus-db.md): Insert a PDF, query it with an agent, and delete Milvus records by name or metadata. - [Milvus Async](https://docs.agno.com/knowledge/vector-stores/milvus/usage/async-milvus-db.md): Insert knowledge and run an agent with ainsert() and aprint_response() on a Milvus backend. - [Milvus Hybrid Search](https://docs.agno.com/knowledge/vector-stores/milvus/usage/milvus-db-hybrid-search.md): Combine dense embeddings and sparse keyword vectors in one Milvus search with SearchType.hybrid. - [Milvus Async Hybrid Search](https://docs.agno.com/knowledge/vector-stores/milvus/usage/async-milvus-db-hybrid-search.md): Run Milvus hybrid search asynchronously with ainsert() and aprint_response(). - [Weaviate Vector Database](https://docs.agno.com/knowledge/vector-stores/weaviate/overview.md): Use Weaviate as a vector database for your Knowledge Base. - [Weaviate](https://docs.agno.com/knowledge/vector-stores/weaviate/usage/weaviate-db.md): Insert a PDF, search it with an agent, and delete Weaviate records by name or metadata. - [Weaviate Async](https://docs.agno.com/knowledge/vector-stores/weaviate/usage/async-weaviate-db.md): Insert knowledge and run an agent with Agno's async methods and a Weaviate backend. - [Weaviate Hybrid Search](https://docs.agno.com/knowledge/vector-stores/weaviate/usage/weaviate-db-hybrid-search.md): Combine vector and keyword scores in one Weaviate query, tuned with hybrid_search_alpha. - [Couchbase Vector Database](https://docs.agno.com/knowledge/vector-stores/couchbase/overview.md): Use Couchbase as a vector database for your Knowledge Base. - [Couchbase](https://docs.agno.com/knowledge/vector-stores/couchbase/usage/couchbase-db.md): Define a Couchbase FTS vector index in code, insert a PDF, and search it with an agent. - [Couchbase Async](https://docs.agno.com/knowledge/vector-stores/couchbase/usage/async-couchbase-db.md): Insert knowledge and run an agent with Agno's async methods and a Couchbase FTS backend. - [Cassandra Vector Database](https://docs.agno.com/knowledge/vector-stores/cassandra/overview.md): Use Cassandra as a vector database for your Knowledge Base. - [Cassandra](https://docs.agno.com/knowledge/vector-stores/cassandra/usage/cassandra-db.md): Insert a PDF into a Cassandra knowledge base, query it with an agent, and delete content by name or metadata. - [Cassandra Async](https://docs.agno.com/knowledge/vector-stores/cassandra/usage/async-cassandra-db.md): Load and query a Cassandra knowledge base asynchronously with ainsert() and aprint_response(). - [ClickHouse Vector Database](https://docs.agno.com/knowledge/vector-stores/clickhouse/overview.md): Use ClickHouse as a vector database for your Knowledge Base. - [ClickHouse](https://docs.agno.com/knowledge/vector-stores/clickhouse/usage/clickhouse-db.md): Insert a PDF into a ClickHouse knowledge base, query it with an agent, and delete content by name or metadata. - [ClickHouse Async](https://docs.agno.com/knowledge/vector-stores/clickhouse/usage/async-clickhouse-db.md): Load and query a ClickHouse knowledge base asynchronously with ainsert() and aprint_response(). - [SingleStore Vector Database](https://docs.agno.com/knowledge/vector-stores/singlestore/overview.md): Use SingleStore as a vector database for your Knowledge Base. - [SingleStore](https://docs.agno.com/knowledge/vector-stores/singlestore/usage/singlestore-db.md): Insert a PDF into a SingleStore knowledge base, query it with an agent, and delete content by name. - [SingleStore Async](https://docs.agno.com/knowledge/vector-stores/singlestore/usage/async-singlestore-db.md): Load and query a SingleStore knowledge base asynchronously with ainsert() and aprint_response(). - [Redis Vector Database](https://docs.agno.com/knowledge/vector-stores/redis/overview.md): Use Redis as a vector database for your Knowledge Base. - [Redis](https://docs.agno.com/knowledge/vector-stores/redis/usage/redis-db.md): Insert a PDF into a Redis knowledge base, query it with an agent, and delete content by name. - [Redis Async](https://docs.agno.com/knowledge/vector-stores/redis/usage/async-redis-db.md): Load and query a Redis knowledge base asynchronously with ainsert() and aprint_response(). - [Valkey Vector Database](https://docs.agno.com/knowledge/vector-stores/valkey/overview.md): Use Valkey as a vector database for your Knowledge Base. - [Valkey](https://docs.agno.com/knowledge/vector-stores/valkey/usage/valkey-db.md): Add and query knowledge with ValkeyDB as the vector database. - [Valkey Async](https://docs.agno.com/knowledge/vector-stores/valkey/usage/async-valkey-db.md): Add and query knowledge asynchronously with ValkeyDB as the vector database. - [MongoDB Vector Database](https://docs.agno.com/knowledge/vector-stores/mongodb/overview.md): Use MongoDB as a vector database for your Knowledge Base. - [MongoDB](https://docs.agno.com/knowledge/vector-stores/mongodb/usage/mongo-db.md): Insert a PDF into a MongoDB collection and query it with an agent. - [MongoDB Async](https://docs.agno.com/knowledge/vector-stores/mongodb/usage/async-mongo-db.md): Insert knowledge and query it asynchronously with ainsert() and aprint_response() on MongoDB. - [MongoDB Hybrid Search](https://docs.agno.com/knowledge/vector-stores/mongodb/usage/mongo-db-hybrid-search.md): Combine vector and keyword retrieval with SearchType.hybrid on a MongoDB collection. - [Azure Cosmos DB MongoDB vCore Vector Database](https://docs.agno.com/knowledge/vector-stores/azure_cosmos_mongodb/overview.md): Use Azure Cosmos DB MongoDB vCore as a vector database for your Knowledge Base. - [SurrealDB Vector Database](https://docs.agno.com/knowledge/vector-stores/surrealdb/overview.md): Use SurrealDB as a vector database for your Knowledge Base. - [SurrealDB](https://docs.agno.com/knowledge/vector-stores/surrealdb/usage/surreal-db.md): Insert a PDF into a SurrealDB collection and query it with an agent. - [SurrealDB Async](https://docs.agno.com/knowledge/vector-stores/surrealdb/usage/async-surreal-db.md): Insert knowledge and query it asynchronously with ainsert() and aprint_response() on SurrealDB. - [LightRAG Vector Database](https://docs.agno.com/knowledge/vector-stores/lightrag/overview.md): Connect your Knowledge Base to a LightRAG server for graph-based retrieval. - [LightRAG](https://docs.agno.com/knowledge/vector-stores/lightrag/usage/lightrag-db.md): Insert a PDF and a Wikipedia page into a LightRAG knowledge base and query it with an agent. - [LightRAG Async](https://docs.agno.com/knowledge/vector-stores/lightrag/usage/async-lightrag-db.md): Load and query a LightRAG knowledge base asynchronously with ainsert() and aprint_response(). - [Upstash Vector Database](https://docs.agno.com/knowledge/vector-stores/upstash/overview.md): Use Upstash Vector as a serverless vector database for your Knowledge Base. - [Upstash](https://docs.agno.com/knowledge/vector-stores/upstash/usage/upstash-db.md): Insert a PDF into an Upstash Vector index, query it with an agent, and delete content by name or metadata. - [Upstash Async](https://docs.agno.com/knowledge/vector-stores/upstash/usage/async-upstash-db.md): Load and query an Upstash Vector knowledge base asynchronously with ainsert() and aprint_response(). - [LangChain Vector Database](https://docs.agno.com/knowledge/vector-stores/langchain/overview.md): Search an existing LangChain vectorstore or retriever from your Knowledge Base. - [LangChain](https://docs.agno.com/knowledge/vector-stores/langchain/usage/langchain-db.md): Load a document into a Chroma vectorstore with LangChain, then search it through Agno Knowledge. - [LangChain Async](https://docs.agno.com/knowledge/vector-stores/langchain/usage/async-langchain-db.md): Load a document into a Chroma vectorstore with LangChain, then query it asynchronously with aprint_response. - [LlamaIndex Vector Database](https://docs.agno.com/knowledge/vector-stores/llamaindex/overview.md): Search an existing LlamaIndex index from your Knowledge Base through a retriever. - [LlamaIndex](https://docs.agno.com/knowledge/vector-stores/llamaindex/usage/llamaindex-db.md): Use a LlamaIndex retriever as the vector store behind Agno Knowledge. - [LlamaIndex Async](https://docs.agno.com/knowledge/vector-stores/llamaindex/usage/async-llamaindex-db.md): Query a LlamaIndex-backed Knowledge base asynchronously with aprint_response. - [Embedders](https://docs.agno.com/knowledge/concepts/embedder/overview.md): Convert text into vector representations for semantic search. - [OpenAI Embedder](https://docs.agno.com/knowledge/concepts/embedder/openai/openai-embedder.md): Embed documents with OpenAIEmbedder. The default model is text-embedding-3-small. - [Cohere Embedder](https://docs.agno.com/knowledge/concepts/embedder/cohere/cohere-embedder.md): Generate Cohere embeddings with an explicit model, input type, and vector dimension. - [Gemini Embedder](https://docs.agno.com/knowledge/concepts/embedder/gemini/gemini-embedder.md): Generate Gemini embeddings with an explicit retrieval task type and vector dimension. - [Ollama Embedder](https://docs.agno.com/knowledge/concepts/embedder/ollama/ollama-embedder.md): Generate embeddings with OllamaEmbedder and an explicit local embedding model. - [Mistral Embedder](https://docs.agno.com/knowledge/concepts/embedder/mistral/mistral-embedder.md): Generate 1024-dimensional embeddings with MistralEmbedder and mistral-embed. - [VoyageAI Embedder](https://docs.agno.com/knowledge/concepts/embedder/voyageai/voyageai-embedder.md): Generate VoyageAI retrieval embeddings with explicit document and query input types. - [AWS Bedrock Embedder](https://docs.agno.com/knowledge/concepts/embedder/aws-bedrock/aws-bedrock-embedder.md): Generate Cohere Embed v3 or v4 embeddings through AWS Bedrock. - [Azure OpenAI Embedder](https://docs.agno.com/knowledge/concepts/embedder/azure-openai/azure-embedder.md): Generate embeddings from an Azure OpenAI model deployment. - [Fireworks Embedder](https://docs.agno.com/knowledge/concepts/embedder/fireworks/fireworks-embedder.md): Generate 768-dimensional embeddings through the Fireworks API. - [Hugging Face Embedder](https://docs.agno.com/knowledge/concepts/embedder/huggingface/huggingface-embedder.md): Call a Hugging Face feature-extraction endpoint with HuggingfaceCustomEmbedder. - [Jina Embedder](https://docs.agno.com/knowledge/concepts/embedder/jina/jina-embedder.md): Generate Jina retrieval embeddings with explicit passage and query tasks. - [LangDB Embedder](https://docs.agno.com/knowledge/concepts/embedder/langdb/langdb-embedder.md): Generate embeddings through a LangDB project endpoint. - [Nebius Embedder](https://docs.agno.com/knowledge/concepts/embedder/nebius/nebius-embedder.md): Generate embeddings with an active Nebius model through its OpenAI-compatible API. - [Qdrant FastEmbed Embedder](https://docs.agno.com/knowledge/concepts/embedder/qdrant-fastembed/qdrant-fastembed.md): Generate local embeddings with Qdrant's FastEmbed library. - [Sentence Transformer Embedder](https://docs.agno.com/knowledge/concepts/embedder/sentencetransformers/sentence-transformer-embedder.md): Generate local embeddings with the sentence-transformers library. - [Together Embedder](https://docs.agno.com/knowledge/concepts/embedder/together/together-embedder.md): Configure Together embeddings through its OpenAI-compatible API. - [vLLM Embedder](https://docs.agno.com/knowledge/concepts/embedder/vllm/vllm-embedder.md): Generate embeddings with vLLM, either in-process or against a vLLM server. - [Culture](https://docs.agno.com/culture/overview.md): Migrate v2 Culture configurations after the Culture feature was removed in Agno v3. - [Custom Logging](https://docs.agno.com/custom-logging.md): Configure custom loggers and formatters for your Agno setup. - [Getting Help](https://docs.agno.com/get-help.md): Connect with the Agno community, reach out to the team, build and share. - [Install & Setup](https://docs.agno.com/other/install.md): Install the Agno SDK with uv or pip in a supported Python virtual environment. - [OpenTelemetry](https://docs.agno.com/observability/overview.md): Agno emits OpenTelemetry traces that integrate with popular tracing and monitoring platforms. - [AgentOps](https://docs.agno.com/observability/agentops.md): Integrate Agno with AgentOps to send traces and logs to a centralized observability platform. - [Arize Phoenix](https://docs.agno.com/observability/arize.md): Integrate Agno with Arize Phoenix to send traces and evaluate your agent's performance. - [Atla](https://docs.agno.com/observability/atla.md): Historical reference for the discontinued Atla Insights integration. - [LangDB](https://docs.agno.com/observability/langdb.md): Send Agno agent runs, team runs, and tool-call traces to LangDB. - [Langfuse](https://docs.agno.com/observability/langfuse.md): Integrate Agno with Langfuse to send traces and gain insights into your agent's performance. - [LangSmith](https://docs.agno.com/observability/langsmith.md): Send Agno traces to a LangSmith project with OpenInference and OpenTelemetry. - [Langtrace](https://docs.agno.com/observability/langtrace.md): Send Agno model-call traces to Langtrace. - [LangWatch](https://docs.agno.com/observability/langwatch.md): Integrate Agno with LangWatch to send traces and gain insights into your agent's performance. - [Latitude](https://docs.agno.com/observability/latitude.md): Integrate Agno with Latitude to send traces and gain insights into your agent's performance. - [Logfire](https://docs.agno.com/observability/logfire.md): Integrate Agno with Logfire to send traces and gain insights into your agent's performance. - [Maxim](https://docs.agno.com/observability/maxim.md): Connect Agno with Maxim to monitor, trace, and evaluate your agent's activity and performance. - [MLflow](https://docs.agno.com/observability/mlflow.md): Integrate Agno with MLflow to automatically capture OpenTelemetry-native traces from your agents with a single line of code. - [OpenLIT](https://docs.agno.com/observability/openlit.md): Integrate Agno with OpenLIT for OpenTelemetry-native observability, tracing, and monitoring of your AI agents. - [The Context Company](https://docs.agno.com/observability/the-context-company.md): Send Agno traces to The Context Company for agent observability and production interaction analysis. - [Traceloop](https://docs.agno.com/observability/traceloop.md): Integrate Agno with Traceloop to send traces and gain insights into your agent's performance. - [Weave](https://docs.agno.com/observability/weave.md): Integrate Agno with Weave by WandB to send traces and gain insights into your agent's performance. - [Memori](https://docs.agno.com/integrations/memory/memori.md): Integrate Agno with Memori to give agents persistent, searchable conversation memory. - [Discord Bot](https://docs.agno.com/integrations/discord/overview.md): Host agents as Discord Bots. - [Basic](https://docs.agno.com/integrations/discord/usage/basic.md): Run a basic Agno agent as a Discord bot with conversation history. - [Agent with Media](https://docs.agno.com/integrations/discord/usage/agent-with-media.md): Run a Discord bot that analyzes images, audio, and video using Gemini. - [Agent with User Memory](https://docs.agno.com/integrations/discord/usage/agent-with-user-memory.md): Discord agent with agentic memory and web search using SqliteDb. - [Scenario Testing](https://docs.agno.com/integrations/testing/overview.md): Simulate conversations and evaluate agent behavior with the Scenario testing framework. - [Basic](https://docs.agno.com/integrations/testing/usage/basic.md): Test a vegetarian recipe agent with Scenario's user simulator and judge agents in a pytest suite. - [AgentSystems Notary](https://docs.agno.com/integrations/governance/agentsystems-notary.md): Cryptographically verifiable audit trails for Agno applications. - [Migrating to Agno v3.0](https://docs.agno.com/other/v3-migration.md): Guide to migrate your Agno applications from v2 to v3. - [Agno v3.0 Changelog](https://docs.agno.com/other/v3-changelog.md): Full list of storage, parameter, and behavior changes introduced in Agno v3.0. - [Migrating to Agno v2.0](https://docs.agno.com/other/v2-migration.md): Guide to migrate your Agno applications from v1 to v2. - [Agno v2.0 Changelog](https://docs.agno.com/other/v2-changelog.md): Full list of class, parameter, and behavior changes introduced in Agno v2.0. - [Database Migrations](https://docs.agno.com/other/database-migrations.md): Migrate Agno database tables between versions. - [Migrating to Workflows 2.0](https://docs.agno.com/other/workflows-migration.md): Migrate your Workflows from 1.0 to 2.0. - [Cursor Rules for Building Agents](https://docs.agno.com/other/cursor-rules.md): Use .cursorrules to improve AI coding assistant suggestions when building agents with Agno - [Contributing to Agno](https://docs.agno.com/other/contribute.md): Contribute to Agno through the fork and pull request workflow. - [AgentUI](https://docs.agno.com/other/agent-ui.md): An open-source AgentUI for your AgentOS - [Agno Telemetry](https://docs.agno.com/telemetry.md): Control what usage data Agno collects ## AgentOS - [What is AgentOS?](https://docs.agno.com/agent-os/introduction.md): Run agents, teams, and workflows using FastAPI. - [Run Your First AgentOS](https://docs.agno.com/agent-os/run-your-os.md): Serve an agent through FastAPI with persistent sessions and a local REST API. - [Connect Your AgentOS](https://docs.agno.com/agent-os/connect-your-os.md): Connect an AgentOS runtime to the Control Plane to manage and monitor it from one web interface. - [AgentOS Control Plane](https://docs.agno.com/agent-os/control-plane.md): Manage and monitor AgentOS runtimes from one web interface. - [Security & Auth](https://docs.agno.com/agent-os/security/overview.md): Authentication modes, scope enforcement, and policies for AgentOS API, MCP, and interface requests. - [AgentOS Runtime](https://docs.agno.com/agent-os/overview.md): Configure the FastAPI runtime that serves your agents, teams, and workflows. - [Using the API](https://docs.agno.com/agent-os/using-the-api.md): Run agents, manage state, and operate AgentOS through its REST API. - [AgentOS as MCP Server](https://docs.agno.com/agent-os/mcp/mcp.md): Serve AgentOS as an MCP server at /mcp, with eight built-in tools, custom tools, and per-call authorization via MCPConfig. - [MCPTools within AgentOS](https://docs.agno.com/agent-os/mcp/tools.md): Give the Agents, Teams, and Workflows in your AgentOS access to external MCP servers with MCPTools. - [Public Surface](https://docs.agno.com/agent-os/public-surface.md): Select agents and teams for bounded public execution while keeping runtime administration private. - [Interfaces](https://docs.agno.com/agent-os/interfaces/overview.md): Expose agents, teams, and workflows through AI frontends, messaging platforms, and agent protocols. - [Slack](https://docs.agno.com/agent-os/interfaces/slack/introduction.md): Run agents, teams, and workflows in Slack with session routing, streaming, file handling, and human-in-the-loop approvals. - [Setup](https://docs.agno.com/agent-os/interfaces/slack/setup.md): Create a Slack App and connect it to your Agno agent. - [Features](https://docs.agno.com/agent-os/interfaces/slack/features.md): Memory, files, streaming, search, and user identity for your Slack agent. - [Human-in-the-Loop](https://docs.agno.com/agent-os/interfaces/slack/hitl.md): Pause agents for approval before executing sensitive tools. - [Slack Reference](https://docs.agno.com/agent-os/interfaces/slack/reference.md): Interface parameters, endpoints, event handling, and OAuth scopes for the Slack interface. - [Telegram](https://docs.agno.com/agent-os/interfaces/telegram/introduction.md): Expose agents, teams, or workflows as Telegram bots with webhook endpoints. - [Setup](https://docs.agno.com/agent-os/interfaces/telegram/setup.md): Create a Telegram bot with BotFather and configure webhooks for local and production deployments. - [Telegram Reference](https://docs.agno.com/agent-os/interfaces/telegram/reference.md): Interface parameters, endpoints, and event handling for the Telegram interface. - [WhatsApp](https://docs.agno.com/agent-os/interfaces/whatsapp/introduction.md): Deploy agents, teams, or workflows as WhatsApp bots via the WhatsApp Interface using Agno. - [Setup](https://docs.agno.com/agent-os/interfaces/whatsapp/setup.md): Configure Meta Developer account, WhatsApp Business API, and webhooks for WhatsApp bots. - [WhatsApp Reference](https://docs.agno.com/agent-os/interfaces/whatsapp/reference.md): Interface parameters, endpoints, and webhook handling for the WhatsApp interface. - [Discord](https://docs.agno.com/agent-os/interfaces/discord/introduction.md): Deploy agents to Discord for community support and moderation. - [A2A](https://docs.agno.com/agent-os/interfaces/a2a/introduction.md): Expose Agno agents via the A2A protocol - [AG-UI](https://docs.agno.com/agent-os/interfaces/ag-ui/introduction.md): Expose Agno agents via the AG-UI protocol - [Bring Your Own FastAPI App](https://docs.agno.com/agent-os/custom-fastapi/overview.md): Integrate your own FastAPI app with AgentOS. - [Overriding Routes](https://docs.agno.com/agent-os/custom-fastapi/override-routes.md): Resolve route conflicts between your custom FastAPI app and AgentOS with the on_route_conflict parameter. - [AgentOS Middleware](https://docs.agno.com/agent-os/middleware/overview.md): Add authentication, logging, monitoring, and security features to your AgentOS application using middleware - [Custom Middleware](https://docs.agno.com/agent-os/middleware/custom.md): Create custom middleware for rate limiting, logging, security, and monitoring in AgentOS - [Auth Middleware](https://docs.agno.com/agent-os/middleware/jwt.md): Configure AuthMiddleware for JWT validation, claim injection, and RBAC across REST, MCP, and WebSocket connections. - [Authorization](https://docs.agno.com/agent-os/security/authorization/overview.md): JWT validation and scope-based permissions for AgentOS endpoints. - [Quickstart](https://docs.agno.com/agent-os/security/authorization/quickstart.md): Enable authorization, set a verification key, and make your first authenticated request. - [JSON Web Tokens (JWT)](https://docs.agno.com/agent-os/security/authorization/tokens.md): JWT claim structure, example tokens, and how AgentOS reads them. - [Self-Hosted (BYO Token)](https://docs.agno.com/agent-os/security/authorization/self-hosted.md): Run AgentOS without the AgentOS control plane by issuing and verifying your own JWTs. - [Service Accounts](https://docs.agno.com/agent-os/security/authorization/service-accounts.md): Mint, scope, and revoke the opaque agno_pat_ tokens that machine identities use to authenticate with AgentOS. - [Scopes](https://docs.agno.com/agent-os/security/authorization/scopes.md): Scope format, built-in endpoint permissions, and custom route policies. - [Roles](https://docs.agno.com/agent-os/security/authorization/roles.md): Default and custom roles in the AgentOS control plane. - [Per-User Data Isolation](https://docs.agno.com/agent-os/security/authorization/user-isolation.md): Scope user-owned AgentOS data and resources to the caller's user ID. - [Dynamic Agents](https://docs.agno.com/agent-os/factories/overview.md): Build agents, teams, and workflows per request from JWT claims, user input, and other request-time context. - [AgentFactory](https://docs.agno.com/agent-os/factories/agent-factory.md): Build an Agent per request from verified middleware claims, client input, or any request-time context. - [TeamFactory](https://docs.agno.com/agent-os/factories/team-factory.md): Build a Team per request whose members, model, and instructions depend on the caller. - [WorkflowFactory](https://docs.agno.com/agent-os/factories/workflow-factory.md): Build a Workflow per request whose steps, agents, and model depend on the caller. - [Manage Knowledge](https://docs.agno.com/agent-os/knowledge/manage-knowledge.md): Attach Knowledge to your AgentOS instance - [Remote Content](https://docs.agno.com/agent-os/knowledge/remote-content.md): Upload content from registered cloud sources via the AgentOS Knowledge API. - [Filter Knowledge](https://docs.agno.com/agent-os/knowledge/filter-knowledge.md): Use filter expressions through the AgentOS API for precise knowledge base filtering. - [Manage Learnings](https://docs.agno.com/agent-os/learnings/manage-learnings.md): Read and manage learning records through the AgentOS /learnings endpoints. - [Overview](https://docs.agno.com/agent-os/studio/introduction.md): A visual editor in AgentOS to build Agents, Teams, and Workflows. - [Agents](https://docs.agno.com/agent-os/studio/agents.md): Build and configure agents visually in AgentOS Studio. - [Teams](https://docs.agno.com/agent-os/studio/teams.md): Compose multi-agent teams visually in AgentOS Studio. - [Overview](https://docs.agno.com/agent-os/studio/workflows.md): Design step-based workflows visually in AgentOS Studio. - [CEL Expressions](https://docs.agno.com/agent-os/studio/cel-expressions.md): Use CEL expressions as evaluators, end conditions, and selectors in workflow steps. - [Registry](https://docs.agno.com/agent-os/studio/registry.md): Register tools, models, databases, workflows, and learning machines for use in AgentOS Studio. - [StudioTools](https://docs.agno.com/agent-os/studio/tools.md): Give an agent tools to create, update, and run Studio components from chat with human-in-the-loop confirmation. - [Multi-Framework Support](https://docs.agno.com/agent-os/multi-framework/overview.md): Serve agents built with the Claude Agent SDK, LangGraph, DSPy and Antigravity from one AgentOS. - [Antigravity](https://docs.agno.com/agent-os/multi-framework/antigravity.md): Run Google's Managed Agents (Gemini API) as an AgentOS agent using AntigravityAgent. - [Claude Agent SDK](https://docs.agno.com/agent-os/multi-framework/claude-agent-sdk.md): Run Claude Code as an AgentOS agent using ClaudeAgent. - [LangGraph](https://docs.agno.com/agent-os/multi-framework/langgraph.md): Wrap a compiled LangGraph graph as an AgentOS agent. - [DSPy](https://docs.agno.com/agent-os/multi-framework/dspy.md): Serve a DSPy program as an AgentOS agent. - [AgentOS Configuration](https://docs.agno.com/agent-os/config.md): Configure quick prompts, display names, and per-database settings with a YAML file or the AgentOSConfig class - [Background Hooks](https://docs.agno.com/agent-os/background-tasks/overview.md): Run agent hooks as non-blocking background tasks in AgentOS - [Background Execution](https://docs.agno.com/agent-os/background-execution/overview.md): Accept runs with background=true, poll or stream them, and make acceptance durable with a database-backed job queue. - [Durable Queue](https://docs.agno.com/agent-os/background-execution/durable-queue.md): QueueConfig(durable=True): accepted background runs become committed rows that survive crashes and deploys. - [Multi-Replica Deployments](https://docs.agno.com/agent-os/background-execution/multi-replica.md): QueueConfig(redis=...) sets the event stream and cancellation manager on every replica so runs can be resumed and cancelled from any of them. - [Human-in-the-Loop Continuations](https://docs.agno.com/agent-os/background-execution/hitl-continuations.md): Continue a paused durable run through the queue with background=true. Same job, same run_id, one more attempt. - [Operations and Monitoring](https://docs.agno.com/agent-os/background-execution/operations.md): The /queue endpoints: dead-letter listing, requeue, queue stats, retention, admin gating, and deployment affinity. - [Scheduler](https://docs.agno.com/agent-os/scheduler/overview.md): Deploy and manage scheduled execution for agents and workflows via AgentOS cron jobs. - [Approvals](https://docs.agno.com/agent-os/approvals/overview.md): Manage approval workflows for agents and teams via the AgentOS Control Plane. - [Custom Lifespan](https://docs.agno.com/agent-os/lifespan.md): Customize the lifespan of your AgentOS app to handle startup and shutdown logic. - [Clients](https://docs.agno.com/agent-os/client/overview.md): Python clients for connecting to agent servers - [AgentOS Client](https://docs.agno.com/agent-os/client/agentos-client.md): Connect to Agno AgentOS instances via REST API - [A2A Client](https://docs.agno.com/agent-os/client/a2a-client.md): Connect to agent servers through A2A REST or JSON-RPC - [Remote Execution](https://docs.agno.com/agent-os/remote-execution/overview.md): Execute agents, teams, and workflows hosted on remote AgentOS instances - [Remote Agent](https://docs.agno.com/agent-os/remote-execution/remote-agent.md): Execute agents hosted on remote AgentOS instances - [Remote Team](https://docs.agno.com/agent-os/remote-execution/remote-team.md): Execute teams hosted on remote AgentOS instances - [Remote Workflow](https://docs.agno.com/agent-os/remote-execution/remote-workflow.md): Execute workflows hosted on remote AgentOS instances - [AgentOS Gateway](https://docs.agno.com/agent-os/remote-execution/gateway.md): Create a unified API gateway for multiple AgentOS instances - [Tracing](https://docs.agno.com/agent-os/tracing/overview.md): Store AgentOS traces to inspect run behavior, latency, errors, model calls, and tool calls. - [Filter Options](https://docs.agno.com/agent-os/tracing/filter-options.md): Filter and search traces using structured queries, time ranges, and view toggles. - [Basic Agent Tracing](https://docs.agno.com/agent-os/tracing/usage/basic-agent-tracing.md): Trace agents with Agno in AgentOS. - [Basic Team Tracing](https://docs.agno.com/agent-os/tracing/usage/basic-team-tracing.md): Trace teams with Agno in AgentOS. - [Basic Workflow Tracing](https://docs.agno.com/agent-os/tracing/usage/basic-workflow-tracing.md): Trace workflows with Agno in AgentOS. - [Agent with Knowledge Tracing](https://docs.agno.com/agent-os/tracing/usage/agent-with-knowledge-tracing.md): Trace agents with knowledge bases in AgentOS. - [Agent with Reasoning Tools Tracing](https://docs.agno.com/agent-os/tracing/usage/agent-with-reasoning-tools-tracing.md): Trace agents with reasoning tools in AgentOS. - [Multi-DB Tracing with setup_tracing()](https://docs.agno.com/agent-os/tracing/usage/tracing-with-multi-db-scenario.md): Trace agents with multiple databases using setup_tracing() in AgentOS. - [Multi-DB Tracing with tracing=True](https://docs.agno.com/agent-os/tracing/usage/tracing-with-multi-db-scenario-and-tracing-flag.md): Trace agents with multiple databases using tracing=True in AgentOS. - [AgentOS Demo](https://docs.agno.com/agent-os/usage/demo.md): AgentOS with a knowledge-backed agent, a web research team, and Postgres storage. - [AgentOS Configuration](https://docs.agno.com/agent-os/usage/extra-configuration.md): Pass a YAML configuration file to AgentOS for quick prompts and database display names. - [Human-in-the-Loop Example](https://docs.agno.com/agent-os/usage/hitl.md): AgentOS with tools requiring user confirmation - [Database Migrations](https://docs.agno.com/agent-os/usage/database-migrations.md): Migrate your AgentOS database schema. - [Enable AgentOS MCP](https://docs.agno.com/agent-os/usage/mcp/enable-mcp-example.md): AgentOS with the MCP server enabled, plus an agent client that authenticates and operates it. - [AgentOS with MCPTools](https://docs.agno.com/agent-os/usage/mcp/mcp-tools-example.md): AgentOS whose agent connects to an external MCP server through MCPTools. - [Custom Middleware](https://docs.agno.com/agent-os/usage/middleware/custom-middleware.md): AgentOS with custom middleware for rate limiting and request logging - [JWT Middleware with Authorization Headers](https://docs.agno.com/agent-os/usage/middleware/jwt-middleware.md): Complete AgentOS setup with JWT middleware for authentication and parameter injection using Authorization headers - [JWT Middleware with Cookies](https://docs.agno.com/agent-os/usage/middleware/jwt-cookies.md): AgentOS with JWT middleware using HTTP-only cookies for secure web authentication - [Custom FastAPI App with JWT Middleware](https://docs.agno.com/agent-os/usage/middleware/custom-fastapi-jwt.md): Custom FastAPI application with JWT middleware for authentication and AgentOS integration - [Basic RBAC (Symmetric)](https://docs.agno.com/agent-os/usage/rbac/basic-symmetric.md): AgentOS with RBAC enabled using JWT HS256 and a shared secret. - [Basic RBAC (Asymmetric)](https://docs.agno.com/agent-os/usage/rbac/basic-asymmetric.md): Enable RBAC with RS256 asymmetric JWT keys. - [WorkOS BYOT (Bring Your Own Token)](https://docs.agno.com/agent-os/usage/rbac/workos-byot.md): Verify WorkOS-issued JWTs and read scopes from the permissions claim. - [Advanced Scopes](https://docs.agno.com/agent-os/usage/rbac/advanced-scopes.md): Use global, per-resource, and wildcard RBAC scope patterns. - [Per-Agent Permissions](https://docs.agno.com/agent-os/usage/rbac/per-agent-permissions.md): Restrict which users can run which agents with per-agent scopes. - [Custom Scope Mappings](https://docs.agno.com/agent-os/usage/rbac/custom-scope-mappings.md): Map custom scopes to AgentOS routes instead of the defaults. - [Background Hooks (Global)](https://docs.agno.com/agent-os/usage/background-hooks-global.md): Run non-guardrail agent hooks as background tasks using AgentOS - [Background Hooks (Per-Hook)](https://docs.agno.com/agent-os/usage/background-hooks-decorator.md): Run specific hooks as background tasks using the @hook decorator - [Background Output Evaluation](https://docs.agno.com/agent-os/usage/background-output-evaluation.md): Use Agent as Judge evaluation to assess responses as a background task - [Basic Client Usage](https://docs.agno.com/agent-os/usage/client/basic-client.md): Connect to an AgentOS instance and inspect its agents, teams, and workflows - [Running Agents](https://docs.agno.com/agent-os/usage/client/run-agents.md): Execute agent runs with streaming, non-streaming, and resumable background responses. - [Running Teams](https://docs.agno.com/agent-os/usage/client/run-teams.md): Execute team runs with streaming and non-streaming responses - [Running Workflows](https://docs.agno.com/agent-os/usage/client/run-workflows.md): Execute workflow runs with streaming and non-streaming responses - [Session Management](https://docs.agno.com/agent-os/usage/client/session-management.md): Create, list, and manage sessions for agents, teams, and workflows - [Memory Operations](https://docs.agno.com/agent-os/usage/client/memory-operations.md): Create, update, list, and delete user memories - [Knowledge Search](https://docs.agno.com/agent-os/usage/client/knowledge-search.md): Search the knowledge base and list stored content - [Remote Agent](https://docs.agno.com/agent-os/usage/remote-execution/remote-agent.md): Execute agents hosted on a remote AgentOS instance - [Remote Team](https://docs.agno.com/agent-os/usage/remote-execution/remote-team.md): Execute teams hosted on a remote AgentOS instance - [AgentOS Gateway](https://docs.agno.com/agent-os/usage/remote-execution/gateway.md): Create a unified API gateway for multiple AgentOS instances - [Basic WhatsApp Agent](https://docs.agno.com/agent-os/usage/interfaces/whatsapp/basic.md): Create a basic AI agent that integrates with the WhatsApp Business API - [WhatsApp Image Generation Agent (Tool-based)](https://docs.agno.com/agent-os/usage/interfaces/whatsapp/image-generation-tools.md): WhatsApp agent that generates images using OpenAI's image generation tools - [WhatsApp Reasoning Finance Agent](https://docs.agno.com/agent-os/usage/interfaces/whatsapp/reasoning-agent.md): WhatsApp agent with advanced reasoning and financial analysis capabilities - [WhatsApp Image Generation Agent (Model-based)](https://docs.agno.com/agent-os/usage/interfaces/whatsapp/image-generation-model.md): WhatsApp agent that generates images using Gemini's built-in capabilities - [WhatsApp Agent with Media Support](https://docs.agno.com/agent-os/usage/interfaces/whatsapp/agent-with-media.md): WhatsApp agent that analyzes images, videos, and audio using multimodal AI - [WhatsApp Agent with User Memory](https://docs.agno.com/agent-os/usage/interfaces/whatsapp/agent-with-user-memory.md): Personalized WhatsApp agent that remembers user information and preferences - [Basic](https://docs.agno.com/agent-os/usage/interfaces/a2a/basic.md): Create a basic AI agent with A2A interface - [Agent with Tools](https://docs.agno.com/agent-os/usage/interfaces/a2a/agent-with-tools.md): Investment analyst agent with financial tools served over the A2A protocol - [Research Team](https://docs.agno.com/agent-os/usage/interfaces/a2a/team.md): Multi-agent research team with specialized roles served over the A2A protocol - [Basic](https://docs.agno.com/agent-os/usage/interfaces/ag-ui/basic.md): Expose an OpenAI agent through the AG-UI protocol. - [Agent with Tools](https://docs.agno.com/agent-os/usage/interfaces/ag-ui/agent-with-tools.md): Investment analyst agent with financial tools served over the AG-UI protocol - [Research Team](https://docs.agno.com/agent-os/usage/interfaces/ag-ui/team.md): Multi-agent research team with specialized roles and web interface - [Basic Slack Agent](https://docs.agno.com/agent-os/usage/interfaces/slack/basic.md): Minimal Slack bot with SQLite session persistence and @mention replies - [Streaming Deep Research](https://docs.agno.com/agent-os/usage/interfaces/slack/streaming.md): Multi-tool research agent with streaming task cards and suggested prompts - [Support Team](https://docs.agno.com/agent-os/usage/interfaces/slack/support-team.md): Multi-agent team with SlackTools that routes support questions to the right specialist. - [Channel Summarizer](https://docs.agno.com/agent-os/usage/interfaces/slack/channel-summarizer.md): SlackTools-powered agent that reads channel history and produces structured summaries - [Slack Workflow](https://docs.agno.com/agent-os/usage/interfaces/slack/workflow.md): Research and write two-step workflow with WebSearch and SQLite sessions - [Multiple Instances](https://docs.agno.com/agent-os/usage/interfaces/slack/multiple-instances.md): Multiple Slack bots on a single AgentOS server with prefix-based routing - [Slack Agent with User Memory](https://docs.agno.com/agent-os/usage/interfaces/slack/agent-with-user-memory.md): MemoryManager captures user preferences and personalizes responses across conversations - [Reasoning Finance Agent](https://docs.agno.com/agent-os/usage/interfaces/slack/reasoning-agent.md): ReasoningTools and WebSearch for step-by-step financial analysis on Slack - [File Analyst](https://docs.agno.com/agent-os/usage/interfaces/slack/file-analyst.md): Slack bot that downloads shared files, analyzes their content, and uploads results back to the channel - [Multi-Bot](https://docs.agno.com/agent-os/usage/interfaces/slack/multi-bot.md): Two independent Slack bots on the same workspace with separate prefixes, tokens, and session isolation - [Multimodal Team](https://docs.agno.com/agent-os/usage/interfaces/slack/multimodal-team.md): Legacy Slack multimodal team with vision analysis and deprecated DALL-E image generation - [Multimodal Workflow](https://docs.agno.com/agent-os/usage/interfaces/slack/multimodal-workflow.md): Legacy Slack workflow with visual analysis, web research, and deprecated DALL-E image generation - [Research Assistant](https://docs.agno.com/agent-os/usage/interfaces/slack/research-assistant.md): Agent that searches Slack message history and the web to answer research questions - [Basic Telegram Agent](https://docs.agno.com/agent-os/usage/interfaces/telegram/basic.md): Gemini agent with session persistence on Telegram - [Streaming Telegram Agent](https://docs.agno.com/agent-os/usage/interfaces/telegram/streaming.md): OpenAI agent with token-by-token streaming via live message edits - [Multi-Agent Telegram Team](https://docs.agno.com/agent-os/usage/interfaces/telegram/team.md): Researcher + Writer team coordinating on Telegram - [Telegram Agent with Media](https://docs.agno.com/agent-os/usage/interfaces/telegram/agent-with-media.md): Legacy Telegram media bot with deprecated DALL-E image generation and ElevenLabs TTS - [Telegram Agent with User Memory](https://docs.agno.com/agent-os/usage/interfaces/telegram/agent-with-user-memory.md): MemoryManager for cross-session user recall on Telegram - [Telegram Reasoning Agent](https://docs.agno.com/agent-os/usage/interfaces/telegram/reasoning-agent.md): ReasoningTools + DuckDuckGo search on Telegram - [Telegram Workflow](https://docs.agno.com/agent-os/usage/interfaces/telegram/workflow.md): Draft + Edit two-step workflow on Telegram - [Streaming Workflow](https://docs.agno.com/agent-os/usage/interfaces/telegram/streaming-workflow.md): Research + Write workflow with live step progress on Telegram. - [Multiple Instances](https://docs.agno.com/agent-os/usage/interfaces/telegram/multiple-instances.md): Multiple Telegram bots on a single AgentOS server ## Templates - [Templates](https://docs.agno.com/deploy/introduction.md) - [Build with Coding Agents](https://docs.agno.com/deploy/coding-agents.md): Use Claude Code, Codex, or Cursor to set up, build, improve, evaluate, review, and deploy an AgentOS Starter. - [AgentOS on Railway](https://docs.agno.com/deploy/templates/railway/deploy.md): AgentOS template for teams that develop locally with Docker and deploy with Postgres on Railway. - [Railway Reference](https://docs.agno.com/deploy/templates/railway/reference.md): Commands, customization, environment variables, and troubleshooting for the Railway template. - [AgentOS on self-hosted Docker](https://docs.agno.com/deploy/templates/docker/deploy.md): AgentOS template for teams that want to run the same Docker Compose stack locally and on their own infrastructure. - [Docker Reference](https://docs.agno.com/deploy/templates/docker/reference.md): Commands, customization, environment variables, and troubleshooting for the self-hosted Docker template. - [AgentOS on AWS](https://docs.agno.com/deploy/templates/aws/deploy.md): AgentOS template for teams that develop locally with Docker and deploy to production on AWS. - [AWS Reference](https://docs.agno.com/deploy/templates/aws/reference.md): Commands, customization, environment variables, and troubleshooting for the AWS template. - [AgentOS on Fly.io](https://docs.agno.com/deploy/templates/fly/deploy.md): AgentOS template for teams that develop locally with Docker and deploy to production on Fly.io. - [Fly.io Reference](https://docs.agno.com/deploy/templates/fly/reference.md): Commands, customization, environment variables, and troubleshooting for the Fly.io template. - [AgentOS on Google Cloud Run](https://docs.agno.com/deploy/templates/gcp/deploy.md): AgentOS template for teams that develop locally with Docker and deploy to production on Google Cloud Run. - [Google Cloud Run Reference](https://docs.agno.com/deploy/templates/gcp/reference.md): Commands, customization, environment variables, and troubleshooting for the Google Cloud Run template. - [AgentOS on Kubernetes](https://docs.agno.com/deploy/templates/helm/deploy.md): AgentOS template for teams that develop locally with Docker and deploy to production on Kubernetes with Helm. - [Kubernetes Reference](https://docs.agno.com/deploy/templates/helm/reference.md): Commands, customization, environment variables, and troubleshooting for the Kubernetes template. - [AgentOS on Azure Container Apps](https://docs.agno.com/deploy/templates/azure/deploy.md): AgentOS template for teams that develop locally with Docker and deploy to production on Azure Container Apps. - [Azure Container Apps Reference](https://docs.agno.com/deploy/templates/azure/reference.md): Commands, customization, environment variables, and troubleshooting for the Azure Container Apps template. - [AgentOS on Render](https://docs.agno.com/deploy/templates/render/deploy.md): AgentOS template for teams that develop locally with Docker and deploy to production on Render. - [Render Reference](https://docs.agno.com/deploy/templates/render/reference.md): Commands, customization, environment variables, and troubleshooting for the Render template. - [AgentOS on Modal](https://docs.agno.com/deploy/templates/modal/deploy.md): AgentOS template for teams that develop locally with Docker and deploy to production on Modal. - [Modal Reference](https://docs.agno.com/deploy/templates/modal/reference.md): Commands, customization, environment variables, and troubleshooting for the Modal template. - [Docs Agent](https://docs.agno.com/deploy/templates/docs-agent.md): A template for building your own documentation agent is coming soon. - [Dash](https://docs.agno.com/deploy/templates/dash/overview.md): Self-learning data agent for teams that need grounded answers from company data, business rules, and proven query patterns. - [Coda](https://docs.agno.com/deploy/templates/coda/overview.md): Code companion for engineering teams that review pull requests, triage issues, and answer architecture questions in Slack. - [Scout](https://docs.agno.com/deploy/templates/scout/overview.md): Company intelligence agent for teams that need answers across live web, Slack, Drive, wiki, CRM, and MCP sources. - [Context](https://docs.agno.com/deploy/templates/context/overview.md): Self-hosted context manager that gives your AI tools one private CRM and knowledge base for your work. - [Agno CLI](https://docs.agno.com/cli/overview.md): Scaffold, run, and connect an AgentOS from the terminal with the agno command. - [Create a Project](https://docs.agno.com/cli/create.md): Scaffold an AgentOS project from a starter template: Docker by default, or AWS, Azure, Fly.io, GCP, Kubernetes, Modal, Railway, and Render. - [Connect Your Clients](https://docs.agno.com/cli/connect.md): Connect Claude Code, Claude Desktop, Codex, and Cursor to a running AgentOS over MCP with one command. - [Operate Your AgentOS](https://docs.agno.com/cli/operate.md): Start, stop, and restart an AgentOS project with Docker Compose, and inspect what is running and connected. - [Manage Tokens](https://docs.agno.com/cli/tokens.md): Mint, list, and revoke AgentOS service-account tokens from the terminal. ## Examples - [Examples](https://docs.agno.com/examples/introduction.md): Runnable patterns for agents, teams, workflows, tools, knowledge, and AgentOS. - [Quickstart](https://docs.agno.com/examples/basics/overview.md): Build your first agent with tools, structured output, memory, knowledge, guardrails. - [Agent with Tools - Your First Useful Agent](https://docs.agno.com/examples/basics/agent-with-tools.md): The agent uses Yahoo Finance to turn a plain-English question into tool calls and a current market brief. - [Agent with Structured Output - Finance Agent with Typed Responses](https://docs.agno.com/examples/basics/agent-with-structured-output.md): Get structured, typed responses from your agent. - [Agent with Typed Input and Output](https://docs.agno.com/examples/basics/agent-with-typed-input-output.md): Define both input and output schemas for your agent. - [Agent with Storage - Finance Agent with Storage](https://docs.agno.com/examples/basics/agent-with-storage.md): Add SQLite session storage so the Finance Agent remembers conversations across runs. - [Agent with Memory - Finance Agent that Remembers You](https://docs.agno.com/examples/basics/agent-with-memory.md): Give your agent memory of user preferences. - [Agent with State Management - Finance Agent with Watchlist](https://docs.agno.com/examples/basics/agent-with-state-management.md): Give your agent persistent state that it can read and modify. - [Agentic Search over Knowledge - Agent with a Knowledge Base](https://docs.agno.com/examples/basics/agent-search-over-knowledge.md): Give an agent a searchable knowledge base. - [Agent with Guardrails - Input Validation and Safety](https://docs.agno.com/examples/basics/agent-with-guardrails.md): Add guardrails to your agent to validate input before processing. - [Human in the Loop - Approve Before the Agent Acts](https://docs.agno.com/examples/basics/human-in-the-loop.md): This example pauses an agent before it executes a tool that has an external effect. - [Multi-Agent Team - Investment Research Team](https://docs.agno.com/examples/basics/multi-agent-team.md): Coordinate bull and bear analyst agents under a team leader that synthesizes a balanced investment recommendation. - [Sequential Workflow - Stock Research Pipeline](https://docs.agno.com/examples/basics/sequential-workflow.md): Chain data gathering, analysis, and report writing agents into a three-step stock research workflow. - [AgentOS - Run the Complete Quickstart](https://docs.agno.com/examples/basics/run.md): Start an AgentOS server that exposes every quickstart agent, team, and workflow at http://localhost:7777. - [Agent with Learning - Research That Improves Across Users](https://docs.agno.com/examples/basics/agent-with-learning.md): This example gives an agent learned knowledge: reusable insights that become available to future users and sessions. - [Agents](https://docs.agno.com/examples/agents/overview.md): Practical examples for building agents with Agno, organized by feature area. - [Basic Agent](https://docs.agno.com/examples/agents/basics/basic-agent.md): Create a minimal agent with an OpenAIResponses model and print a streamed response. - [Agent With Instructions](https://docs.agno.com/examples/agents/basics/agent-with-instructions.md): Steer agent responses with an instructions string that requests a three-bullet answer format. - [Agent With Tools](https://docs.agno.com/examples/agents/basics/agent-with-tools.md): Give an agent DuckDuckGo web search tools and stream a summary of a recent AI safety headline. - [Quickstart](https://docs.agno.com/examples/agents/basics/overview.md): Starter examples for creating and running agents with core settings. - [Input Output](https://docs.agno.com/examples/agents/input-output/overview.md): Examples for input formats, validation schemas, streaming, and structured outputs. - [Streaming](https://docs.agno.com/examples/agents/input-output/streaming.md): Demonstrates streaming agent responses token by token. - [Input Formats](https://docs.agno.com/examples/agents/input-output/input-formats.md): Send text and images through Agno media objects or an explicit Chat-compatible model. - [Input Schema](https://docs.agno.com/examples/agents/input-output/input-schema.md): Validate agent input against a Pydantic input_schema, passing either a dict or a model instance. - [Output Model](https://docs.agno.com/examples/agents/input-output/output-model.md): Regenerate the final answer with a separate output model. - [Output Schema](https://docs.agno.com/examples/agents/input-output/output-schema.md): Use `output_schema` to return structured data that matches a Pydantic model. - [Parser Model](https://docs.agno.com/examples/agents/input-output/parser-model.md): Pair output_schema with a parser_model that turns the response into a NationalParkAdventure object. - [Expected Output](https://docs.agno.com/examples/agents/input-output/expected-output.md): Guide agent responses using the expected_output parameter. - [Response As Variable](https://docs.agno.com/examples/agents/input-output/response-as-variable.md): Capture agent.run() output in a RunOutput variable and inspect it with pprint. - [Save To File](https://docs.agno.com/examples/agents/input-output/save-to-file.md): Save agent responses to a file automatically. - [Followups (Built-in)](https://docs.agno.com/examples/agents/input-output/followup-suggestions.md): Enable built-in followup prompts on any agent with a single flag. - [Followups: Streaming](https://docs.agno.com/examples/agents/input-output/followup-suggestions-streaming.md): Stream the main response token-by-token and capture followup suggestions via events at the end. - [Tools](https://docs.agno.com/examples/agents/tools/overview.md): Examples for callable tool factories, tool choice, tool call limits, and tools driven by runtime dependencies. - [Callable Tools Factory](https://docs.agno.com/examples/agents/tools/callable-tools.md): Pass a function as `tools` instead of a list. - [Session State Tools](https://docs.agno.com/examples/agents/tools/session-state-tools.md): Use `session_state` as a parameter name in your factory to receive the session state dict directly (no need for run_context). - [Team Callable Members](https://docs.agno.com/examples/agents/tools/team-callable-members.md): Pass a function as `members` to a Team. - [Tool Call Limit](https://docs.agno.com/examples/agents/tools/tool-call-limit.md): Use tool call limit to control the number of tool calls an agent can make. - [Tool Choice](https://docs.agno.com/examples/agents/tools/tool-choice.md): Compare tool_choice values none, auto, and a forced get_weather call across three agents. - [Tools with Literal Type Parameters](https://docs.agno.com/examples/agents/tools/tools-with-literal-type-param.md): Use typing.Literal for function parameters in Agno toolkits and standalone tools. - [Dependencies In Context](https://docs.agno.com/examples/agents/dependencies/dependencies-in-context.md): Resolve a HackerNews fetch function as a runtime dependency and add its output to the agent's context. - [Dependencies In Tools](https://docs.agno.com/examples/agents/dependencies/dependencies-in-tools.md): Access dependencies passed to Agent.run() from inside a tool through the injected run_context parameter. - [Dynamic Tools](https://docs.agno.com/examples/agents/dependencies/dynamic-tools.md): Build the tool list at runtime from a function that reads session state off the RunContext. - [Dependencies](https://docs.agno.com/examples/agents/dependencies/overview.md): Examples for runtime dependency injection and dynamic runtime inputs. - [State And Session](https://docs.agno.com/examples/agents/state-and-session/overview.md): Examples for session state management, chat history, and session persistence. - [Session State Basic](https://docs.agno.com/examples/agents/state-and-session/session-state-basic.md): Maintain a shopping list in session state and update it from a tool through RunContext. - [Chat History](https://docs.agno.com/examples/agents/state-and-session/chat-history.md): Persist chat history in Postgres and read it back with get_chat_history() between turns. - [Persistent Session](https://docs.agno.com/examples/agents/state-and-session/persistent-session.md): Persist agent sessions in Postgres with a fixed session_id so history carries across runs. - [Session State Events](https://docs.agno.com/examples/agents/state-and-session/session-state-events.md): Stream an agent run and read the final session state from RunCompletedEvent. - [Dynamic Session State](https://docs.agno.com/examples/agents/state-and-session/dynamic-session-state.md): Update session state inside a tool hook during an agent run. - [Session State Manual Update](https://docs.agno.com/examples/agents/state-and-session/session-state-manual-update.md): Modify session state between runs with get_session_state() and update_session_state(). - [Session State Multiple Users](https://docs.agno.com/examples/agents/state-and-session/session-state-multiple-users.md): Separate in-process shopping lists by user and session while persisting agent session records in SQLite. - [Session State Advanced](https://docs.agno.com/examples/agents/state-and-session/session-state-advanced.md): Manage a shopping list in session_state with tools that add, remove, and list items through RunContext. - [Agentic Session State](https://docs.agno.com/examples/agents/state-and-session/agentic-session-state.md): Let the agent update its own session_state shopping list with enable_agentic_state. - [Session Options](https://docs.agno.com/examples/agents/state-and-session/session-options.md): Use conversation history while omitting copied history messages from each later run record. - [Session Summary](https://docs.agno.com/examples/agents/state-and-session/session-summary.md): Enable session summaries with enable_session_summaries or a custom SessionSummaryManager, stored in Postgres. - [Last N Session Messages](https://docs.agno.com/examples/agents/state-and-session/last-n-session-messages.md): Search a user's previous sessions with search_past_sessions, capped at the last two by num_past_sessions_to_search. - [Metadata Resolution](https://docs.agno.com/examples/agents/state-and-session/metadata-resolution.md): Demonstrates the three-layer metadata resolution for Agents: agent.metadata < session.metadata < call-site metadata. - [Search Past Sessions](https://docs.agno.com/examples/agents/state-and-session/search-past-sessions.md): Demonstrates the two-step list-then-read pattern for accessing previous sessions. - [Knowledge](https://docs.agno.com/examples/agents/knowledge/overview.md): Examples for retrieval-augmented generation, knowledge filters, and custom retrievers. - [Traditional RAG](https://docs.agno.com/examples/agents/knowledge/traditional-rag.md): Traditional RAG that injects PgVector search results into the prompt instead of using a search tool. - [Agentic RAG](https://docs.agno.com/examples/agents/knowledge/agentic-rag.md): Agentic RAG with PgVector hybrid search: the agent queries a recipe knowledge base on demand. - [Agentic RAG With Reasoning](https://docs.agno.com/examples/agents/knowledge/agentic-rag-with-reasoning.md): Demonstrates agentic RAG with reranking and explicit reasoning tools. - [Agentic RAG With Reranking](https://docs.agno.com/examples/agents/knowledge/agentic-rag-with-reranking.md): Agentic RAG over LanceDB with hybrid search and a Cohere reranker ordering results. - [RAG Custom Embeddings](https://docs.agno.com/examples/agents/knowledge/rag-custom-embeddings.md): Build agentic RAG over multilingual documents with SentenceTransformer embeddings and a BAAI reranker in PgVector. - [Knowledge Filters](https://docs.agno.com/examples/agents/knowledge/knowledge-filters.md): Filter knowledge base searches using static filters or agentic filters. - [Custom Retriever](https://docs.agno.com/examples/agents/knowledge/custom-retriever.md): Use knowledge_retriever to provide a custom retrieval function. - [References Format](https://docs.agno.com/examples/agents/knowledge/references-format.md): Control how knowledge base references are formatted for the agent. - [Guardrails](https://docs.agno.com/examples/agents/guardrails/overview.md): Examples for input/output safety checks and policy enforcement. - [Custom Guardrail](https://docs.agno.com/examples/agents/guardrails/custom-guardrail.md): Use a keyword-based BaseGuardrail pre-hook to reject input containing configured terms. - [Output Guardrail](https://docs.agno.com/examples/agents/guardrails/output-guardrail.md): Reject responses under 20 characters with a post-hook that raises OutputCheckError. - [OpenAI Moderation](https://docs.agno.com/examples/agents/guardrails/openai-moderation.md): Block flagged input with OpenAIModerationGuardrail as a pre-hook, including custom category lists and image moderation. - [PII Detection](https://docs.agno.com/examples/agents/guardrails/pii-detection.md): Reject or mask the PII patterns recognized by PIIDetectionGuardrail. - [Prompt Injection](https://docs.agno.com/examples/agents/guardrails/prompt-injection.md): Check input for the patterns recognized by PromptInjectionGuardrail and handle rejected runs. - [Mixed Hooks and Guardrails](https://docs.agno.com/examples/agents/guardrails/mixed-hooks.md): Combine a logging pre-hook with PIIDetectionGuardrail; blocked runs return RunStatus.error instead of raising. - [DeepKeep AI Firewall Guardrails](https://docs.agno.com/examples/agents/guardrails/deepkeep-ai-firewall.md): Use DeepKeep AI Firewall as custom guardrails for Agno Agents. - [Human In The Loop](https://docs.agno.com/examples/agents/human-in-the-loop/overview.md): Browse agent examples for confirmation, user input, external execution, and approval-backed HITL. - [Agentic User Input](https://docs.agno.com/examples/agents/human-in-the-loop/agentic-user-input.md): Let the agent request missing fields at runtime with UserControlFlowTools, then continue the run. - [Confirmation Advanced](https://docs.agno.com/examples/agents/human-in-the-loop/confirmation-advanced.md): Confirm or reject tool calls across a custom HackerNews tool and WikipediaTools in one run. - [Confirmation Required](https://docs.agno.com/examples/agents/human-in-the-loop/confirmation-required.md): Gate a custom HackerNews tool behind requires_confirmation and resume the paused run with agent.continue_run(). - [Confirmation Required MCP Toolkit](https://docs.agno.com/examples/agents/human-in-the-loop/confirmation-required-mcp-toolkit.md): Require confirmation for an MCP server tool and resume an async streamed run after approval. - [Confirmation Toolkit](https://docs.agno.com/examples/agents/human-in-the-loop/confirmation-toolkit.md): Gate a toolkit's web_search tool behind user confirmation with requires_confirmation_tools. - [External Tool Execution](https://docs.agno.com/examples/agents/human-in-the-loop/external-tool-execution.md): Pause the run for a shell tool marked external_execution, execute it yourself, and continue with the result. - [User Input Required](https://docs.agno.com/examples/agents/human-in-the-loop/user-input-required.md): Mark a tool with requires_user_input so the run pauses to collect the to_address field. - [Confirmation with Session State](https://docs.agno.com/examples/agents/human-in-the-loop/confirmation-with-session-state.md): Pause before a tool changes session_state, then apply the change after approval. - [Mixed External and Regular Tools](https://docs.agno.com/examples/agents/human-in-the-loop/mixed-external-and-regular-tools.md): Combine a regular tool with an external_execution tool; the agent auto-runs one and pauses for the other. - [User Feedback (Structured Questions)](https://docs.agno.com/examples/agents/human-in-the-loop/user-feedback.md): Pause a trip-planning agent with UserFeedbackTools and collect answers to multiple-choice questions. - [Deterministic approval flow for a side-effecting tool](https://docs.agno.com/examples/agents/human-in-the-loop/side-effecting-tool-approval.md): This example uses a local mock model, so it runs without provider credentials. - [Approval Async](https://docs.agno.com/examples/agents/approvals/approval-async.md): Async approval-backed HITL: @approval with async agent run. - [Approval Basic](https://docs.agno.com/examples/agents/approvals/approval-basic.md): Approval-backed HITL: @approval + @tool(requires_confirmation=True) with persistent DB record. - [Approval External Execution](https://docs.agno.com/examples/agents/approvals/approval-external-execution.md): Approval + external execution HITL: @approval + @tool(external_execution=True). - [Approval List And Resolve](https://docs.agno.com/examples/agents/approvals/approval-list-and-resolve.md): Full approval lifecycle: pause, list, filter, resolve, delete. - [Approval Team](https://docs.agno.com/examples/agents/approvals/approval-team.md): Team-level approval: member agent tool with @approval. - [Approval User Input](https://docs.agno.com/examples/agents/approvals/approval-user-input.md): Approval + user input HITL: @approval + @tool(requires_user_input=True). - [Audit Approval Async](https://docs.agno.com/examples/agents/approvals/audit-approval-async.md): Async audit approval: @approval(type="audit") + @tool(requires_confirmation=True) with async. - [Audit Approval Confirmation](https://docs.agno.com/examples/agents/approvals/audit-approval-confirmation.md): Audit approval with confirmation: @approval(type="audit") + @tool(requires_confirmation=True). - [Audit Approval External](https://docs.agno.com/examples/agents/approvals/audit-approval-external.md): Audit approval with external execution: @approval(type="audit") + @tool(external_execution=True). - [Audit Approval Overview](https://docs.agno.com/examples/agents/approvals/audit-approval-overview.md): Compare required approval records with audit records written on HITL resolution, including rejection. - [Audit Approval User Input](https://docs.agno.com/examples/agents/approvals/audit-approval-user-input.md): Audit approval with user input: @approval(type="audit") + @tool(requires_user_input=True). - [Approval Post Hook](https://docs.agno.com/examples/agents/approvals/approval-post-hook.md): Demonstrates the post-hook reading the resolved approval record from run_output.metadata["approval"] after a paused run resumes via DB resolution. - [Approvals](https://docs.agno.com/examples/agents/approvals/overview.md): These cookbooks demonstrate the **@approval** decorator for human-in-the-loop (HITL) approval workflows. - [Multimodal](https://docs.agno.com/examples/agents/multimodal/overview.md): Examples for image/audio/video processing patterns. - [Image To Text](https://docs.agno.com/examples/agents/multimodal/image-to-text.md): Pass a local sample.jpg to GPT-5.2 with OpenAIResponses and print a three-sentence story about the image. - [Image To Structured Output](https://docs.agno.com/examples/agents/multimodal/image-to-structured-output.md): Generate a fictional MovieScript inspired by an image URL and stream the available output. - [Image To Image](https://docs.agno.com/examples/agents/multimodal/image-to-image.md): Give an agent FalTools and a source image URL so it can generate a transformed image with Fal's image_to_image model. - [Image To Audio](https://docs.agno.com/examples/agents/multimodal/image-to-audio.md): Write a story about a local image, validate the result, then narrate it with gpt-audio and save a WAV. - [Audio to Text](https://docs.agno.com/examples/agents/multimodal/audio-to-text.md): Transcribe an MP3 conversation with Gemini, labeling each speaker in the output. - [Audio Input Output](https://docs.agno.com/examples/agents/multimodal/audio-input-output.md): Send a WAV recording to gpt-audio and save the model's spoken reply to tmp/result.wav with write_audio_to_file(). - [Audio Streaming](https://docs.agno.com/examples/agents/multimodal/audio-streaming.md): Stream pcm16 audio from gpt-audio and write the chunks to a WAV file while printing the transcript. - [Audio Sentiment Analysis](https://docs.agno.com/examples/agents/multimodal/audio-sentiment-analysis.md): Analyze speaker sentiment in a recorded conversation with Gemini, then ask a follow-up question answered from SQLite-backed session history. - [Media Input For Tool](https://docs.agno.com/examples/agents/multimodal/media-input-for-tool.md): Declare a files parameter on a tool and Agno injects the files passed to the agent, with send_media_to_model=False keeping the PDF out of the model request. - [Video Caption](https://docs.agno.com/examples/agents/multimodal/video-caption.md): Extract video audio, request timestamped subtitles, and embed captions with MoviePyVideoTools. - [Hooks](https://docs.agno.com/examples/agents/hooks/overview.md): Examples for pre-hooks, post-hooks, tool hooks, stream hooks, and agent context management. - [Post Hook Output](https://docs.agno.com/examples/agents/hooks/post-hook-output.md): Validate agent responses for completeness, tone, safety, and length with post_hooks that raise OutputCheckError. - [Pre Hook Input](https://docs.agno.com/examples/agents/hooks/pre-hook-input.md): Validate financial-advice input with a pre-hook, then detect blocked calls by checking for RunStatus.error. - [Session State Hooks](https://docs.agno.com/examples/agents/hooks/session-state-hooks.md): Track conversation topics in session_state by updating RunContext from a pre_hook. - [Stream Hook](https://docs.agno.com/examples/agents/hooks/stream-hook.md): Send a notification from a post_hook after a streamed agent response completes. - [Tool Hooks](https://docs.agno.com/examples/agents/hooks/tool-hooks.md): Use tool_hooks to add middleware that wraps every tool call. - [Message History Hooks](https://docs.agno.com/examples/agents/hooks/message-history-hooks.md): Access the current run's message history inside tool pre/post hooks via run_context.messages. - [Few Shot Learning](https://docs.agno.com/examples/agents/context-management/few-shot-learning.md): Use additional_input with an Agent. - [Filter Tool Calls From History](https://docs.agno.com/examples/agents/context-management/filter-tool-calls-from-history.md): Demonstrates `max_tool_calls_from_history` by showing that tool-call filtering only affects model input history while full run history remains in storage. - [Instructions](https://docs.agno.com/examples/agents/context-management/instructions.md): Add the current date and time to the agent's context with add_datetime_to_context and a timezone. - [Instructions With State](https://docs.agno.com/examples/agents/context-management/instructions-with-state.md): Pass a callable as instructions to build the agent's system message from session_state on every run. - [Introduction Message](https://docs.agno.com/examples/agents/context-management/introduction-message.md): Use the introduction parameter to set an initial greeting message. - [System Message](https://docs.agno.com/examples/agents/context-management/system-message.md): Customize the agent's system message and role. - [Custom Datetime Format](https://docs.agno.com/examples/agents/context-management/datetime-format.md): Customize the datetime format injected into the agent's system context. - [Context Management](https://docs.agno.com/examples/agents/context-management/overview.md): Examples for instructions, system messages, introduction messages, and context shaping. - [Advanced](https://docs.agno.com/examples/agents/advanced/overview.md): Advanced examples covering caching, compression, concurrency, events, retries, debugging, serialization, and migration notices. - [Advanced Compression](https://docs.agno.com/examples/agents/advanced/advanced-compression.md): Set a context token based limit for tool call compression. - [Agent Serialization](https://docs.agno.com/examples/agents/advanced/agent-serialization.md): Serialize an agent with to_dict and from_dict, and persist versions with save and load. - [Background Execution Concurrency](https://docs.agno.com/examples/agents/advanced/background-execution-concurrency.md): Example demonstrating the concurrency limit for background runs. - [Background Streaming Resume](https://docs.agno.com/examples/agents/advanced/background-streaming-resume.md): Example demonstrating background streaming with disconnect and resume. - [Redis Event Stream Resume](https://docs.agno.com/examples/agents/advanced/redis-event-stream-resume.md): Example demonstrating cross-process streaming resume with RedisEventStream. - [Automatic Cultural Management](https://docs.agno.com/examples/agents/advanced/automatic-cultural-management.md): Migration notice for the retired Automatic Cultural Management example. - [Background Execution](https://docs.agno.com/examples/agents/advanced/background-execution.md): Start a background agent run that returns PENDING immediately, then poll for completion or cancel it. - [Background Execution Structured](https://docs.agno.com/examples/agents/advanced/background-execution-structured.md): Example demonstrating background execution with structured output. - [SSE Reconnection](https://docs.agno.com/examples/agents/advanced/sse-reconnect.md): Migrate the legacy agent SSE reconnect route to the current AgentOS v3 example. - [Basic Agent Events](https://docs.agno.com/examples/agents/advanced/basic-agent-events.md): Stream run lifecycle, tool call, and content events from an agent with stream_events=True. - [Cache Model Response](https://docs.agno.com/examples/agents/advanced/cache-model-response.md): Example showing how to cache model responses to avoid redundant API calls. - [Cancel Run](https://docs.agno.com/examples/agents/advanced/cancel-run.md): Example demonstrating how to cancel a running agent execution. - [Compression Events](https://docs.agno.com/examples/agents/advanced/compression-events.md): Stream CompressionStarted and CompressionCompleted events from an agent using compress_tool_results=True. - [Concurrent Execution](https://docs.agno.com/examples/agents/advanced/concurrent-execution.md): Concurrent Agent Execution with asyncio.gather. - [Create Cultural Knowledge](https://docs.agno.com/examples/agents/advanced/create-cultural-knowledge.md): Migrate the removed cultural-knowledge example to supported Agno v3 features. - [Example demonstrating a custom cancellation manager](https://docs.agno.com/examples/agents/advanced/custom-cancellation-manager.md): Extend BaseRunCancellationManager to implement your own cancellation backend (e.g., a database, a message queue, an API, etc.). - [Custom Logging](https://docs.agno.com/examples/agents/advanced/custom-logging.md): Configure the default agno.utils.log logger with a custom Python logger. - [Debug](https://docs.agno.com/examples/agents/advanced/debug.md): Enable verbose debug output for every run with debug_mode, or turn it on for a single run. - [Manually Add Culture](https://docs.agno.com/examples/agents/advanced/manually-add-culture.md): Migrate the removed manual-culture example to supported Agno v3 features. - [Multi-Model Metrics](https://docs.agno.com/examples/agents/advanced/multi-model-metrics.md): When an agent uses a MemoryManager, each manager's model calls are tracked under separate detail keys in metrics.details. - [Culture Manager Metrics](https://docs.agno.com/examples/agents/advanced/culture-metrics.md): Migrate the removed culture-metrics example to supported Agno v3 metrics surfaces. - [Session Metrics](https://docs.agno.com/examples/agents/advanced/session-metrics.md): Demonstrates session-level metrics that accumulate across multiple runs. - [Session Summary Metrics](https://docs.agno.com/examples/agents/advanced/session-summary-metrics.md): When an agent uses a SessionSummaryManager, the summary model's token usage is tracked separately under the "session_summary_model" detail key. - [Streaming Metrics](https://docs.agno.com/examples/agents/advanced/streaming-metrics.md): Capture metrics from streaming responses. - [Tool Call Metrics](https://docs.agno.com/examples/agents/advanced/tool-call-metrics.md): Demonstrates tool execution timing metrics. - [Background Execution Metrics](https://docs.agno.com/examples/agents/advanced/background-execution-metrics.md): Demonstrates that metrics are fully tracked for background runs. - [Reasoning Agent Events](https://docs.agno.com/examples/agents/advanced/reasoning-agent-events.md): Capture lifecycle and content-delta events from a separate reasoning stage. - [Retries](https://docs.agno.com/examples/agents/advanced/retries.md): Configure retries, delay_between_retries, and exponential_backoff on an Agent. - [Tool Call Compression](https://docs.agno.com/examples/agents/advanced/tool-call-compression.md): Set compress_tool_results=True to compress tool call results and save context on search-heavy runs. - [Use Cultural Knowledge in Agent](https://docs.agno.com/examples/agents/advanced/use-cultural-knowledge-in-agent.md): Migration notice for the retired Use Cultural Knowledge in Agent example. - [Cancel Run Persistence](https://docs.agno.com/examples/agents/advanced/agent-run-cancel-persistence.md): Cancel an agent run mid-stream and verify that partial content and messages are preserved in the database. - [Combined Metrics](https://docs.agno.com/examples/agents/advanced/combined-metrics.md): Migrate the removed combined-metrics example to supported Agno v3 metrics surfaces. - [Interchange Model: All 5 Providers](https://docs.agno.com/examples/agents/advanced/interchange-model/all-providers.md): Cycles through OpenAI Chat, OpenAI Responses, Claude, Gemini, and AWS Claude. - [Claude Gemini](https://docs.agno.com/examples/agents/advanced/interchange-model/claude-gemini.md): Switch a single agent between Claude and Gemini mid-conversation with shared Postgres history. - [OpenAI Chat Responses](https://docs.agno.com/examples/agents/advanced/interchange-model/openai-chat-responses.md): Alternate one agent between OpenAIChat and OpenAIResponses mid-session over shared Postgres history. - [OpenAI Claude](https://docs.agno.com/examples/agents/advanced/interchange-model/openai-claude.md): Swap a single agent between gpt-5.6-luna and Claude mid-session with tool-call history stored in Postgres. - [OpenAI Gemini](https://docs.agno.com/examples/agents/advanced/interchange-model/openai-gemini.md): Move one conversation between gpt-5.6-luna and Gemini across turns with history persisted in Postgres. - [Metrics](https://docs.agno.com/examples/agents/advanced/metrics.md): Inspect per-message, run, and session metrics after a YFinance tool call on a Postgres-backed agent. - [Learning Machine](https://docs.agno.com/examples/agents/memory-and-learning/learning-machine.md): Attach a LearningMachine that builds an agentic user profile and recalls it in a new session. - [Memory Manager](https://docs.agno.com/examples/agents/memory-and-learning/memory-manager.md): Use a MemoryManager to give agents persistent memory across sessions. - [Memory And Learning](https://docs.agno.com/examples/agents/memory-and-learning/overview.md): Examples for persistent memory and learning behavior. - [Basic Reasoning](https://docs.agno.com/examples/agents/reasoning/basic-reasoning.md): Run a separate native OpenAI reasoning stage before the main response and display available reasoning output. - [Reasoning With Model](https://docs.agno.com/examples/agents/reasoning/reasoning-with-model.md): Run a separate native OpenAI reasoning stage before the main response and display available reasoning output. - [Reasoning](https://docs.agno.com/examples/agents/reasoning/overview.md): Examples using a separate native reasoning model. - [Basic Skills](https://docs.agno.com/examples/agents/skills/basic-skills.md): Load skills from a local directory with LocalSkills and use them in a code review agent. - [Check Style](https://docs.agno.com/examples/agents/skills/sample-skills/code-review/scripts/check-style.md): Check Python code for style issues. - [Scripts](https://docs.agno.com/examples/agents/skills/sample-skills/code-review/scripts/overview.md): Examples for Scripts. - [Code Review](https://docs.agno.com/examples/agents/skills/sample-skills/code-review/overview.md): Examples for Code Review. - [Commit Message](https://docs.agno.com/examples/agents/skills/sample-skills/git-workflow/scripts/commit-message.md): Validate or generate conventional commit messages. - [Scripts](https://docs.agno.com/examples/agents/skills/sample-skills/git-workflow/scripts/overview.md): Examples for Scripts. - [Git Workflow](https://docs.agno.com/examples/agents/skills/sample-skills/git-workflow/overview.md): Examples for Git Workflow. - [Sample Skills](https://docs.agno.com/examples/agents/skills/sample-skills/overview.md): Examples for Sample Skills. - [Skills](https://docs.agno.com/examples/agents/skills/overview.md): Examples for defining and using agent skills and helper scripts. - [Fallback Models: Basic](https://docs.agno.com/examples/agents/fallback-models/basic-fallback.md): Try fallback models in order after a retryable provider failure exhausts the primary model retries. - [Fallback Models: Error-Specific](https://docs.agno.com/examples/agents/fallback-models/error-specific-fallbacks.md): Use FallbackConfig for error-specific fallback routing. - [Fallback Models: Mid-Run Failure](https://docs.agno.com/examples/agents/fallback-models/mid-run-fallback.md): Trigger a fallback when the primary model fails mid-run, after a tool call has already executed. - [Fallback Models: Callback Notification](https://docs.agno.com/examples/agents/fallback-models/fallback-callback.md): Register a FallbackConfig callback that fires when a fallback model serves the request. - [Crash recovery with checkpoint="tool-batch"](https://docs.agno.com/examples/agents/checkpointing/crash-recovery.md): Crash an in-flight run with SIGKILL, then resume it from the last persisted tool-batch checkpoint via /continue. - [Tool Error Persistence](https://docs.agno.com/examples/agents/checkpointing/tool-error-persistence.md): Persist an agent conversation through tool exceptions and model failures, then retry the failed run in place with `Agent.acontinue_run()`. - [Checkpoint Endpoints](https://docs.agno.com/examples/agents/checkpointing/checkpoint-endpoints.md): List a run's checkpoint boundaries and fetch truncated snapshots via the AgentOS checkpoint endpoints, then resume from a chosen index. - [Regenerate](https://docs.agno.com/examples/agents/regenerate/regenerate.md): Regenerate the last response via /continue with regenerate=True. - [Continue From](https://docs.agno.com/examples/agents/time-travel/continue-from.md): Choose a message boundary and resume from there. - [Fork Run](https://docs.agno.com/examples/agents/time-travel/fork-run.md): Fork a completed run at a message boundary into a new sibling run in the same session, leaving the original intact. - [Fork a session via agent.fork_session()](https://docs.agno.com/examples/agents/fork-session/fork-session.md): Fork a session into a new session with a fresh session_id and fresh run IDs while the source session stays untouched. - [Offload Tool Results](https://docs.agno.com/examples/agents/result-offloading/offload-tool-results.md): Store large tool results and keep bounded previews with references in model context. - [The Result Store](https://docs.agno.com/examples/agents/result-offloading/result-store.md): `ResultStore` holds the settings and is usable without an agent: offload a payload, read a bounded page back, search it, list a session's live result ids, and sweep expired rows. - [Where Results Live](https://docs.agno.com/examples/agents/result-offloading/where-results-live.md): Inspect a successfully offloaded result in session storage, the index, and the file store. - [Custom Store Settings](https://docs.agno.com/examples/agents/result-offloading/custom-store-settings.md): Configure offloading thresholds, head previews, and payload expiry. - [Payloads on Disk](https://docs.agno.com/examples/agents/result-offloading/payloads-on-disk.md): By default the stored payload goes to AgentFS on the agent's database. - [PostgreSQL Layout](https://docs.agno.com/examples/agents/result-offloading/postgres-layout.md): This example runs one agent against PostgreSQL, then queries both schemas. - [Delete Session Cascade](https://docs.agno.com/examples/agents/result-offloading/delete-session-cascade.md): A stored result belongs to its session. - [Teams](https://docs.agno.com/examples/teams/overview.md): Cookbooks for building multi-agent teams in Agno. - [Quickstart](https://docs.agno.com/examples/teams/basics/overview.md): Quickstart team examples: coordination, routing, delegation, shared history, broadcast, and task modes. - [Basic Coordination](https://docs.agno.com/examples/teams/basics/basic-coordination.md): Demonstrates a simple two-member team working together on one task. - [Broadcast Mode](https://docs.agno.com/examples/teams/basics/broadcast-mode.md): Delegate the same task to every team member with TeamMode.broadcast. - [Task Mode](https://docs.agno.com/examples/teams/basics/task-mode.md): Demonstrates autonomous task decomposition and execution using TeamMode.tasks. - [Respond Directly Router Team](https://docs.agno.com/examples/teams/basics/respond-directly-router-team.md): Route each question to the member agent that speaks its language with TeamMode.route and return that member's answer directly. - [Delegate To All Members](https://docs.agno.com/examples/teams/basics/delegate-to-all-members.md): Broadcast the same research task to every member with TeamMode.broadcast, fanning it out to Reddit and HackerNews researchers. - [Share Member Interactions](https://docs.agno.com/examples/teams/basics/share-member-interactions.md): Give support team members each other's requests and responses from the current run with share_member_interactions=True. - [Concurrent Member Agents](https://docs.agno.com/examples/teams/basics/concurrent-member-agents.md): Stream member events with timestamps while the team leader delegates to HackerNews and news agents concurrently. - [Cache Team Response](https://docs.agno.com/examples/teams/basics/caching.md): Cache team leader and member responses in two layers. - [History Of Members](https://docs.agno.com/examples/teams/basics/history-of-members.md): Give each member its own conversation history with add_history_to_context while the leader routes questions by language. - [Respond Directly With History](https://docs.agno.com/examples/teams/basics/respond-directly-with-history.md): Demonstrates direct member responses with team history persisted in SQLite. - [Team History](https://docs.agno.com/examples/teams/basics/team-history.md): Demonstrates sharing team history with member agents across a session. - [Nested Teams](https://docs.agno.com/examples/teams/basics/nested-teams.md): Demonstrates using teams as members in a higher-level coordinating team. - [Tasks](https://docs.agno.com/examples/teams/modes/tasks/overview.md): Index of tasks mode team examples: sequential decomposition, parallel execution, dependency chains, and streaming events. - [Basic Tasks Mode Example](https://docs.agno.com/examples/teams/modes/tasks/basic.md): Tasks mode: the leader splits a request into planner, writer, and editor tasks and runs them in order. - [Tasks with Dependencies Example](https://docs.agno.com/examples/teams/modes/tasks/dependencies.md): Demonstrates task mode with dependency chains. - [Parallel Tasks Execution Example](https://docs.agno.com/examples/teams/modes/tasks/parallel.md): Demonstrates task mode with parallel execution. - [Task Mode Streaming Events](https://docs.agno.com/examples/teams/modes/tasks/streaming-events.md): Consume streaming events programmatically in `mode=tasks`. - [Basic Broadcast Mode Example](https://docs.agno.com/examples/teams/modes/broadcast/basic.md): Send the same delegated task to all members and synthesize their perspectives in broadcast mode. - [Broadcast Mode for Structured Debate](https://docs.agno.com/examples/teams/modes/broadcast/debate.md): Demonstrates broadcast mode for a structured debate between agents with opposing viewpoints. - [Broadcast Mode for Parallel Research Sweep](https://docs.agno.com/examples/teams/modes/broadcast/research-sweep.md): Gather research from specialized members in broadcast mode. - [Structured Debate](https://docs.agno.com/examples/teams/modes/broadcast/structured-debate.md): Same task is sent to every agent in the team. - [Broadcast](https://docs.agno.com/examples/teams/modes/broadcast/overview.md): Examples for Broadcast. - [Basic Coordinate Mode Example](https://docs.agno.com/examples/teams/modes/coordinate/basic.md): Coordinate mode: the team leader delegates to a researcher and writer, then synthesizes the answer. - [Coordinate Mode with Structured Output](https://docs.agno.com/examples/teams/modes/coordinate/structured-output.md): Demonstrates coordination that produces a Pydantic-validated structured response. - [Coordinate Mode with Tools](https://docs.agno.com/examples/teams/modes/coordinate/with-tools.md): Demonstrates coordination where member agents have specialized tools. - [Coordinate](https://docs.agno.com/examples/teams/modes/coordinate/overview.md): Examples for Coordinate. - [Basic Route Mode Example](https://docs.agno.com/examples/teams/modes/route/basic.md): Demonstrates `mode=route` where the team leader routes each request to a single specialist agent and returns their response directly (no synthesis). - [Specialist Router Example](https://docs.agno.com/examples/teams/modes/route/specialist-router.md): Demonstrates routing to domain specialist agents. - [Route Mode with Fallback Agent](https://docs.agno.com/examples/teams/modes/route/with-fallback.md): Demonstrates routing with a general-purpose fallback agent that handles requests when no specialist is a clear match. - [Route](https://docs.agno.com/examples/teams/modes/route/overview.md): Examples for Route. - [Task Mode Streaming Example - Real-time Task List with Dedicated Events](https://docs.agno.com/examples/teams/modes/tasks-stream.md): Stream TaskCreated, TaskUpdated, TaskIteration, and TaskStateUpdated events to render a real-time task list. - [Modes](https://docs.agno.com/examples/teams/modes/overview.md): Agno teams support four execution modes that control how the team leader coordinates work with member agents. - [Structured Input Output](https://docs.agno.com/examples/teams/structured-input-output/overview.md): Validate team inputs and return typed, schema-constrained outputs in sync and streaming runs. - [Input Formats](https://docs.agno.com/examples/teams/structured-input-output/input-formats.md): Demonstrates different input formats accepted by team run methods. - [Input Schema](https://docs.agno.com/examples/teams/structured-input-output/input-schema.md): Demonstrates team-level automatic input validation using input_schema. - [Pydantic Input](https://docs.agno.com/examples/teams/structured-input-output/pydantic-input.md): Demonstrates passing validated Pydantic models as team inputs. - [Output Model](https://docs.agno.com/examples/teams/structured-input-output/output-model.md): Demonstrates setting a dedicated model for final team response generation. - [Pydantic Output](https://docs.agno.com/examples/teams/structured-input-output/pydantic-output.md): Demonstrates team-level typed output using Pydantic schemas. - [JSON Schema Output](https://docs.agno.com/examples/teams/structured-input-output/json-schema-output.md): Return a parsed stock-analysis dictionary through JSON mode with a JSON schema included in the prompt. - [Output Schema Override](https://docs.agno.com/examples/teams/structured-input-output/output-schema-override.md): Demonstrates per-run output_schema overrides across sync/async and streaming modes. - [Structured Output Streaming](https://docs.agno.com/examples/teams/structured-input-output/structured-output-streaming.md): Demonstrates sync and async streaming with structured team outputs. - [Response As Variable](https://docs.agno.com/examples/teams/structured-input-output/response-as-variable.md): Demonstrates capturing typed team responses as variables for downstream logic. - [Parser Model](https://docs.agno.com/examples/teams/structured-input-output/parser-model.md): Demonstrates parser-model assisted team output parsing into rich schemas. - [Expected Output](https://docs.agno.com/examples/teams/structured-input-output/expected-output.md): Demonstrates setting a team-level `expected_output` to describe the desired run result shape. - [Human In The Loop](https://docs.agno.com/examples/teams/human-in-the-loop/overview.md): Pause and resume team runs for confirmation, user input, and external tool execution. - [Confirmation Required](https://docs.agno.com/examples/teams/human-in-the-loop/confirmation-required.md): Demonstrates team-level pause/continue flow for confirmation-required member tools. - [Team HITL: Rejecting a member agent tool call](https://docs.agno.com/examples/teams/human-in-the-loop/confirmation-rejected.md): The team handles rejection of a tool call. - [User Input Required](https://docs.agno.com/examples/teams/human-in-the-loop/user-input-required.md): Demonstrates collecting required user input during paused team tool execution. - [Team Tool Confirmation](https://docs.agno.com/examples/teams/human-in-the-loop/team-tool-confirmation.md): Team HITL: Tool on the team itself requiring confirmation. - [External Tool Execution](https://docs.agno.com/examples/teams/human-in-the-loop/external-tool-execution.md): Demonstrates resolving external tool execution requirements in team flows. - [Team HITL: Async member agent tool confirmation](https://docs.agno.com/examples/teams/human-in-the-loop/confirmation-required-async.md): Pause an asynchronous team run for confirmation, approve its production-deployment tool call, and resume it with team.acontinue_run(). - [Confirmation Required Stream](https://docs.agno.com/examples/teams/human-in-the-loop/confirmation-required-stream.md): Team HITL Streaming: Member agent tool requiring confirmation. - [Confirmation Rejected Stream](https://docs.agno.com/examples/teams/human-in-the-loop/confirmation-rejected-stream.md): Team HITL Streaming: Rejecting a member agent tool call. - [Confirmation Required Async Stream](https://docs.agno.com/examples/teams/human-in-the-loop/confirmation-required-async-stream.md): Team HITL Async Streaming: Member agent tool requiring confirmation. - [External Tool Execution Stream](https://docs.agno.com/examples/teams/human-in-the-loop/external-tool-execution-stream.md): Team HITL Streaming: Member agent tool with external execution. - [Team Tool Confirmation Stream](https://docs.agno.com/examples/teams/human-in-the-loop/team-tool-confirmation-stream.md): Team HITL Streaming: Tool on the team itself requiring confirmation. - [User Input Required Stream](https://docs.agno.com/examples/teams/human-in-the-loop/user-input-required-stream.md): Team HITL Streaming: Member agent tool requiring user input. - [Confirmation Required with Dependencies](https://docs.agno.com/examples/teams/human-in-the-loop/confirmation-required-with-dependencies.md): Re-pass dependencies when continuing a paused team run so its member tool receives them. - [Multi-Round User Input](https://docs.agno.com/examples/teams/human-in-the-loop/multi-round-user-input.md): Demonstrates chained HITL where a member pauses multiple times for user input. - [Guardrails](https://docs.agno.com/examples/teams/guardrails/overview.md): Apply moderation, PII, and prompt-injection guardrails to team runs. - [OpenAI Moderation](https://docs.agno.com/examples/teams/guardrails/openai-moderation.md): Demonstrates OpenAI moderation guardrails for team inputs. - [PII Detection](https://docs.agno.com/examples/teams/guardrails/pii-detection.md): Demonstrates PII detection guardrails for team input protection. - [Prompt Injection](https://docs.agno.com/examples/teams/guardrails/prompt-injection.md): Demonstrates prompt-injection guardrails for team input validation. - [Knowledge](https://docs.agno.com/examples/teams/knowledge/overview.md): Give teams shared knowledge, filters, custom retrievers, and coordinated RAG search. - [Team With Agentic Knowledge Filters](https://docs.agno.com/examples/teams/knowledge/team-with-agentic-knowledge-filters.md): Demonstrates AI-driven dynamic knowledge filtering for team retrieval. - [Team With Custom Retriever](https://docs.agno.com/examples/teams/knowledge/team-with-custom-retriever.md): Demonstrates a custom team knowledge retriever that uses runtime dependencies. - [Team With Knowledge](https://docs.agno.com/examples/teams/knowledge/team-with-knowledge.md): Demonstrates a team that combines knowledge-base retrieval with web search support. - [Team With Knowledge Filters](https://docs.agno.com/examples/teams/knowledge/team-with-knowledge-filters.md): Demonstrates static metadata-based knowledge filtering in team retrieval. - [Team Update Knowledge](https://docs.agno.com/examples/teams/knowledge/team-update-knowledge.md): Demonstrates enabling `update_knowledge` so teams can persist new facts. - [Distributed RAG With LanceDB](https://docs.agno.com/examples/teams/distributed-rag/distributed-rag-lancedb.md): Demonstrates distributed team-based RAG with primary and context retrieval over LanceDB. - [Distributed RAG With PgVector](https://docs.agno.com/examples/teams/distributed-rag/distributed-rag-pgvector.md): Demonstrates distributed team-based RAG using PostgreSQL + pgvector. - [Distributed RAG With Reranking](https://docs.agno.com/examples/teams/distributed-rag/distributed-rag-with-reranking.md): Demonstrates distributed RAG with hybrid retrieval and Cohere reranking. - [Distributed RAG](https://docs.agno.com/examples/teams/distributed-rag/overview.md): Distribute RAG searches across team members with LanceDB, PgVector, and reranking. - [Coordinated Agentic RAG](https://docs.agno.com/examples/teams/search-coordination/coordinated-agentic-rag.md): Demonstrates coordinated team search, analysis, and synthesis over shared knowledge. - [Coordinated Reasoning RAG](https://docs.agno.com/examples/teams/search-coordination/coordinated-reasoning-rag.md): Demonstrates distributed reasoning roles for coordinated RAG responses. - [Distributed Infinity Search](https://docs.agno.com/examples/teams/search-coordination/distributed-infinity-search.md): Demonstrates distributed search coordination with Infinity reranking. - [Search Coordination](https://docs.agno.com/examples/teams/search-coordination/overview.md): Coordinate distributed and reasoning-guided RAG searches across team members. - [Learning](https://docs.agno.com/examples/teams/learning/overview.md): Capture user profiles, memories, entities, session context, knowledge, and decisions from team runs. - [Team Learning: Always Mode](https://docs.agno.com/examples/teams/learning/team-always-learn.md): Set learning=True on a Team to enable automatic learning. - [Team Learning: Configured Stores](https://docs.agno.com/examples/teams/learning/team-configured-learning.md): Configure specific learning stores on a Team using LearningMachine. - [Team Learning: Decision Logging](https://docs.agno.com/examples/teams/learning/team-decision-log.md): Teams can log decisions for auditing, debugging, and learning using the DecisionLogStore. - [Team Learning: Entity Memory](https://docs.agno.com/examples/teams/learning/team-entity-memory.md): Teams can track entities (people, projects, companies) across conversations using the EntityMemory store. - [Team Learning: Learned Knowledge](https://docs.agno.com/examples/teams/learning/team-learned-knowledge.md): Teams can build a shared knowledge base from conversations using LearnedKnowledge with a vector database. - [Team Learning: Session Planning](https://docs.agno.com/examples/teams/learning/team-session-planning.md): Teams can track session goals and progress using SessionContext with planning mode enabled. - [Team Learning: User Profile](https://docs.agno.com/examples/teams/learning/team-user-profile.md): Team learns and recalls user profile across sessions. - [Team Learning: User Memory](https://docs.agno.com/examples/teams/learning/team-user-memory.md): Team learns observations and context about the user across sessions. - [Team Learning: Async Mode](https://docs.agno.com/examples/teams/learning/team-async-learning.md): Demonstrates Team learning with async database operations. - [Team Learning: Agentic Mode](https://docs.agno.com/examples/teams/learning/team-agentic-learning.md): Team decides when to update user memory using tools. - [Learning Machine](https://docs.agno.com/examples/teams/memory/learning-machine.md): Demonstrates team learning with LearningMachine and user profile extraction. - [Team With Agentic Memory](https://docs.agno.com/examples/teams/memory/team-with-agentic-memory.md): Demonstrates team-level agentic memory creation and updates during runs. - [Team With Memory Manager](https://docs.agno.com/examples/teams/memory/team-with-memory-manager.md): Demonstrates persistent team memory updates through MemoryManager. - [Memories in Context](https://docs.agno.com/examples/teams/memory/memories-in-context.md): Demonstrates `add_memories_to_context` with team memory capture. - [Memory](https://docs.agno.com/examples/teams/memory/overview.md): Persist and inject team memories with LearningMachine, MemoryManager, and agentic memory. - [State & Session](https://docs.agno.com/examples/teams/state/overview.md): Share state across team members and persist sessions, chat history, searches, and summaries. - [Agentic Session State](https://docs.agno.com/examples/teams/state/agentic-session-state.md): Demonstrates team and member agentic state updates on shared session state. - [Change State On Run](https://docs.agno.com/examples/teams/state/change-state-on-run.md): Demonstrates per-run session state overrides for different users/sessions. - [Nested Shared State](https://docs.agno.com/examples/teams/state/nested-shared-state.md): Demonstrates hierarchical teams that coordinate over shared session state. - [Overwrite Stored Session State](https://docs.agno.com/examples/teams/state/overwrite-stored-session-state.md): Demonstrates replacing persisted session_state with run-time session_state. - [State Sharing](https://docs.agno.com/examples/teams/state/state-sharing.md): Demonstrates sharing session state and member interactions across team members. - [Chat History](https://docs.agno.com/examples/teams/session/chat-history.md): Demonstrates retrieving chat history and limiting included history messages. - [Persistent Session](https://docs.agno.com/examples/teams/session/persistent-session.md): Demonstrates persistent team sessions with optional history injection. - [Session Options](https://docs.agno.com/examples/teams/session/session-options.md): Demonstrates session naming, in-memory DB usage, and session caching options. - [Session Summary](https://docs.agno.com/examples/teams/session/session-summary.md): Demonstrates session summary creation, context reuse, and async summary retrieval. - [Share Session With Agent](https://docs.agno.com/examples/teams/session/share-session-with-agent.md): Demonstrates sharing one session across team and single-agent interactions. - [Custom Session Summary](https://docs.agno.com/examples/teams/session/custom-session-summary.md): Demonstrates configuring a custom session summary manager and reusing summaries in context. - [Metadata Resolution](https://docs.agno.com/examples/teams/session/metadata-resolution.md): Demonstrates the three-layer metadata resolution for Teams: team.metadata < session.metadata < call-site metadata. - [Deep Nested Team History](https://docs.agno.com/examples/teams/session/nested-team-deep-history.md): Use case: Customer reports a database issue. - [Nested Team History](https://docs.agno.com/examples/teams/session/nested-team-history.md): Demonstrates how nested teams (teams as members of other teams) maintain their own conversation history across multiple delegations. - [Nested Team History for Members](https://docs.agno.com/examples/teams/session/nested-team-history-to-members.md): Demonstrates `add_team_history_to_members` for nested teams: when a parent team delegates to a nested sub-team, the sub-team is given ITS OWN prior history (filtered by the sub-team's id) rather than the root team leader's history. - [Search Past Sessions (Team)](https://docs.agno.com/examples/teams/session/search-past-sessions.md): Demonstrates the two-step list-then-read pattern for accessing previous team sessions with user-scoped history access. - [Session](https://docs.agno.com/examples/teams/session/overview.md): Persist team sessions, history, summaries, and shared agent interactions. - [Tools](https://docs.agno.com/examples/teams/tools/overview.md): Configure team and member tools, tool hooks, tool choice, and call limits. - [Async Tools](https://docs.agno.com/examples/teams/tools/async-tools.md): Demonstrates async team execution with mixed research and scraping tools. - [Custom Tools](https://docs.agno.com/examples/teams/tools/custom-tools.md): Demonstrates a team using a custom FAQ tool plus web-search fallback. - [Member Tool Hooks](https://docs.agno.com/examples/teams/tools/member-tool-hooks.md): Demonstrates permission-aware tool hooks that gate member delegation. - [Tool Hooks](https://docs.agno.com/examples/teams/tools/tool-hooks.md): Demonstrates team/member tool hooks for logging delegation and tool execution timing. - [Async Toolkit Context](https://docs.agno.com/examples/teams/tools/async-toolkit-context.md): Demonstrates that async-only toolkit functions are correctly included in the team system message when add_member_tools_to_context=True. - [Member Information](https://docs.agno.com/examples/teams/tools/member-information.md): Demonstrates enabling the `get_member_information_tool` capability on a Team. - [Message History In Tool Hooks](https://docs.agno.com/examples/teams/tools/message-history-in-tool-hooks.md): Access the current run's message history inside tool hooks in a team via run_context.messages. - [Tool Call Limit](https://docs.agno.com/examples/teams/tools/tool-call-limit.md): Demonstrates constraining how many tool calls a Team can make in a single run. - [Tool Choice](https://docs.agno.com/examples/teams/tools/tool-choice.md): Demonstrates using `tool_choice` to force the Team to execute a specific tool. - [Post Hook Output](https://docs.agno.com/examples/teams/hooks/post-hook-output.md): Demonstrates output validation and transformation post-hooks for team runs. - [Pre Hook Input](https://docs.agno.com/examples/teams/hooks/pre-hook-input.md): Demonstrates input validation and transformation pre-hooks for team runs. - [Stream Hook](https://docs.agno.com/examples/teams/hooks/stream-hook.md): Demonstrates post-hook notifications after team response generation. - [Hooks](https://docs.agno.com/examples/teams/hooks/overview.md): Validate and transform team inputs and outputs, and send a notification after a streamed response. - [Skills](https://docs.agno.com/examples/teams/skills/overview.md): Examples for attaching skills to team leaders. - [Basic Skills on a Team](https://docs.agno.com/examples/teams/skills/basic-skills-team.md): Attach Skills to a Team leader so it gets domain expertise (instructions, references, scripts) directly. - [Advanced](https://docs.agno.com/examples/teams/task-mode/overview.md): Advanced team examples for task mode, run control, context management, multimodal input, metrics, reasoning, and dependencies. - [Async Task Mode Example](https://docs.agno.com/examples/teams/task-mode/async-task-mode.md): Demonstrates task mode using the async API (arun / aprint_response). - [Basic Task Mode Example](https://docs.agno.com/examples/teams/task-mode/basic-task-mode.md): A tasks-mode team decomposes a briefing request into research, writing, and review tasks. - [Task Mode with Custom Tools](https://docs.agno.com/examples/teams/task-mode/custom-tools.md): Demonstrates task mode where member agents use custom Python function tools. - [Task Dependencies Example](https://docs.agno.com/examples/teams/task-mode/dependency-chain.md): Demonstrates complex task dependency chains in task mode. - [Multi-Run Session with Task Mode](https://docs.agno.com/examples/teams/task-mode/multi-run-session.md): Demonstrates that task state persists across multiple runs within the same session. - [Parallel Task Execution Example](https://docs.agno.com/examples/teams/task-mode/parallel-tasks.md): Demonstrates the `execute_tasks_parallel` tool in task mode. - [Task Mode with Tool-Equipped Agents](https://docs.agno.com/examples/teams/task-mode/task-mode-with-tools.md): Demonstrates task mode where member agents have real tools. - [Tool Call Compression](https://docs.agno.com/examples/teams/context-compression/tool-call-compression.md): Demonstrates team-level tool result compression in both sync and async workflows. - [Tool Call Compression With Manager](https://docs.agno.com/examples/teams/context-compression/tool-call-compression-with-manager.md): Demonstrates custom tool result compression using CompressionManager. - [Context Compression](https://docs.agno.com/examples/teams/context-compression/overview.md): Compress team tool results to reduce the context sent to the model. - [Few Shot Learning](https://docs.agno.com/examples/teams/context-management/few-shot-learning.md): Demonstrates using additional_input examples to guide team support responses. - [Filter Tool Calls From History](https://docs.agno.com/examples/teams/context-management/filter-tool-calls-from-history.md): Demonstrates limiting historical tool call results in team context. - [Team Introduction](https://docs.agno.com/examples/teams/context-management/introduction.md): Demonstrates setting a reusable team introduction message for a session. - [Additional Context](https://docs.agno.com/examples/teams/context-management/additional-context.md): Demonstrates adding custom `additional_context` and resolving placeholders at run time through Team context resolution. - [Custom Team System Message](https://docs.agno.com/examples/teams/context-management/custom-system-message.md): Replace the generated team system message with a custom message and role. - [Custom Datetime Format](https://docs.agno.com/examples/teams/context-management/datetime-format.md): Customize the datetime format injected into the team's system context. - [Location Context](https://docs.agno.com/examples/teams/context-management/location-context.md): Demonstrates adding location and timezone context to team prompts. - [Context Management](https://docs.agno.com/examples/teams/context-management/overview.md): Control team context with instructions, messages, history filters, dates, and locations. - [Dependencies In Context](https://docs.agno.com/examples/teams/dependencies/dependencies-in-context.md): Demonstrates team-level dependencies referenced directly in instructions and member context. - [Dependencies In Tools](https://docs.agno.com/examples/teams/dependencies/dependencies-in-tools.md): Demonstrates passing dependencies at runtime and accessing them inside team tools. - [Dependencies To Members](https://docs.agno.com/examples/teams/dependencies/dependencies-to-members.md): Demonstrates passing dependencies on run and propagating them to member agents. - [Dependencies](https://docs.agno.com/examples/teams/dependencies/overview.md): Pass runtime dependencies into teams, members, instructions, and tools. - [Team Metrics](https://docs.agno.com/examples/teams/metrics/team-metrics.md): Demonstrates retrieving team, session, and member-level execution metrics. - [Team Streaming Metrics](https://docs.agno.com/examples/teams/metrics/team-streaming-metrics.md): Capture metrics from team streaming responses. - [Team Session Metrics](https://docs.agno.com/examples/teams/metrics/team-session-metrics.md): Demonstrates session-level metrics for teams with PostgreSQL persistence. - [Team Tool Metrics](https://docs.agno.com/examples/teams/metrics/team-tool-metrics.md): Demonstrates metrics for teams where members use tools. - [Team Eval Metrics](https://docs.agno.com/examples/teams/metrics/team-eval-metrics.md): Demonstrates that eval model metrics are accumulated back into the team's run_output when AgentAsJudgeEval is used as a post_hook. - [Loop Through Team Lead and Member Metrics](https://docs.agno.com/examples/teams/metrics/loop-team-and-member-metrics.md): Walk the metrics surface on a TeamRunOutput. - [Metrics](https://docs.agno.com/examples/teams/metrics/overview.md): Team-level metrics for runs, sessions, streaming, tools, and evals. - [Audio Sentiment Analysis](https://docs.agno.com/examples/teams/multimodal/audio-sentiment-analysis.md): Demonstrates team-based transcription and sentiment analysis for audio conversations. - [Audio To Text](https://docs.agno.com/examples/teams/multimodal/audio-to-text.md): Demonstrates team-based audio transcription and follow-up content analysis. - [Generate Image With Team](https://docs.agno.com/examples/teams/multimodal/generate-image-with-team.md): Legacy DalleTools team example for prompt refinement and image generation. - [Image To Image Transformation](https://docs.agno.com/examples/teams/multimodal/image-to-image-transformation.md): Demonstrates collaborative style planning and image transformation. - [Image To Structured Output](https://docs.agno.com/examples/teams/multimodal/image-to-structured-output.md): Demonstrates collaborative visual analysis with structured movie script output. - [Image To Text](https://docs.agno.com/examples/teams/multimodal/image-to-text.md): Demonstrates collaborative image analysis and narrative generation. - [Media Input For Tool](https://docs.agno.com/examples/teams/multimodal/media-input-for-tool.md): Demonstrates team tools accessing uploaded media files directly. - [Video Caption Generation](https://docs.agno.com/examples/teams/multimodal/video-caption-generation.md): Demonstrates team-based video caption generation and embedding workflow. - [Multimodal](https://docs.agno.com/examples/teams/multimodal/overview.md): Process audio, images, video, and media-aware tools with teams. - [Example demonstrating background execution with a Team](https://docs.agno.com/examples/teams/other/background-execution.md): Background execution allows you to start a team run that returns immediately with a PENDING status, while the actual work continues in the background. - [Team SSE Reconnection](https://docs.agno.com/examples/teams/other/sse-reconnect.md): Migrate the removed team SSE reconnect example to the current AgentOS v3 reconnect flow. - [Other](https://docs.agno.com/examples/teams/other/overview.md): Additional team patterns that don't fit into the main categories. - [Reasoning Multi Purpose Team](https://docs.agno.com/examples/teams/reasoning/reasoning-multi-purpose-team.md): Demonstrates multi-purpose team reasoning with both sync and async patterns. - [Reasoning](https://docs.agno.com/examples/teams/reasoning/overview.md): Coordinate reasoning-enabled team members across research and decision tasks. - [Cancel Run](https://docs.agno.com/examples/teams/run-control/cancel-run.md): Demonstrates cancelling an in-flight team run from a separate thread. - [Model Inheritance](https://docs.agno.com/examples/teams/run-control/model-inheritance.md): Demonstrates how member models inherit from parent team models. - [Remote Team](https://docs.agno.com/examples/teams/run-control/remote-team.md): Demonstrates calling and streaming a team hosted on a remote AgentOS instance. - [Retries](https://docs.agno.com/examples/teams/run-control/retries.md): Demonstrates team retry configuration for transient run errors. - [Team Background Execution Metrics](https://docs.agno.com/examples/teams/run-control/background-execution-metrics.md): Demonstrates that metrics are fully tracked for team background runs. - [Cancel Run Persistence](https://docs.agno.com/examples/teams/run-control/cancel-run-persistence.md): Cancel a team run mid-stream and verify that partial content and messages are preserved in the database. - [Cancel While Member Runs](https://docs.agno.com/examples/teams/run-control/team-cancel-while-member-runs.md): Cancel a team run while a member agent is actively streaming. - [Run Control](https://docs.agno.com/examples/teams/run-control/overview.md): Control team background execution, cancellation, retries, remote access, and model inheritance. - [Team Events](https://docs.agno.com/examples/teams/streaming/team-events.md): Inspect team and member lifecycle and tool events in an async stream. - [Team Streaming](https://docs.agno.com/examples/teams/streaming/team-streaming.md): Demonstrates sync and async streaming responses from a team. - [Streaming](https://docs.agno.com/examples/teams/streaming/overview.md): Stream team and member content, tool calls, and lifecycle events. - [Team Fallback Models: Basic](https://docs.agno.com/examples/teams/fallback-models/basic-fallback.md): When the team leader's primary model fails (after exhausting retries), fallback models are tried in order until one succeeds. - [Team Fallback Models: Error-Specific](https://docs.agno.com/examples/teams/fallback-models/error-specific-fallbacks.md): Use FallbackConfig for error-specific fallback routing on Teams. - [Crash recovery for a Team with checkpoint="tool-batch"](https://docs.agno.com/examples/teams/checkpointing/crash-recovery.md): Simulate a hard crash during a team run, then resume its last RUNNING checkpoint with `Team.acontinue_run()`. - [Tool Error Persistence](https://docs.agno.com/examples/teams/checkpointing/tool-error-persistence.md): Persist a team conversation through tool exceptions and model failures, then retry the failed run in place with `Team.acontinue_run()`. - [Checkpoint Endpoints](https://docs.agno.com/examples/teams/checkpointing/checkpoint-endpoints.md): Inspect a team run's checkpoint timeline via the new HTTP endpoints. - [Remote Agent as Team Member](https://docs.agno.com/examples/teams/remote-agents/basic-remote-member.md): Use a RemoteAgent as a team member. - [Regenerate](https://docs.agno.com/examples/teams/regenerate/regenerate.md): Regenerate a team leader response while preserving prior member exchanges. - [Continue From](https://docs.agno.com/examples/teams/time-travel/continue-from.md): ``continue_from`` chooses the message boundary to resume from. - [Fork a team run at a specific message](https://docs.agno.com/examples/teams/time-travel/fork-run.md): ``fork=True`` + ``continue_from="last_user"`` creates a new team run by truncating the source's messages at the last user boundary. - [Fork Session](https://docs.agno.com/examples/teams/fork-session/fork-session.md): Use ``fork_session`` when you want a completely independent conversation thread that starts from the current state. - [Handing A Result To A Member](https://docs.agno.com/examples/teams/result-offloading/handing-a-result-to-a-member.md): Members share the leader's result store, and every member of a team runs under the same session id. - [Member Store Settings](https://docs.agno.com/examples/teams/result-offloading/member-store-settings.md): The team's own setting is never written onto a member, and the binding is redone every time the team initializes, so a member moved to another team follows that team. - [Offload Member Results](https://docs.agno.com/examples/teams/result-offloading/offload-member-results.md): A member's answer reaches the team leader as the result of the delegation tool, so it is the payload that grows a team session. - [Workflows](https://docs.agno.com/examples/workflows/overview.md): Workflow examples covering steps, loops, parallel execution, routing, advanced controls, and CEL. - [Step With Function](https://docs.agno.com/examples/workflows/basic-workflows/step-with-function/overview.md): Run custom functions, classes, and additional-data executors as workflow steps. - [Step With Additional Data](https://docs.agno.com/examples/workflows/basic-workflows/step-with-function/step-with-additional-data.md): Demonstrates custom step executors that consume `additional_data` in sync and async workflow runs. - [Step With Class Executor](https://docs.agno.com/examples/workflows/basic-workflows/step-with-function/step-with-class.md): Demonstrates class-based step executors with sync and async workflow execution. - [Step With Function](https://docs.agno.com/examples/workflows/basic-workflows/step-with-function/step-with-function.md): Demonstrates custom function executors in step-based workflows with sync, sync-streaming, and async-streaming runs. - [Function Workflow](https://docs.agno.com/examples/workflows/basic-workflows/function-workflows/function-workflow.md): Demonstrates using a single execution function in place of explicit step lists across sync and async run modes. - [Function Workflows](https://docs.agno.com/examples/workflows/basic-workflows/function-workflows/overview.md): Runnable workflow examples under: cookbook/04_workflows/01_basic_workflows/03_function_workflows. - [Sequence Of Steps](https://docs.agno.com/examples/workflows/basic-workflows/sequence-of-steps/sequence-of-steps.md): Demonstrates sequential workflow execution with sync, async, streaming, and event-streaming run modes. - [Sequence With Functions](https://docs.agno.com/examples/workflows/basic-workflows/sequence-of-steps/sequence-with-functions.md): Demonstrates sequencing function steps and agent/team steps with sync, async, and streaming runs. - [Workflow Using Steps](https://docs.agno.com/examples/workflows/basic-workflows/sequence-of-steps/workflow-using-steps.md): Compose a workflow from a `Steps` sequence with research, writing, and editing steps. - [Workflow Using Nested Steps](https://docs.agno.com/examples/workflows/basic-workflows/sequence-of-steps/workflow-using-steps-nested.md): Demonstrates nested workflow composition using `Steps`, `Condition`, and `Parallel`. - [Workflow With File Input](https://docs.agno.com/examples/workflows/basic-workflows/sequence-of-steps/workflow-with-file-input.md): Demonstrates passing file inputs through workflow steps for reading and summarization. - [Workflow With Session Metrics](https://docs.agno.com/examples/workflows/basic-workflows/sequence-of-steps/workflow-with-session-metrics.md): Demonstrates collecting and printing workflow session metrics after execution. - [Sequence Of Steps](https://docs.agno.com/examples/workflows/basic-workflows/sequence-of-steps/overview.md): Runnable workflow examples under: cookbook/04_workflows/01_basic_workflows/01_sequence_of_steps. - [Basic Workflows](https://docs.agno.com/examples/workflows/basic-workflows/overview.md): Workflow examples for function executors, step sequences, nested steps, files, and session metrics. - [Conditional Execution](https://docs.agno.com/examples/workflows/conditional-execution/overview.md): Condition workflow examples for branching on input and previous-step output. - [Condition Basic](https://docs.agno.com/examples/workflows/conditional-execution/condition-basic.md): Demonstrates conditional step execution using a fact-check gate in a linear workflow. - [Condition With Else](https://docs.agno.com/examples/workflows/conditional-execution/condition-with-else.md): Demonstrates `Condition(..., else_steps=[...])` for routing between technical and general support branches. - [Condition With List](https://docs.agno.com/examples/workflows/conditional-execution/condition-with-list.md): Demonstrates condition branches that execute a list of multiple steps, including parallel conditional blocks. - [Condition With Parallel](https://docs.agno.com/examples/workflows/conditional-execution/condition-with-parallel.md): Run Hacker News, web, and Exa research conditions in parallel before two synthesis steps. - [Loop Basic](https://docs.agno.com/examples/workflows/loop-execution/loop-basic.md): Demonstrates loop-based workflow execution with an end-condition evaluator and max-iteration guard. - [Loop With Parallel](https://docs.agno.com/examples/workflows/loop-execution/loop-with-parallel.md): Demonstrates a loop body that mixes `Parallel` and sequential steps before final content generation. - [Loop Iterative Accumulation](https://docs.agno.com/examples/workflows/loop-execution/loop-iterative-accumulation.md): Demonstrates that Loop iterations carry forward the output from the previous iteration. - [Loop Execution](https://docs.agno.com/examples/workflows/loop-execution/overview.md): Loop workflows for iterative processing, parallel branches, and accumulated outputs. - [Parallel Basic](https://docs.agno.com/examples/workflows/parallel-execution/parallel-basic.md): Demonstrates running independent research steps in parallel before sequential writing and review steps. - [Parallel With Condition](https://docs.agno.com/examples/workflows/parallel-execution/parallel-with-condition.md): Demonstrates combining conditional branches with parallel execution for adaptive research pipelines. - [Parallel Execution](https://docs.agno.com/examples/workflows/parallel-execution/overview.md): Parallel workflows for concurrent agents, teams, conditions, and synthesis steps. - [Conditional Branching](https://docs.agno.com/examples/workflows/conditional-branching/overview.md): Router and conditional workflow examples for dynamic branch selection. - [Loop In Choices](https://docs.agno.com/examples/workflows/conditional-branching/loop-in-choices.md): Demonstrates using a `Loop` component as one of the router choices. - [Nested Choices](https://docs.agno.com/examples/workflows/conditional-branching/nested-choices.md): Route to a single step or a nested sequence of steps based on selector logic. - [Router Basic](https://docs.agno.com/examples/workflows/conditional-branching/router-basic.md): Route to HackerNews or web research based on topic keywords. - [Router With Loop](https://docs.agno.com/examples/workflows/conditional-branching/router-with-loop.md): Route to simple web search or iterative deep research loops based on topic complexity. - [Selector Media Pipeline](https://docs.agno.com/examples/workflows/conditional-branching/selector-media-pipeline.md): Route input strings to image generation or video planning and text critique. - [Selector Types](https://docs.agno.com/examples/workflows/conditional-branching/selector-types.md): Use string, step, list, and nested return types in router selectors. - [Step Choices Parameter](https://docs.agno.com/examples/workflows/conditional-branching/step-choices-parameter.md): Access available choices in selector functions to dynamically route between steps. - [String Selector](https://docs.agno.com/examples/workflows/conditional-branching/string-selector.md): Return a step name string from a router selector. - [Condition with CEL expression: branching on additional_data](https://docs.agno.com/examples/workflows/cel-expressions/condition/cel-additional-data.md): Uses additional_data.priority to route high-priority requests to a specialized agent. - [Condition with CEL expression: route based on input content](https://docs.agno.com/examples/workflows/cel-expressions/condition/cel-basic.md): Uses input.contains() to check whether the request is urgent, branching to different agents via if/else steps. - [Condition with CEL expression: branching on previous step output](https://docs.agno.com/examples/workflows/cel-expressions/condition/cel-previous-step.md): Runs a classifier step first, then uses previous_step_content.contains() to decide the next step. - [Condition with CEL: branch based on a named step's output](https://docs.agno.com/examples/workflows/cel-expressions/condition/cel-previous-step-outputs.md): Uses previous_step_outputs map to check the output of a specific step by name, enabling multi-step pipelines with conditional logic. - [Condition with CEL expression: branching on session_state](https://docs.agno.com/examples/workflows/cel-expressions/condition/cel-session-state.md): Uses session_state.retry_count to implement retry logic. - [Condition](https://docs.agno.com/examples/workflows/cel-expressions/condition/overview.md): Runnable workflow examples under: cookbook/04_workflows/07_cel_expressions/condition. - [Loop with CEL end condition: compound exit condition](https://docs.agno.com/examples/workflows/cel-expressions/loop/cel-compound-exit.md): Combines all_success and current_iteration to stop when both conditions are met: all steps succeeded AND enough iterations ran. - [Loop with CEL end condition: stop when agent signals completion](https://docs.agno.com/examples/workflows/cel-expressions/loop/cel-content-keyword.md): Uses last_step_content.contains() to detect a keyword in the output that signals the loop should stop. - [Loop with CEL end condition: stop after N iterations](https://docs.agno.com/examples/workflows/cel-expressions/loop/cel-iteration-limit.md): Use current_iteration to stop a loop early, within its max_iterations cap. - [Loop with CEL end condition: check a named step's output](https://docs.agno.com/examples/workflows/cel-expressions/loop/cel-step-outputs-check.md): Uses step_outputs map to access a specific step by name and check its content before deciding to stop the loop. - [Loop](https://docs.agno.com/examples/workflows/cel-expressions/loop/overview.md): Runnable workflow examples under: cookbook/04_workflows/07_cel_expressions/loop. - [Router with CEL expression: route from additional_data field](https://docs.agno.com/examples/workflows/cel-expressions/router/cel-additional-data-route.md): Uses additional_data.route to let the caller specify which step to run, useful when the routing decision is made upstream (e.g. UI). - [Router with CEL: route based on a named previous step's output](https://docs.agno.com/examples/workflows/cel-expressions/router/cel-previous-step-route.md): Uses previous_step_outputs map to access the classifier step by name, then routes to the appropriate handler based on the classification. - [Router with CEL expression: route from session_state](https://docs.agno.com/examples/workflows/cel-expressions/router/cel-session-state-route.md): Route using session_state.preferred_handler and change the configured default for subsequent runs. - [Router with CEL expression: ternary operator on input content](https://docs.agno.com/examples/workflows/cel-expressions/router/cel-ternary.md): Uses a CEL ternary to pick between two steps based on whether the input mentions "video" or not. - [Router with CEL: route using step_choices index](https://docs.agno.com/examples/workflows/cel-expressions/router/cel-using-step-choices.md): Uses step_choices[0], step_choices[1], etc. to reference steps by their position in the choices list, rather than hardcoding step names. - [Router](https://docs.agno.com/examples/workflows/cel-expressions/router/overview.md): Runnable workflow examples under: cookbook/04_workflows/07_cel_expressions/router. - [Cel Expressions](https://docs.agno.com/examples/workflows/cel-expressions/overview.md): Use CEL expressions in workflow conditions, loops, and routers. - [Workflow Agent](https://docs.agno.com/examples/workflows/advanced-concepts/workflow-agent/overview.md): Runnable workflow examples under: cookbook/04_workflows/06_advanced_concepts/workflow_agent. - [Basic Workflow Agent](https://docs.agno.com/examples/workflows/advanced-concepts/workflow-agent/basic-workflow-agent.md): WorkflowAgent decides whether to execute steps or answer from history. - [Workflow Agent With Condition](https://docs.agno.com/examples/workflows/advanced-concepts/workflow-agent/workflow-agent-with-condition.md): Add conditional steps to a WorkflowAgent graph. - [Background Poll](https://docs.agno.com/examples/workflows/advanced-concepts/background-execution/background-poll.md): Run a workflow in async background mode and poll run status until completion. - [Background Execution WebSocket Client](https://docs.agno.com/examples/workflows/advanced-concepts/background-execution/websocket-client.md): Build an interactive WebSocket client for authenticating, starting workflows, and rendering streamed workflow events. - [Background Execution WebSocket Server](https://docs.agno.com/examples/workflows/advanced-concepts/background-execution/websocket-server.md): Run background workflows and stream workflow events over WebSocket. - [Background Execution](https://docs.agno.com/examples/workflows/advanced-concepts/background-execution/overview.md): Runnable workflow examples under: cookbook/04_workflows/06_advanced_concepts/background_execution. - [Early Stop Basic](https://docs.agno.com/examples/workflows/advanced-concepts/early-stopping/early-stop-basic.md): Implement early termination with `StepOutput(stop=True)` across direct steps, `Steps` containers, and agent/function workflows. - [Early Stop Condition](https://docs.agno.com/examples/workflows/advanced-concepts/early-stopping/early-stop-condition.md): Stop an entire workflow when a Condition branch detects policy violations. - [Early Stop Loop](https://docs.agno.com/examples/workflows/advanced-concepts/early-stopping/early-stop-loop.md): Halt a Loop when a safety-check step detects sensitive AI-related content. - [Early Stop Parallel](https://docs.agno.com/examples/workflows/advanced-concepts/early-stopping/early-stop-parallel.md): Stop an entire workflow from a Parallel step when checking input for dangerous content. - [Early Stopping](https://docs.agno.com/examples/workflows/advanced-concepts/early-stopping/overview.md): Runnable workflow examples under: cookbook/04_workflows/06_advanced_concepts/early_stopping. - [Prompt Injection Guardrail](https://docs.agno.com/examples/workflows/advanced-concepts/guardrails/prompt-injection.md): Block prompt-injection attacks using PromptInjectionGuardrail in async workflows. - [Guardrails](https://docs.agno.com/examples/workflows/advanced-concepts/guardrails/overview.md): Runnable workflow examples under: cookbook/04_workflows/06_advanced_concepts/guardrails. - [Continuous Execution](https://docs.agno.com/examples/workflows/advanced-concepts/history/continuous-execution.md): Give a tutoring workflow access to its three most recent runs for a continuing conversation. - [History In Function](https://docs.agno.com/examples/workflows/advanced-concepts/history/history-in-function.md): Access workflow history from function steps to analyze content strategy and prevent topic overlap. - [Intent Routing With History](https://docs.agno.com/examples/workflows/advanced-concepts/history/intent-routing-with-history.md): Route customer requests to specialist steps while maintaining shared workflow history for context continuity. - [Step History](https://docs.agno.com/examples/workflows/advanced-concepts/history/step-history.md): Control workflow-level and step-level history access for multi-step conversation workflows. - [History](https://docs.agno.com/examples/workflows/advanced-concepts/history/overview.md): Runnable workflow examples under: cookbook/04_workflows/06_advanced_concepts/history. - [Disruption Catchup](https://docs.agno.com/examples/workflows/advanced-concepts/long-running/disruption-catchup.md): Tests full catch-up behavior for a running workflow when reconnecting with `last_event_index=None`. - [Events Replay](https://docs.agno.com/examples/workflows/advanced-concepts/long-running/events-replay.md): Tests replay behavior when reconnecting to a completed workflow run. - [WebSocket Reconnect](https://docs.agno.com/examples/workflows/advanced-concepts/long-running/websocket-reconnect.md): Tests reconnect behavior for a running workflow: initial subscription, disconnection, reconnect, and missed-event catch-up. - [Long Running](https://docs.agno.com/examples/workflows/advanced-concepts/long-running/overview.md): Reconnect to active workflow runs and replay persisted events over WebSockets. - [Access Previous Outputs](https://docs.agno.com/examples/workflows/advanced-concepts/previous-step-outputs/access-previous-outputs.md): Access output from multiple prior steps using both named steps and implicit step keys. - [Previous Step Outputs](https://docs.agno.com/examples/workflows/advanced-concepts/previous-step-outputs/overview.md): Runnable workflow examples under: cookbook/04_workflows/06_advanced_concepts/previous_step_outputs. - [Cancel Run](https://docs.agno.com/examples/workflows/advanced-concepts/run-control/cancel-run.md): Cancel a workflow run from another thread while it's executing. - [Workflow Deep Copy](https://docs.agno.com/examples/workflows/advanced-concepts/run-control/deep-copy.md): Create independent workflow copies with isolated state using `deep_copy()`. - [Event Storage](https://docs.agno.com/examples/workflows/advanced-concepts/run-control/event-storage.md): Store workflow events while filtering out high-volume event types. - [Executor Events](https://docs.agno.com/examples/workflows/advanced-concepts/run-control/executor-events.md): Suppress intermediate executor events while preserving terminal executor events when streaming. - [Workflow Metrics](https://docs.agno.com/examples/workflows/advanced-concepts/run-control/metrics.md): Extract workflow and step-level execution metrics including duration and token counts. - [Remote Workflow](https://docs.agno.com/examples/workflows/advanced-concepts/run-control/remote-workflow.md): Execute workflows hosted on a remote AgentOS server using async calls. - [Workflow CLI](https://docs.agno.com/examples/workflows/advanced-concepts/run-control/workflow-cli.md): Use `Workflow.cli_app()` for interactive command-line workflow runs. - [Workflow Serialization](https://docs.agno.com/examples/workflows/advanced-concepts/run-control/workflow-serialization.md): Persist workflows with `to_dict()`, `save()`, and `load()`. - [Run Control](https://docs.agno.com/examples/workflows/advanced-concepts/run-control/overview.md): Runnable workflow examples under: cookbook/04_workflows/06_advanced_concepts/run_control. - [Rename Session](https://docs.agno.com/examples/workflows/advanced-concepts/session-state/rename-session.md): Auto-generate a workflow session name after a run. - [State In Condition](https://docs.agno.com/examples/workflows/advanced-concepts/session-state/state-in-condition.md): Use workflow session state in `Condition` evaluator and executor functions. - [State In Function](https://docs.agno.com/examples/workflows/advanced-concepts/session-state/state-in-function.md): Read and mutate workflow session state in custom function executors. - [State In Router](https://docs.agno.com/examples/workflows/advanced-concepts/session-state/state-in-router.md): Use router selectors with workflow session state for adaptive routing. - [State With Agent](https://docs.agno.com/examples/workflows/advanced-concepts/session-state/state-with-agent.md): Share workflow session state across agent tool calls. - [State With Team](https://docs.agno.com/examples/workflows/advanced-concepts/session-state/state-with-team.md): Share session state across team and agent steps in project workflows. - [Job Application Tracker](https://docs.agno.com/examples/workflows/advanced-concepts/session-state/job-application-tracker.md): Extract job applications with structured output and persist them in session state across workflow runs. - [Session State](https://docs.agno.com/examples/workflows/advanced-concepts/session-state/overview.md): Runnable workflow examples under: cookbook/04_workflows/06_advanced_concepts/session_state. - [Image Input](https://docs.agno.com/examples/workflows/advanced-concepts/structured-io/image-input.md): Pass images to workflows and chain vision analysis with web search. - [Input Schema](https://docs.agno.com/examples/workflows/advanced-concepts/structured-io/input-schema.md): Validate workflow input with Pydantic schemas. - [Pydantic Input](https://docs.agno.com/examples/workflows/advanced-concepts/structured-io/pydantic-input.md): Pass Pydantic model instances as workflow input. - [Structured I/O Agent](https://docs.agno.com/examples/workflows/advanced-concepts/structured-io/structured-io-agent.md): Chain agents with structured output schemas. - [Structured I/O Function](https://docs.agno.com/examples/workflows/advanced-concepts/structured-io/structured-io-function.md): Return strings and BaseModel objects from custom function steps in workflows with structured I/O and data flow analysis. - [Structured I/O Team](https://docs.agno.com/examples/workflows/advanced-concepts/structured-io/structured-io-team.md): Use structured output schemas across team steps in multi-step workflows. - [Structured I/O](https://docs.agno.com/examples/workflows/advanced-concepts/structured-io/overview.md): Runnable workflow examples under: cookbook/04_workflows/06_advanced_concepts/structured_io. - [Workflow Tools](https://docs.agno.com/examples/workflows/advanced-concepts/tools/workflow-tools.md): Expose workflows as tools for agent execution. - [Tools](https://docs.agno.com/examples/workflows/advanced-concepts/tools/overview.md): Runnable workflow examples under: cookbook/04_workflows/06_advanced_concepts/tools. - [Nested Workflows](https://docs.agno.com/examples/workflows/advanced-concepts/nested-workflows/overview.md): Compose complex workflows from simpler sub-workflows using nested workflows. - [Nested Workflow Example](https://docs.agno.com/examples/workflows/advanced-concepts/nested-workflows/nested-workflow.md): Use a workflow as a step within another workflow. - [Nested Workflow - Auto-Wrap (Passing Workflow Directly)](https://docs.agno.com/examples/workflows/advanced-concepts/nested-workflows/nested-workflow-auto-wrap.md): Pass a Workflow directly in steps list for auto-wrapping without explicit Step() wrapper. - [Nested Workflow Example - Event Inspection](https://docs.agno.com/examples/workflows/advanced-concepts/nested-workflows/nested-workflow-events.md): Runs a nested workflow and prints every workflow/step event with full details so you can see workflow_id, workflow_name, nested_depth, and how inner vs outer events differ. - [Nested Workflow with Condition](https://docs.agno.com/examples/workflows/advanced-concepts/nested-workflows/nested-workflow-with-condition.md): Use Condition steps in nested workflows to conditionally branch execution based on content analysis. - [Nested Workflow with Loop](https://docs.agno.com/examples/workflows/advanced-concepts/nested-workflows/nested-workflow-with-loop.md): Run bounded repeated research attempts inside a nested workflow. - [Nested Workflow with Router](https://docs.agno.com/examples/workflows/advanced-concepts/nested-workflows/nested-workflow-with-router.md): Use Router steps in nested workflows to delegate tasks to specialist agents based on input keywords. - [Deeply Nested Workflow (3 Levels)](https://docs.agno.com/examples/workflows/advanced-concepts/nested-workflows/deeply-nested-workflow.md): Compose a three-level workflow with parallel research branches and nested mini-workflows. - [File Generation Workflow](https://docs.agno.com/examples/workflows/advanced-concepts/file-propagation/file-generation-workflow.md): Generate a PDF with FileGenerationTools and propagate it to the next workflow step for analysis. - [Workflow All Run Params](https://docs.agno.com/examples/workflows/advanced-concepts/run-params/workflow-all-params.md): Configure workflow behavior with metadata, dependencies, and session state in a content pipeline. - [Workflow Dependencies](https://docs.agno.com/examples/workflows/advanced-concepts/run-params/workflow-dependencies.md): Inject configuration through workflow dependencies to control agent behavior. - [Metadata Resolution](https://docs.agno.com/examples/workflows/advanced-concepts/run-params/metadata-resolution.md): Demonstrates the three-layer metadata resolution for Workflows: workflow.metadata < session.metadata < call-site metadata. - [Advanced Concepts](https://docs.agno.com/examples/workflows/advanced-concepts/overview.md): Advanced workflow examples for run control, state, history, nesting, guardrails, structured I/O, and background execution. - [Human in the Loop](https://docs.agno.com/examples/workflows/human-in-the-loop/overview.md): Add approval, route selection, user input, and output review to workflows. - [Condition with User Decision HITL Example](https://docs.agno.com/examples/workflows/human-in-the-loop/condition/condition-user-decision.md): Use HITL with a Condition component, allowing the user to decide which branch to execute at runtime. - [Condition on_error Handling](https://docs.agno.com/examples/workflows/human-in-the-loop/condition/condition-on-error.md): Control error handling within Condition steps using the `on_error` parameter. - [Basic Step Confirmation Example](https://docs.agno.com/examples/workflows/human-in-the-loop/confirmation/basic-step-confirmation.md): Pause a workflow for user confirmation before executing a step. - [Custom Function Step Confirmation](https://docs.agno.com/examples/workflows/human-in-the-loop/confirmation/custom-function-step-confirmation.md): Require confirmation for workflow steps with the @pause decorator. - [Step Confirmation with Streaming](https://docs.agno.com/examples/workflows/human-in-the-loop/confirmation/step-confirmation-streaming.md): Pause a workflow for user confirmation before executing a step, with streaming execution for real-time event updates. - [Async Step Confirmation](https://docs.agno.com/examples/workflows/human-in-the-loop/confirmation/async-step-confirmation.md): Demonstrates that the @pause decorator works correctly with async functions. - [StepContinuedEvent Demo (Streaming)](https://docs.agno.com/examples/workflows/human-in-the-loop/confirmation/step-continued-event.md): Demonstrates the StepContinuedEvent that is emitted when a paused step resumes execution after step-level HITL is resolved. - [Decision Tree with Sequential HITL Conditions](https://docs.agno.com/examples/workflows/human-in-the-loop/decision-tree/decision-tree.md): Build a multi-step decision tree with sequential Condition pauses at each branch. - [Multi-Component Decision Tree](https://docs.agno.com/examples/workflows/human-in-the-loop/decision-tree/multi-component-decision-tree.md): Decision tree mixing Condition and Loop components with user confirmation at each pause point. - [Dual HITL: Step Confirmation + Executor Tool Confirmation (Streaming)](https://docs.agno.com/examples/workflows/human-in-the-loop/dual-level-hitl/step-confirmation-and-tool-confirmation.md): Require step-level confirmation, then tool-level confirmation, on a single step. - [Dual HITL: Step User Input + Executor Tool Confirmation (Streaming)](https://docs.agno.com/examples/workflows/human-in-the-loop/dual-level-hitl/step-user-input-and-tool-confirmation.md): Collect step-level user input, then require confirmation for the agent's tool call. - [Dual HITL: Condition Confirmation + Executor Tool Confirmation (Streaming)](https://docs.agno.com/examples/workflows/human-in-the-loop/dual-level-hitl/condition-and-tool-confirmation.md): Require confirmation for branch selection, then confirm the tool call inside the chosen branch. - [Dual HITL: Router User Selection + Executor Tool Confirmation (Streaming)](https://docs.agno.com/examples/workflows/human-in-the-loop/dual-level-hitl/router-selection-and-tool-confirmation.md): Router-level user selection of route, then tool confirmation within the chosen agent. - [Dual HITL: Post-Execution Output Review + Executor Tool Confirmation (Streaming)](https://docs.agno.com/examples/workflows/human-in-the-loop/dual-level-hitl/output-review-and-tool-confirmation.md): Confirm the agent's tool call during execution, then review the step output after it completes. - [Dual HITL: Loop Confirmation + Executor Tool Confirmation (Streaming)](https://docs.agno.com/examples/workflows/human-in-the-loop/dual-level-hitl/loop-confirmation-and-tool-confirmation.md): Confirm the Loop before it starts, then confirm the agent's tool call on each iteration. - [Dual HITL: Router Confirmation + Executor Tool Confirmation (Streaming)](https://docs.agno.com/examples/workflows/human-in-the-loop/dual-level-hitl/router-confirmation-and-tool-confirmation.md): Confirm the Router before its selector runs, then confirm the tool call made by the agent on the chosen route. - [Dual HITL: Multi-Step Workflow with Mixed HITL Types (Streaming)](https://docs.agno.com/examples/workflows/human-in-the-loop/dual-level-hitl/multi-step-mixed-hitl.md): Combine step confirmation, user input, and executor tool confirmation in one streaming workflow. - [Error HITL: Retry or Skip Failed Steps](https://docs.agno.com/examples/workflows/human-in-the-loop/error/error-retry-skip.md): Handle step errors by pausing workflow and letting user choose to retry or skip the step. - [Error HITL: Retry or Skip Failed Steps (Streaming)](https://docs.agno.com/examples/workflows/human-in-the-loop/error/error-retry-skip-streaming.md): Handle step errors with streaming event updates, pausing to let user retry or skip failed steps. - [Agent Confirmation in Workflow Step](https://docs.agno.com/examples/workflows/human-in-the-loop/executor-hitl/agent-confirmation.md): Pause workflow execution until the user confirms an agent tool call. - [Agent Confirmation in Workflow Step (Streaming)](https://docs.agno.com/examples/workflows/human-in-the-loop/executor-hitl/agent-confirmation-stream.md): Emit StepExecutorPausedEvent in workflow stream when agent tool needs confirmation. - [Team-in-Step Executor HITL](https://docs.agno.com/examples/workflows/human-in-the-loop/executor-hitl/team-in-step.md): Propagate a team member's tool-confirmation pause through the containing workflow. - [Condition with Executor HITL Example (Streaming)](https://docs.agno.com/examples/workflows/human-in-the-loop/executor-hitl/agent-confirmation-in-condition-step.md): A Condition evaluates which branch to take, and the agent inside the chosen branch has a tool with requires_confirmation=True (executor HITL). - [Loop with Executor HITL Example (Streaming)](https://docs.agno.com/examples/workflows/human-in-the-loop/executor-hitl/agent-confirmation-in-loop-step.md): A Loop repeatedly runs steps, and one of its inner steps has an agent with a tool that requires_confirmation=True (executor HITL). - [Steps Container with Executor HITL Example (Streaming)](https://docs.agno.com/examples/workflows/human-in-the-loop/executor-hitl/agent-confirmation-in-steps-container.md): A Steps container runs multiple inner steps sequentially, and one of its inner steps has an agent with a tool that requires_confirmation=True (executor HITL). - [Router with Executor HITL Example (Streaming)](https://docs.agno.com/examples/workflows/human-in-the-loop/executor-hitl/agent-confirmation-in-router-step.md): A Router selects which branch to execute, and the chosen branch has an agent with a tool that requires_confirmation=True (executor HITL). - [Agent User Input in Workflow Step (Streaming)](https://docs.agno.com/examples/workflows/human-in-the-loop/executor-hitl/agent-user-input-step.md): An agent's tool has requires_user_input=True, so it pauses for user-provided values before execution. - [StepExecutorContinuedEvent Demo (Streaming)](https://docs.agno.com/examples/workflows/human-in-the-loop/executor-hitl/executor-continued-event.md): The StepExecutorContinuedEvent: emitted when a paused executor (agent/team) resumes after executor-level HITL is resolved. - [Loop with User Confirmation HITL Example](https://docs.agno.com/examples/workflows/human-in-the-loop/loop/loop-confirmation.md): Start confirmation for Loop components. - [Loop with User Confirmation HITL Example (Streaming)](https://docs.agno.com/examples/workflows/human-in-the-loop/loop/loop-confirmation-streaming.md): Start confirmation for Loop components with streaming. - [Loop Iteration Review Example](https://docs.agno.com/examples/workflows/human-in-the-loop/loop/loop-iteration-review.md): Per-iteration review in a Loop component using the HITL config class. - [Basic Output Review Example](https://docs.agno.com/examples/workflows/human-in-the-loop/output-review/basic-output-review.md): Post-execution output review using the HITL config, where the workflow pauses AFTER a step runs so a human can review the output before it flows to the next step. - [Output Review with Retry Example](https://docs.agno.com/examples/workflows/human-in-the-loop/output-review/output-review-with-retry.md): Uses on_reject=OnReject.retry with reject(feedback=...) to send feedback to the agent on retry. - [Edit Output Example](https://docs.agno.com/examples/workflows/human-in-the-loop/output-review/edit-output.md): Allow humans to edit step output directly instead of retrying, avoiding extra LLM calls. - [Conditional Output Review Example](https://docs.agno.com/examples/workflows/human-in-the-loop/output-review/conditional-output-review.md): Conditional HITL using the HITL config: the output review only triggers when a condition is met. - [Full Review Cycle Example](https://docs.agno.com/examples/workflows/human-in-the-loop/output-review/full-review-cycle.md): Handle approve, reject with feedback and retry, and cancel decisions in one HITL workflow. - [Router with User Selection HITL Example](https://docs.agno.com/examples/workflows/human-in-the-loop/router/router-user-selection.md): Create a user-driven decision tree using a Router where the user selects which path to take at runtime. - [Router with Multiple Selection HITL Example](https://docs.agno.com/examples/workflows/human-in-the-loop/router/router-multi-selection.md): Let users select MULTIPLE paths to execute in sequence using a Router with allow_multiple_selections=True. - [Router with Nested Choices HITL Example](https://docs.agno.com/examples/workflows/human-in-the-loop/router/router-nested-choices.md): Let users choose from pre-configured processing packages. - [Router with Confirmation HITL Example](https://docs.agno.com/examples/workflows/human-in-the-loop/router/router-confirmation.md): Require user confirmation before a router selects and executes a branch. - [Router Output Review](https://docs.agno.com/examples/workflows/human-in-the-loop/router/router-output-review.md): Router Post-Execution Review -- Approve / Re-route / Cancel. - [Steps Pipeline with User Confirmation HITL Example](https://docs.agno.com/examples/workflows/human-in-the-loop/steps/steps-pipeline-confirmation.md): Use HITL with a Steps component, allowing the user to confirm before executing an entire pipeline of steps. - [HITL Timeout Example](https://docs.agno.com/examples/workflows/human-in-the-loop/timeout/hitl-timeout.md): Timeout handling for HITL pauses using the HumanReview config class. - [Confirmation Timeout Example](https://docs.agno.com/examples/workflows/human-in-the-loop/timeout/confirmation-timeout.md): Apply confirmation timeout policies when a paused workflow is continued. - [Basic User Input HITL Example](https://docs.agno.com/examples/workflows/human-in-the-loop/user-input/basic-user-input.md): Pause a workflow to collect user input before executing a step. - [Step-Level User Input HITL Example](https://docs.agno.com/examples/workflows/human-in-the-loop/user-input/step-user-input.md): Pause a workflow to collect user input using Step parameters directly (without the @pause decorator). - [Step-Level User Input HITL Example (Streaming)](https://docs.agno.com/examples/workflows/human-in-the-loop/user-input/step-user-input-streaming.md): Handle human-in-the-loop pauses with streaming workflow events. - [Storage](https://docs.agno.com/examples/storage/overview.md): Integrate various databases with Agno agents, teams, and workflows. - [DynamoDB](https://docs.agno.com/examples/storage/dynamodb/overview.md): Store agent and team sessions in DynamoDB. - [Use DynamoDB as the database for an agent](https://docs.agno.com/examples/storage/dynamodb/dynamo-for-agent.md): Store agent sessions and runs in DynamoDB using AWS credentials from the environment. - [Use DynamoDB as the database for a team](https://docs.agno.com/examples/storage/dynamodb/dynamo-for-team.md): Store team sessions and runs in DynamoDB with environment variables or constructor parameters. - [Examples](https://docs.agno.com/examples/storage/examples/overview.md): Patterns and examples for database integration with Agno. - [Multi-User Multi-Session](https://docs.agno.com/examples/storage/examples/multi-user-multi-session.md): Handle multiple users and sessions with SQLite-backed agent storage. - [Selecting Tables](https://docs.agno.com/examples/storage/examples/selecting-tables.md): Use SQLite as the database for an Agent, selecting custom names for the tables. - [Firestore](https://docs.agno.com/examples/storage/firestore/overview.md): Examples demonstrating Google Cloud Firestore integration with Agno agents. - [Firestore for Agent](https://docs.agno.com/examples/storage/firestore/firestore-for-agent.md): Store agent sessions in a Firestore database. - [GCS](https://docs.agno.com/examples/storage/gcs/overview.md): Google Cloud Storage (GCS) integration examples: store agent sessions in GCS buckets. - [GCS JSON Storage for Agent](https://docs.agno.com/examples/storage/gcs/gcs-json-for-agent.md): Store agent sessions in Google Cloud Storage as JSON blobs. - [In Memory](https://docs.agno.com/examples/storage/in-memory/overview.md): Store agent, team, and workflow sessions in memory with InMemoryDb. - [In Memory Storage for Agent](https://docs.agno.com/examples/storage/in-memory/in-memory-storage-for-agent.md): Keep agent sessions in an in-memory database that clears when the process exits. - [In-Memory Storage for Team](https://docs.agno.com/examples/storage/in-memory/in-memory-storage-for-team.md): HackerNews research team stores sessions in an in-memory database with a structured Article output. - [In Memory Storage for Workflow](https://docs.agno.com/examples/storage/in-memory/in-memory-storage-for-workflow.md): Use in-memory storage for Workflow demos. - [JSON DB](https://docs.agno.com/examples/storage/json-db/overview.md): Store agent, team, and workflow sessions in JSON files. - [JSON for Agent](https://docs.agno.com/examples/storage/json-db/json-for-agent.md): Use JSON files as the database for an Agent. - [JSON for Team](https://docs.agno.com/examples/storage/json-db/json-for-team.md): Use JSON files as the database for a Team. - [JSON for Workflows](https://docs.agno.com/examples/storage/json-db/json-for-workflows.md): Use JSON files as the database for a Workflow. - [Mongo](https://docs.agno.com/examples/storage/mongo/overview.md): Store agent, team, and workflow sessions in MongoDB. - [MongoDB for Agent](https://docs.agno.com/examples/storage/mongo/mongodb-for-agent.md): Use MongoDb as the database for an agent. - [MongoDB for Team](https://docs.agno.com/examples/storage/mongo/mongodb-for-team.md): HackerNews research team stores its sessions in MongoDB with a structured Article output. - [Async MongoDB](https://docs.agno.com/examples/storage/mongo/async-mongo/overview.md): Examples demonstrating AsyncMongoDb integration with Agno agents, teams, and workflows. - [Use AsyncMongoDb as the database for an agent](https://docs.agno.com/examples/storage/mongo/async-mongo/async-mongodb-for-agent.md): Persist agent sessions in MongoDB with AsyncMongoDb and async run methods. - [Use AsyncMongoDb as the database for a team](https://docs.agno.com/examples/storage/mongo/async-mongo/async-mongodb-for-team.md): HackerNews team persists sessions in MongoDB with AsyncMongoDb and aprint_response. - [Async MongoDB for Workflow](https://docs.agno.com/examples/storage/mongo/async-mongo/async-mongodb-for-workflow.md): Two-step content workflow stores its sessions in MongoDB through AsyncMongoDb. - [MySQL](https://docs.agno.com/examples/storage/mysql/overview.md): Examples demonstrating MySQL database integration with Agno agents, teams, and workflows. - [Use MySQL as the database for an agent](https://docs.agno.com/examples/storage/mysql/mysql-for-agent.md): Store agent sessions in MySQL and add history to context across runs. - [Use MySQL as the database for a team](https://docs.agno.com/examples/storage/mysql/mysql-for-team.md): HackerNews research team stores its sessions in MySQL with structured Article output. - [Async MySQL for Agent](https://docs.agno.com/examples/storage/mysql/async-mysql/async-mysql-for-agent.md): Store agent sessions in async MySQL while using web search and conversation history. - [Async MySQL for Team](https://docs.agno.com/examples/storage/mysql/async-mysql/async-mysql-for-team.md): Store a HackerNews research team's structured article output in MySQL asynchronously. - [Async MySQL for Workflow](https://docs.agno.com/examples/storage/mysql/async-mysql/async-mysql-for-workflow.md): Store a multi-step research and blog-writing workflow in MySQL asynchronously. - [Async MySQL](https://docs.agno.com/examples/storage/mysql/async-mysql/overview.md): Examples demonstrating asynchronous MySQL integration with Agno agents, teams, and workflows. - [Postgres](https://docs.agno.com/examples/storage/postgres/overview.md): Store agents, teams, and workflows in PostgreSQL with session persistence. - [Use Postgres as the database for an agent](https://docs.agno.com/examples/storage/postgres/postgres-for-agent.md): Persist agent sessions in Postgres and carry conversation history across runs. - [Postgres for Team](https://docs.agno.com/examples/storage/postgres/postgres-for-team.md): HackerNews research team stores sessions in Postgres with a structured Article output. - [Postgres Storage for Workflow](https://docs.agno.com/examples/storage/postgres/postgres-for-workflow.md): Store workflow sessions in PostgreSQL with a multi-step content creation example. - [Async Postgres for Agent](https://docs.agno.com/examples/storage/postgres/async-postgres/async-postgres-for-agent.md): Persist agent sessions in Postgres with AsyncPostgresDb and async runs. - [Async Postgres for Team](https://docs.agno.com/examples/storage/postgres/async-postgres/async-postgres-for-team.md): HackerNews team persists sessions in Postgres using AsyncPostgresDb. - [Async Postgres Storage for Workflow](https://docs.agno.com/examples/storage/postgres/async-postgres/async-postgres-for-workflow.md): Use async Postgres as the storage backend for a workflow. - [Async Postgres](https://docs.agno.com/examples/storage/postgres/async-postgres/overview.md): Examples demonstrating asynchronous PostgreSQL integration with Agno agents, teams, and workflows. - [Redis](https://docs.agno.com/examples/storage/redis/overview.md): Examples demonstrating Redis integration with Agno agents, teams, and workflows. - [Redis for Agent](https://docs.agno.com/examples/storage/redis/redis-for-agent.md): Use Redis as the storage backend for an agent. - [Redis for Team](https://docs.agno.com/examples/storage/redis/redis-for-team.md): Use Redis as the storage backend for a team. - [Redis Storage for Workflow](https://docs.agno.com/examples/storage/redis/redis-for-workflow.md): Use Redis as the storage backend for a workflow. - [Valkey](https://docs.agno.com/examples/storage/valkey/overview.md): Examples demonstrating Valkey integration with Agno agents, teams, and workflows. - [Valkey for Agent](https://docs.agno.com/examples/storage/valkey/valkey-for-agent.md): Example showing how to use Valkey as the database for an agent. - [Valkey for Team](https://docs.agno.com/examples/storage/valkey/valkey-for-team.md): Example showing how to use Valkey as the database for a team. - [Valkey Storage for Workflow](https://docs.agno.com/examples/storage/valkey/valkey-for-workflow.md): Demonstrates using ValkeyDb as the session storage backend for a workflow. - [SingleStore](https://docs.agno.com/examples/storage/singlestore/overview.md): Examples demonstrating SingleStore database integration with Agno agents and teams. - [Use SingleStore as the database for an agent](https://docs.agno.com/examples/storage/singlestore/singlestore-for-agent.md): Store agent sessions in SingleStore using connection settings from environment variables. - [SingleStore for Team](https://docs.agno.com/examples/storage/singlestore/singlestore-for-team.md): HackerNews team persists its sessions in SingleStore configured from environment variables. - [SQLite](https://docs.agno.com/examples/storage/sqlite/overview.md): Store agent, team, and workflow sessions in SQLite. - [Use SQLite as the database for an Agent](https://docs.agno.com/examples/storage/sqlite/sqlite-for-agent.md): Store agent sessions in a SQLite file and recall earlier messages in the conversation. - [SQLite for Team](https://docs.agno.com/examples/storage/sqlite/sqlite-for-team.md): HackerNews research team stores sessions in a SQLite file with structured Article output. - [SQLite Storage for Workflow](https://docs.agno.com/examples/storage/sqlite/sqlite-for-workflow.md): Use SqliteDb as the session storage backend for a workflow. - [Use SQLite as the database for an agent](https://docs.agno.com/examples/storage/sqlite/async-sqlite/async-sqlite-for-agent.md): Persist agent sessions in SQLite with AsyncSqliteDb and async run methods. - [Async SQLite for Team](https://docs.agno.com/examples/storage/sqlite/async-sqlite/async-sqlite-for-team.md): HackerNews team persists sessions in a SQLite file using AsyncSqliteDb. - [Async SQLite for Workflow](https://docs.agno.com/examples/storage/sqlite/async-sqlite/async-sqlite-for-workflow.md): Two-step content workflow stores its sessions in SQLite through AsyncSqliteDb. - [Async SQLite](https://docs.agno.com/examples/storage/sqlite/async-sqlite/overview.md): Examples demonstrating asynchronous SQLite integration with Agno agents, teams, and workflows. - [SurrealDB](https://docs.agno.com/examples/storage/surrealdb/overview.md): Store agent, team, and workflow sessions in SurrealDB. - [SurrealDB for Agent](https://docs.agno.com/examples/storage/surrealdb/surrealdb-for-agent.md): Claude agent persists sessions in SurrealDB over a WebSocket connection. - [SurrealDB for Team](https://docs.agno.com/examples/storage/surrealdb/surrealdb-for-team.md): Persist a HackerNews research team's sessions in SurrealDB with structured Article output. - [SurrealDB for Workflow](https://docs.agno.com/examples/storage/surrealdb/surrealdb-for-workflow.md): Persist workflow sessions in SurrealDB for a two-step research and content planning pipeline. - [Persistent Session Storage](https://docs.agno.com/examples/storage/persistent-session-storage.md): Store and retrieve team sessions across runs using PostgreSQL. - [Session Summary](https://docs.agno.com/examples/storage/session-summary.md): Generate and persist session summaries with SessionSummaryManager. - [Chat History](https://docs.agno.com/examples/storage/chat-history.md): Retrieve and inspect user and assistant messages from persisted agent sessions. - [Session Summary with Limits](https://docs.agno.com/examples/storage/session-summary-limits.md): Limit the conversation history sent to the summary model using `last_n_runs` and `conversation_limit` on SessionSummaryManager. - [Media Storage](https://docs.agno.com/examples/storage/media-storage/overview.md): Offload agent media to the filesystem, S3, or Google Cloud Storage. - [Local Media Storage](https://docs.agno.com/examples/storage/media-storage/local.md): Offload agent media to the local filesystem and persist a MediaReference after successful storage. - [S3 Media Storage](https://docs.agno.com/examples/storage/media-storage/s3.md): Offload agent media to S3 and persist a MediaReference after successful storage. - [GCS Media Storage](https://docs.agno.com/examples/storage/media-storage/gcs.md): Offload agent media to Google Cloud Storage and persist a MediaReference after successful storage. - [Media Storage Across Turns](https://docs.agno.com/examples/storage/media-storage/multiturn.md): Read successfully offloaded S3 media across multiple turns of one session. - [Deleting Offloaded Media](https://docs.agno.com/examples/storage/media-storage/delete.md): Delete session rows, then attempt a best-effort sweep of their offloaded media. - [Workflow Media Storage (S3)](https://docs.agno.com/examples/storage/media-storage-workflow.md): Demonstrates media offload across a workflow. - [Generated File Storage (S3)](https://docs.agno.com/examples/storage/media-storage-file-generation.md): Demonstrates media offload for files the agent *generates*, rather than files you attach. - [Knowledge](https://docs.agno.com/examples/knowledge/overview.md): Build knowledge bases from basic RAG through production patterns, vector databases, and cloud storage integrations. - [Basic RAG: Context Injection](https://docs.agno.com/examples/knowledge/getting-started/basic-rag.md): The simplest way to give an agent access to documents. - [Agentic RAG: Tool-Based Search](https://docs.agno.com/examples/knowledge/getting-started/agentic-rag.md): The agent gets a search_knowledge_base tool and decides when to query the knowledge base. - [Loading Content: All Source Types](https://docs.agno.com/examples/knowledge/getting-started/loading-content.md): Knowledge supports loading content from many sources: local files, URLs, raw text, topics (Wikipedia/ArXiv), and batch operations. - [Website Ingestion: One Row Per Page](https://docs.agno.com/examples/knowledge/getting-started/website-per-page.md): Load a website into a knowledge base page by page from its sitemap: one content row per page with its source URL kept, so the agent can cite the page it answered from and a re-run refreshes only pages that changed. - [Chunking Strategies: Side-by-Side Comparison](https://docs.agno.com/examples/knowledge/building-blocks/chunking-strategies.md): Compare four chunking strategies for different content types and use cases. - [Search Types: Vector, Keyword, and Hybrid](https://docs.agno.com/examples/knowledge/building-blocks/hybrid-search.md): Compare vector search, keyword search, and hybrid approaches to find the right strategy for your knowledge base. - [Reranking: Improving Search Quality](https://docs.agno.com/examples/knowledge/building-blocks/reranking.md): Implement two-stage retrieval with Cohere reranking to improve search result relevance. - [Filtering: Metadata-Based Search Refinement](https://docs.agno.com/examples/knowledge/building-blocks/filtering.md): Filters let you narrow search results based on document metadata. - [Agentic Filtering: Agent-Driven Search Refinement](https://docs.agno.com/examples/knowledge/building-blocks/agentic-filtering.md): With agentic filtering enabled, the agent inspects available metadata keys in the knowledge base and dynamically builds filters from the user query. - [Embedders: Choosing and Configuring Embedding Models](https://docs.agno.com/examples/knowledge/building-blocks/embedders.md): Embedders convert text into vectors for semantic search. - [Multi-Source RAG: Combining Different Content Types](https://docs.agno.com/examples/knowledge/production/multi-source-rag.md): In production, agents often need knowledge from multiple sources: PDFs, web pages, text snippets, and databases. - [Knowledge Lifecycle: Insert, Update, Remove, Track](https://docs.agno.com/examples/knowledge/production/knowledge-lifecycle.md): The full content lifecycle with a contents database for tracking what has been ingested and its current status. - [Multi-Tenant Knowledge: Isolating Data Per Tenant](https://docs.agno.com/examples/knowledge/production/multi-tenant.md): When multiple Knowledge instances share the same vector database, use isolate_vector_search to ensure each instance only searches its own data. - [AgentOS: Serving Knowledge via API](https://docs.agno.com/examples/knowledge/production/agent-os.md): AgentOS wraps your agents and knowledge instances in a FastAPI server, exposing them as API endpoints. - [Error Handling: Production Patterns](https://docs.agno.com/examples/knowledge/production/error-handling.md): Handle knowledge ingestion failures with skip_if_exists, batch error logging, and verification patterns. - [SSRF Hardening: allowed_hosts on URL-fetching Readers](https://docs.agno.com/examples/knowledge/production/ssrf-allowed-hosts.md): Knowledge readers that fetch arbitrary URLs (WebsiteReader, FirecrawlReader, DoclingReader, LLMsTxtReader, WebSearchReader) accept an opt-in `allowed_hosts` argument that restricts outbound requests to a hostname allowlist. - [Custom Retriever: Bypass the Knowledge Class](https://docs.agno.com/examples/knowledge/advanced/custom-retriever.md): Provide a custom retriever function for non-vector data sources such as SQL queries, API calls, or file lookups. - [Custom Chunking: Implementing Your Own Strategy](https://docs.agno.com/examples/knowledge/advanced/custom-chunking.md): Implement a custom ParagraphChunking strategy that splits documents on double newlines for well-structured prose. - [Graph RAG: LightRAG Integration](https://docs.agno.com/examples/knowledge/advanced/graph-rag.md): LightRAG is a managed knowledge backend that builds a knowledge graph from your documents. - [Knowledge Tools: Think, Search, Analyze](https://docs.agno.com/examples/knowledge/advanced/knowledge-tools.md): Provide agents with think, search, and analyze tools for multi-step knowledge reasoning. - [Knowledge Protocol: Custom Knowledge Sources](https://docs.agno.com/examples/knowledge/advanced/knowledge-protocol.md): Implement KnowledgeProtocol for an in-memory source with custom context and synchronous or asynchronous document retrieval. - [Prefix Search: Help Center with search-as-you-type](https://docs.agno.com/examples/knowledge/advanced/prefix-search.md): Real-world use case: A help center where users search for articles while typing, getting instant results for partial words. - [Per-User Isolation: Cassandra](https://docs.agno.com/examples/knowledge/advanced/per-user-isolation/cassandra-db.md): Each user gets a private view of one shared knowledge base. - [Per-User Isolation: Chroma](https://docs.agno.com/examples/knowledge/advanced/per-user-isolation/chroma-db.md): Each user gets a private view of one shared knowledge base. - [Per-User Isolation: ClickHouse](https://docs.agno.com/examples/knowledge/advanced/per-user-isolation/clickhouse-db.md): Each user gets a private view of one shared knowledge base. - [Per-User Isolation: Couchbase](https://docs.agno.com/examples/knowledge/advanced/per-user-isolation/couchbase-db.md): Each user gets a private view of one shared knowledge base. - [Per-User Isolation: LanceDB](https://docs.agno.com/examples/knowledge/advanced/per-user-isolation/lance-db.md): Each user gets a private view of one shared knowledge base. - [Per-User Isolation: Milvus](https://docs.agno.com/examples/knowledge/advanced/per-user-isolation/milvus-db.md): Each user gets a private view of one shared knowledge base. - [Per-User Isolation: MongoDB](https://docs.agno.com/examples/knowledge/advanced/per-user-isolation/mongo-db.md): Each user gets a private view of one shared knowledge base. - [Per-User Isolation: OpenSearch](https://docs.agno.com/examples/knowledge/advanced/per-user-isolation/opensearch-db.md): Each user gets a private view of one shared knowledge base. - [Per-User Isolation: PgVector](https://docs.agno.com/examples/knowledge/advanced/per-user-isolation/pgvector-db.md): Each user gets a private view of one shared knowledge base. - [Per-User Isolation: Pinecone](https://docs.agno.com/examples/knowledge/advanced/per-user-isolation/pinecone-db.md): Each user gets a private view of one shared knowledge base. - [Per-User Isolation: Qdrant](https://docs.agno.com/examples/knowledge/advanced/per-user-isolation/qdrant-db.md): Each user gets a private view of one shared knowledge base. - [Per-User Isolation: Redis](https://docs.agno.com/examples/knowledge/advanced/per-user-isolation/redis-db.md): Each user gets a private view of one shared knowledge base. - [Per-User Isolation: SingleStore](https://docs.agno.com/examples/knowledge/advanced/per-user-isolation/singlestore-db.md): Each user gets a private view of one shared knowledge base. - [Per-User Isolation: SurrealDB](https://docs.agno.com/examples/knowledge/advanced/per-user-isolation/surreal-db.md): Each user gets a private view of one shared knowledge base. - [Per-User Isolation: Upstash](https://docs.agno.com/examples/knowledge/advanced/per-user-isolation/upstash-db.md): Each user gets a private view of one shared knowledge base. - [Per-User Isolation: Valkey](https://docs.agno.com/examples/knowledge/advanced/per-user-isolation/valkey-db.md): Each user gets a private view of one shared knowledge base. - [Per-User Isolation: Weaviate](https://docs.agno.com/examples/knowledge/advanced/per-user-isolation/weaviate-db.md): Each user gets a private view of one shared knowledge base. - [AWS Integration: S3 Content Source](https://docs.agno.com/examples/knowledge/integrations/cloud/aws.md): Load files and folders from S3 buckets into your Knowledge base. - [Azure Integration: Blob Storage](https://docs.agno.com/examples/knowledge/integrations/cloud/azure.md): Load files and folders from Azure Blob Storage containers into your Knowledge base. - [Azure Integration: Blob Storage (SAS Token)](https://docs.agno.com/examples/knowledge/integrations/cloud/azure-sas.md): Load files and folders from Azure Blob Storage containers using SAS token authentication. - [GCP Integration: Google Cloud Storage](https://docs.agno.com/examples/knowledge/integrations/cloud/gcp.md): Load files and folders from GCS buckets into your Knowledge base. - [SharePoint Integration](https://docs.agno.com/examples/knowledge/integrations/cloud/sharepoint.md): Load files and folders from SharePoint document libraries into your Knowledge base. - [GitHub Integration: Per-Request Repo Override](https://docs.agno.com/examples/knowledge/integrations/cloud/github-dynamic-repo.md): Use a single GitHubConfig with no default repo to load content from multiple repositories by passing the repo at request time. - [Multi-Source Remote Content](https://docs.agno.com/examples/knowledge/integrations/cloud/multi-source.md): Combine multiple remote content sources in a single Knowledge instance. - [Agentic RAG Infinity Reranker](https://docs.agno.com/examples/knowledge/integrations/rag/agentic-rag-infinity-reranker.md): Run hybrid LanceDB search with Cohere embeddings and a local Infinity reranker (BAAI/bge-reranker-base on localhost:7997) behind a Claude agent. - [Agentic RAG With Lightrag](https://docs.agno.com/examples/knowledge/integrations/rag/agentic-rag-with-lightrag.md): Ingest a PDF, a Wikipedia topic, and a URL into a LightRag-backed knowledge base and query it with an async agent. - [Local RAG Langchain Qdrant](https://docs.agno.com/examples/knowledge/integrations/rag/local-rag-langchain-qdrant.md): Local RAG with Ollama answering from a LangChain retriever backed by Qdrant and FastEmbed embeddings. - [Document Readers: PDF and Excel](https://docs.agno.com/examples/knowledge/integrations/readers/documents.md): Load a local PDF, an Excel file via an explicit ExcelReader, and a remote PDF URL into Qdrant hybrid search, then query them with an agent. - [Data Readers: CSV and JSON](https://docs.agno.com/examples/knowledge/integrations/readers/data.md): Ingest inline CSV and JSON text with CSVReader and JSONReader into Qdrant hybrid search and query the rows with an agent. - [Web Readers: Website and PDF](https://docs.agno.com/examples/knowledge/integrations/readers/web.md): Crawl docs.agno.com with WebsiteReader (max_depth=1, max_links=5) and auto-detect a remote PDF URL into Qdrant hybrid search. - [Docling Reader: Audio Files](https://docs.agno.com/examples/knowledge/integrations/readers/docling/docling-audio.md): Examples of using Docling to process audio files with speech-to-text transcription. - [Docling Reader: Office Documents](https://docs.agno.com/examples/knowledge/integrations/readers/docling/docling-documents.md): Examples of using Docling to process Microsoft Office documents. - [Docling Reader: Image Documents](https://docs.agno.com/examples/knowledge/integrations/readers/docling/docling-images.md): Examples of using Docling to process image files with OCR capabilities. - [Docling Reader: Markup and Structured Documents](https://docs.agno.com/examples/knowledge/integrations/readers/docling/docling-markup.md): Examples of using Docling to process markup and structured document formats. - [Docling Reader: PDF Documents](https://docs.agno.com/examples/knowledge/integrations/readers/docling/docling-pdf.md): Examples of using Docling to process PDF files with different output formats. - [Docling Reader: Data Files](https://docs.agno.com/examples/knowledge/integrations/readers/docling/docling-xlsx.md): Read an XLSX spreadsheet with DoclingReader in HTML output mode and query products and prices through a LanceDB-backed agent. - [Docling Reader: Input Types](https://docs.agno.com/examples/knowledge/integrations/readers/docling/docling-input-types.md): DoclingReader.read() accepts several kinds of `file` input. - [Qdrant: Recommended Vector Database](https://docs.agno.com/examples/knowledge/integrations/vector-dbs/qdrant.md): Run Qdrant-backed knowledge two ways: basic vector search, and hybrid search with a Cohere reranker. - [Local Vector Databases: ChromaDB and LanceDB](https://docs.agno.com/examples/knowledge/integrations/vector-dbs/local.md): Compare embedded ChromaDB and hybrid-search LanceDB knowledge bases over the same PDF, with graceful fallback when either package is missing. - [Managed Vector Databases: Pinecone](https://docs.agno.com/examples/knowledge/integrations/vector-dbs/managed.md): Pinecone is a fully managed, serverless vector database for production workloads where you want zero infrastructure management. - [PgVector: PostgreSQL Vector Search](https://docs.agno.com/examples/knowledge/integrations/vector-dbs/pgvector.md): PgVector adds vector similarity search to PostgreSQL, giving you vectors alongside your existing relational data in one database. - [ScyllaDB: high-performance, real-time database for AI](https://docs.agno.com/examples/knowledge/integrations/vector-dbs/scylladb.md): ScyllaDB is a high-performance, real-time, distributed database that provides low latency reads/writes and supports vector similarity search. - [Memory](https://docs.agno.com/examples/memory/overview.md): Examples of persisting user memories in a database and sharing them across runs, sessions, and multiple agents. - [Agent With Persistent Memory](https://docs.agno.com/examples/memory/agent-with-memory.md): Persist user memories in PostgresDb with automatic memory extraction during agent runs. - [Agentic Memory Management](https://docs.agno.com/examples/memory/agentic-memory.md): Enable enable_agentic_memory so the agent itself creates, updates, and deletes user memories during a run. - [Agents Sharing Memory](https://docs.agno.com/examples/memory/agents-share-memory.md): Two agents sharing the same user memory. - [Custom Memory Manager Configuration](https://docs.agno.com/examples/memory/custom-memory-manager.md): Configure a MemoryManager separately from the Agent and apply custom memory capture instructions. - [Memory Tools With Web Search](https://docs.agno.com/examples/memory/memory-tools.md): Use MemoryTools alongside WebSearchTools so an agent can store and use user memory while planning a trip. - [Multi-User Multi-Session Chat](https://docs.agno.com/examples/memory/multi-user-multi-session-chat.md): A multi-user, multi-session chat flow where user memory is shared across sessions for the same user. - [Concurrent Multi-User Multi-Session Chat](https://docs.agno.com/examples/memory/multi-user-multi-session-chat-concurrent.md): Run three user conversations concurrently with asyncio.gather while a shared agent persists user memories across sessions. - [Share Memory and History Between Agents](https://docs.agno.com/examples/memory/share-memory-and-history-between-agents.md): Two agents sharing both conversation history and user memory through a common database, user ID, and session ID. - [Memory Manager](https://docs.agno.com/examples/memory/memory-manager/overview.md): The Memory Manager handles user memory CRUD and retrieval operations. - [Custom Memory Capture Instructions](https://docs.agno.com/examples/memory/memory-manager/custom-memory-instructions.md): Run a custom OpenAI memory-capture scenario for academic interests, then a separate default Claude scenario over multi-turn messages. - [Control Memory Database Tools](https://docs.agno.com/examples/memory/memory-manager/db-tools-control.md): Control which memory database operations are available to the AI model using DB tool flags. - [Create Memories From Text and Message History](https://docs.agno.com/examples/memory/memory-manager/memory-creation.md): Create user memories from direct text and from a message list using MemoryManager. - [Search User Memories](https://docs.agno.com/examples/memory/memory-manager/memory-search.md): Search user memories using different retrieval methods such as last_n, first_n, and agentic retrieval. - [Standalone Memory Manager CRUD](https://docs.agno.com/examples/memory/memory-manager/standalone-memory.md): Add, get, delete, and replace user memories manually. - [Optimize Memories](https://docs.agno.com/examples/memory/optimize-memories/overview.md): Memory optimization strategies: the built-in summarize strategy and custom strategies via MemoryOptimizationStrategy. - [Custom Memory Optimization Strategy](https://docs.agno.com/examples/memory/optimize-memories/custom-memory-strategy.md): Create and apply a custom memory optimization strategy by subclassing MemoryOptimizationStrategy. - [Optimize Memories With Summarize Strategy](https://docs.agno.com/examples/memory/optimize-memories/memory-summarize-strategy.md): Memory optimization using the summarize strategy, which combines all memories into one summary for token reduction. - [Dakera Integration](https://docs.agno.com/examples/memory/integrations/dakera-integration.md): Pinned Dakera memory integration that requires current API migration and sends recalled context to OpenAI. - [Learning](https://docs.agno.com/examples/learning/overview.md): A comprehensive guide to building agents that learn, adapt, and improve. - [Basics](https://docs.agno.com/examples/learning/basics/overview.md): Core learning primitives and default patterns. - [Session Context: Summary Mode](https://docs.agno.com/examples/learning/basics/a-session-context-summary.md): Summary mode provides lightweight tracking - a running summary without goal/plan structure. - [User Memory: Always Mode](https://docs.agno.com/examples/learning/basics/a-user-memory-always.md): ALWAYS mode extracts memories automatically in parallel while the agent responds - no explicit tool calls needed. - [User Profile: Always Mode](https://docs.agno.com/examples/learning/basics/a-user-profile-always.md): ALWAYS mode extracts profile information automatically in parallel while the agent responds - no explicit tool calls needed. - [Session Context: Planning Mode](https://docs.agno.com/examples/learning/basics/b-session-context-planning.md): Planning mode (enable_planning=True) adds structured goal tracking - summary plus goal, plan steps, and progress markers. - [User Memory: Agentic Mode](https://docs.agno.com/examples/learning/basics/b-user-memory-agentic.md): AGENTIC mode gives the agent explicit tools to save and update memories. - [User Profile: Agentic Mode](https://docs.agno.com/examples/learning/basics/b-user-profile-agentic.md): AGENTIC mode gives the agent explicit tools to update profile fields. - [Learned Knowledge: Agentic Mode](https://docs.agno.com/examples/learning/basics/learned-knowledge.md): The agent decides when to save and apply learnings. - [Entity Memory: The Four Tools](https://docs.agno.com/examples/learning/basics/entity-memory.md): Entity memory is the agent's knowledge about the WORLD - the people, projects, companies and systems around the user - as opposed to user memory, which is about the user themselves. - [Extraction Limits: Preventing Runaway Loops](https://docs.agno.com/examples/learning/basics/extraction-limits.md): Configure max_updates_per_run to cap memory updates per extraction. - [Custom Stores](https://docs.agno.com/examples/learning/custom-stores/overview.md): Custom learning store implementations and integration patterns. - [Custom Store: Database-Backed Example](https://docs.agno.com/examples/learning/custom-stores/custom-store-with-db.md): Create a custom learning store with database persistence. - [Custom Store: Minimal Example](https://docs.agno.com/examples/learning/custom-stores/minimal-custom-store.md): Create a custom learning store by implementing the LearningStore protocol. - [Decision Logs](https://docs.agno.com/examples/learning/decision-logs/overview.md): Examples for capturing and reviewing agent decision logs. - [Decision Logs: Basic Usage](https://docs.agno.com/examples/learning/decision-logs/basic-decision-log.md): Use DecisionLogStore to record and retrieve agent decisions. - [Decision Logs: Recording Outcomes](https://docs.agno.com/examples/learning/decision-logs/record-outcomes.md): The feedback half of the decision log: log_decision records the choice with its reasoning, and record_outcome closes the loop later with what actually happened. - [Entity Memory](https://docs.agno.com/examples/learning/entity-memory/overview.md): Capture entity facts, link related entities, and archive outdated records. - [Entity Memory: The Four Tools](https://docs.agno.com/examples/learning/entity-memory/the-four-tools.md): Entity memory is the agent's knowledge base about the world: the people, projects, companies and systems around the user. - [Entity Memory: Links, Browse and Forget](https://docs.agno.com/examples/learning/entity-memory/links-and-forget.md): Create reciprocal entity links, browse recent entities, archive one, and revive it through entity memory. - [Learned Knowledge](https://docs.agno.com/examples/learning/learned-knowledge/overview.md): Deep-dive examples for reusable learned knowledge. - [Learned Knowledge: Agentic Mode (Deep Dive)](https://docs.agno.com/examples/learning/learned-knowledge/agentic-mode.md): Agent decides when to save and retrieve learnings. - [Learned Knowledge: Propose Mode (Deep Dive)](https://docs.agno.com/examples/learning/learned-knowledge/propose-mode.md): Prompt the agent to seek confirmation before saving reusable learnings. - [Patterns](https://docs.agno.com/examples/learning/patterns/overview.md): End-to-end multi-store learning patterns for real workflows. - [Pattern: Personal Assistant with Learning](https://docs.agno.com/examples/learning/patterns/personal-assistant.md): A personal assistant that learns about the user over time. - [Pattern: Support Agent with Learning](https://docs.agno.com/examples/learning/patterns/support-agent.md): A customer support agent that learns from interactions. - [Pattern: Research Assistant with Tools + Learning](https://docs.agno.com/examples/learning/patterns/research-assistant.md): A research assistant that uses web search tools and learns about the user. - [Quick Tests](https://docs.agno.com/examples/learning/quick-tests/overview.md): Quick validation scripts for critical learning paths. - [Async User Profile Test](https://docs.agno.com/examples/learning/quick-tests/async-user-profile.md): Tests the async path for user profile learning. - [Claude Model Test](https://docs.agno.com/examples/learning/quick-tests/claude-model.md): Tests learning with Claude instead of OpenAI. - [Learning=True Shorthand Test](https://docs.agno.com/examples/learning/quick-tests/learning-true-shorthand.md): Tests the simplest way to enable learning: `learning=True`. - [No-DB Graceful Handling Test](https://docs.agno.com/examples/learning/quick-tests/no-db-graceful.md): Tests that learning gracefully handles missing database. - [Quickstart](https://docs.agno.com/examples/learning/quickstart/overview.md): Quick start examples for enabling learning in an agent. - [Learning Machines: Agentic Mode](https://docs.agno.com/examples/learning/quickstart/agentic-learn.md): In AGENTIC mode, the agent receives tools to explicitly manage learning. - [Learning Machines](https://docs.agno.com/examples/learning/quickstart/always-learn.md): Set learning=True to turn an agent into a learning machine. - [Learning Machines: Learned Knowledge](https://docs.agno.com/examples/learning/quickstart/learned-knowledge.md): Learned Knowledge stores insights that transfer across users. - [Session Context](https://docs.agno.com/examples/learning/session-context/overview.md): Deep-dive examples for session context tracking. - [Session Context: Planning Mode (Deep Dive)](https://docs.agno.com/examples/learning/session-context/planning-mode.md): Goal, plan, and progress tracking for task-oriented sessions. - [Session Context: Summary Mode (Deep Dive)](https://docs.agno.com/examples/learning/session-context/summary-mode.md): Running summary of conversation state. - [User Profile](https://docs.agno.com/examples/learning/user-profile/overview.md): Deep-dive examples focused on user profile extraction and schema control. - [User Profile: Agentic Mode (Deep Dive)](https://docs.agno.com/examples/learning/user-profile/agentic-mode.md): Agent-controlled profile updates via explicit tools. - [User Profile: Always Extraction (Deep Dive)](https://docs.agno.com/examples/learning/user-profile/always-extraction.md): Automatic profile extraction from natural conversation. - [User Profile: Custom Schema](https://docs.agno.com/examples/learning/user-profile/custom-schema.md): Define your own profile structure with a dataclass. - [Learning Demo: Shared Agent](https://docs.agno.com/examples/learning/demo/agents.md): Shared ops assistant configured with all six learning stores, backed by PostgresDb and a PgVector knowledge base. - [Learning Demo: AgentOS Server](https://docs.agno.com/examples/learning/demo/run.md): Serves the ops assistant on an AgentOS instance, which exposes the /learnings CRUD endpoints and powers the Learning pages at os.agno.com. - [Learning Demo: Seed Data](https://docs.agno.com/examples/learning/demo/seed.md): Runs a few short conversations through the ops assistant so that every Learning page in AgentOS has data: user profiles, user memories, session context, entity memories, and decision logs. - [Composition: ALWAYS Capture Through the Manual Door](https://docs.agno.com/examples/learning/composition/always-capture.md): The manual door has no automatic post-run extraction - the tools are the capture mechanism. - [Composition: The Manual Door](https://docs.agno.com/examples/learning/composition/basic.md): learning= is the automatic door: the framework injects context, instructions and tools for you. - [Composition: The Data Block via additional_context](https://docs.agno.com/examples/learning/composition/context-block.md): build_context() returns the recalled-data block on its own, for when you want the data injected but not the tools - a read-only view of what the machine knows, placed exactly where you choose. - [Composition: LearningMachine + FileSystem, One Deliberate Order](https://docs.agno.com/examples/learning/composition/with-filesystem.md): The point of the manual door: LearningMachine, FileSystem and your own system prompt compose in one order you can read off the page. - [Filesystem Context Provider](https://docs.agno.com/examples/context/filesystem.md): FilesystemContextProvider wraps a local directory and gives the agent a single `query_` tool. - [Web Context Provider with Exa](https://docs.agno.com/examples/context/web-exa.md): Run WebContextProvider with ExaBackend so an agent searches and fetches web pages through Exa's search + contents API. - [Web Context Provider with Exa's keyless MCP endpoint](https://docs.agno.com/examples/context/web-exa-mcp.md): `ExaMCPBackend` speaks to Exa's public MCP server at https://mcp.exa.ai/mcp. - [Web Context Provider with Parallel](https://docs.agno.com/examples/context/web-parallel.md): `ParallelBackend` speaks directly to Parallel's web API via the `parallel-web` SDK (>= 1.0). - [Database Context Provider (SQLite, read + write)](https://docs.agno.com/examples/context/database-read-write.md): Use one SQLite engine for read and write operations through DatabaseContextProvider. - [Slack Context Provider](https://docs.agno.com/examples/context/slack.md): Separate sub-agents under the hood keep scopes minimal: read agents never see `send_message`, and the write agent never sees history or search tools. - [MCP Context Provider](https://docs.agno.com/examples/context/mcp-server.md): MCPContextProvider wraps a single MCP server as a context provider. - [Slack Search & Media Tools](https://docs.agno.com/examples/context/slack-search-media.md): Run SlackContextProvider with Gemini sub-agents, enabling search_messages and opt-in media tools for file download and upload. - [Google Drive Context Provider](https://docs.agno.com/examples/context/google-drive.md): GoogleDriveContextProvider wraps a read-only slice of `GoogleDriveTools` with `corpora="allDrives"` so a service account can see folders shared with it and files in Shared Drives. - [Multiple Context Providers on One Agent](https://docs.agno.com/examples/context/multi-provider.md): Compose filesystem, Exa MCP web, and SQLite database context providers on one agent, bracketing only the web provider's MCP session with asetup/aclose. - [Team briefing: Slack + Web](https://docs.agno.com/examples/context/web-plus-slack.md): Cross-reference internal Slack discussion with external industry news to produce a short briefing. - [Custom Context Provider](https://docs.agno.com/examples/context/custom-provider.md): When a built-in provider doesn't fit, subclass `ContextProvider`. - [Web Context Provider with Parallel's MCP endpoint](https://docs.agno.com/examples/context/web-parallel-mcp.md): `ParallelMCPBackend` speaks to Parallel's public MCP server at https://search.parallel.ai/mcp. - [Engineering briefing: Slack + Workspace + Parallel Web](https://docs.agno.com/examples/context/engineering-briefing.md): Synthesizes a briefing by chaining Slack, workspace, and Parallel web context providers, then posting the result back to Slack. - [Workspace Context Provider](https://docs.agno.com/examples/context/workspace.md): WorkspaceContextProvider wraps a project directory and gives the agent a single `query_` tool. - [Wiki Context Provider (filesystem backend)](https://docs.agno.com/examples/context/wiki-filesystem.md): Seeds a local markdown wiki and uses WikiContextProvider with FileSystemBackend so the agent writes and reads back a runbook using OpenAI. - [Wiki Context Provider (git backend)](https://docs.agno.com/examples/context/wiki-git.md): Same WikiContextProvider as `14_wiki_filesystem.py`, but the wiki lives in a real git repository. - [Wiki Context Provider (Notion database backend)](https://docs.agno.com/examples/context/wiki-notion.md): Mirror a Notion database to local Markdown and update it through WikiContextProvider. - [Wiki Context Provider (filesystem + web ingestion)](https://docs.agno.com/examples/context/wiki-with-web.md): Wires an ExaMCPBackend into WikiContextProvider so a single update_wiki call can search or fetch the web, digest the result, and file it as a wiki page. - [Wiki Context Provider (dual: company knowledge + company voice)](https://docs.agno.com/examples/context/wiki-dual.md): Mount two WikiContextProvider instances on one agent: a read-write company_knowledge wiki and a read-only company_voice wiki. - [Gmail Context Provider](https://docs.agno.com/examples/context/gmail.md): GmailContextProvider gives agents read/write access to Gmail through specialized sub-agents. - [Google Calendar Context Provider](https://docs.agno.com/examples/context/calendar.md): GoogleCalendarContextProvider gives agents read/write access to Google Calendar through specialized sub-agents. - [Google Workspace Multi-Provider](https://docs.agno.com/examples/context/google-workspace.md): Combines GDrive, Gmail, and Calendar context providers into a single agent for cross-service workflows. - [Google Drive Office Document Reading](https://docs.agno.com/examples/context/gdrive-office.md): Reads .docx, .xlsx and .pptx files from Google Drive via GoogleDriveContextProvider, extracting text with python-docx, openpyxl and python-pptx. - [Wiki Context Provider: Sub-Agent Event Streaming](https://docs.agno.com/examples/context/wiki-streaming-events.md): When the parent agent calls a context provider's query tool, the sub-agent's events (tool calls, content) are streamed back automatically. - [Wiki Context Provider: AgentOS Streaming](https://docs.agno.com/examples/context/wiki-agentos-streaming.md): Serve a WikiContextProvider-backed agent on AgentOS at localhost:7777 so sub-agent tool calls and content stream live into os.agno.com. - [Multi Context Provider: Streaming Demo](https://docs.agno.com/examples/context/multi-context-streaming.md): Attach two WikiContextProviders (architecture and operations) to one AgentOS agent and stream nested events from parallel sub-agent tool calls. - [Restricting Write Operations with write_tools](https://docs.agno.com/examples/context/write-tools-restriction.md): Context providers expose ``write_tools`` to customize or restrict what write operations an agent can perform. - [Models](https://docs.agno.com/examples/models/overview.md): Provider examples for Agno models, with setup requirements and historical integrations identified. - [Responses](https://docs.agno.com/examples/models/openai/responses/overview.md): OpenAI Responses API examples for structured output, tool use, multimodal input, deep research, reasoning, and storage controls. - [Responses Agent Flex Tier](https://docs.agno.com/examples/models/openai/responses/agent-flex-tier.md): Run o4-mini on the flex service tier with the OpenAIResponses model. - [Responses Basic](https://docs.agno.com/examples/models/openai/responses/basic.md): Run a gpt-5.6-luna agent through the Responses API in sync, async, and streaming modes. - [Responses DB](https://docs.agno.com/examples/models/openai/responses/db.md): Persist Responses API sessions in Postgres and answer a history-dependent follow-up. - [OpenAI Deep Research Agent](https://docs.agno.com/examples/models/openai/responses/deep-research-agent.md): Build a cited web research agent with OpenAI Responses, with a current alternative to the retired Deep Research recipe. - [Responses Image Agent](https://docs.agno.com/examples/models/openai/responses/image-agent.md): Describe an image from a URL and fetch related news via web search with OpenAIResponses. - [Responses Image Agent Bytes](https://docs.agno.com/examples/models/openai/responses/image-agent-bytes.md): Pass image bytes to a Responses API agent that pairs vision with web search. - [Responses Image Agent with Memory](https://docs.agno.com/examples/models/openai/responses/image-agent-with-memory.md): Reference an earlier image in follow-up questions after adding a database for session history. - [Image Generation Agent](https://docs.agno.com/examples/models/openai/responses/image-generation-agent.md): Generate and save an image with OpenAITools and GPT Image 2. - [Responses Knowledge](https://docs.agno.com/examples/models/openai/responses/knowledge.md): Answer questions from a PDF stored in PgVector knowledge with OpenAIResponses. - [Responses Memory](https://docs.agno.com/examples/models/openai/responses/memory.md): Store user memories and session summaries in Postgres while chatting through the Responses API. - [Responses PDF Input Local](https://docs.agno.com/examples/models/openai/responses/pdf-input-local.md): Query a PDF with an explicit File Search index, bounded indexing checks, and cleanup of owned resources. - [Responses PDF Input URL](https://docs.agno.com/examples/models/openai/responses/pdf-input-url.md): Query a PDF with an explicit File Search index, bounded indexing checks, and cleanup of owned resources. - [Responses Reasoning O3 Mini](https://docs.agno.com/examples/models/openai/responses/reasoning-o3-mini.md): Stream retrieved stock quotes from o3-mini with YFinance tools via the Responses API. - [Responses Structured Output](https://docs.agno.com/examples/models/openai/responses/structured-output.md): Generate a MovieScript Pydantic model via JSON mode, strict, and guided structured output. - [OpenAI Structured Output With Tools](https://docs.agno.com/examples/models/openai/responses/structured-output-with-tools.md): Combine web search tools with a Pydantic output schema on gpt-5-mini. - [Responses Tool Use](https://docs.agno.com/examples/models/openai/responses/tool-use.md): Answer a news question with WebSearchTools through the Responses API. - [OpenAI Tool Use GPT 5](https://docs.agno.com/examples/models/openai/responses/tool-use-gpt-5.md): Fetch stock quotes through the Responses API with cached YFinance tool results. - [OpenAI Tool Use O3](https://docs.agno.com/examples/models/openai/responses/tool-use-o3.md): Fetch stock quotes through the Responses API with cached YFinance tool results. - [Responses Verbosity Control](https://docs.agno.com/examples/models/openai/responses/verbosity-control.md): Set desired response detail through OpenAI Responses while comparing retrieved stock quotes. - [OpenAI WebSearch Builtin Tool](https://docs.agno.com/examples/models/openai/responses/websearch-builtin-tool.md): Combine the built-in web_search_preview tool with FileTools to search and save results to disk. - [ZDR Reasoning Agent](https://docs.agno.com/examples/models/openai/responses/zdr-reasoning-agent.md): Disable Responses application-state storage with store=False while retaining multi-turn context in InMemoryDb. - [Background Mode](https://docs.agno.com/examples/models/openai/responses/background.md): Run GPT-5.4 in background mode with automatic polling and a 600-second default maximum wait. - [OpenAI File Input Direct](https://docs.agno.com/examples/models/openai/responses/file-input-direct.md): Attach files by URL, local path, or raw bytes to a Responses API agent. - [Responses Image Agent File](https://docs.agno.com/examples/models/openai/responses/image-agent-file.md): Attach a local image by filepath to a Responses API agent, overriding the MIME type if needed. - [OpenAI Reasoning Effort](https://docs.agno.com/examples/models/openai/responses/reasoning-effort.md): Set xhigh reasoning effort and request an automatic reasoning summary from OpenAI Responses. - [Access Memories in Memory Completed Event](https://docs.agno.com/examples/models/openai/chat/access-memories-in-memory-completed-event.md): Stream an agent run with stream_events=True and read user memories off the MemoryUpdateCompleted event, backed by PostgresDb. - [Chat Agent Flex Tier](https://docs.agno.com/examples/models/openai/chat/agent-flex-tier.md): Run o4-mini on the flex service tier by setting service_tier on OpenAIChat. - [OpenAI Audio Input Agent](https://docs.agno.com/examples/models/openai/chat/audio-input-agent.md): Send a WAV file fetched from a URL to gpt-audio and stream a text answer about it. - [OpenAI Audio Input And Output Multi Turn](https://docs.agno.com/examples/models/openai/chat/audio-input-and-output-multi-turn.md): Send audio input to gpt-audio and carry the conversation across turns with audio replies. - [OpenAI Audio Input Local File Upload](https://docs.agno.com/examples/models/openai/chat/audio-input-local-file-upload.md): Send a local MP3 file to gpt-audio and get a text description of the audio. - [OpenAI Audio Output Agent](https://docs.agno.com/examples/models/openai/chat/audio-output-agent.md): Get text and audio output from gpt-audio and save each spoken reply to a WAV file. - [OpenAI Audio Output Stream](https://docs.agno.com/examples/models/openai/chat/audio-output-stream.md): Stream pcm16 audio from gpt-audio, printing the transcript while writing frames to a WAV file. - [Chat Basic](https://docs.agno.com/examples/models/openai/chat/basic.md): Run a gpt-5.6-luna agent in sync, async, and streaming modes with OpenAIChat. - [OpenAI Basic Stream Metrics](https://docs.agno.com/examples/models/openai/chat/basic-stream-metrics.md): Stream a response, then read run-level and per-message metrics from the last run output. - [Custom Role Map](https://docs.agno.com/examples/models/openai/chat/custom-role-map.md): Point OpenAIChat at Mistral's base_url and remap the `model` role to `assistant` via role_map. - [Chat DB](https://docs.agno.com/examples/models/openai/chat/db.md): Store session history in Postgres so a follow-up question can reference the previous answer. - [OpenAI Generate Images](https://docs.agno.com/examples/models/openai/chat/generate-images.md): Legacy DalleTools example that generates an image and reads its URL from the run output. - [Chat Image Agent](https://docs.agno.com/examples/models/openai/chat/image-agent.md): Analyze an image from a URL and search the web for related news, streaming the reply. - [Chat Image Agent Bytes](https://docs.agno.com/examples/models/openai/chat/image-agent-bytes.md): Pass an image as raw bytes and combine vision with web search for current news. - [Chat Image Agent with Memory](https://docs.agno.com/examples/models/openai/chat/image-agent-with-memory.md): Analyze an image with web search, keeping chat history so follow-ups can reference it. - [Chat Knowledge](https://docs.agno.com/examples/models/openai/chat/knowledge.md): Load a PDF into PgVector-backed knowledge and answer questions from its contents. - [Chat Memory](https://docs.agno.com/examples/models/openai/chat/memory.md): Store user memories and session summaries in Postgres and print them after each turn. - [OpenAI Metrics](https://docs.agno.com/examples/models/openai/chat/metrics.md): Print per-message and aggregated run metrics after a YFinance tool call. - [PDF Input File Upload](https://docs.agno.com/examples/models/openai/chat/pdf-input-file-upload.md): Attach a local PDF file to an OpenAI chat agent and ask it to suggest a recipe from the document. - [Chat PDF Input Local](https://docs.agno.com/examples/models/openai/chat/pdf-input-local.md): Attach a local PDF by filepath and query its contents with gpt-5.6-luna. - [Chat PDF Input URL](https://docs.agno.com/examples/models/openai/chat/pdf-input-url.md): Attach a PDF by URL and ask the agent for a recipe from the file. - [Chat Reasoning O3 Mini](https://docs.agno.com/examples/models/openai/chat/reasoning-o3-mini.md): Run o3-mini with high reasoning effort and YFinance tools to write a stock report. - [Retry](https://docs.agno.com/examples/models/openai/chat/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [Chat Structured Output](https://docs.agno.com/examples/models/openai/chat/structured-output.md): Compare JSON mode, strict, and guided structured output for a Pydantic movie schema. - [Text-to-Speech Agent](https://docs.agno.com/examples/models/openai/chat/text-to-speech-agent.md): Generate speech with the OpenAITools speech toolkit and save the returned audio to tmp/speech_output.mp3. - [Chat Tool Use](https://docs.agno.com/examples/models/openai/chat/tool-use.md): Answer questions with web search tools in sync, streaming, and async modes. - [Chat Verbosity Control](https://docs.agno.com/examples/models/openai/chat/verbosity-control.md): Control response length with the gpt-5 verbosity parameter in a finance report agent. - [OpenAI With Retries](https://docs.agno.com/examples/models/openai/chat/with-retries.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [OpenAI Chat Citations](https://docs.agno.com/examples/models/openai/chat/citations.md): OpenAI web-search chat models return `url_citation` annotations alongside the content. - [Chat Image Agent File](https://docs.agno.com/examples/models/openai/chat/image-agent-file.md): Attach a local image by filepath, with auto-detected or explicit MIME types. - [OpenAI PDF Input Bytes](https://docs.agno.com/examples/models/openai/chat/pdf-input-bytes.md): Attach a PDF as raw bytes and have gpt-5-mini summarize its contents. - [Chat](https://docs.agno.com/examples/models/openai/chat/overview.md): OpenAI Chat examples for multimodal input and output, tools, reasoning, structured output, storage, and retries. - [OpenAI](https://docs.agno.com/examples/models/openai/overview.md): OpenAI Chat and Responses API examples for multimodal input, tools, reasoning, structured output, storage, and streaming. - [Skills](https://docs.agno.com/examples/models/anthropic/skills/overview.md): Browse Claude Agent Skills examples for PowerPoint, Excel, Word, and multi-skill workflows. - [Agno Agent with Word Document Skills](https://docs.agno.com/examples/models/anthropic/skills/agent-with-documents.md): Use Claude's docx skill to create Word documents through Agno agents. - [Agno Agent with Excel Skills](https://docs.agno.com/examples/models/anthropic/skills/agent-with-excel.md): Use Claude's xlsx skill to create Excel spreadsheets through Agno agents. - [Agno Agent with PowerPoint Skills](https://docs.agno.com/examples/models/anthropic/skills/agent-with-powerpoint.md): Use Claude's pptx skill to create PowerPoint presentations through Agno agents. - [Multi-Skill Agent - PowerPoint, Excel, and Word](https://docs.agno.com/examples/models/anthropic/skills/multi-skill-agent.md): Create an agent with multiple Claude Agent Skills that can create presentations, spreadsheets, and documents in a single workflow. - [Anthropic Basic](https://docs.agno.com/examples/models/anthropic/basic.md): Run a minimal Claude agent in sync, async, and streaming modes. - [Anthropic Basic With Timeout](https://docs.agno.com/examples/models/anthropic/basic-with-timeout.md): Set a one-second request timeout on Claude with the timeout parameter. - [Betas](https://docs.agno.com/examples/models/anthropic/betas.md): Inspect beta names known to the installed Anthropic SDK and select a model with the required context window. - [Anthropic Code Execution](https://docs.agno.com/examples/models/anthropic/code-execution.md): Compute statistics with Claude's server-side code execution tool via a beta flag. - [Self-managed Context Management](https://docs.agno.com/examples/models/anthropic/context-management.md): Claude's context management feature for automatic tool result clearing. - [Anthropic CSV Input](https://docs.agno.com/examples/models/anthropic/csv-input.md): Pass a downloaded IMDB CSV to Claude as a File attachment for box office analysis. - [DB](https://docs.agno.com/examples/models/anthropic/db.md): Persist Claude chat history in Postgres so follow-up questions keep context. - [Anthropic Financial Analyst Thinking](https://docs.agno.com/examples/models/anthropic/financial-analyst-thinking.md): Analyze a stock portfolio with interleaved thinking, calculator, and YFinance tools. - [Anthropic Image Input Bytes](https://docs.agno.com/examples/models/anthropic/image-input-bytes.md): Send an image to Claude as raw bytes and search the web for related news. - [Image Input File Upload](https://docs.agno.com/examples/models/anthropic/image-input-file-upload.md): Review the legacy uploaded-image recipe and run a current local-image alternative. - [Image Input Local File](https://docs.agno.com/examples/models/anthropic/image-input-local-file.md): Download a PNG locally and pass it to Claude by filepath as an image input. - [Anthropic Image Input URL](https://docs.agno.com/examples/models/anthropic/image-input-url.md): Pass an image URL to Claude and combine vision with web search tools. - [Knowledge](https://docs.agno.com/examples/models/anthropic/knowledge.md): Give Claude a PgVector knowledge base built from a recipe PDF with Azure OpenAI embeddings. - [Anthropic MCP Connector](https://docs.agno.com/examples/models/anthropic/mcp-connector.md): Connect Claude to the DeepWiki MCP server with the native MCP connector beta. - [Memory](https://docs.agno.com/examples/models/anthropic/memory.md): Store Claude agent memories and session summaries in Postgres across multiple turns. - [Anthropic PDF Input Bytes](https://docs.agno.com/examples/models/anthropic/pdf-input-bytes.md): Send a PDF to Claude as raw bytes and read citations from the run output. - [PDF Input File Upload](https://docs.agno.com/examples/models/anthropic/pdf-input-file-upload.md): Upload a PDF through the Anthropic Files API beta and pass the file handle to a Claude agent for summarization. - [Anthropic PDF Input Local](https://docs.agno.com/examples/models/anthropic/pdf-input-local.md): Attach a local PDF file to a Claude prompt and print the response citations. - [Anthropic PDF Input URL](https://docs.agno.com/examples/models/anthropic/pdf-input-url.md): Summarize a PDF fetched from a URL by passing it to Claude as a File. - [Prompt Caching](https://docs.agno.com/examples/models/anthropic/prompt-caching.md): Enable Claude cache_system_prompt for a large system message and print cache write and read token counts across two runs. - [Prompt Caching Extended](https://docs.agno.com/examples/models/anthropic/prompt-caching-extended.md): Set the extended-cache-ttl beta and extended_cache_time on Claude to hold the cached system prompt for one hour, and compare cache write and read tokens. - [Retry](https://docs.agno.com/examples/models/anthropic/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [Anthropic Structured Output](https://docs.agno.com/examples/models/anthropic/structured-output.md): Generate a typed MovieScript from Claude with a Pydantic output schema. - [Structured Output Strict Tools](https://docs.agno.com/examples/models/anthropic/structured-output-strict-tools.md): Combine strict tool arguments with a Pydantic response schema in a simulated weather example. - [Anthropic Thinking](https://docs.agno.com/examples/models/anthropic/thinking.md): Enable Claude extended thinking with a token budget and stream the response. - [Tool Use](https://docs.agno.com/examples/models/anthropic/tool-use.md): Call web search tools from Claude in sync, streaming, and async modes. - [Anthropic Web Fetch](https://docs.agno.com/examples/models/anthropic/web-fetch.md): Fetch and summarize a web page with Anthropic's native web_fetch tool. - [Anthropic Web Search](https://docs.agno.com/examples/models/anthropic/web-search.md): Use Anthropic's native web_search tool and inspect search metrics on the run output. - [Anthropic Adaptive Thinking](https://docs.agno.com/examples/models/anthropic/adaptive-thinking.md): Let Claude 4.6 decide its own thinking depth with thinking={"type": "adaptive"} and an effort level. - [Anthropic Append Trailing User Message](https://docs.agno.com/examples/models/anthropic/append-trailing-user-message.md): Append a trailing user turn with append_trailing_user_message when a Claude 4.6 conversation ends on an assistant message. - [Anthropic Markdown Input](https://docs.agno.com/examples/models/anthropic/markdown-input.md): Demonstrates passing markdown files to Claude using the correct text/markdown MIME type. - [Prompt Caching Multi Block](https://docs.agno.com/examples/models/anthropic/prompt-caching-multi-block.md): Configure one-hour system-cache blocks and refresh uncached context per request. - [Prompt Caching with Dynamic Block](https://docs.agno.com/examples/models/anthropic/prompt-caching-with-dynamic-block.md): Augment the agent-built system prompt with a dynamic per-request block. - [Anthropic Pydantic Tool Input](https://docs.agno.com/examples/models/anthropic/pydantic-tool-input.md): Pass nested, Optional, Union, List, and deeply nested Pydantic models as tool inputs to Claude. - [Anthropic Server Tools: Multi-Turn](https://docs.agno.com/examples/models/anthropic/server-tools-multi-turn.md): Combines web_search, web_fetch, and code_execution in a multi-turn conversation. - [Anthropic](https://docs.agno.com/examples/models/anthropic/overview.md): Claude examples for multimodal input, context management, caching, knowledge, memory, thinking, structured output, server tools, and skills. - [Anthropic Advisor](https://docs.agno.com/examples/models/anthropic/advisor.md): Use Anthropic's advisor tool beta with Claude Sonnet and an Opus advisor model. - [Google](https://docs.agno.com/examples/models/google/overview.md): Google Gemini and Gemini Interactions examples for multimodal input, search, thinking, tools, and deep research. - [Gemini 3.8 Flash Market Brief](https://docs.agno.com/examples/models/google/gemini/gemini-3-8-flash-market-brief.md): Combine Google Search, URL context, and a Pydantic schema for a research brief. - [Gemini 3.8 Flash](https://docs.agno.com/examples/models/google/gemini/gemini-3-8-flash.md): Use web search tools with Gemini 3.8 Flash in synchronous, asynchronous, and streaming calls. - [Agent with Thinking Budget](https://docs.agno.com/examples/models/google/gemini/agent-with-thinking-budget.md): Cap Gemini 2.5 Pro's reasoning with thinking_budget and surface thought summaries with include_thoughts. - [Google Audio Input Bytes Content](https://docs.agno.com/examples/models/google/gemini/audio-input-bytes-content.md): Pass a downloaded WAV file to Gemini as raw audio bytes for analysis. - [Google Audio Input File Upload](https://docs.agno.com/examples/models/google/gemini/audio-input-file-upload.md): Upload an MP3 through the Gemini Files API and reuse the remote file across runs. - [Google Audio Input Local File Upload](https://docs.agno.com/examples/models/google/gemini/audio-input-local-file-upload.md): Send a local MP3 to Gemini with Audio(filepath) and stream the analysis. - [Google Basic](https://docs.agno.com/examples/models/google/gemini/basic.md): Run a Gemini 3.7 Flash agent with sync, async, and streaming responses. - [DB](https://docs.agno.com/examples/models/google/gemini/db.md): Persist Gemini agent sessions in Postgres and carry history across turns. - [External URL Input](https://docs.agno.com/examples/models/google/gemini/external-url-input.md): Pass a public HTTPS PDF URL straight to Gemini 3.7 Flash with File(url=...), no download step. - [Google File Search Advanced](https://docs.agno.com/examples/models/google/gemini/file-search-advanced.md): Manage multiple Gemini File Search stores with custom chunking and metadata filters. - [Google File Search Basic](https://docs.agno.com/examples/models/google/gemini/file-search-basic.md): Create a Gemini File Search store, upload a document, and query it with citations. - [Google File Search RAG Pipeline](https://docs.agno.com/examples/models/google/gemini/file-search-rag-pipeline.md): Build an async RAG pipeline over a directory of files with Gemini File Search. - [File Upload with Cache](https://docs.agno.com/examples/models/google/gemini/file-upload-with-cache.md): Upload a transcript with the Gemini Files API, cache it with a 5-minute TTL, and reuse the cached content across requests. - [GCS File Input](https://docs.agno.com/examples/models/google/gemini/gcs-file-input.md): Summarize a PDF straight from a Google Cloud Storage gs:// URI using Gemini on Vertex AI, with no download or re-upload. - [Gemini 2 to 3](https://docs.agno.com/examples/models/google/gemini/gemini-2-to-3.md): Continue a Gemini 3.7 Flash session to Gemini 3.1 Pro Preview by sharing history through SqliteDb. - [Gemini 3 Pro](https://docs.agno.com/examples/models/google/gemini/gemini-3-pro.md): Use Gemini 3.1 Pro Preview with web search and SQLite chat history. - [Gemini 3 Pro Thinking Level](https://docs.agno.com/examples/models/google/gemini/gemini-3-pro-thinking-level.md): Set Gemini 3.1 Pro Preview thinking level and stream an asynchronous response. - [Grounding with Gemini](https://docs.agno.com/examples/models/google/gemini/grounding.md): Grounding enables Gemini to search the web and provide responses backed by real-time information with citations. - [Google Image Editing](https://docs.agno.com/examples/models/google/gemini/image-editing.md): Send an image to Gemini and get back an edited version using image response modalities. - [Google Image Generation](https://docs.agno.com/examples/models/google/gemini/image-generation.md): Generate an image with Gemini response modalities and open it with PIL. - [Google Image Input](https://docs.agno.com/examples/models/google/gemini/image-input.md): Pass an image URL to a Gemini agent and fetch related news with web search. - [Google Image Input File Upload](https://docs.agno.com/examples/models/google/gemini/image-input-file-upload.md): Upload an image through the Gemini Files API and combine it with web search. - [Imagen Tool](https://docs.agno.com/examples/models/google/gemini/imagen-tool.md): Adapt a historical Imagen toolkit example to a Gemini image-generation function tool. - [Imagen Tool Advanced](https://docs.agno.com/examples/models/google/gemini/imagen-tool-advanced.md): Adapt a historical Imagen toolkit example to a Gemini image-generation function tool. - [Knowledge](https://docs.agno.com/examples/models/google/gemini/knowledge.md): Query a PDF knowledge base stored in PgVector with Gemini embeddings. - [PDF Input File Upload](https://docs.agno.com/examples/models/google/gemini/pdf-input-file-upload.md): Upload a PDF to the Gemini Files API, then ask an agent to summarize it and suggest a recipe from it. - [Google PDF Input Local](https://docs.agno.com/examples/models/google/gemini/pdf-input-local.md): Attach a local PDF to a Gemini agent and ask follow-up questions with history. - [Google PDF Input URL](https://docs.agno.com/examples/models/google/gemini/pdf-input-url.md): Summarize a PDF from a URL and ask follow-ups using InMemoryDb chat history. - [Retry](https://docs.agno.com/examples/models/google/gemini/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [S3 URL File Input](https://docs.agno.com/examples/models/google/gemini/s3-url-file-input.md): Generate an S3 pre-signed URL with boto3 and pass it straight to Gemini to summarize the PDF without downloading it. - [Google Search with Gemini](https://docs.agno.com/examples/models/google/gemini/search.md): The search tool enables Gemini to access current information from Google Search. - [Storage and Memory](https://docs.agno.com/examples/models/google/gemini/storage-and-memory.md): Combine PgVector knowledge, Postgres memory, session summaries, and web search on Gemini. - [Google Structured Output](https://docs.agno.com/examples/models/google/gemini/structured-output.md): Return an event plan as a Pydantic schema with enums, formats, and nested objects. - [Google Text To Speech](https://docs.agno.com/examples/models/google/gemini/text-to-speech.md): Generate speech with a Gemini TTS model and write the audio to a WAV file. - [Google Thinking Agent](https://docs.agno.com/examples/models/google/gemini/thinking-agent.md): Solve a logic puzzle with a Gemini thinking budget and thought summaries enabled. - [Tool Use](https://docs.agno.com/examples/models/google/gemini/tool-use.md): Add web search tools to a Gemini agent and run sync, streaming, and async. - [URL Context](https://docs.agno.com/examples/models/google/gemini/url-context.md): Compare two recipe pages by enabling Gemini's url_context to fetch page content. - [URL Context with Search](https://docs.agno.com/examples/models/google/gemini/url-context-with-search.md): Combine URL context with Google Search for comprehensive web analysis. - [Vertex AI Search with Gemini](https://docs.agno.com/examples/models/google/gemini/vertex-ai-search.md): Vertex AI Search allows Gemini to search through your data stores, providing grounded responses based on your private knowledge base. - [Vertex AI](https://docs.agno.com/examples/models/google/gemini/vertexai.md): Authenticate Gemini against Vertex AI with environment variables or explicit `project_id` and `location` parameters on the Gemini model. - [Google Vertex AI With Credentials](https://docs.agno.com/examples/models/google/gemini/vertexai-with-credentials.md): Authenticate Gemini on Vertex AI with explicit service account credentials. - [Google Video Input Bytes Content](https://docs.agno.com/examples/models/google/gemini/video-input-bytes-content.md): Download an MP4 and pass it to Gemini as raw video bytes for analysis. - [Google Video Input File Upload](https://docs.agno.com/examples/models/google/gemini/video-input-file-upload.md): Upload a video through the Gemini Files API and poll until it is ready to query. - [Google Video Input Local File Upload](https://docs.agno.com/examples/models/google/gemini/video-input-local-file-upload.md): Pass a local video file to Gemini and ask the agent to describe its contents. - [Google Video Input YouTube](https://docs.agno.com/examples/models/google/gemini/video-input-youtube.md): Send a YouTube URL as video input to Gemini and have the agent describe it. - [Google CSV Input](https://docs.agno.com/examples/models/google/gemini/csv-input.md): Analyze an IMDB movie dataset by attaching a CSV file to a Gemini agent. - [Google File Search Image Upload](https://docs.agno.com/examples/models/google/gemini/file-search-image-upload.md): Demonstrates uploading images (JPEG, PNG) to Gemini File Search stores using the multimodal embedding model (gemini-embedding-2). - [Grounding with Parallel Web Search on Vertex AI](https://docs.agno.com/examples/models/google/gemini/parallel-grounding.md): Ground Gemini 3.7 Flash responses with Parallel web search on Vertex AI. - [Gemini Timeout](https://docs.agno.com/examples/models/google/gemini/timeout.md): Set a request timeout (in seconds) for the Gemini model. - [Gemini](https://docs.agno.com/examples/models/google/gemini/overview.md): Gemini examples for multimodal input, file search, grounding, knowledge, thinking, structured output, tools, and Vertex AI. - [Gemini Interactions - Antigravity agent](https://docs.agno.com/examples/models/google/gemini-interactions/antigravity.md): Run Google's managed Antigravity agent for planning, code, web browsing, and artifacts in a remote sandbox. - [Gemini Interactions - Antigravity environment configuration](https://docs.agno.com/examples/models/google/gemini-interactions/antigravity-environment-config.md): Reuse an existing Antigravity sandbox by ID, or pass a full EnvironmentConfig to set sources and network rules for a new one. - [Gemini Interactions - Antigravity multi-turn](https://docs.agno.com/examples/models/google/gemini-interactions/antigravity-multi-turn.md): Continue an Antigravity interaction across turns. - [Gemini Interactions - Antigravity streaming](https://docs.agno.com/examples/models/google/gemini-interactions/antigravity-streaming.md): Stream the Antigravity agent's progress (tool calls, intermediate text, generated artifacts) instead of waiting for the final result. - [Gemini Interactions - Audio Understanding](https://docs.agno.com/examples/models/google/gemini-interactions/audio-understanding.md): Send an audio clip URL to a GeminiInteractions agent and have it describe what it hears. - [Gemini Interactions - Basic](https://docs.agno.com/examples/models/google/gemini-interactions/basic.md): Run a GeminiInteractions agent through sync, streaming, and async response calls. - [Gemini Interactions - Deep Research](https://docs.agno.com/examples/models/google/gemini-interactions/deep-research.md): Setting `agent` to a deep-research agent id switches GeminiInteractions to the agent path (agent + agent_config) instead of the model path. - [Gemini Interactions - Deep Research with Collaborative Planning](https://docs.agno.com/examples/models/google/gemini-interactions/deep-research-collaborative-planning.md): Demonstrate plan, refinement, and execution modes, with an explicit approval gate for interactive use. - [Gemini Interactions - Deep Research with File Search](https://docs.agno.com/examples/models/google/gemini-interactions/deep-research-file-search.md): Ground the Deep Research agent on your own documents. - [Gemini Interactions - Deep Research with MCP servers](https://docs.agno.com/examples/models/google/gemini-interactions/deep-research-mcp.md): Give the Deep Research agent access to external tools via remote MCP servers. - [Gemini Interactions - Deep Research multi-turn](https://docs.agno.com/examples/models/google/gemini-interactions/deep-research-multi-turn.md): Continue a Deep Research interaction across turns. - [Gemini Interactions - Deep Research with multimodal input](https://docs.agno.com/examples/models/google/gemini-interactions/deep-research-multimodal.md): Deep Research accepts images and documents (PDFs) as input, then conducts web-based research grounded in that content. - [Gemini Interactions - Deep Research streaming](https://docs.agno.com/examples/models/google/gemini-interactions/deep-research-streaming.md): Stream real-time progress (thought summaries, text, generated images) from a Deep Research task instead of waiting for the final report. - [Gemini Interactions - Deep Research with Visualization](https://docs.agno.com/examples/models/google/gemini-interactions/deep-research-visualization.md): With `visualization="auto"` the agent can generate charts and graphs to support its findings. - [Gemini Interactions - Document Processing](https://docs.agno.com/examples/models/google/gemini-interactions/document-processing.md): Example showing document (PDF) processing with the Interactions API. - [Gemini Interactions - Image Generation](https://docs.agno.com/examples/models/google/gemini-interactions/image-generation.md): Example showing image generation with the Interactions API. - [Gemini Interactions - Image Understanding](https://docs.agno.com/examples/models/google/gemini-interactions/image-understanding.md): Example showing image understanding with the Interactions API. - [Gemini Interactions - Multi-turn Conversation](https://docs.agno.com/examples/models/google/gemini-interactions/multi-turn.md): Demonstrates server-side conversation history with the Interactions API. - [Gemini Interactions - Google Search](https://docs.agno.com/examples/models/google/gemini-interactions/search.md): Example using the built-in Google Search tool with the Interactions API. - [Gemini Interactions - Structured Output](https://docs.agno.com/examples/models/google/gemini-interactions/structured-output.md): Example showing structured output with the Interactions API. - [Gemini Interactions - Thinking](https://docs.agno.com/examples/models/google/gemini-interactions/thinking.md): Example showing thinking/reasoning with the Gemini Interactions API. - [Gemini Interactions - Tool Use](https://docs.agno.com/examples/models/google/gemini-interactions/tool-use.md): Give a GeminiInteractions agent WebSearchTools for live searches across sync, streaming, and async calls. - [Gemini Interactions - Video Understanding](https://docs.agno.com/examples/models/google/gemini-interactions/video-understanding.md): Example showing video understanding with the Interactions API. - [Reasoning](https://docs.agno.com/examples/models/groq/reasoning/overview.md): Run current Groq reasoning examples with model substitutions for historical cookbook sources. - [Groq Basic](https://docs.agno.com/examples/models/groq/reasoning/basic.md): Run GPT-OSS 120B on Groq using the current replacement for the historical reasoning model. - [Demo DeepSeek Qwen](https://docs.agno.com/examples/models/groq/reasoning/demo-deepseek-qwen.md): Use Qwen3.6 on Groq for a separate reasoning stage and final response. - [Demo Qwen 2.5 32B](https://docs.agno.com/examples/models/groq/reasoning/demo-qwen-2-5-32b.md): Query a recipe knowledge base with the current Qwen3.6 replacement on Groq. - [Groq Finance Agent](https://docs.agno.com/examples/models/groq/reasoning/finance-agent.md): Write a stock report with GPT-OSS 120B on Groq and explicitly enabled YFinance tools. - [Groq Agent Team](https://docs.agno.com/examples/models/groq/agent-team.md): Coordinate web-search and finance agents with a GPT-OSS 120B team on Groq. - [Groq Basic](https://docs.agno.com/examples/models/groq/basic.md): Run GPT-OSS 120B on Groq with synchronous, asynchronous and streaming responses. - [Groq Browser Search](https://docs.agno.com/examples/models/groq/browser-search.md): Answer live questions with Groq's built-in browser_search tool on GPT-OSS 20B. - [DB](https://docs.agno.com/examples/models/groq/db.md): Store Groq agent sessions in Postgres so follow-up questions keep context. - [Deep Knowledge](https://docs.agno.com/examples/models/groq/deep-knowledge.md): DeepKnowledge - An AI Agent that iteratively searches a knowledge base to answer questions. - [Groq Image Agent](https://docs.agno.com/examples/models/groq/image-agent.md): Describe an image from a URL using a vision model on Groq. - [Knowledge](https://docs.agno.com/examples/models/groq/knowledge.md): Answer questions from a PDF knowledge base in PgVector with a Groq agent. - [Groq Metrics](https://docs.agno.com/examples/models/groq/metrics.md): Inspect token and timing metrics per message after a Groq agent run. - [Groq Reasoning Agent](https://docs.agno.com/examples/models/groq/reasoning-agent.md): Use GPT-OSS 120B on Groq for a separate reasoning stage and final response. - [Research Agent Exa](https://docs.agno.com/examples/models/groq/research-agent-exa.md): Research a topic with Exa search on Groq and save the markdown report to a file. - [Research Agent Seltz](https://docs.agno.com/examples/models/groq/research-agent-seltz.md): Generate a referenced research report with Seltz search and a Groq agent. - [Retry](https://docs.agno.com/examples/models/groq/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [Groq Structured Output](https://docs.agno.com/examples/models/groq/structured-output.md): Request JSON from Groq and validate it locally as a MovieScript. - [Tool Use](https://docs.agno.com/examples/models/groq/tool-use.md): Combine web search and Newspaper4k article extraction in a Groq research agent. - [Transcription Agent](https://docs.agno.com/examples/models/groq/transcription-agent.md): Give an agent GroqTools to transcribe a hosted WAV file to English. - [Groq Translation Agent](https://docs.agno.com/examples/models/groq/translation-agent.md): Translate French audio and generate English speech with GroqTools. - [Groq](https://docs.agno.com/examples/models/groq/overview.md): Groq examples for agents and teams, multimodal input, knowledge, reasoning, research, transcription, translation, structured output, and tools. - [DeepSeek](https://docs.agno.com/examples/models/deepseek/overview.md): Run DeepSeek models with reasoning, thinking mode, structured output, retries, and tool use. - [DeepSeek Basic](https://docs.agno.com/examples/models/deepseek/basic.md): Run a DeepSeek V4 Flash agent with sync, async, and streaming responses. - [DeepSeek Reasoning Agent](https://docs.agno.com/examples/models/deepseek/reasoning-agent.md): Solve the missionaries and cannibals puzzle with DeepSeek V4 Pro reasoning. - [DeepSeek Reasoning Effort](https://docs.agno.com/examples/models/deepseek/reasoning-effort.md): Control reasoning effort with DeepSeek V4 models. - [Retry](https://docs.agno.com/examples/models/deepseek/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [DeepSeek Structured Output](https://docs.agno.com/examples/models/deepseek/structured-output.md): Request JSON from DeepSeek and validate it locally as a MovieScript. - [DeepSeek Thinking Mode](https://docs.agno.com/examples/models/deepseek/thinking-mode.md): Enable or disable DeepSeek V4 thinking mode and inspect reasoning_content. - [Thinking Tool Calls](https://docs.agno.com/examples/models/deepseek/thinking-tool-calls.md): Combine DeepSeek thinking mode with web search tool calls and show full reasoning. - [Tool Use](https://docs.agno.com/examples/models/deepseek/tool-use.md): Answer news questions with a DeepSeek V4 Flash agent using web search tools. - [Mistral](https://docs.agno.com/examples/models/mistral/overview.md): Run Mistral models with image input, memory, structured output, retries, and tool use. - [Mistral Basic](https://docs.agno.com/examples/models/mistral/basic.md): Run a Mistral Small agent with plain and streaming print_response calls. - [Mistral Image Bytes Input Agent](https://docs.agno.com/examples/models/mistral/image-bytes-input-agent.md): Analyze raw image bytes with a Ministral 14B agent. - [Mistral Image Compare Agent](https://docs.agno.com/examples/models/mistral/image-compare-agent.md): Compare two image URLs with a Ministral 14B agent. - [Mistral Image File Input Agent](https://docs.agno.com/examples/models/mistral/image-file-input-agent.md): Analyze sample.jpeg with Ministral 14B and search for related news. - [Mistral Image OCR With Structured Output](https://docs.agno.com/examples/models/mistral/image-ocr-with-structured-output.md): Extract receipt data into a structured schema with Ministral 14B. - [Image Transcribe Document Agent](https://docs.agno.com/examples/models/mistral/image-transcribe-document-agent.md): Transcribe a document image with a Ministral 14B agent. - [Memory](https://docs.agno.com/examples/models/mistral/memory.md): Persist user memories and session summaries for a Mistral Large agent in Postgres. - [Mistral Small](https://docs.agno.com/examples/models/mistral/mistral-small.md): Query Mistral Small with WebSearchTools and stream a news summary. - [Retry](https://docs.agno.com/examples/models/mistral/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [Mistral Structured Output](https://docs.agno.com/examples/models/mistral/structured-output.md): Produce a structured MovieScript from Mistral Large in both sync and async runs. - [Mistral Structured Output With Tool Use](https://docs.agno.com/examples/models/mistral/structured-output-with-tool-use.md): Combine web search with a Person output schema on Mistral Medium in a researcher agent. - [Tool Use](https://docs.agno.com/examples/models/mistral/tool-use.md): Mistral tool use example with a custom function tool. - [Together](https://docs.agno.com/examples/models/together/overview.md): Run Together models with streaming, image input, reasoning, structured output, web search, and retry configuration. - [Together Basic](https://docs.agno.com/examples/models/together/basic.md): Run a current Together chat model with sync, streaming, and async calls. - [Together Image Agent](https://docs.agno.com/examples/models/together/image-agent.md): Describe an image from a URL with a current Together vision model. - [Together Image Agent Bytes](https://docs.agno.com/examples/models/together/image-agent-bytes.md): Send local image bytes to a current Together vision model and stream the description. - [Together Image Agent With Memory](https://docs.agno.com/examples/models/together/image-agent-with-memory.md): Keep image context across turns with a current Together vision model and an in-memory database. - [Together Reasoning Agent](https://docs.agno.com/examples/models/together/reasoning-agent.md): Current alternatives for the retired Together Reasoning Agent example. - [Retry](https://docs.agno.com/examples/models/together/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [Together Structured Output](https://docs.agno.com/examples/models/together/structured-output.md): Request JSON through Together and validate a MovieScript with Pydantic. - [Tool Use](https://docs.agno.com/examples/models/together/tool-use.md): Give a function-calling Together model web search tools and run it sync, streaming, and async. - [Fireworks](https://docs.agno.com/examples/models/fireworks/overview.md): Run Fireworks models with streaming, structured output, web search, and retry configuration. - [Fireworks Basic](https://docs.agno.com/examples/models/fireworks/basic.md): Run Fireworks GPT OSS 120B with synchronous, asynchronous, and streaming responses. - [Retry](https://docs.agno.com/examples/models/fireworks/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [Fireworks Structured Output](https://docs.agno.com/examples/models/fireworks/structured-output.md): Request a MovieScript JSON schema on Fireworks and validate the response locally. - [Tool Use](https://docs.agno.com/examples/models/fireworks/tool-use.md): Stream web search results from a Fireworks GPT OSS 120B agent. - [Cohere](https://docs.agno.com/examples/models/cohere/overview.md): Run Cohere Command A examples and migrate pinned Aya Vision 8B image examples to Command A Vision. - [Cohere Basic](https://docs.agno.com/examples/models/cohere/basic.md): Run Cohere Command A with sync, async, and streaming responses. - [DB](https://docs.agno.com/examples/models/cohere/db.md): Persist Cohere agent sessions in Postgres and reuse history across runs. - [Cohere Image Agent](https://docs.agno.com/examples/models/cohere/image-agent.md): Migrate the pinned Aya Vision 8B example to Command A Vision, then describe an image from a URL. - [Cohere Image Agent Bytes](https://docs.agno.com/examples/models/cohere/image-agent-bytes.md): Migrate the pinned Aya Vision 8B example to Command A Vision, then analyze raw image bytes. - [Cohere Image Agent Local File](https://docs.agno.com/examples/models/cohere/image-agent-local-file.md): Migrate the pinned Aya Vision 8B example to Command A Vision, then analyze a local image with `Image(filepath=...)`. - [Knowledge](https://docs.agno.com/examples/models/cohere/knowledge.md): Query a recipe PDF stored in PgVector from a Cohere Command A agent. - [Memory](https://docs.agno.com/examples/models/cohere/memory.md): Store personalized memories and session summaries with Cohere Command A and PostgreSQL. - [Retry](https://docs.agno.com/examples/models/cohere/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [Cohere Structured Output](https://docs.agno.com/examples/models/cohere/structured-output.md): Request JSON from Cohere Command A and validate it locally as a MovieScript. - [Tool Use](https://docs.agno.com/examples/models/cohere/tool-use.md): Add WebSearchTools to a Cohere Command A agent for live news queries. - [Meta](https://docs.agno.com/examples/models/meta/overview.md): Llama and Llama OpenAI examples covering tool use, knowledge, memory, metrics, storage, and retries. - [Llama OpenAI Basic](https://docs.agno.com/examples/models/meta/llama-openai/basic.md): Run Llama 4 Maverick through Meta's OpenAI-compatible API with sync, async, and streaming calls. - [Llama OpenAI Image Input Bytes](https://docs.agno.com/examples/models/meta/llama-openai/image-input-bytes.md): Send image bytes to Llama 4 Maverick and fetch related news with web search tools. - [Llama OpenAI Image Input File](https://docs.agno.com/examples/models/meta/llama-openai/image-input-file.md): Attach a local image file to Llama 4 Maverick via the OpenAI-compatible client and stream a description. - [Llama OpenAI Knowledge](https://docs.agno.com/examples/models/meta/llama-openai/knowledge.md): Query a PgVector knowledge base of PDF recipes with Llama 4 Maverick over the OpenAI-compatible API. - [Llama OpenAI Memory](https://docs.agno.com/examples/models/meta/llama-openai/memory.md): Use personalized memories and summaries in an agent. - [Llama OpenAI Metrics](https://docs.agno.com/examples/models/meta/llama-openai/metrics.md): Stream a Llama 4 Maverick run with YFinance tools and inspect message, run, and session metrics. - [Storage](https://docs.agno.com/examples/models/meta/llama-openai/storage.md): Store sessions in a named Postgres table so Llama 4 Maverick keeps history across runs. - [Llama OpenAI Structured Output](https://docs.agno.com/examples/models/meta/llama-openai/structured-output.md): Return a MovieScript Pydantic model from Llama 4 Maverick through the OpenAI-compatible client. - [Llama OpenAI Tool Use](https://docs.agno.com/examples/models/meta/llama-openai/tool-use.md): Call YFinance tools from Llama 4 Maverick over the OpenAI-compatible API in sync and async modes. - [Llama OpenAI](https://docs.agno.com/examples/models/meta/llama-openai/overview.md): OpenAI-compatible Llama examples for runs, images, knowledge, memory, metrics, storage, structured output, and tools. - [Async Knowledge](https://docs.agno.com/examples/models/meta/llama/async-knowledge.md): Query a PgVector knowledge base asynchronously with a Llama 4 Maverick agent. - [Llama Basic](https://docs.agno.com/examples/models/meta/llama/basic.md): Run Llama 4 Maverick through Meta's Llama API with sync, async, and streaming calls. - [DB](https://docs.agno.com/examples/models/meta/llama/db.md): Add a Postgres database to a Llama 4 Maverick agent to keep multi-turn chat history. - [Llama Image Input Bytes](https://docs.agno.com/examples/models/meta/llama/image-input-bytes.md): Pass a downloaded image as bytes to Llama 4 Maverick and search the web for related news. - [Llama Image Input File](https://docs.agno.com/examples/models/meta/llama/image-input-file.md): Attach a local image file to a Llama 4 Maverick agent and stream its description. - [Llama Knowledge](https://docs.agno.com/examples/models/meta/llama/knowledge.md): Answer recipe questions from a PgVector knowledge base with a Llama 4 Maverick agent. - [Llama Memory](https://docs.agno.com/examples/models/meta/llama/memory.md): Use personalized memories and summaries in an agent. - [Llama Metrics](https://docs.agno.com/examples/models/meta/llama/metrics.md): Inspect per-message and run-level metrics from a Llama 4 Maverick run with YFinance tools. - [Llama Structured Output](https://docs.agno.com/examples/models/meta/llama/structured-output.md): Generate a MovieScript Pydantic object from Llama 4 Maverick with a JSON schema output. - [Llama Tool Use](https://docs.agno.com/examples/models/meta/llama/tool-use.md): Fetch stock prices with YFinance tools from a Llama 4 Maverick agent in sync and async modes. - [Llama](https://docs.agno.com/examples/models/meta/llama/overview.md): Meta Llama API examples for runs, images, knowledge, memory, metrics, storage, structured output, and tools. - [Retry](https://docs.agno.com/examples/models/meta/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [Hugging Face](https://docs.agno.com/examples/models/huggingface/overview.md): Hugging Face examples for basic and streaming runs, essay generation, retries, and web-search tool use. - [Hugging Face Basic](https://docs.agno.com/examples/models/huggingface/basic.md): Run a Hugging Face Mistral 7B agent in sync, async, and streaming modes. - [Hugging Face GPT-OSS Essay Writer](https://docs.agno.com/examples/models/huggingface/llama-essay-writer.md): Write a 300-word essay on a user topic with GPT-OSS 120B on Hugging Face. - [Retry](https://docs.agno.com/examples/models/huggingface/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [Hugging Face Tool Use](https://docs.agno.com/examples/models/huggingface/tool-use.md): Add web search tools to a GPT-OSS 120B agent on Hugging Face. - [AWS](https://docs.agno.com/examples/models/aws/overview.md): Run Claude and Amazon Nova models on AWS Bedrock. - [Bedrock Basic](https://docs.agno.com/examples/models/aws/bedrock/basic.md): Run a minimal agent on AWS Bedrock in sync, async, and streaming modes. - [AWS Image Agent Bytes](https://docs.agno.com/examples/models/aws/bedrock/image-agent-bytes.md): Send image bytes to Amazon Nova Pro on Bedrock and fetch related news with web search. - [AWS PDF Agent Bytes](https://docs.agno.com/examples/models/aws/bedrock/pdf-agent-bytes.md): Pass a PDF as bytes to Amazon Nova Pro on Bedrock and extract a recipe from it. - [Bedrock Structured Output](https://docs.agno.com/examples/models/aws/bedrock/structured-output.md): Return a typed MovieScript from a Bedrock model with a Pydantic output schema. - [Bedrock Tool Use](https://docs.agno.com/examples/models/aws/bedrock/tool-use.md): Call web search tools from a Bedrock model in sync, streaming, and async modes. - [Bedrock](https://docs.agno.com/examples/models/aws/bedrock/overview.md): Amazon Bedrock examples for basic runs, image and PDF input, structured output, and tool use. - [Claude Basic](https://docs.agno.com/examples/models/aws/claude/basic.md): Run Claude on AWS Bedrock through the aws.Claude model class in sync, async, and streaming modes. - [DB](https://docs.agno.com/examples/models/aws/claude/db.md): Store session history for Claude on Bedrock in Postgres with add_history_to_context. - [AWS Image Agent](https://docs.agno.com/examples/models/aws/claude/image-agent.md): Describe an image URL with Claude on Bedrock and enrich it with web search. - [Knowledge](https://docs.agno.com/examples/models/aws/claude/knowledge.md): Attach a PgVector knowledge base of recipes to Claude running on Bedrock. - [Claude Structured Output](https://docs.agno.com/examples/models/aws/claude/structured-output.md): Generate a typed MovieScript from Claude on Bedrock with a Pydantic output schema. - [Claude Tool Use](https://docs.agno.com/examples/models/aws/claude/tool-use.md): Call web search tools from Claude on Bedrock in sync, streaming, and async modes. - [AWS Bedrock Claude Adaptive Thinking](https://docs.agno.com/examples/models/aws/claude/adaptive-thinking.md): Configure Claude 4.6 adaptive thinking with effort levels on AWS Bedrock. - [AWS Append Trailing User Message](https://docs.agno.com/examples/models/aws/claude/append-trailing-user-message.md): Append trailing user messages when Claude 4.6+ ends with an assistant response. - [Claude](https://docs.agno.com/examples/models/aws/claude/overview.md): Claude on AWS Bedrock examples for runs, storage, images, knowledge, structured output, tools, and adaptive thinking. - [Retry](https://docs.agno.com/examples/models/aws/retry.md): Review Bedrock retry settings and the current adapter limitation when classifying AWS errors. - [Azure](https://docs.agno.com/examples/models/azure/overview.md): Choose Azure OpenAI, Claude on Foundry, or migration examples for the classic Azure AI Inference adapter. - [Azure AI Foundry Basic](https://docs.agno.com/examples/models/azure/ai-foundry/basic.md): Run a Phi-4 agent through the classic Azure AI Inference adapter, with a link to the current Foundry API. - [AI Foundry DB](https://docs.agno.com/examples/models/azure/ai-foundry/db.md): Persist a classic Azure AI Inference session in Postgres and test history with tool-free prompts. - [Azure Demo Cohere](https://docs.agno.com/examples/models/azure/ai-foundry/demo-cohere.md): Migrate a retired Azure Cohere recipe to a current Foundry Chat Completions deployment. - [Azure Demo Mistral](https://docs.agno.com/examples/models/azure/ai-foundry/demo-mistral.md): Migrate a retired Azure Mistral recipe to a current Foundry Chat Completions deployment. - [Azure Image Agent](https://docs.agno.com/examples/models/azure/ai-foundry/image-agent.md): Migrate an Azure AI Foundry image-URL agent from retired Llama 3.2 Vision to Llama 4 Scout. - [Azure Image Agent Bytes](https://docs.agno.com/examples/models/azure/ai-foundry/image-agent-bytes.md): Migrate an Azure AI Foundry image-bytes agent from retired Llama 3.2 Vision to Llama 4 Scout. - [AI Foundry Knowledge](https://docs.agno.com/examples/models/azure/ai-foundry/knowledge.md): Query PgVector knowledge through a current Foundry deployment with separate Azure OpenAI embeddings. - [AI Foundry Structured Output](https://docs.agno.com/examples/models/azure/ai-foundry/structured-output.md): Understand prompt-guided output in the classic Foundry adapter and use Azure OpenAI for native structured output. - [AI Foundry Tool Use](https://docs.agno.com/examples/models/azure/ai-foundry/tool-use.md): Use WebSearchTools with a current Foundry deployment that supports function calling. - [AI Foundry](https://docs.agno.com/examples/models/azure/ai-foundry/overview.md): Classic Azure AI Inference recipes and current Foundry migration paths for text, tools, images, knowledge, and structured output. - [Azure OpenAI Basic](https://docs.agno.com/examples/models/azure/openai/basic.md): Create a minimal AzureOpenAI agent and print sync, async, and streamed responses. - [OpenAI DB](https://docs.agno.com/examples/models/azure/openai/db.md): Persist AzureOpenAI agent sessions in Postgres and add history to follow-up runs. - [OpenAI Knowledge](https://docs.agno.com/examples/models/azure/openai/knowledge.md): Load a recipe PDF into PgVector with AzureOpenAIEmbedder and query it from an agent. - [OpenAI Structured Output](https://docs.agno.com/examples/models/azure/openai/structured-output.md): Return a typed MovieScript from an AzureOpenAI agent with output_schema. - [OpenAI Tool Use](https://docs.agno.com/examples/models/azure/openai/tool-use.md): Stream an AzureOpenAI agent's response as it calls WebSearchTools. - [OpenAI](https://docs.agno.com/examples/models/azure/openai/overview.md): Azure OpenAI examples for runs, storage, knowledge, structured output, and tools. - [Retry](https://docs.agno.com/examples/models/azure/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [Azure AI Foundry Claude Basic](https://docs.agno.com/examples/models/azure/claude/basic.md): Run Claude Sonnet on Azure AI Foundry with sync, async, and streaming responses. - [Azure AI Foundry Claude Thinking](https://docs.agno.com/examples/models/azure/claude/thinking.md): Enable extended thinking with a token budget on Claude via Azure AI Foundry. - [Tool Use](https://docs.agno.com/examples/models/azure/claude/tool-use.md): Add WebSearchTools to a Claude agent running on Azure AI Foundry. - [Vertex AI](https://docs.agno.com/examples/models/vertexai/overview.md): Vertex AI examples for Claude models, retries, multimodal input, knowledge, memory, caching, structured output, and tools. - [Vertex AI Basic](https://docs.agno.com/examples/models/vertexai/claude/basic.md): Run Claude Sonnet 4 on Vertex AI with sync, streaming, and async calls. - [Vertex AI Basic With Timeout](https://docs.agno.com/examples/models/vertexai/claude/basic-with-timeout.md): Set a five-second request timeout on Claude Sonnet 4 running on Vertex AI. - [Betas](https://docs.agno.com/examples/models/vertexai/claude/betas.md): Pass a beta option to Claude on Vertex AI and inspect the SDK’s known beta names. - [DB](https://docs.agno.com/examples/models/vertexai/claude/db.md): Persist session history in SQLite so a Vertex AI Claude agent can answer follow-ups. - [Vertex AI Image Input Bytes](https://docs.agno.com/examples/models/vertexai/claude/image-input-bytes.md): Send a downloaded image as bytes to Claude on Vertex AI and search the web about it. - [Vertex AI Image Input URL](https://docs.agno.com/examples/models/vertexai/claude/image-input-url.md): Pass an image URL to Claude on Vertex AI and search the web for more context. - [Knowledge](https://docs.agno.com/examples/models/vertexai/claude/knowledge.md): Answer questions from a PgVector knowledge base with Claude on Vertex AI. - [Memory](https://docs.agno.com/examples/models/vertexai/claude/memory.md): Store personalized memories and session summaries with Claude on Vertex AI using PostgreSQL. - [Vertex AI PDF Input Bytes](https://docs.agno.com/examples/models/vertexai/claude/pdf-input-bytes.md): Summarize a PDF passed as raw bytes to Claude Sonnet 4 on Vertex AI. - [Vertex AI PDF Input Local](https://docs.agno.com/examples/models/vertexai/claude/pdf-input-local.md): Summarize a local PDF file passed by path to Claude Sonnet 4 on Vertex AI. - [Prompt Caching](https://docs.agno.com/examples/models/vertexai/claude/prompt-caching.md): Mark a reusable system prompt for caching and inspect Claude’s cache usage on Vertex AI. - [Vertex AI Structured Output](https://docs.agno.com/examples/models/vertexai/claude/structured-output.md): Request JSON from Claude on Vertex AI and validate it locally as a MovieScript. - [Vertex AI Thinking](https://docs.agno.com/examples/models/vertexai/claude/thinking.md): Enable extended thinking with a 1024-token budget on Claude Sonnet 4 via Vertex AI. - [Tool Use](https://docs.agno.com/examples/models/vertexai/claude/tool-use.md): Give Claude on Vertex AI web search tools and run it sync, streaming, and async. - [Vertex AI Claude Adaptive Thinking](https://docs.agno.com/examples/models/vertexai/claude/adaptive-thinking.md): Use adaptive thinking with effort levels to control reasoning depth on Claude VertexAI. - [Claude](https://docs.agno.com/examples/models/vertexai/claude/overview.md): Claude on Vertex AI examples for runs, storage, multimodal input, knowledge, memory, caching, thinking, structured output, and tools. - [Retry](https://docs.agno.com/examples/models/vertexai/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [Watsonx](https://docs.agno.com/examples/models/ibm/watsonx/overview.md): Run IBM watsonx models with basic responses, tools, knowledge, storage, retries, and structured output. - [IBM Basic](https://docs.agno.com/examples/models/ibm/watsonx/basic.md): Run a watsonx Mistral Small agent in sync, async, and streaming modes. - [DB](https://docs.agno.com/examples/models/ibm/watsonx/db.md): Persist watsonx agent sessions in Postgres with history added to context. - [IBM Image Agent Bytes](https://docs.agno.com/examples/models/ibm/watsonx/image-agent-bytes.md): Pass JPEG bytes to a vision-capable Mistral Small model on watsonx. - [Knowledge](https://docs.agno.com/examples/models/ibm/watsonx/knowledge.md): Search a PgVector knowledge base loaded from a PDF with a watsonx agent. - [IBM Structured Output](https://docs.agno.com/examples/models/ibm/watsonx/structured-output.md): Request JSON from a watsonx agent and validate it locally as a MovieScript. - [Tool Use](https://docs.agno.com/examples/models/ibm/watsonx/tool-use.md): Stream web search results through a watsonx agent, sync and async. - [Retry](https://docs.agno.com/examples/models/ibm/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [IBM](https://docs.agno.com/examples/models/ibm/overview.md): IBM watsonx examples for model retries, storage, knowledge, structured output, and tools. - [NVIDIA](https://docs.agno.com/examples/models/nvidia/overview.md): NVIDIA API examples: basic runs, retry configuration, and tool use. - [NVIDIA Basic](https://docs.agno.com/examples/models/nvidia/basic.md): Run Mistral Nemotron through the NVIDIA API in sync, async, and streaming modes. - [Retry](https://docs.agno.com/examples/models/nvidia/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [Tool Use](https://docs.agno.com/examples/models/nvidia/tool-use.md): Use web search tools with Mistral Nemotron served through the NVIDIA API. - [Cerebras](https://docs.agno.com/examples/models/cerebras/overview.md): Cerebras examples: basic runs, storage, knowledge, structured output, retries, and tool use. - [Cerebras Basic](https://docs.agno.com/examples/models/cerebras/basic.md): Compare synchronous, asynchronous, and streaming calls with the Cerebras model client. - [DB](https://docs.agno.com/examples/models/cerebras/db.md): Store Cerebras agent sessions in Postgres so follow-up questions keep context. - [Knowledge](https://docs.agno.com/examples/models/cerebras/knowledge.md): Answer questions from a PDF knowledge base in PgVector with a Cerebras agent. - [Cerebras OSS GPT](https://docs.agno.com/examples/models/cerebras/oss-gpt.md): Run gpt-oss-120b on Cerebras with web search via WebSearchTools. - [Retry](https://docs.agno.com/examples/models/cerebras/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [Cerebras Structured Output](https://docs.agno.com/examples/models/cerebras/structured-output.md): Compare strict and guided structured output on Cerebras with a MovieScript schema. - [Cerebras Tool Use](https://docs.agno.com/examples/models/cerebras/tool-use.md): Call WebSearchTools from a Cerebras agent in synchronous and asynchronous modes. - [xAI](https://docs.agno.com/examples/models/xai/overview.md): xAI model examples for building agents with Grok, including vision, web search, and financial analysis. - [xAI Basic](https://docs.agno.com/examples/models/xai/basic.md): Run a Grok agent in sync, async, and streaming modes with xAI. - [Finance Agent](https://docs.agno.com/examples/models/xai/finance-agent.md): Use YFinanceTools with Grok to report stock prices, analyst recommendations, company data, and news. - [xAI Image Agent](https://docs.agno.com/examples/models/xai/image-agent.md): Describe an image URL with Grok 4.5 and search for related news. - [xAI Image Agent Bytes](https://docs.agno.com/examples/models/xai/image-agent-bytes.md): Analyze downloaded image bytes with Grok 4.5. - [xAI Image Agent With Memory](https://docs.agno.com/examples/models/xai/image-agent-with-memory.md): Analyze an image with Grok 4.5 and retain history for follow-up questions. - [xAI Live Search Agent](https://docs.agno.com/examples/models/xai/live-search-agent.md): Request a news digest using the current xAI Responses web search tool. - [xAI Live Search Agent Stream](https://docs.agno.com/examples/models/xai/live-search-agent-stream.md): Stream a news digest using the current xAI Responses web search tool. - [xAI Reasoning Agent](https://docs.agno.com/examples/models/xai/reasoning-agent.md): Use ReasoningTools and explicit financial data tools with Grok 4.3 for a stock report. - [Retry](https://docs.agno.com/examples/models/xai/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [xAI Structured Output](https://docs.agno.com/examples/models/xai/structured-output.md): Request JSON and validate a MovieScript with Pydantic through Grok 4.3. - [Build a Web Search Agent using xAI](https://docs.agno.com/examples/models/xai/tool-use.md): Build a web search agent on Grok and run it sync, streaming, and async. - [xAI SuperGrok Chat Sign-In](https://docs.agno.com/examples/models/xai/oauth-chat-signin.md): Sign in with a SuperGrok subscription from inside the conversation. - [xAI SuperGrok Device Login](https://docs.agno.com/examples/models/xai/oauth-device-login.md): Sign in with a SuperGrok subscription instead of an API key and run an agent through xAIResponses. - [xAI SuperGrok Per-User Sign-In](https://docs.agno.com/examples/models/xai/oauth-multi-user.md): Several people share one deployment and each spends their own SuperGrok subscription. - [Responses](https://docs.agno.com/examples/models/ollama/responses/overview.md): Ollama examples on the OpenAI-compatible /v1/responses endpoint: basic runs, structured output, and tool use. - [Basic](https://docs.agno.com/examples/models/ollama/responses/basic.md): Use Ollama's OpenAI-compatible /v1/responses endpoint with an Agent. - [Structured Output](https://docs.agno.com/examples/models/ollama/responses/structured-output.md): Request JSON and validate a MovieScript with Pydantic through Ollama's local Responses API. - [Tool Use](https://docs.agno.com/examples/models/ollama/responses/tool-use.md): Call DuckDuckGoTools through the local Ollama Responses endpoint. - [Ollama Basic](https://docs.agno.com/examples/models/ollama/chat/basic.md): Run a local Llama 3.1 agent on Ollama in sync, async, and streaming modes. - [DB](https://docs.agno.com/examples/models/ollama/chat/db.md): Store local Ollama sessions in Postgres and include earlier turns. - [Ollama Demo DeepSeek R1](https://docs.agno.com/examples/models/ollama/chat/demo-deepseek-r1.md): Ask a local DeepSeek-R1 model to write Python for quadratic equations and explain its reasoning. - [Ollama Demo Gemma](https://docs.agno.com/examples/models/ollama/chat/demo-gemma.md): Stream a short story about a local image from Gemma 3 12B running on Ollama. - [Ollama Demo Phi4](https://docs.agno.com/examples/models/ollama/chat/demo-phi4.md): Run Microsoft Phi-4 locally through Ollama with a one-shot story prompt. - [Ollama Demo Qwen](https://docs.agno.com/examples/models/ollama/chat/demo-qwen.md): Build an NVDA stock report with Qwen3 on Ollama using YFinanceTools. - [Ollama Image Agent](https://docs.agno.com/examples/models/ollama/chat/image-agent.md): Describe a local image file with Llama 3.2 Vision running on Ollama. - [Knowledge](https://docs.agno.com/examples/models/ollama/chat/knowledge.md): Search a PgVector knowledge base embedded with OllamaEmbedder from a local Llama agent. - [Memory](https://docs.agno.com/examples/models/ollama/chat/memory.md): Persist user memories and session summaries in Postgres with a local qwen2.5 Ollama agent. - [Ollama Cloud](https://docs.agno.com/examples/models/ollama/chat/ollama-cloud.md): Run gpt-oss:120b on Ollama Cloud with OLLAMA_API_KEY instead of a local Ollama server. - [Retry](https://docs.agno.com/examples/models/ollama/chat/retry.md): Understand the native Ollama retry limitation and configure retries through its compatible endpoint. - [Set Client](https://docs.agno.com/examples/models/ollama/chat/set-client.md): Supply an explicit Ollama Python client to a local agent. - [Ollama Set Temperature](https://docs.agno.com/examples/models/ollama/chat/set-temperature.md): Set sampling temperature on an Ollama model through the options dict. - [Ollama Structured Output](https://docs.agno.com/examples/models/ollama/chat/structured-output.md): Request JSON and validate a MovieScript with Pydantic through the native local Ollama API. - [Tool Use](https://docs.agno.com/examples/models/ollama/chat/tool-use.md): Call WebSearchTools from a local Llama 3.2 agent. - [Chat](https://docs.agno.com/examples/models/ollama/chat/overview.md): Use native Ollama chat for local and cloud models, images, history, knowledge, and tools. - [Ollama](https://docs.agno.com/examples/models/ollama/overview.md): Ollama Chat and Responses API examples for local and cloud models, knowledge, memory, reasoning, structured output, and tools. - [vLLM](https://docs.agno.com/examples/models/vllm/overview.md): vLLM is a fast and easy-to-use library for running LLM models locally. - [vLLM Basic](https://docs.agno.com/examples/models/vllm/basic.md): Run a Qwen2.5-7B agent on a local vLLM server in sync, async, and streaming modes. - [Code Generation](https://docs.agno.com/examples/models/vllm/code-generation.md): Code generation example with DeepSeek-Coder. - [DB](https://docs.agno.com/examples/models/vllm/db.md): Store a vLLM agent's session history in Postgres and reuse it across turns. - [Memory](https://docs.agno.com/examples/models/vllm/memory.md): Personalized memory and session summaries with vLLM. - [Retry](https://docs.agno.com/examples/models/vllm/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [vLLM Structured Output](https://docs.agno.com/examples/models/vllm/structured-output.md): Request JSON and validate a MovieScript with Pydantic through vLLM. - [vLLM Tool Use](https://docs.agno.com/examples/models/vllm/tool-use.md): Add web search tools to a vLLM agent and stream responses sync and async. - [Lmstudio](https://docs.agno.com/examples/models/lmstudio/overview.md): LM Studio examples for local models, images, knowledge, memory, storage, retries, structured output, and tools. - [Lmstudio Basic](https://docs.agno.com/examples/models/lmstudio/basic.md): Run a Qwen model served by LM Studio with sync and streamed responses. - [DB](https://docs.agno.com/examples/models/lmstudio/db.md): Store LM Studio agent sessions in Postgres and answer follow-ups with history in context. - [Lmstudio Image Agent](https://docs.agno.com/examples/models/lmstudio/image-agent.md): Send image bytes to a llama3.2-vision model in LM Studio and stream the description. - [Knowledge](https://docs.agno.com/examples/models/lmstudio/knowledge.md): Query a PgVector knowledge base of PDF recipes from an agent running on LM Studio. - [Memory](https://docs.agno.com/examples/models/lmstudio/memory.md): Store and retrieve personalized user memories and conversation summaries in an LM Studio agent. - [Retry](https://docs.agno.com/examples/models/lmstudio/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [Lmstudio Structured Output](https://docs.agno.com/examples/models/lmstudio/structured-output.md): Request a MovieScript with JSON Schema and validate the model response locally. - [Tool Use](https://docs.agno.com/examples/models/lmstudio/tool-use.md): Add web search tools to an LM Studio agent and run sync and streaming queries. - [Llama Cpp](https://docs.agno.com/examples/models/llama-cpp/overview.md): Run agents against a local llama.cpp server serving `ggml-org/gpt-oss-20b-GGUF` at `http://127.0.0.1:8080/v1`. - [Llama Cpp Basic](https://docs.agno.com/examples/models/llama-cpp/basic.md): Run a local GGUF model with LlamaCpp and print sync and streamed responses. - [Retry](https://docs.agno.com/examples/models/llama-cpp/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [Llama Cpp Structured Output](https://docs.agno.com/examples/models/llama-cpp/structured-output.md): Request a MovieScript with JSON Schema and validate the model response locally. - [Tool Use](https://docs.agno.com/examples/models/llama-cpp/tool-use.md): Give a local LlamaCpp model web search tools and stream the tool-assisted answer. - [Aimlapi](https://docs.agno.com/examples/models/aimlapi/overview.md): AIML API examples for basic runs, multimodal input, memory, retries, structured output, and tool use. - [Aimlapi Basic](https://docs.agno.com/examples/models/aimlapi/basic.md): Run an AIMLAPI agent four ways: sync, async, and streaming variants of each. - [Aimlapi Image Agent](https://docs.agno.com/examples/models/aimlapi/image-agent.md): Use the current gateway adaptation to analyze an image URL through AIMLAPI. - [Aimlapi Image Agent Bytes](https://docs.agno.com/examples/models/aimlapi/image-agent-bytes.md): Use the current gateway adaptation to analyze raw image bytes through AIMLAPI. - [Aimlapi Image Agent With Memory](https://docs.agno.com/examples/models/aimlapi/image-agent-with-memory.md): Analyze an image through AIMLAPI and retain it for a follow-up using a database. - [Retry](https://docs.agno.com/examples/models/aimlapi/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [Aimlapi Structured Output](https://docs.agno.com/examples/models/aimlapi/structured-output.md): Return a typed MovieScript from AIML API using JSON mode and a Pydantic output schema. - [Tool Use](https://docs.agno.com/examples/models/aimlapi/tool-use.md): Answer current-events questions with web search tools on an AIML API model, sync and async. - [Cerebras OpenAI](https://docs.agno.com/examples/models/cerebras-openai/overview.md): Run Cerebras models through the OpenAI-compatible endpoint: streaming, tools, structured output, storage, and knowledge. - [Cerebras OpenAI Basic](https://docs.agno.com/examples/models/cerebras-openai/basic.md): Compare synchronous, asynchronous, and streaming calls through Cerebras' OpenAI-compatible endpoint. - [DB](https://docs.agno.com/examples/models/cerebras-openai/db.md): Store CerebrasOpenAI agent sessions in Postgres with history added to context. - [Knowledge](https://docs.agno.com/examples/models/cerebras-openai/knowledge.md): Query a PgVector knowledge base built from a PDF with a CerebrasOpenAI agent. - [Cerebras OpenAI OSS GPT](https://docs.agno.com/examples/models/cerebras-openai/oss-gpt.md): Use gpt-oss-120b on the Cerebras OpenAI-compatible endpoint with WebSearchTools. - [Cerebras OpenAI Structured Output](https://docs.agno.com/examples/models/cerebras-openai/structured-output.md): Return a typed MovieScript from a CerebrasOpenAI agent with output_schema. - [Cerebras OpenAI Tool Use](https://docs.agno.com/examples/models/cerebras-openai/tool-use.md): Call WebSearchTools from a CerebrasOpenAI agent in sync and async modes. - [Clients](https://docs.agno.com/examples/models/clients/overview.md): Pass custom HTTP clients to model instances for headers, logging and connection reuse. - [Custom HTTP Clients](https://docs.agno.com/examples/models/clients/http-client-caching.md): Pass a custom HTTP client to model instances for request headers, logging and connection reuse. - [Cloudflare](https://docs.agno.com/examples/models/cloudflare/overview.md): Cloudflare AI Gateway model examples. - [Cloudflare AI Gateway (basic)](https://docs.agno.com/examples/models/cloudflare/basic.md): Run a Workers AI model through Cloudflare AI Gateway in sync, async, and streaming modes. - [Cloudflare AI Gateway: switching models (OpenRouter-style ids)](https://docs.agno.com/examples/models/cloudflare/switch-model.md): Switch Cloudflare AI Gateway models by passing a catalog binding to `Cloudflare(id=...)` or a `cloudflare:` model string. - [Cloudflare AI Gateway: tool use](https://docs.agno.com/examples/models/cloudflare/tool-use.md): Give a Cloudflare AI Gateway agent web search with WebSearchTools and stream the tool-calling run. - [Cloudflare Structured Output](https://docs.agno.com/examples/models/cloudflare/structured-output.md): Generate JSON with Cloudflare Workers AI and validate it locally as a MovieScript. - [Cometapi](https://docs.agno.com/examples/models/cometapi/overview.md): Run GPT, Claude, Gemini, DeepSeek, and Qwen models through CometAPI's OpenAI-compatible gateway. - [Cometapi Basic](https://docs.agno.com/examples/models/cometapi/basic.md): Run a GPT model through the CometAPI gateway in sync, async, and streaming modes. - [Image analysis example using CometAPI with vision models](https://docs.agno.com/examples/models/cometapi/image-agent.md): Stream an image description from a vision model through CometAPI. - [Image analysis with memory example using CometAPI](https://docs.agno.com/examples/models/cometapi/image-agent-with-memory.md): Analyze an image with CometAPI and include SQLite session history in a follow-up request. - [Multi Model](https://docs.agno.com/examples/models/cometapi/multi-model.md): Test and compare different models available through CometAPI. - [Retry](https://docs.agno.com/examples/models/cometapi/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [CometAPI Structured Output](https://docs.agno.com/examples/models/cometapi/structured-output.md): Request JSON from CometAPI and validate it locally as a MovieScript. - [Cometapi Tool Use](https://docs.agno.com/examples/models/cometapi/tool-use.md): Call WebSearchTools through CometAPI for prices, weather, and news queries. - [Dashscope](https://docs.agno.com/examples/models/dashscope/overview.md): Browse DashScope model examples with Qwen models, image analysis, knowledge tools, and retry patterns. - [Dashscope Basic](https://docs.agno.com/examples/models/dashscope/basic.md): Run Qwen Plus on DashScope with sync, async, and streaming responses. - [DashScope Image Agent](https://docs.agno.com/examples/models/dashscope/image-agent.md): Analyze image URLs with a tool-capable Qwen3-VL model and enrich answers with web search. - [Dashscope Image Agent Bytes](https://docs.agno.com/examples/models/dashscope/image-agent-bytes.md): Analyze image bytes with a tool-capable Qwen3-VL model and search the web for context. - [Knowledge Tools](https://docs.agno.com/examples/models/dashscope/knowledge-tools.md): Use OpenAI embeddings with LanceDB for hybrid search to build a knowledge-augmented DashScope agent. - [Retry](https://docs.agno.com/examples/models/dashscope/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [Dashscope Structured Output](https://docs.agno.com/examples/models/dashscope/structured-output.md): Request JSON from Qwen Plus on DashScope and validate it locally as a MovieScript. - [Dashscope Thinking Agent](https://docs.agno.com/examples/models/dashscope/thinking-agent.md): Enable thinking on DashScope's QVQ Max to reason through an image problem. - [DashScope Tool Use](https://docs.agno.com/examples/models/dashscope/tool-use.md): Give a DashScope qwen-plus agent web search tools and stream sync and async responses. - [DeepInfra](https://docs.agno.com/examples/models/deepinfra/overview.md): DeepInfra examples: basic agent runs, JSON output, tool use, and retries. - [DeepInfra Basic](https://docs.agno.com/examples/models/deepinfra/basic.md): Run DeepSeek V3 on DeepInfra with synchronous, asynchronous, and streaming responses. - [DeepInfra JSON Output](https://docs.agno.com/examples/models/deepinfra/json-output.md): Request JSON from DeepInfra phi-4 and validate it locally as a MovieScript. - [Retry](https://docs.agno.com/examples/models/deepinfra/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [Tool Use](https://docs.agno.com/examples/models/deepinfra/tool-use.md): Add web search to a DeepSeek V3 agent on DeepInfra and stream synchronous and asynchronous responses. - [Inception](https://docs.agno.com/examples/models/inception/overview.md): Inception Labs Mercury model examples. - [Inception Basic](https://docs.agno.com/examples/models/inception/basic.md): Run an Inception Mercury 2 agent in sync, async, and streaming modes. - [Inception Tool Use](https://docs.agno.com/examples/models/inception/tool-use.md): Add web search tools to an Inception Mercury 2 agent and stream the response. - [Inception Structured Output](https://docs.agno.com/examples/models/inception/structured-output.md): Compare JSON mode and native JSON Schema requests with Inception Mercury 2. - [Internlm](https://docs.agno.com/examples/models/internlm/overview.md): Configure InternLM API access and review model retry behavior. - [Retry](https://docs.agno.com/examples/models/internlm/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [LangDB](https://docs.agno.com/examples/models/langdb/overview.md): Run LangDB models with basic responses, tools, retries, and structured output. - [Agent](https://docs.agno.com/examples/models/langdb/agent.md): Fetch NVDA and TSLA stock prices with YFinance tools through LangDB. - [LangDB Basic](https://docs.agno.com/examples/models/langdb/basic.md): Run a Llama 3.1 70B agent through the LangDB gateway with streaming. - [Data Analyst](https://docs.agno.com/examples/models/langdb/data-analyst.md): Load an IMDB movie CSV into DuckDB and query it with a LangDB agent. - [Finance Agent](https://docs.agno.com/examples/models/langdb/finance-agent.md): Summarize stock fundamentals with a YFinance analyst agent on LangDB. - [Retry](https://docs.agno.com/examples/models/langdb/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [LangDB Structured Output](https://docs.agno.com/examples/models/langdb/structured-output.md): Compare JSON Object and JSON Schema requests through LangDB with local MovieScript validation. - [Web Search](https://docs.agno.com/examples/models/langdb/web-search.md): Answer current-events questions with web search tools through LangDB. - [LiteLLM](https://docs.agno.com/examples/models/litellm/overview.md): Call providers through the LiteLLM Python SDK with tools, knowledge, history, structured output and media. - [LiteLLM Audio Input Agent](https://docs.agno.com/examples/models/litellm/audio-input-agent.md): Pass an MP3 file as audio input to a gpt-audio model routed through LiteLLM. - [LiteLLM Basic](https://docs.agno.com/examples/models/litellm/basic.md): Run a Hugging Face Mistral model through LiteLLM with sync, async, and streaming calls. - [LiteLLM Basic GPT](https://docs.agno.com/examples/models/litellm/basic-gpt.md): Call OpenAI's gpt-5.6-luna through the LiteLLM model class for a basic agent response. - [DB](https://docs.agno.com/examples/models/litellm/db.md): Persist LiteLLM agent sessions in SQLite and answer follow-ups with chat history in context. - [LiteLLM Image Agent](https://docs.agno.com/examples/models/litellm/image-agent.md): Analyze an image from a URL with gpt-5.6-luna via LiteLLM and pull related news using web search. - [LiteLLM Image Agent Bytes](https://docs.agno.com/examples/models/litellm/image-agent-bytes.md): Send an image as raw bytes to gpt-5.6-luna via LiteLLM and fetch related news with web search. - [LiteLLM Knowledge](https://docs.agno.com/examples/models/litellm/knowledge.md): Answer questions from a PDF knowledge base stored in PgVector using a LiteLLM agent. - [LiteLLM Memory](https://docs.agno.com/examples/models/litellm/memory.md): Keep up to three previous runs in context and inspect session messages using an in-memory database. - [LiteLLM Metrics](https://docs.agno.com/examples/models/litellm/metrics.md): Inspect per-message and run-level token metrics from a LiteLLM agent using YFinance tools. - [LiteLLM PDF Input Bytes](https://docs.agno.com/examples/models/litellm/pdf-input-bytes.md): Pass a downloaded PDF as raw bytes to a LiteLLM agent and summarize its contents. - [LiteLLM PDF Input Local](https://docs.agno.com/examples/models/litellm/pdf-input-local.md): Attach a local PDF file to a LiteLLM agent and ask questions about a specific recipe. - [LiteLLM PDF Input URL](https://docs.agno.com/examples/models/litellm/pdf-input-url.md): Attach a PDF by URL to a LiteLLM agent and ask for a recipe from the document. - [LiteLLM Reasoning Agent Example](https://docs.agno.com/examples/models/litellm/reasoning-agent.md): Stream DeepSeek V4 Flash reasoning content through the LiteLLM SDK. - [Retry](https://docs.agno.com/examples/models/litellm/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [LiteLLM Structured Output](https://docs.agno.com/examples/models/litellm/structured-output.md): Request JSON mode and JSON Schema through LiteLLM, then validate responses with MovieScript. - [LiteLLM Tool Use](https://docs.agno.com/examples/models/litellm/tool-use.md): Call YFinance tools from a LiteLLM agent with sync, streaming, and async runs. - [LiteLLM Append Trailing User Message](https://docs.agno.com/examples/models/litellm/append-trailing-user-message.md): Append a trailing user turn so Claude 4.6+ models that reject assistant prefill work through LiteLLM. - [LiteLLM OpenAI](https://docs.agno.com/examples/models/litellm-openai/overview.md): Connect an Agno agent to a local LiteLLM proxy using its OpenAI-compatible HTTP API. - [Audio Input Agent](https://docs.agno.com/examples/models/litellm-openai/audio-input-agent.md): Send an MP3 to gpt-audio through a local LiteLLM proxy with LiteLLMOpenAI. - [LiteLLM OpenAI Basic](https://docs.agno.com/examples/models/litellm-openai/basic.md): Run GPT-5.6 Luna through a LiteLLM proxy server with sync and streaming responses. - [Tool Use](https://docs.agno.com/examples/models/litellm-openai/tool-use.md): Give a GPT-5.6 Luna agent web search tools through a LiteLLM proxy. - [MiniMax](https://docs.agno.com/examples/models/minimax/overview.md): MiniMax M3 agent examples: basic runs, web search tool use, and JSON requests with local validation. - [MiniMax Basic](https://docs.agno.com/examples/models/minimax/basic.md): Run a MiniMax M3 agent across sync, async, and streaming response modes. - [MiniMax Tool Use](https://docs.agno.com/examples/models/minimax/tool-use.md): Equip a MiniMax M3 agent with WebSearchTools and stream a news query. - [MiniMax Structured Output](https://docs.agno.com/examples/models/minimax/structured-output.md): Request JSON from MiniMax M3 and validate the response locally with Pydantic. - [Moonshot](https://docs.agno.com/examples/models/moonshot/overview.md): Kimi K3 runs, files, reasoning effort, schemas, and tools, plus K2.6 thinking control. - [Moonshot Basic](https://docs.agno.com/examples/models/moonshot/basic.md): Run a Moonshot Kimi K3 thinking agent with and without response streaming. - [Moonshot Tool Use](https://docs.agno.com/examples/models/moonshot/tool-use.md): Add WebSearchTools to a Moonshot Kimi K3 agent and stream a current-events answer. - [Moonshot File Input](https://docs.agno.com/examples/models/moonshot/file-input.md): Upload a file to Kimi, extract its text, and reuse retrievable file IDs across stored history. - [Moonshot Reasoning Effort](https://docs.agno.com/examples/models/moonshot/reasoning-effort.md): Kimi K3 always reasons before answering. - [Moonshot Structured Output](https://docs.agno.com/examples/models/moonshot/structured-output.md): Compare JSON Schema and JSON Object requests with Kimi K3 and validate a movie script. - [Moonshot Thinking Mode](https://docs.agno.com/examples/models/moonshot/thinking-mode.md): Toggle thinking for Kimi K2.6; Kimi K3 always reasons. - [N1N](https://docs.agno.com/examples/models/n1n/overview.md): N1N gateway examples: running OpenAI models via N1N with basic streaming and web-search tool calls. - [N1N Basic](https://docs.agno.com/examples/models/n1n/basic.md): Run GPT-5.6 Luna through the N1N gateway, with and without streaming. - [N1N Tool Use](https://docs.agno.com/examples/models/n1n/tool-use.md): Call web search tools from GPT-5 mini running through the N1N gateway. - [Nebius](https://docs.agno.com/examples/models/nebius/overview.md): Nebius model examples: basic runs, Postgres sessions, PgVector knowledge, retries, structured output, and tool use. - [Nebius Basic](https://docs.agno.com/examples/models/nebius/basic.md): Prompt a Nebius agent synchronously, asynchronously, and with streaming. - [DB](https://docs.agno.com/examples/models/nebius/db.md): Store Nebius agent sessions in Postgres and carry history across a follow-up question. - [Knowledge](https://docs.agno.com/examples/models/nebius/knowledge.md): Answer recipe questions using a Token Factory model and a PgVector knowledge base. - [Retry](https://docs.agno.com/examples/models/nebius/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [Nebius Structured Output](https://docs.agno.com/examples/models/nebius/structured-output.md): Request a MovieScript schema from a Token Factory model and validate the response. - [Nebius Tool Use](https://docs.agno.com/examples/models/nebius/tool-use.md): Use WebSearchTools with a Token Factory model across sync, async, and streaming runs. - [Neosantara](https://docs.agno.com/examples/models/neosantara/overview.md): Neosantara examples covering basic runs, structured output, and web-search tool use. - [Neosantara Basic](https://docs.agno.com/examples/models/neosantara/basic.md): Run a Grok model on Neosantara with sync, async, and streaming responses. - [Neosantara Structured Output](https://docs.agno.com/examples/models/neosantara/structured-output.md): Request JSON and validate a MovieScript with Pydantic through Neosantara. - [Neosantara Tool Use](https://docs.agno.com/examples/models/neosantara/tool-use.md): Answer stock price questions on Neosantara with WebSearchTools, sync and async. - [Nexus](https://docs.agno.com/examples/models/nexus/overview.md): Nexus examples covering basic runs, retry configuration, and tool use. - [Nexus Basic](https://docs.agno.com/examples/models/nexus/basic.md): Run Claude Sonnet 4.6 through a local Nexus gateway in sync, async, and streaming modes. - [Retry](https://docs.agno.com/examples/models/nexus/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [Tool Use](https://docs.agno.com/examples/models/nexus/tool-use.md): Use web search tools with Claude Sonnet 4.6 through a local Nexus gateway. - [Responses](https://docs.agno.com/examples/models/openrouter/responses/overview.md): Access multiple AI models through a unified, stateless API. - [Basic Usage](https://docs.agno.com/examples/models/openrouter/responses/basic.md): Run openai/gpt-oss-20b through OpenRouterResponses with reasoning enabled in sync and async modes. - [Fallback Routing](https://docs.agno.com/examples/models/openrouter/responses/fallback.md): Configure ordered OpenRouter fallback models for unavailable primary routes. - [Streaming](https://docs.agno.com/examples/models/openrouter/responses/stream.md): Stream an OpenRouter Responses agent's output with print_response(stream=True). - [Structured Output](https://docs.agno.com/examples/models/openrouter/responses/structured-output.md): Use of Pydantic models for structured output with OpenRouter's Responses API. - [Tools](https://docs.agno.com/examples/models/openrouter/responses/tool-use.md): Call DuckDuckGoTools from an OpenRouter Responses agent and stream the answer. - [Chat](https://docs.agno.com/examples/models/openrouter/chat/overview.md): OpenRouter Chat API examples: basic agents, image generation, ordered model fallback, retries, structured output, and tool use. - [OpenRouter Basic](https://docs.agno.com/examples/models/openrouter/chat/basic.md): Run a minimal OpenRouter agent with sync, streaming, and async response variants. - [Dynamic Model Router](https://docs.agno.com/examples/models/openrouter/chat/dynamic-model-router.md): Configure an OpenRouter agent with an ordered fallback model list so a request failing on rate limits, timeouts, or overload retries on the next model. - [Retry](https://docs.agno.com/examples/models/openrouter/chat/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [OpenRouter Structured Output](https://docs.agno.com/examples/models/openrouter/chat/structured-output.md): Compare JSON mode and native structured outputs on OpenRouter by generating a MovieScript Pydantic model. - [Tools](https://docs.agno.com/examples/models/openrouter/chat/tool-use.md): Give an OpenRouter agent WebSearchTools and stream the answer in both sync and async runs. - [Image Generation](https://docs.agno.com/examples/models/openrouter/chat/image-generation.md): Generate an image through OpenRouter, decode the returned bytes, and display the result with Pillow. - [OpenRouter](https://docs.agno.com/examples/models/openrouter/overview.md): OpenRouter Chat and Responses API examples for model routing, retries, structured output, and tools. - [Perplexity](https://docs.agno.com/examples/models/perplexity/overview.md): Index of Perplexity sonar-pro agent examples: basic runs, knowledge, memory, retries, structured output, and web search. - [Perplexity Basic](https://docs.agno.com/examples/models/perplexity/basic.md): Run a basic Perplexity sonar-pro agent and print sync, streaming, and async responses. - [Knowledge](https://docs.agno.com/examples/models/perplexity/knowledge.md): Answer questions from a PDF knowledge base in PgVector using a Perplexity sonar-pro agent. - [Memory](https://docs.agno.com/examples/models/perplexity/memory.md): Use Perplexity for answers and an OpenAI memory manager to persist user memories in Postgres. - [Retry](https://docs.agno.com/examples/models/perplexity/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [Perplexity Structured Output](https://docs.agno.com/examples/models/perplexity/structured-output.md): Request a MovieScript JSON schema from Perplexity sonar-pro and validate the response. - [Perplexity Web Search](https://docs.agno.com/examples/models/perplexity/web-search.md): Use Perplexity sonar-pro's native web search to answer a current news question. - [Portkey](https://docs.agno.com/examples/models/portkey/overview.md): Index of Agno examples routing agents through the Portkey AI gateway: basic runs, retries, structured output, and tool use. - [Portkey Basic](https://docs.agno.com/examples/models/portkey/basic.md): Route a basic agent through the Portkey AI gateway with sync, streaming, and async runs. - [Retry](https://docs.agno.com/examples/models/portkey/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [Portkey Structured Output](https://docs.agno.com/examples/models/portkey/structured-output.md): Request a MovieScript schema through Portkey and validate the response with Pydantic. - [Portkey Tool Use](https://docs.agno.com/examples/models/portkey/tool-use.md): Call web search tools from an agent routed through the Portkey gateway. - [Requesty](https://docs.agno.com/examples/models/requesty/overview.md): Index of Agno examples routing agents through the Requesty gateway: basic runs, retries, structured output, and tool use. - [Requesty Basic](https://docs.agno.com/examples/models/requesty/basic.md): Run a minimal agent through the Requesty gateway with sync, streaming, and async calls. - [Retry](https://docs.agno.com/examples/models/requesty/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [Requesty Structured Output](https://docs.agno.com/examples/models/requesty/structured-output.md): Request JSON through Requesty and validate a MovieScript with Pydantic. - [Tool Use](https://docs.agno.com/examples/models/requesty/tool-use.md): Stream a web-search agent through the Requesty gateway, sync and async. - [Sambanova](https://docs.agno.com/examples/models/sambanova/overview.md): SambaNova model examples: basic sync/stream/async runs and retry configuration. - [Sambanova Basic](https://docs.agno.com/examples/models/sambanova/basic.md): Run Llama 3.3 70B on SambaCloud with sync, streaming, and async calls. - [Retry](https://docs.agno.com/examples/models/sambanova/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [Siliconflow](https://docs.agno.com/examples/models/siliconflow/overview.md): Examples for SiliconFlow model integration. - [Siliconflow Basic](https://docs.agno.com/examples/models/siliconflow/basic.md): Run gpt-oss-120b on SiliconFlow with sync, streaming, and async response calls. - [Retry](https://docs.agno.com/examples/models/siliconflow/retry.md): Review retry settings and why invalid model IDs cannot reliably exercise the retry path. - [Siliconflow Structured Output](https://docs.agno.com/examples/models/siliconflow/structured-output.md): Request JSON through SiliconFlow and validate a MovieScript with Pydantic. - [Tool Use](https://docs.agno.com/examples/models/siliconflow/tool-use.md): Call web search from a SiliconFlow model with function-calling support. - [Vercel](https://docs.agno.com/examples/models/vercel/overview.md): Historical Vercel v0 Model API example index. - [Vercel Basic](https://docs.agno.com/examples/models/vercel/basic.md): Historical Vercel v0 Model API example; use the current alternatives below. - [Vercel Image Agent](https://docs.agno.com/examples/models/vercel/image-agent.md): Historical Vercel v0 Model API example; use the current alternatives below. - [Knowledge](https://docs.agno.com/examples/models/vercel/knowledge.md): Historical Vercel v0 Model API example; use the current alternatives below. - [Retry](https://docs.agno.com/examples/models/vercel/retry.md): Historical Vercel v0 Model API example; use the current alternatives below. - [Vercel v0 Tool Use](https://docs.agno.com/examples/models/vercel/tool-use.md): Historical Vercel v0 Model API example; use the current alternatives below. - [Xiaomi MiMo](https://docs.agno.com/examples/models/xiaomi/overview.md): Xiaomi MiMo agent examples for basic runs, string model shorthand, web search, structured output, thinking mode, and reasoning. - [Xiaomi MiMo Basic](https://docs.agno.com/examples/models/xiaomi/basic.md): The minimal MiMo agent, run four ways: sync, sync + streaming, async, and async + streaming. - [Xiaomi MiMo String Model](https://docs.agno.com/examples/models/xiaomi/string-model.md): Create a MiMo agent without importing the model class, using the `model="xiaomi:"` string shorthand. - [Xiaomi MiMo Tool Use](https://docs.agno.com/examples/models/xiaomi/tool-use.md): Give the agent a web search tool and let it call tools while thinking mode is on (`use_thinking=True`). - [Xiaomi MiMo Structured Output](https://docs.agno.com/examples/models/xiaomi/structured-output.md): Request JSON and validate a MovieScript with Pydantic through MiMo. - [Xiaomi MiMo Thinking Mode](https://docs.agno.com/examples/models/xiaomi/thinking-mode.md): Toggle thinking mode with the `use_thinking` flag. - [Xiaomi MiMo Reasoning Agent](https://docs.agno.com/examples/models/xiaomi/reasoning-agent.md): Solve a logic puzzle with thinking mode on. - [Basic](https://docs.agno.com/examples/models/tuning-engines/basic.md): Use Agno with Tuning Engines as an OpenAI-compatible endpoint. - [Router Basic](https://docs.agno.com/examples/models/ramp/basic.md): Run RampRouter directly and through a model string in sync, async, and streaming modes. - [Router Fallback](https://docs.agno.com/examples/models/ramp/fallback.md): Configure Router candidate fallback before streaming starts, with separate startup and idle deadlines. - [Router Structured Output](https://docs.agno.com/examples/models/ramp/structured-output.md): Request JSON through Router and validate a MovieScript with Pydantic. - [Router Tool Use](https://docs.agno.com/examples/models/ramp/tool-use.md): Give a RampRouter agent WebSearchTools and run it in sync, async, and streaming modes. - [Synthorai Basic](https://docs.agno.com/examples/models/synthorai/basic.md): Generate short prose with Synthorai in synchronous and streaming modes. - [Synthorai Tool Use](https://docs.agno.com/examples/models/synthorai/tool-use.md): Give a Synthorai agent WebSearchTools and stream a current-events response. - [TokenLab Basic](https://docs.agno.com/examples/models/tokenlab/basic.md): Run TokenLab in sync, async, and streaming modes. - [TrustedRouter Basic](https://docs.agno.com/examples/models/trustedrouter/basic.md): Run TrustedRouter with synchronous and asynchronous streaming calls. - [TrustedRouter Structured Output](https://docs.agno.com/examples/models/trustedrouter/structured-output.md): Request JSON through TrustedRouter and validate a MovieScript with Pydantic. - [TrustedRouter Tool Use](https://docs.agno.com/examples/models/trustedrouter/tool-use.md): Give a TrustedRouter agent WebSearchTools and run it in sync and async streaming modes. - [Tools](https://docs.agno.com/examples/tools/overview.md): Examples for using and creating tools in Agno. - [Overview](https://docs.agno.com/examples/tools/tool-hooks/overview.md): Using Tool Hooks with Agno agents. - [Pre And Post Hooks](https://docs.agno.com/examples/tools/tool-hooks/pre-and-post-hooks.md): Log tool arguments and results with pre_hook and post_hook callbacks, sync and async. - [Tool Hook](https://docs.agno.com/examples/tools/tool-hooks/tool-hook.md): Run logic before and after tool execution with a tool hook. - [Tool Hook in Toolkit](https://docs.agno.com/examples/tools/tool-hooks/tool-hook-in-toolkit.md): Block deletion and retrieval of customer ID 123 with a validation tool_hook wrapped around a custom Toolkit. - [Tool Hook in Toolkit with State](https://docs.agno.com/examples/tools/tool-hooks/tool-hook-in-toolkit-with-state.md): Swap a customer ID for the full profile stored in session_state by rewriting tool arguments inside a tool_hook. - [Tool Hook in Toolkit with State Nested](https://docs.agno.com/examples/tools/tool-hooks/tool-hook-in-toolkit-with-state-nested.md): Chain two nested tool hooks so the first swaps a customer ID for the profile stored in session_state before the toolkit function runs. - [Tool Hooks in Toolkit Nested](https://docs.agno.com/examples/tools/tool-hooks/tool-hooks-in-toolkit-nested.md): Nest validation and logging hooks around toolkit tools to block customer ID 123 and strip the name field from results, in sync and async variants. - [Message History In Hooks](https://docs.agno.com/examples/tools/tool-hooks/message-history-in-hooks.md): Access the current run's message history inside tool pre/post hooks via run_context.messages. - [Resilience and Error Handling](https://docs.agno.com/examples/tools/exceptions/overview.md): Build reliable agents using retries, post-hook error management, and explicit stop conditions. - [Retry Tool Call](https://docs.agno.com/examples/tools/exceptions/retry-tool-call.md): Raise RetryAgentRun after adding an item to ask the model for more items. - [Post-Hook Retry](https://docs.agno.com/examples/tools/exceptions/retry-tool-call-from-post-hook.md): Request another model decision from a tool post-hook with RetryAgentRun. - [Stop Agent Exception](https://docs.agno.com/examples/tools/exceptions/stop-agent-exception.md): Raise StopAgentRun inside a tool to halt the agent run before it completes. - [Overview](https://docs.agno.com/examples/tools/tool-decorator/overview.md): Index of @tool decorator examples: sync and async tools, class methods, hooks, instructions, caching, and stop-after-tool-call. - [Tool Decorator](https://docs.agno.com/examples/tools/tool-decorator/tool-decorator.md): Decorate generator functions and class static methods with @tool, including show_result and async variants. - [Async Tool Decorator](https://docs.agno.com/examples/tools/tool-decorator/async-tool-decorator.md): Define an async generator tool with @tool and stream its yielded results. - [Tool Decorator On Class Method](https://docs.agno.com/examples/tools/tool-decorator/tool-decorator-on-class-method.md): Apply @tool to Toolkit class methods so decorated functions stay bound to the instance. - [Tool Decorator with Hook](https://docs.agno.com/examples/tools/tool-decorator/tool-decorator-with-hook.md): Attach a custom duration-logging hook to a tool via @tool(tool_hooks=[...]) to time each tool call. - [Tool Decorator With Instructions](https://docs.agno.com/examples/tools/tool-decorator/tool-decorator-with-instructions.md): Attach usage instructions, a name, and a description to a tool via @tool parameters. - [Cache Tool Calls](https://docs.agno.com/examples/tools/tool-decorator/cache-tool-calls.md): Combine cache_results and stop_after_tool_call on a @tool-decorated function. - [Stop After Tool Call](https://docs.agno.com/examples/tools/tool-decorator/stop-after-tool-call.md): Return a tool result without a further model response using stop_after_tool_call. - [Toolkit Per-Tool Instructions](https://docs.agno.com/examples/tools/tool-decorator/toolkit-per-tool-instructions.md): Verify per-tool instructions from a bare @tool function and a Toolkit both reach agent._tool_instructions via parse_tools. - [Overview](https://docs.agno.com/examples/tools/mcp/overview.md): MCPTools examples for local, hosted, authenticated, and multi-server MCP connections. - [Overview](https://docs.agno.com/examples/tools/mcp/mcp-toolbox-demo/overview.md): Run Agno agents and workflows against a PostgreSQL database through the MCP Toolbox for Databases server. - [Agent](https://docs.agno.com/examples/tools/mcp/mcp-toolbox-demo/agent.md): Load MCPToolbox hotel and booking toolsets for an interactive CLI agent, with a documented limitation on the manual-loading alternative. - [Agent OS](https://docs.agno.com/examples/tools/mcp/mcp-toolbox-demo/agent-os.md): Serve a hotel booking assistant in AgentOS using MCPToolbox hotel and booking toolsets. - [Hotel Management Typesafe](https://docs.agno.com/examples/tools/mcp/mcp-toolbox-demo/hotel-management-typesafe.md): Typed hotel search with Pydantic input and output schemas over MCPToolbox database tools. - [Sequential Workflow Demo: Hotel Search → Hotel Booking](https://docs.agno.com/examples/tools/mcp/mcp-toolbox-demo/hotel-management-workflows.md): Chain hotel search and booking agents in a workflow, each with its own scoped MCP Toolbox toolset. - [Overview](https://docs.agno.com/examples/tools/mcp/dynamic-headers/overview.md): Dynamically send information to the MCP server via HTTP headers. - [Client](https://docs.agno.com/examples/tools/mcp/dynamic-headers/client.md): Send per-user headers to an MCP server with a header_provider that reads RunContext fields. - [Server](https://docs.agno.com/examples/tools/mcp/dynamic-headers/server.md): FastMCP server with a greet tool that reads user and tenant IDs from incoming HTTP headers. - [Overview](https://docs.agno.com/examples/tools/mcp/local-server/overview.md): Run a local FastMCP weather server over stdio and connect an Agno agent to it with MCPTools. - [Client](https://docs.agno.com/examples/tools/mcp/local-server/client.md): Launch a local FastMCP server by command and connect an agent to it with MCPTools. - [FastMCP Local Server](https://docs.agno.com/examples/tools/mcp/local-server/server.md): Serve weather tools from a minimal FastMCP stdio server. - [Overview](https://docs.agno.com/examples/tools/mcp/sse-transport/overview.md): Connect agents to SSE MCP servers with MCPTools. - [Client](https://docs.agno.com/examples/tools/mcp/sse-transport/client.md): Connect an OpenAI agent to one SSE MCP server, then combine separate SSE and stdio MCPTools instances. - [Server](https://docs.agno.com/examples/tools/mcp/sse-transport/server.md): Start an example MCP server that uses the SSE transport. - [Overview](https://docs.agno.com/examples/tools/mcp/streamable-http-transport/overview.md): Connect agents to Streamable HTTP MCP servers with MCPTools. - [Client](https://docs.agno.com/examples/tools/mcp/streamable-http-transport/client.md): Show how to connect to MCP servers that use the Streamable HTTP transport using our MCPTools class. - [Server](https://docs.agno.com/examples/tools/mcp/streamable-http-transport/server.md): Start an example MCP server that uses the Streamable HTTP transport. - [Agno MCP](https://docs.agno.com/examples/tools/mcp/agno-mcp.md): Query the Agno docs MCP server over streamable HTTP with MCPTools and a Claude agent. - [MCP Airbnb Agent - Search for Airbnb listings!](https://docs.agno.com/examples/tools/mcp/airbnb.md): Search Airbnb listings with an OpenAI gpt-5.6-luna agent connected to the @openbnb/mcp-server-airbnb stdio MCP server. - [MCP Brave Agent - Search for Brave](https://docs.agno.com/examples/tools/mcp/brave.md): Create an agent that uses Anthropic to search for information using the Brave MCP server. - [MCP CLI](https://docs.agno.com/examples/tools/mcp/cli.md): Run an interactive CLI chat loop against a GitHub MCP server agent. - [MCP Filesystem Agent - Your Personal File Explorer!](https://docs.agno.com/examples/tools/mcp/filesystem.md): Create a filesystem agent that uses MCP to explore, analyze, and provide insights about files and directories. - [GibsonAI MCP Server](https://docs.agno.com/examples/tools/mcp/gibsonai.md): Connect a local GibsonAI MCP server to an Agno agent. - [MCP GitHub Agent - Your Personal GitHub Explorer!](https://docs.agno.com/examples/tools/mcp/github.md): Create a GitHub agent that uses MCP to explore, analyze, and provide insights about GitHub repositories. - [MCP Graphiti Agent - A personal diary assistant](https://docs.agno.com/examples/tools/mcp/graphiti.md): Use Agno's MCP integration together with Graphiti, to build a personal diary assistant. - [Groq + MCP = Lightning Fast Agents](https://docs.agno.com/examples/tools/mcp/groq-mcp.md): Create a high-performance filesystem agent by combining Groq's fast LLM inference with the Model Context Protocol (MCP). - [Include Exclude Tools](https://docs.agno.com/examples/tools/mcp/include-exclude-tools.md): Filter which MCP server tools an agent can use with include_tools and exclude_tools. - [Include Tools](https://docs.agno.com/examples/tools/mcp/include-tools.md): Limit an MCP filesystem server to three functions with the include_tools filter. - [MCP Toolbox for DB](https://docs.agno.com/examples/tools/mcp/mcp-toolbox-for-db.md): Connect an agent to an MCP Toolbox for Databases server and load hotel-management and booking-system toolsets via MCPToolbox. - [‍ Mem0 MCP - Personalized Code Reviewer](https://docs.agno.com/examples/tools/mcp/mem0.md): Use Agno's MCP integration together with Mem0, to build a personalized code reviewer. - [Multiple Servers](https://docs.agno.com/examples/tools/mcp/multiple-servers.md): Use multiple MCP servers in a single agent. - [Multiple Servers with Partial Failure](https://docs.agno.com/examples/tools/mcp/multiple-servers-allow-partial-failure.md): Migrate the removed multi-server partial-failure example to supported MCPTools instances. - [Notion MCP Agent](https://docs.agno.com/examples/tools/mcp/notion-mcp-agent.md): Query and update connected Notion pages through the official Notion MCP server. - [Oxylabs](https://docs.agno.com/examples/tools/mcp/oxylabs.md): Connect a Gemini agent to the Oxylabs MCP server to scrape a careers page for job titles. - [MCP Parallel Agent](https://docs.agno.com/examples/tools/mcp/parallel.md): Create an agent that uses Parallel for searching information using the Parallel MCP server. - [Using Pipedream MCP servers with authentication](https://docs.agno.com/examples/tools/mcp/pipedream-auth.md): Call an authenticated Pipedream MCP server over streamable HTTP, passing a bearer token plus project and environment headers on behalf of an end user. - [Pipedream Google Calendar MCP](https://docs.agno.com/examples/tools/mcp/pipedream-google-calendar.md): Review a legacy Google Calendar MCP integration that uses Pipedream's retired per-app SSE endpoint. - [Pipedream LinkedIn MCP](https://docs.agno.com/examples/tools/mcp/pipedream-linkedin.md): Review a legacy LinkedIn MCP integration that uses Pipedream's retired per-app SSE endpoint. - [Pipedream Slack MCP](https://docs.agno.com/examples/tools/mcp/pipedream-slack.md): Review a legacy Slack MCP integration that uses Pipedream's retired per-app SSE endpoint. - [Qdrant](https://docs.agno.com/examples/tools/mcp/qdrant.md): Store and retrieve agent output in a Qdrant collection through the Qdrant MCP server. - [Sequential Thinking](https://docs.agno.com/examples/tools/mcp/sequential-thinking.md): Combine the sequential-thinking MCP server with YFinanceTools to reason step by step over stock data. - [Stagehand MCP Agent - Hacker News Reader's Digest](https://docs.agno.com/examples/tools/mcp/stagehand.md): Drive a Browserbase Stagehand MCP browser to scrape Hacker News headlines and comments into a structured reader's digest. - [Stripe MCP Agent](https://docs.agno.com/examples/tools/mcp/stripe.md): Create an Agno agent that interacts with the Stripe API via MCP. - [Supabase MCP Agent](https://docs.agno.com/examples/tools/mcp/supabase.md): Use the Supabase MCP server to create projects, database schemas, and edge functions. - [Tool Name Prefix](https://docs.agno.com/examples/tools/mcp/tool-name-prefix.md): Add a prefix to the name of your MCP tools. - [MCP BGPT Agent - Evidence-grounded scientific paper search](https://docs.agno.com/examples/tools/mcp/bgpt.md): Query the hosted BGPT MCP server over Streamable HTTP to search papers and surface methods, sample sizes, limitations, and conflicts of interest. - [Emem](https://docs.agno.com/examples/tools/mcp/emem.md): MCP emem Agent - Shared Memory for AI Agents Working in the Real World. - [Peer Cash MCP Agent - Prepare custody-separated cash-outs](https://docs.agno.com/examples/tools/mcp/peer-cash.md): This example connects Agno to Peer Cash through its published MCP server. - [Structured Content Agent](https://docs.agno.com/examples/tools/mcp/structured-content.md): Some MCP servers declare an output schema and return their answer as `structuredContent`. - [Baidusearch Tools](https://docs.agno.com/examples/tools/baidusearch-tools.md): Search Baidu in English and Chinese and return the top three results on a topic. - [Bravesearch Tools](https://docs.agno.com/examples/tools/bravesearch-tools.md): Fetch the latest news on a topic with BraveSearchTools, enabling specific or all functions. - [DuckDuckGo Tools](https://docs.agno.com/examples/tools/duckduckgo-tools.md): Toggle DuckDuckGo search and news functions, plus WebSearchTools for other backends like Yandex. - [Exa Tools](https://docs.agno.com/examples/tools/exa-tools.md): Search, answer, and find similar pages with ExaTools, restricting results to specific news domains. - [Firecrawl Tools](https://docs.agno.com/examples/tools/firecrawl-tools.md): This is an example of how to use the FirecrawlTools. - [HackerNews Tools](https://docs.agno.com/examples/tools/hackernews-tools.md): Pull top HackerNews stories with HackerNewsTools and stream a report on trending startups. - [Jinareader Tools](https://docs.agno.com/examples/tools/jinareader-tools.md): Fetch a GitHub page with JinaReaderTools and have the agent summarize its content. - [Linkup Tools](https://docs.agno.com/examples/tools/linkup-tools.md): Answer a current-events question with web results from LinkupTools. - [Newspaper Tools](https://docs.agno.com/examples/tools/newspaper-tools.md): Read and summarize an article from a URL with NewspaperTools. - [Newspaper4K Tools](https://docs.agno.com/examples/tools/newspaper4k-tools.md): Summarize a blog post from its URL with the Newspaper4kTools article reader. - [Reddit](https://docs.agno.com/examples/tools/reddit-tools.md): Query subreddit posts, stats, and trends with RedditTools using praw script-app credentials. - [Scrapegraph Tools](https://docs.agno.com/examples/tools/scrapegraph-tools.md): Extract structured data, markdown, and raw HTML from a page using ScrapeGraphTools smartscraper, markdownify, and scrape. - [Searxng Tools](https://docs.agno.com/examples/tools/searxng-tools.md): Query a self-hosted SearxNG instance at localhost:53153 for web, news, and science results. - [Serpapi Tools](https://docs.agno.com/examples/tools/serpapi-tools.md): Run Google and YouTube searches through SerpAPI with per-function enable flags. - [Serper](https://docs.agno.com/examples/tools/serper-tools.md): Run Google web, Scholar, and page-scrape queries via SerperTools using a SERPER_API_KEY. - [Spider Tools](https://docs.agno.com/examples/tools/spider-tools.md): Search, scrape, and crawl websites into LLM-ready Markdown with SpiderTools. - [Tavily Tools](https://docs.agno.com/examples/tools/tavily-tools.md): Search the web and extract page content with Tavily, tuning depth and output format. - [Tavily Tools - Advanced Search Parameters](https://docs.agno.com/examples/tools/tavily-tools-advanced.md): Demonstrates scoping Tavily web search with the advanced parameters: domain restriction, recency, topic, and country localization. - [Trafilatura Tools](https://docs.agno.com/examples/tools/trafilatura-tools.md): Extract clean page text with TrafilaturaTools across txt, markdown, JSON and XML output, precision/recall tuning, metadata-only mode, crawling and HTML-to-text. - [Web Tools](https://docs.agno.com/examples/tools/web-tools.md): Expand a shortened URL to its destination. - [WebBrowser Tools](https://docs.agno.com/examples/tools/webbrowser-tools.md): Search for a page with WebSearchTools and open it in a local browser with WebBrowserTools. - [WebSearch Tools](https://docs.agno.com/examples/tools/websearch-tools.md): Search the web and news with WebSearchTools across DuckDuckGo, Google, Bing, and Brave backends. - [You.com Tools](https://docs.agno.com/examples/tools/youcom-tools.md): Search the web with YouTools, including a domain-allowlisted agent limited to cnbc.com, reuters.com and bloomberg.com. - [Adanos Market Sentiment](https://docs.agno.com/examples/tools/adanos-tools.md): Research stock sentiment across Reddit, X, financial news, and Polymarket with AdanosTools. - [ArXiv Tools - Academic Paper Search and Research](https://docs.agno.com/examples/tools/arxiv-tools.md): Configure ArxivTools with enable_search_arxiv / enable_read_arxiv_papers and all=True to control which ArXiv functions an agent can call. - [CSV Tools](https://docs.agno.com/examples/tools/csv-tools.md): CSV Tools - Data Analysis and Processing for CSV Files. - [Custom API](https://docs.agno.com/examples/tools/custom-api-tools.md): Call arbitrary REST endpoints with CustomApiTools, hitting the dog.ceo API for a random dog image and the full breed list. - [DuckDB Tools](https://docs.agno.com/examples/tools/duckdb-tools.md): Query a remote IMDB movie CSV with SQL using DuckDbTools to compute average ratings. - [File Generation Tools](https://docs.agno.com/examples/tools/file-generation-tools.md): Generate JSON, CSV, PDF, TXT, DOCX and HTML files from agent responses with FileGenerationTools, persisting them to an output directory. - [File Tools - File System Operations and Management](https://docs.agno.com/examples/tools/file-tools.md): Use FileTools for file operations including reading, writing, searching files, and searching file contents. - [Financial Datasets](https://docs.agno.com/examples/tools/financial-datasets-tools.md): Query income statements and balance sheets through the current Financial Datasets API contracts. - [Google BigQuery Tools](https://docs.agno.com/examples/tools/google-bigquery-tools.md): Inspect a BigQuery dataset with a Vertex AI-powered agent. - [Google Sheets Tools](https://docs.agno.com/examples/tools/googlesheets-tools.md): Read a configured range with GoogleSheetsTools using service-account or OAuth credentials. - [Knowledge Tool](https://docs.agno.com/examples/tools/knowledge-tool.md): Let an agent and team write new facts into a PgVector knowledge base with update_knowledge. - [Neo4j](https://docs.agno.com/examples/tools/neo4j-tools.md): Translate natural language into Cypher with Neo4jTools to inspect the schema, list labels and traverse multi-hop relationships. - [Openbb Tools](https://docs.agno.com/examples/tools/openbb-tools.md): Pull stock prices, company news, and price targets with OpenBBTools function toggles. - [Pandas Tools - Data Analysis and DataFrame Operations](https://docs.agno.com/examples/tools/pandas-tools.md): Use PandasTools for data manipulation and analysis. - [Postgres Tools](https://docs.agno.com/examples/tools/postgres-tools.md): Inspect and query a Postgres database with PostgresTools, including read-only tool filtering. - [Pubmed Tools](https://docs.agno.com/examples/tools/pubmed-tools.md): Search PubMed for medical research papers with PubmedTools function toggles. - [Amazon Redshift Tools Example](https://docs.agno.com/examples/tools/redshift-tools.md): Use Amazon Redshift tools with Agno agents. - [SQL Tools](https://docs.agno.com/examples/tools/sql-tools.md): List tables and query a Postgres database from a connection URL with SQLTools. - [Wikipedia Tools](https://docs.agno.com/examples/tools/wikipedia-tools.md): Search Wikipedia and optionally add results to a knowledge base with WikipediaTools. - [Yfinance Tools](https://docs.agno.com/examples/tools/yfinance-tools.md): YFinance Tools - Stock Market Analysis and Financial Data. - [YouTube Tools](https://docs.agno.com/examples/tools/youtube-tools.md): Fetch YouTube video captions and answer questions about the video with YouTubeTools. - [Calcom Tools](https://docs.agno.com/examples/tools/calcom-tools.md): Use Calcom scheduling with Agno Agents. - [Discord Tools](https://docs.agno.com/examples/tools/discord-tools.md): Send messages, read history, inspect channels, and delete messages with DiscordTools. - [Email Tools](https://docs.agno.com/examples/tools/email-tools.md): Send an email from an agent with EmailTools configured with sender credentials and a receiver. - [Google Calendar](https://docs.agno.com/examples/tools/googlecalendar-tools.md): List, create, update and delete Google Calendar events and find free slots via OAuth with GoogleCalendarTools. - [Notion](https://docs.agno.com/examples/tools/notion-tools.md): Auto-categorize content into a tagged Notion database and search or update existing pages with NotionTools. - [Plivo](https://docs.agno.com/examples/tools/plivo-tools.md): Send SMS messages, place calls, and look up numbers with PlivoTools. - [Resend Tools](https://docs.agno.com/examples/tools/resend-tools.md): Send an email from your agent with ResendTools and a configured from address. - [Slack Tools](https://docs.agno.com/examples/tools/slack-tools.md): Compare all-tools, selected-function, and read-only SlackTools configurations for messaging, channel listing, history, and file access. - [Telegram](https://docs.agno.com/examples/tools/telegram-tools.md): Send text messages to a Telegram chat by default and enable additional TelegramTools functions explicitly. - [Todoist Tools](https://docs.agno.com/examples/tools/todoist-tools.md): Example showing how to use the Todoist Tools with Agno. - [Trello](https://docs.agno.com/examples/tools/trello-tools.md): Create and organize Trello boards, lists, and cards from an agent with TrelloTools. - [Twilio](https://docs.agno.com/examples/tools/twilio-tools.md): Send SMS messages and retrieve call or message details with TwilioTools. - [Webex](https://docs.agno.com/examples/tools/webex-tools.md): List Webex spaces and post messages to them from an agent with WebexTools. - [WhatsApp Cookbook](https://docs.agno.com/examples/tools/whatsapp-tools.md): Use WhatsApp integration with Agno. - [Zendesk Tools](https://docs.agno.com/examples/tools/zendesk-tools.md): Answer support questions by searching Zendesk help center articles with ZendeskTools. - [Zoom](https://docs.agno.com/examples/tools/zoom-tools.md): List Zoom meetings with Server-to-Server OAuth and understand the scheduling adapter limitation. - [Brandfetch](https://docs.agno.com/examples/tools/brandfetch-tools.md): Retrieve company brand assets and metadata with BrandfetchTools using the Brand and Brand Search APIs. - [Calculator Tools](https://docs.agno.com/examples/tools/calculator-tools.md): Solve step-by-step arithmetic with CalculatorTools, filtering functions via include and exclude lists. - [Cartesia](https://docs.agno.com/examples/tools/cartesia-tools.md): Generate speech with CartesiaTools and save the agent's audio response to an MP3 file. - [Dalle](https://docs.agno.com/examples/tools/dalle-tools.md): Legacy DalleTools reference for configuring deprecated DALL-E models. - [Desi Vocal](https://docs.agno.com/examples/tools/desi-vocal-tools.md): Reference the retired DesiVocalTools integration and migrate text-to-speech agents to an active provider. - [Elevenlabs](https://docs.agno.com/examples/tools/elevenlabs-tools.md): Produce voice audio and sound effects with ElevenLabsTools on a Gemini-powered agent and save the result as MP3. - [Fal Tools](https://docs.agno.com/examples/tools/fal-tools.md): Generate a video from a text prompt with FalTools and the fal-ai/hunyuan-video model. - [Giphy Tools](https://docs.agno.com/examples/tools/giphy-tools.md): Search Giphy for a fitting GIF with GiphyTools, limiting results and enabled functions. - [Lumalabs Tools](https://docs.agno.com/examples/tools/lumalabs-tools.md): Generate videos from text prompts or images with LumaLabTools and the Luma AI API. - [MLX Transcribe Tools](https://docs.agno.com/examples/tools/mlx-transcribe-tools.md): MLX Transcribe: A tool for transcribing audio files using MLX Whisper. - [Models Lab Tools](https://docs.agno.com/examples/tools/models-lab-tools.md): Configure ModelsLabTools agents for image, video, and audio generation, then run image and sound-effect prompts. - [Moviepy Video Tools](https://docs.agno.com/examples/tools/moviepy-video-tools.md): Extract audio, transcribe it, and embed SRT captions into a video with MoviePyVideoTools. - [OpenCV](https://docs.agno.com/examples/tools/opencv-tools.md): Capture webcam images and video with OpenCVTools, using enable_ flags and show_preview to scope camera access. - [Replicate Tools](https://docs.agno.com/examples/tools/replicate-tools.md): Generate images and video on Replicate models with the generate_media tool. - [Spotify Tools](https://docs.agno.com/examples/tools/spotify-tools.md): Explore Spotify catalog search and the playlist recipe for eligible Extended Quota Mode apps. - [Unsplash](https://docs.agno.com/examples/tools/unsplash-tools.md): Search, fetch, and randomly sample Unsplash photos, with opt-in download tracking for API compliance. - [Visualization Tools](https://docs.agno.com/examples/tools/visualization-tools.md): Generate bar, line, pie, scatter, and histogram charts with VisualizationTools and matplotlib, scoped per agent via enable_ flags and output_dir. - [AgentQL](https://docs.agno.com/examples/tools/agentql-tools.md): Scrape pages with AgentQLTools, using the default extractor or a custom AgentQL query over a live Playwright browser. - [Airflow](https://docs.agno.com/examples/tools/airflow-tools.md): Save and read Airflow DAG files from a dags_dir with AirflowTools, toggling save/read access via enable_ flags. - [Apify](https://docs.agno.com/examples/tools/apify-tools.md): Use Apify actors as agent tools for web data extraction. - [AWS Lambda](https://docs.agno.com/examples/tools/aws-lambda-tools.md): List and invoke AWS Lambda functions in a chosen region with AWSLambdaTools, gating each operation with enable_ flags. - [AWS SES](https://docs.agno.com/examples/tools/aws-ses-tools.md): Research AI news with web search and email the summary from a verified SES sender address using AWSSESTool. - [Bitbucket](https://docs.agno.com/examples/tools/bitbucket-tools.md): List open pull requests, repositories and commits for a Bitbucket workspace and repo slug with BitbucketTools. - [Brightdata Tools](https://docs.agno.com/examples/tools/brightdata-tools.md): Scrape a webpage as Markdown with BrightDataTools, using include and exclude tool filters. - [Browserbase Tools](https://docs.agno.com/examples/tools/browserbase-tools.md): Navigate quotes.toscrape.com and extract paginated quotes and authors with BrowserbaseTools. - [ClickUp Tools](https://docs.agno.com/examples/tools/clickup-tools.md): List ClickUp spaces and manage tasks with ClickUpTools using CLICKUP_API_KEY and MASTER_SPACE_ID. - [Composio Tools](https://docs.agno.com/examples/tools/composio-tools.md): Give an agent a Composio GitHub action and ask it to star a repository. - [Confluence Tools](https://docs.agno.com/examples/tools/confluence-tools.md): List Confluence spaces, read page content, and create new pages with ConfluenceTools. - [Crawl4AI Tools - Web Scraping and Content Extraction](https://docs.agno.com/examples/tools/crawl4ai-tools.md): Define three Crawl4aiTools configurations and execute the two pruning configurations. - [Custom Tool Events](https://docs.agno.com/examples/tools/custom-tool-events.md): Yield a custom CustomEvent subclass from an async @tool and consume it while streaming agent.arun(). - [Custom Tools](https://docs.agno.com/examples/tools/custom-tools.md): Pass plain Python functions returning dicts, generators, Pydantic models, and dataclasses as tools. - [Daytona](https://docs.agno.com/examples/tools/daytona-tools.md): Run agent-generated Python, JavaScript, and TypeScript in a remote Daytona sandbox with file and shell operations. - [Basic Examples](https://docs.agno.com/examples/tools/docling-tools/basic-examples.md): Convert PDFs, DOCX, HTML, and more to Markdown, JSON, YAML, and other formats with DoclingTools. - [OCR Example](https://docs.agno.com/examples/tools/docling-tools/ocr-example.md): Convert PDFs with DoclingTools using forced full-page EasyOCR in Portuguese and English. - [Paths](https://docs.agno.com/examples/tools/docling-tools/paths.md): Helper module that resolves test document paths for the Docling tool examples. - [Docling Tools Run](https://docs.agno.com/examples/tools/docling-tools/run.md): Entry point that runs the basic conversion and OCR Docling examples in sequence. - [Docker Tools](https://docs.agno.com/examples/tools/docker-tools.md): Configure DockerTools with include_tools and exclude_tools for container-management, delete-free, and full-access agents. - [E2B Tools](https://docs.agno.com/examples/tools/e2b-tools.md): Run Python, manage files, and expose sandbox servers with E2BTools. - [EVM](https://docs.agno.com/examples/tools/evm-tools.md): Send native-token transfers on an EIP-1559-compatible chain using an RPC URL and private key. - [GitHub](https://docs.agno.com/examples/tools/github-tools.md): Query agno-agi/agno repositories, issues, and pull requests with GithubTools using include_tools and exclude_tools filters. - [Jira Tools](https://docs.agno.com/examples/tools/jira-tools.md): Search Jira issues, fetch details, and log work with different JiraTools configurations. - [Linear](https://docs.agno.com/examples/tools/linear-tools.md): Create, update, and query Linear issues by id, assignee, and priority with LinearTools. - [MCP Tools](https://docs.agno.com/examples/tools/mcp-tools.md): Connect MCPTools to the filesystem MCP server over a stdio session and summarize a file. - [Mem0](https://docs.agno.com/examples/tools/mem0-tools.md): Add, search, list, and delete user memories across sessions with Mem0Tools using all-functions and restricted configurations. - [Multi-tools Usage](https://docs.agno.com/examples/tools/multiple-tools.md): Combine WebSearchTools and YFinanceTools in one agent to produce a streamed NVDA report from news and financials. - [Nano Banana](https://docs.agno.com/examples/tools/nano-banana-tools.md): Generate images with Google's Nano Banana model, including custom aspect ratios and saving output to disk. - [OpenWeather](https://docs.agno.com/examples/tools/openweather-tools.md): Fetch current weather, forecasts, air-quality data, and geocoding from the OpenWeather API inside an Agno agent. - [Oxylabs Tools](https://docs.agno.com/examples/tools/oxylabs-tools.md): Search Google through Oxylabs and summarize the top results with OxylabsTools. - [Parallel](https://docs.agno.com/examples/tools/parallel-tools.md): Give agents Parallel's Search, Task (deep research), and Monitor APIs for grounded web lookups, cited research, and scheduled web tracking. - [Python Function As Tool](https://docs.agno.com/examples/tools/python-function-as-tool.md): Pass a plain Python function to an agent as a tool to fetch top HackerNews stories. - [Python](https://docs.agno.com/examples/tools/python-tools.md): Let an agent write, save, and execute Python code and install packages with PythonTools, including include/exclude tool restrictions. - [Salesforce](https://docs.agno.com/examples/tools/salesforce-tools.md): Use Salesforce CRM with Agno agents to query, create, and manage records. - [Shell Tools](https://docs.agno.com/examples/tools/shell-tools.md): Let an agent run shell commands to list directory contents with ShellTools. - [Shopify](https://docs.agno.com/examples/tools/shopify-tools.md): Analyze a bounded sample of Shopify orders for sales and product bundles. - [Sleep Tools](https://docs.agno.com/examples/tools/sleep-tools.md): Pause agent execution for a set number of seconds with SleepTools. - [Superserve](https://docs.agno.com/examples/tools/superserve-tools.md): Use Superserve sandbox tools with Agno agents. - [Valyu](https://docs.agno.com/examples/tools/valyu-tools.md): Search academic papers, arXiv, and high-fidelity web sources with ValyuTools. - [X](https://docs.agno.com/examples/tools/x-tools.md): Read your X profile and home timeline, and post or reply, with XTools. - [Async generator tools with Pydantic BaseModel arguments](https://docs.agno.com/examples/tools/async-generator-tool-with-pydantic-args.md): Sync-generator, async-generator, and async-coroutine toolkit tools that each receive a Pydantic SearchParams model and stream custom progress events. - [DuckDuckGo Tools - Advanced Configuration](https://docs.agno.com/examples/tools/duckduckgo-tools-advanced.md): Configure DuckDuckGoTools with timelimit, region, backend, fixed_max_results, and timeout to build week-scoped, region-localized, and news-only search agents. - [GitLab Tools](https://docs.agno.com/examples/tools/gitlab-tools.md): List and summarize GitLab merge requests and issues with read-only GitlabTools functions. - [LLMs.txt Tools - Agentic Documentation Discovery](https://docs.agno.com/examples/tools/llms-txt-tools.md): Read an llms.txt index with LLMsTxtTools, then fetch only the documentation pages the agent decides are relevant. - [LLMs.txt Tools with Knowledge Base](https://docs.agno.com/examples/tools/llms-txt-tools-knowledge.md): Load up to twenty linked documentation pages into PgVector, then answer questions with semantic search. - [Local File System Tools](https://docs.agno.com/examples/tools/local-file-system-tools.md): Write and read files in a target directory with LocalFileSystemTools, including a write-only agent that disables read_file. - [Perplexity Search Tools](https://docs.agno.com/examples/tools/perplexity-tools.md): Search the web with PerplexitySearch, including a variant filtered by recency window and allowed domains. - [Scavio Tools](https://docs.agno.com/examples/tools/scavio-tools.md): Gate ScavioTools providers with enable_* flags to build web-only, commerce-only, and all-provider search agents over Google, YouTube, Amazon, Walmart, Reddit, TikTok, and Instagram. - [Scheduler Tools](https://docs.agno.com/examples/tools/scheduler-tools.md): Create and manage schedule records from a standalone agent; this page covers record operations only. - [SearchAPI Tools](https://docs.agno.com/examples/tools/searchapi-tools.md): Configure SearchApiTools for Google web, News, Images, and YouTube SERP results using per-engine enable flags or all=True. - [Sofya Tools](https://docs.agno.com/examples/tools/sofya-tools.md): Toggle SofyaTools between web search, markdown URL extraction, and cited deep-research report generation. - [Tool Calls Accessing the Agent](https://docs.agno.com/examples/tools/tool-calls-accesing-agent.md): Read agent.dependencies inside a tool by accepting the agent as a parameter. - [TwelveLabs Tools](https://docs.agno.com/examples/tools/twelvelabs-tools.md): Answer questions about a video URL with TwelveLabs Pegasus analyze_video, and generate Marengo multimodal embeddings with embed_text and embed_video. - [WebSearch Tools - Advanced Configuration](https://docs.agno.com/examples/tools/websearch-tools-advanced.md): Configure WebSearchTools time limits, regions, and DDGS backends for text and news search. - [x402scan MCP Tools](https://docs.agno.com/examples/tools/x402scan-mcp-tools.md): Connect an agent or researcher team to x402scan for wallet inspection and paid API access. - [Atomic Mail Workflow](https://docs.agno.com/examples/tools/atomic-mail-tools.md): Runs AtomicMailTools (https://atomicmail.ai) end to end inside a deterministic Agno workflow -- no LLM required. - [Knowledge Management Tools](https://docs.agno.com/examples/tools/knowledge-management-tools.md): An operator agent that manages a knowledge base by chat: ingest a website page by page, list what is loaded grouped by site, check a site's status, and remove content. - [MiniMax Tools](https://docs.agno.com/examples/tools/minimax-tools.md): Demonstrates MiniMax video generation tools. - [Openrouteservice Tools](https://docs.agno.com/examples/tools/openrouteservice-tools.md): OpenRouteService Tools - Accurate distance and travel time between locations. - [Redmine Tools](https://docs.agno.com/examples/tools/redmine-tools.md): Demonstrates the Redmine tools for searching, reading, creating, updating, and commenting on issues. - [Serply Tools](https://docs.agno.com/examples/tools/serply-tools.md): Demonstrates Serply tools for Google web, Google News, and Google Scholar search. - [Smallest AI text-to-speech tools](https://docs.agno.com/examples/tools/smallest-tools.md): Generate speech with Smallest AI Lightning voices and save the returned audio to a WAV file. - [WaveSpeed Tools](https://docs.agno.com/examples/tools/wavespeed-tools.md): Generate images and videos with WaveSpeedTools and return the raw media URL. - [Google Drive](https://docs.agno.com/examples/tools/google-drive.md): Search, read, upload, and download Google Drive files with OAuth or service account authentication. - [Google Maps](https://docs.agno.com/examples/tools/google-maps-tools.md): Use Google Maps with Agno agents. - [Azure OpenAI Tools](https://docs.agno.com/examples/tools/models/azure-openai-tools.md): Legacy AzureOpenAITools example for image generation with retired DALL-E deployments. - [Gemini Image Generation](https://docs.agno.com/examples/tools/models/gemini-image-generation.md): Legacy GeminiTools example using the retired Imagen endpoint in the Gemini API. - [Gemini Video Generation](https://docs.agno.com/examples/tools/models/gemini-video-generation.md): Migrate GeminiTools to Veo 3.1 on Vertex AI and save the returned MP4 correctly. - [Morph](https://docs.agno.com/examples/tools/models/morph.md): Simple example showing Morph Fast Apply with file creation and editing. - [Nebius Tools](https://docs.agno.com/examples/tools/models/nebius-tools.md): Legacy NebiusTools example for the discontinued Token Factory text-to-image service. - [OpenAI Tools](https://docs.agno.com/examples/tools/models/openai-tools.md): Transcribe audio and generate an image with OpenAITools and GPT Image 2. - [Models](https://docs.agno.com/examples/tools/models/overview.md): Model-backed toolkit examples for image and video generation, code editing, and provider-specific tools. - [Add Tool After Initialization](https://docs.agno.com/examples/tools/other/add-tool-after-initialization.md): Attach a new tool to an existing agent at runtime with add_tool(). - [Cache Tool Calls](https://docs.agno.com/examples/tools/other/cache-tool-calls.md): Cache YFinance tool results with cache_results=True to skip repeat API calls. - [Complex Input Types](https://docs.agno.com/examples/tools/other/complex-input-types.md): Pass nested Pydantic models and enums as tool arguments so the agent fills validated UserProfile and Task schemas. - [Human in the Loop](https://docs.agno.com/examples/tools/other/human-in-the-loop.md): Require user confirmation in a tool pre-hook before fetching Hacker News stories. - [Include Exclude Tools](https://docs.agno.com/examples/tools/other/include-exclude-tools.md): Filter toolkit functions with include_tools and exclude_tools on CalculatorTools and WebSearchTools. - [Include Exclude Tools Custom Toolkit](https://docs.agno.com/examples/tools/other/include-exclude-tools-custom-toolkit.md): Restrict a custom customer database Toolkit to read-only functions with include_tools. - [Session State Tool](https://docs.agno.com/examples/tools/other/session-state-tool.md): Read and write run_context.session_state inside a @tool to record the last FAQ answer across runs, persisted with SqliteDb. - [Stop After Tool Call](https://docs.agno.com/examples/tools/other/stop-after-tool-call.md): End the run after web_search executes with stop_after_tool_call_tools and show the raw result. - [Stop After Tool Call Dual Inheritance](https://docs.agno.com/examples/tools/other/stop-after-tool-call-dual-inheritance.md): Confirm stop_after_tool_call_tools flags still apply when a Toolkit subclass also inherits from a non-Toolkit base class. - [Stop After Tool Call in Toolkit](https://docs.agno.com/examples/tools/other/stop-after-tool-call-in-toolkit.md): Register Toolkit methods with stop_after_tool_call_tools so one tool halts the agent run while the others continue. - [Other](https://docs.agno.com/examples/tools/other/overview.md): Tool configuration examples for caching, runtime registration, input schemas, HITL, session state, filtering, and stop-after-call behavior. - [Seltz](https://docs.agno.com/examples/tools/seltz-tools.md): Search the web with SeltzTools and log raw tool results for debugging. - [Website Tools - Web Scraping and Content Analysis](https://docs.agno.com/examples/tools/website-tools.md): Scrape and summarize a web page with WebsiteTools, which registers its read_url function when no knowledge base is attached. - [Website Knowledge](https://docs.agno.com/examples/tools/website-tools-knowledge.md): Ingest web pages and PDFs into a PgVector knowledge base with WebsiteTools. - [Zep](https://docs.agno.com/examples/tools/zep-tools.md): Persist and recall user facts across sessions with ZepTools and ZepAsyncTools injected as agent context. - [Antigravity Agents Crud Tools](https://docs.agno.com/examples/tools/antigravity/antigravity-agents-crud-tools.md): Manage Antigravity custom agents via the Agents API toolkit. - [Antigravity Directory Tools](https://docs.agno.com/examples/tools/antigravity/antigravity-directory-tools.md): Use a local Antigravity agent directory through AntigravityTools. - [Antigravity Snapshot Tools](https://docs.agno.com/examples/tools/antigravity/antigravity-snapshot-tools.md): Download an Antigravity sandbox snapshot through AntigravityTools. - [Agent with Antigravity tools](https://docs.agno.com/examples/tools/antigravity/antigravity-tools.md): Use Agno's integration with Google's Gemini Agents API (Antigravity) as a tool. - [CodingTools: Minimal Tools for Coding Agents](https://docs.agno.com/examples/tools/coding-tools/basic-usage.md): A single toolkit with 4 core tools (read, edit, write, shell) that lets an agent perform any coding task. - [CodingTools: All 7 Tools Enabled](https://docs.agno.com/examples/tools/coding-tools/all-tools.md): Enable all tools including the exploration tools (grep, find, ls) by setting all=True or enabling them individually. - [Calendar Daily Briefing](https://docs.agno.com/examples/tools/google/calendar/daily-briefing.md): Summarizes today's schedule into a structured briefing with meeting prep notes. - [Calendar Event Creator](https://docs.agno.com/examples/tools/google/calendar/event-creator.md): Creates detailed calendar events from natural language descriptions. - [Calendar Meeting Scheduler](https://docs.agno.com/examples/tools/google/calendar/meeting-scheduler.md): Finds a time that works for all attendees and creates the meeting. - [Google Drive Tools](https://docs.agno.com/examples/tools/google/drive/basic.md): Core examples: read-only agent, full-access agent with upload and download. - [Drive Document Reader](https://docs.agno.com/examples/tools/google/drive/document-reader.md): Reads and summarizes large documents from Google Drive. - [Drive File Search](https://docs.agno.com/examples/tools/google/drive/file-search.md): Search and inspect Drive files with structured output. - [Drive Folder Organizer](https://docs.agno.com/examples/tools/google/drive/folder-organizer.md): Browses Drive folders with list_files and uploads or downloads local files with write tools enabled. - [Company-Wide Document Search](https://docs.agno.com/examples/tools/google/drive/shared-drive-search.md): Search across personal and shared drives to find documents organization-wide. - [Gmail Action Item Extractor](https://docs.agno.com/examples/tools/google/gmail/action-items.md): Extracts action items from email threads and returns a structured checklist. - [Basic](https://docs.agno.com/examples/tools/google/gmail/basic.md): Configure Gmail agents with include_tools/exclude_tools for read-only, send-blocked, label-management, and full-access variants. - [Gmail Daily Digest](https://docs.agno.com/examples/tools/google/gmail/daily-digest.md): Summarize recent emails into a structured daily digest grouped by category and tagged with priority. - [Gmail Draft Reply Agent](https://docs.agno.com/examples/tools/google/gmail/draft-reply.md): Reads a conversation thread and drafts a contextual reply. - [Gmail Follow-Up Tracker](https://docs.agno.com/examples/tools/google/gmail/followup-tracker.md): Finds sent emails that never received a reply and drafts follow-ups. - [Gmail Inbox Triage](https://docs.agno.com/examples/tools/google/gmail/inbox-triage.md): A personal inbox triage agent that learns your preferences across sessions. - [Action Item Tracker](https://docs.agno.com/examples/tools/google/sheets/action-tracker.md): Read meeting notes from a sheet, extract action items with owners and due dates, and write them to a tracking sheet. - [Sales Pipeline Forecaster](https://docs.agno.com/examples/tools/google/sheets/sales-pipeline.md): Read a deals spreadsheet, calculate weighted pipeline by stage, and forecast revenue. - [Google Slides Tools](https://docs.agno.com/examples/tools/google/slides/basic.md): Create, manage, and read Google Slides presentations. - [Slides Content Reader](https://docs.agno.com/examples/tools/google/slides/content-reader.md): Reads and summarizes content from existing Google Slides presentations. - [Slides Media and Rich Content](https://docs.agno.com/examples/tools/google/slides/media-slides.md): Adds images, YouTube videos, and styled backgrounds to presentations. - [Slides Presentation Builder](https://docs.agno.com/examples/tools/google/slides/presentation-builder.md): Creates a complete multi-slide presentation from a natural language brief. - [Document Workflow Agent](https://docs.agno.com/examples/tools/google/workspace/document-workflow.md): Work with Drive, Sheets, and Slides together for document workflows. - [Meeting Prep Agent (Calendar + Gmail)](https://docs.agno.com/examples/tools/google/workspace/meeting-prep.md): Prepares you for upcoming meetings by combining calendar and email context. - [Google Workspace Agent](https://docs.agno.com/examples/tools/google/workspace/multi-toolkit.md): Multi-toolkit agent with Gmail, Calendar, and Drive. - [Google Service Account Authentication](https://docs.agno.com/examples/tools/google/workspace/service-account.md): Authenticate Gmail, Calendar, Drive, and Sheets toolkits with a delegated Google Workspace service account. - [Task API: Company Data Enrichment](https://docs.agno.com/examples/tools/parallel/company-enrichment.md): Enrich CRM records or company databases with web intelligence. - [Monitor API: Competitive Intelligence](https://docs.agno.com/examples/tools/parallel/competitor-tracker.md): Track competitors for product launches, news, and strategic moves. - [Monitor API: Investment Tracking](https://docs.agno.com/examples/tools/parallel/investment-monitor.md): Track funding announcements, M&A activity, and market movements. - [Task API: Market Research Reports](https://docs.agno.com/examples/tools/parallel/market-research.md): Configure ParallelTools Task API with text and JSON output schemas to produce cited market reports and structured industry data. - [Search API: Fast Web Lookup](https://docs.agno.com/examples/tools/parallel/news-search.md): Build general, tech, and finance search agents with ParallelTools using include_domains filters and result-count limits. - [Task API: Output Schema Types](https://docs.agno.com/examples/tools/parallel/output-schemas.md): Configure the Parallel Task API's auto, JSON, string, and text output schemas, then run the JSON variant. - [Parallel Tools](https://docs.agno.com/examples/tools/parallel/parallel-tools.md): Answer research questions with the Parallel web search API and stream the response. - [Workspace: basic usage](https://docs.agno.com/examples/tools/workspace-tools/basic-usage.md): Read a local README and write a summary using Workspace permission lists. - [Workspace: human-in-the-loop confirmation](https://docs.agno.com/examples/tools/workspace-tools/workspace-tools-with-confirmation.md): Approve or reject Workspace write/edit/delete tool calls from the console using pause/resume with a SQLite-backed agent. - [Basic Advisor](https://docs.agno.com/examples/tools/advisor-tools/basic.md): The simplest usage of AdvisorTools: give your agent a single advisor model it can ask for feedback, a second opinion, or additional context. - [Multiple Advisors](https://docs.agno.com/examples/tools/advisor-tools/multi-advisor.md): Give the agent several advisors, each with a description of what it is good at. - [Escalation: Small Primary, Large Advisors](https://docs.agno.com/examples/tools/advisor-tools/escalation.md): A common pattern: run a small, fast, cheap model as the primary agent and let it escalate hard sub-problems to larger models. - [Custom Advisor System Message](https://docs.agno.com/examples/tools/advisor-tools/custom-system-message.md): Shape how advisors respond by overriding the `system_message` sent to them. - [Async Advisors](https://docs.agno.com/examples/tools/advisor-tools/async.md): All advisor tools have async variants. - [Market Brief with FinanceTools](https://docs.agno.com/examples/tools/finance/market-brief.md): Build a market brief with Yahoo Finance data and an OpenAI-powered agent. - [FinanceTools with financialdatasets.AI](https://docs.agno.com/examples/tools/finance/financial-datasets.md): Same agent as 01_market_brief.py, different data provider. - [Swap the Provider, Keep the Agent](https://docs.agno.com/examples/tools/finance/swap-provider.md): The point of FinanceTools: the agent is written once against a fixed tool surface, and the data provider is a configuration choice. - [Analyst Mode: Statements, Insiders, Earnings, Filings](https://docs.agno.com/examples/tools/finance/analyst-mode.md): By default FinanceTools registers the seven "market brief" tools. - [Async FinanceTools](https://docs.agno.com/examples/tools/finance/async.md): Every FinanceTools tool has an async variant registered under the same name, so `agent.arun()` / `agent.aprint_response()` use them automatically. - [Bring Your Own Provider](https://docs.agno.com/examples/tools/finance/custom-provider.md): A provider is a `FinanceProvider` subclass that declares which capabilities it serves and returns the normalized dataclasses from `agno.tools.finance`. - [Serve an Agent on WhatsApp](https://docs.agno.com/examples/agent-os/whatsapp/basic.md): Mount one Agent on AgentOS's WhatsApp interface. - [Send WhatsApp-Native Interactive Messages](https://docs.agno.com/examples/agent-os/whatsapp/interactive.md): Give an Agent focused WhatsApp tools for reply buttons, lists, location pins, and reactions. - [Receive and Return WhatsApp Media](https://docs.agno.com/examples/agent-os/whatsapp/media.md): Use one multimodal Agent for inbound WhatsApp images, video, audio, and documents. - [Mount Multiple WhatsApp Bot Instances](https://docs.agno.com/examples/agent-os/whatsapp/multiple-instances.md): Mount two independently configured WhatsApp interfaces on one AgentOS. - [AgentOS Default Database](https://docs.agno.com/examples/agent-os/databases/basic.md): Pass a database to AgentOS once and every listed agent, team, or workflow that does not define its own database inherits it. - [Postgres Database Backend](https://docs.agno.com/examples/agent-os/databases/postgres.md): Use Postgres for production AgentOS persistence. - [SurrealDB Database Backend](https://docs.agno.com/examples/agent-os/databases/surreal.md): Configure SurrealDB with its client, URL, credentials, namespace, and database instead of a SQL connection string. - [Full AgentOS Tour](https://docs.agno.com/examples/agent-os/getting-started/full-os.md): Mount one agent, team, workflow, and knowledge base on a single AgentOS. - [Run Over HTTP](https://docs.agno.com/examples/agent-os/getting-started/run-over-http.md): Call the server from full_os.py through its raw FastAPI surface. - [Connect to AgentOS and inspect its configuration](https://docs.agno.com/examples/agent-os/python-client/connect.md): Both the synchronous ``get_config`` method and its asynchronous ``aget_config`` twin. - [Run and Stream](https://docs.agno.com/examples/agent-os/python-client/run-and-stream.md): The stream is parsed into typed Agno events so applications can handle content, tools, completion, and errors explicitly. - [Sessions and Memory](https://docs.agno.com/examples/agent-os/python-client/sessions-and-memory.md): Manage AgentOS sessions and user memories with the Python client. - [Knowledge](https://docs.agno.com/examples/agent-os/python-client/knowledge.md): Upload, monitor, search, and delete AgentOS knowledge content. - [Evals](https://docs.agno.com/examples/agent-os/python-client/evals.md): The reliability case verifies the assistant calls the calculator's ``multiply`` tool with the expected arguments. - [Auth](https://docs.agno.com/examples/agent-os/python-client/auth.md): Authenticate AgentOSClient calls with the central OS security key. - [Server](https://docs.agno.com/examples/agent-os/python-client/-server.md): Serve the AgentOS used by every Python client example in this folder. - [Background Run](https://docs.agno.com/examples/agent-os/run-lifecycle/background-run.md): Submit a non-streaming background run, observe the HTTP 202/PENDING contract, then poll the persisted run until it reaches a terminal status. - [Cancel a background AgentOS run](https://docs.agno.com/examples/agent-os/run-lifecycle/cancel-run.md): Start a long non-streaming background run, cancel it through the nested run route, and poll the database-backed run until its status becomes CANCELLED. - [List and continue from AgentOS run checkpoints](https://docs.agno.com/examples/agent-os/run-lifecycle/checkpoints.md): Create a run with ``checkpoint="tool-batch"``, list its persisted continuation boundaries over HTTP, then continue from a selected ``message_index``. - [Hooks in Background](https://docs.agno.com/examples/agent-os/run-lifecycle/hooks-in-background.md): Show both levels of background-hook control without conflating their behavior: the ``global`` server mode applies ``run_hooks_in_background=True`` to every non-guardrail hook, while the ``mixed`` mode keeps one blocking evaluator and marks selected hooks and a second evaluator for background execution. - [Reconnect to background AgentOS SSE streams](https://docs.agno.com/examples/agent-os/run-lifecycle/sse-reconnect.md): Use raw ``httpx`` to track ``event_index``, deliberately disconnect, and POST to the nested ``/resume`` route. - [JWT authentication with asymmetric keys](https://docs.agno.com/examples/agent-os/security/asymmetric-keys.md): Use an RS256 private key only in the token issuer and give AgentOS only the public verification key. - [JWT scopes and audience verification](https://docs.agno.com/examples/agent-os/security/basic-scopes.md): Protect AgentOS with an HS256 JWT, the default scope map, and an audience bound to this AgentOS instance. - [JWT authentication from an HTTP-only cookie](https://docs.agno.com/examples/agent-os/security/cookie-auth.md): Mount login/logout routes on the base FastAPI app, read JWTs from a secure cookie, and enforce AgentOS scopes normally. - [Custom route-to-scope mappings](https://docs.agno.com/examples/agent-os/security/custom-scope-mappings.md): Override route scopes and apply an explicit application namespace for agent-run authorization. - [JWT claims into request state and agent dependencies](https://docs.agno.com/examples/agent-os/security/jwt-claims.md): Extract trusted claims into request.state, session state, and the dependencies carried by the RunContext passed to agent tools. - [Per-resource and wildcard scopes](https://docs.agno.com/examples/agent-os/security/per-resource-scopes.md): Grant access to one agent, team, or workflow by id, or use a wildcard scope deliberately. - [Service accounts for machine-to-machine authentication](https://docs.agno.com/examples/agent-os/security/service-accounts.md): Mint an opaque agno_pat_ token, use its current default scopes, and revoke it. - [Executable and pytest RBAC enforcement test](https://docs.agno.com/examples/agent-os/security/test-scopes.md): Exercise JWT authentication, audience validation, default agent/team/workflow scope mappings, component protection, admin bypass, and a fully local workflow run. - [JWT RBAC with per-user data isolation](https://docs.agno.com/examples/agent-os/security/user-isolation.md): Turn on AuthorizationConfig(user_isolation=True) so non-admin session reads and writes are pinned to the JWT subject. - [Per-user knowledge ownership](https://docs.agno.com/examples/agent-os/security/user-isolation-knowledge.md): Turn on AuthorizationConfig(user_isolation=True) so every knowledge content row is owned by the JWT subject, and a row with no owner is shared, org-wide content. - [Bring your own WorkOS token issuer](https://docs.agno.com/examples/agent-os/security/workos-byot.md): Configure AgentOS to verify a WorkOS JWKS, read scopes from the permissions claim, and enforce the token audience. - [Serve a Workflow with AgentOS](https://docs.agno.com/examples/agent-os/serving-workflows/basic.md): Serve one two-step workflow as an HTTP resource. - [Run Over API](https://docs.agno.com/examples/agent-os/serving-workflows/run-over-api.md): Call basic.py through raw HTTP: create a non-streaming run, consume an SSE event stream, and list the persisted runs for the shared workflow session. - [Expose a Workflow input schema](https://docs.agno.com/examples/agent-os/serving-workflows/with-input-schema.md): Define a Pydantic input_schema on a served workflow, then use --demo to fetch GET /workflows/{id} and inspect the JSON schema AgentOS exposes to clients. - [Chat with a WorkflowAgent through AgentOS](https://docs.agno.com/examples/agent-os/serving-workflows/with-workflow-agent.md): A WorkflowAgent decides whether a new message should execute the workflow or can be answered from workflow history. - [Stream a served Workflow over WebSocket](https://docs.agno.com/examples/agent-os/serving-workflows/ws-stream.md): Connect to the workflow-only /workflows/ws surface, send start-workflow, and consume indexed workflow events until the served workflow completes. - [Enable AgentOS Tracing](https://docs.agno.com/examples/agent-os/observability/basic.md): Enable OpenTelemetry tracing for a served agent with one AgentOS flag. - [Filter Traces Through AgentOS](https://docs.agno.com/examples/agent-os/observability/filtering.md): Build a composable FilterExpr, apply it directly to the tracing database, and send the same expression to POST /traces/search. - [Refresh and Read AgentOS Metrics](https://docs.agno.com/examples/agent-os/observability/metrics.md): Create a persisted agent run, refresh daily aggregates through POST /metrics/refresh, then read the stored result through GET /metrics. - [Read Trace Trees Through AgentOS](https://docs.agno.com/examples/agent-os/observability/read-traces.md): Generate one synchronous and one asynchronous agent run, list their traces through GET /traces, then fetch GET /traces/{trace_id} and print each nested span tree. - [Route AgentOS Traces to ClickHouse](https://docs.agno.com/examples/agent-os/observability/traces-to-clickhouse.md): Keep transactional sessions in SQLite while batching trace spans into a dedicated ClickHouse OLAP store. - [Serve agents as MCP tools](https://docs.agno.com/examples/agent-os/mcp/agents-as-tools.md): Turn the default MCP surface off and serve agents directly as tools. - [Serve AgentOS over MCP](https://docs.agno.com/examples/agent-os/mcp/basic.md): Expose one agent through the eight built-in AgentOS MCP tools at ``/mcp``. - [Expose one custom MCP tool](https://docs.agno.com/examples/agent-os/mcp/custom-tools.md): Replace the eight built-in AgentOS MCP tools with one purpose-built tool. - [Drive the AgentOS MCP run lifecycle](https://docs.agno.com/examples/agent-os/mcp/mcp-client.md): Use a protocol-level FastMCP client to discover the eight built-in tools, pause and continue one agent run, cancel a second paused run, and read the continued session back. - [Use WorkOS AuthKit for MCP OAuth](https://docs.agno.com/examples/agent-os/mcp/oauth-authkit.md): Keep AgentOS as the MCP resource server while WorkOS AuthKit owns login, consent, and token issuance. - [Use the built-in MCP authorization server](https://docs.agno.com/examples/agent-os/mcp/oauth-builtin.md): Make AgentOS its own OAuth 2.1 authorization server for MCP connector clients. - [Secure and scope an AgentOS MCP server](https://docs.agno.com/examples/agent-os/mcp/secure-mcp.md): Protect ``/mcp`` with an OS security key plus a database-backed ``agno_pat_`` service account. - [Serve a toolkit as MCP tools](https://docs.agno.com/examples/agent-os/mcp/toolkit-tools.md): Pass a Toolkit to MCPConfig.tools and AgentOS flattens it into one MCP tool per method, the way an agent takes it apart. - [MCP server identity](https://docs.agno.com/examples/agent-os/mcp/server-identity.md): Configure the name, version, and instructions that MCP clients receive. - [Stateless MCP transport](https://docs.agno.com/examples/agent-os/mcp/stateless.md): Give each MCP request its own transport without retaining MCP sessions. - [Discover an A2A Agent Card](https://docs.agno.com/examples/agent-os/a2a/agent-card.md): Read the Agent card from the entity-scoped discovery route with both the synchronous and asynchronous first-party client methods. - [Serve an Agent over A2A](https://docs.agno.com/examples/agent-os/a2a/basic.md): Expose one persistent Agent through AgentOS's built-in A2A interface. - [Call an A2A Agent with the First-Party Client](https://docs.agno.com/examples/agent-os/a2a/client.md): Use `A2AClient` to send a message, thread a returned `context_id` into a follow-up, stream another turn, and handle an unavailable server. - [Serve an Airbnb Specialist over A2A](https://docs.agno.com/examples/agent-os/a2a/multi-agent/airbnb-agent.md): Connect an Agent to the OpenBNB MCP server and expose it on the dedicated A2A topology port. - [Orchestrate Multiple A2A Agents](https://docs.agno.com/examples/agent-os/a2a/multi-agent/trip-planning-a2a-client.md): Serve a trip-planning Agent whose async tools call the weather and Airbnb specialists through the first-party `A2AClient`. - [Serve a Weather Specialist over A2A](https://docs.agno.com/examples/agent-os/a2a/multi-agent/weather-agent.md): Expose a weather Agent on the dedicated topology port. - [Serve a Team over A2A](https://docs.agno.com/examples/agent-os/a2a/team.md): Expose a research Team through the A2A team namespace. - [Send Media to an Agent over AG-UI](https://docs.agno.com/examples/agent-os/agui/agent-with-media.md): Accept AG-UI image, audio, video, and document content parts and pass them to a Gemini multimodal agent through one AgentOS interface. - [Use Backend and Frontend Tools over AG-UI](https://docs.agno.com/examples/agent-os/agui/agent-with-tools.md): Expose one server-side weather tool and accept a browser-side change_background tool supplied in an AG-UI request. - [Serve an Agent over AG-UI](https://docs.agno.com/examples/agent-os/agui/basic.md): Mount one Agent on AgentOS's AG-UI interface and expose its response as an AG-UI server-sent event stream. - [Confirm a Backend Tool over AG-UI](https://docs.agno.com/examples/agent-os/agui/human-in-the-loop.md): Pause a real server-side tool call with requires_confirmation, stream the requirement to the frontend, and resume the persisted run after the user accepts or rejects it. - [Mount Multiple AG-UI Instances](https://docs.agno.com/examples/agent-os/agui/multiple-instances.md): Expose two agents from one AgentOS under independent AG-UI prefixes. - [Render an Agno Agent with OpenUI](https://docs.agno.com/examples/agent-os/agui/openui/server.md): Serve OpenUI Lang from an Agno Agent over AG-UI. - [Stream Reasoning Events over AG-UI](https://docs.agno.com/examples/agent-os/agui/reasoning-agent.md): Stream available reasoning events from a separate native reasoning stage over AG-UI. - [Serve a Research Team over AG-UI](https://docs.agno.com/examples/agent-os/agui/research-team.md): Mount an AgentOS Team on AG-UI so member tool calls and the coordinated final answer share one protocol event stream. - [Synchronize Shared State over AG-UI](https://docs.agno.com/examples/agent-os/agui/shared-state.md): Give an AG-UI agent a recipe-shaped session state and let its update_session_state tool emit state snapshots and JSON Patch deltas. - [Stream Structured Output over AG-UI](https://docs.agno.com/examples/agent-os/agui/structured-output.md): Request a movie-pitch schema over AG-UI and validate the assembled answer before using its fields. - [Serve an Agent in Slack](https://docs.agno.com/examples/agent-os/slack/basic.md): Mount one persistent Agent on the Slack interface. - [Confirm a Destructive Tool in Slack](https://docs.agno.com/examples/agent-os/slack/hitl-confirmation.md): Look up a subscription, then pause the persisted run with Approve and Deny actions before an irreversible cancellation tool executes. - [Return External Tool Execution to Slack](https://docs.agno.com/examples/agent-os/slack/hitl-external-execution.md): Pause on a Kubernetes diagnostic the Agent cannot execute. - [Coordinate a Compound Incident in Slack](https://docs.agno.com/examples/agent-os/slack/hitl-incident-commander.md): Combine user feedback, external execution, confirmation, and required user input in one persisted incident run. - [Collect Structured User Input in Slack](https://docs.agno.com/examples/agent-os/slack/hitl-user-input.md): Draft a support ticket from the conversation, then pause with a Slack form so the requester supplies the priority and owning component. - [Mount Multiple Slack Bots](https://docs.agno.com/examples/agent-os/slack/multiple-bots.md): Serve a research bot and an analysis bot from one AgentOS. - [Let One Slack App Address Another](https://docs.agno.com/examples/agent-os/slack/peer-agents.md): Use two Slack apps in one workspace for one-way delegation. - [Search Slack and Work with Files](https://docs.agno.com/examples/agent-os/slack/slack-tools.md): Combine channel history, thread expansion, workspace search, and file download/upload in one focused SlackTools Agent. - [Configure Slack Streaming UX](https://docs.agno.com/examples/agent-os/slack/streaming-ux.md): Show a streaming response with rotating loading messages, dynamic suggested prompts, and plan-mode task cards for tool calls. - [Serve a Support Team in Slack](https://docs.agno.com/examples/agent-os/slack/team.md): Route each Slack request to a technical specialist or a documentation specialist that can search current workspace discussions. - [Remember Slack Users Across Threads](https://docs.agno.com/examples/agent-os/slack/user-memory.md): Resolve a Slack member to an email-backed user ID when available, then use a MemoryManager to retain stable preferences across otherwise separate threads. - [Serve a Sequential Workflow in Slack](https://docs.agno.com/examples/agent-os/slack/workflow.md): Serve a two-step research and writing workflow in Slack with SQLite-backed history. - [Serve an Agent through Telegram](https://docs.agno.com/examples/agent-os/telegram/basic.md): Expose one persistent Agent through AgentOS's Telegram webhook interface. - [Send and Receive Media through Telegram](https://docs.agno.com/examples/agent-os/telegram/media.md): Use Gemini to understand photos, voice notes, audio, video, and documents received from Telegram. - [Mount Multiple Telegram Bots](https://docs.agno.com/examples/agent-os/telegram/multiple-instances.md): Mount two independently credentialed Telegram bots on one AgentOS server. - [Inspect Registry resources and manage persisted components](https://docs.agno.com/examples/agent-os/studio/registry-and-components.md): The Registry endpoint lists code-defined primitives. - [Wire Studio-built components to a Registry LearningMachine](https://docs.agno.com/examples/agent-os/studio/registry-learning.md): Configure Studio components with a named shared LearningMachine or their default learning stores. - [Compose and version an Agent without starting AgentOS](https://docs.agno.com/examples/agent-os/studio/standalone-studio-agent.md): StudioTools can persist components directly through a synchronous database. - [Resolve Studio composition pauses in the console](https://docs.agno.com/examples/agent-os/studio/studio-hitl-agent.md): This standalone Studio Agent gathers a structured tool choice, requests free-text instructions, and pauses again before create_agent persists anything. - [Resolve Studio composition pauses through AgentOS](https://docs.agno.com/examples/agent-os/studio/studio-hitl-agent-os.md): This AgentOS lesson exposes the same three Studio pauses as the console lesson, but serializes them as Agent run tools. - [StudioRunnerTools called directly: list, run, and refusal semantics](https://docs.agno.com/examples/agent-os/studio/studio-runner-direct.md): The runner's tools are plain methods, so a platform can call them without a wielding model. - [Dispatch Studio-built components from a runner-only Agent](https://docs.agno.com/examples/agent-os/studio/studio-runner-dispatcher.md): StudioRunnerTools is the dispatch half of the Studio: list the components in the platform database and run one by id. - [Serve a Studio Agent that composes persisted components](https://docs.agno.com/examples/agent-os/studio/studio-tools-agent.md): AgentOS exposes code-defined Agents alongside components created by StudioTools. - [Create the Showcase Agents](https://docs.agno.com/examples/agent-os/showcase/-agents.md): Define the shared PostgreSQL database, pgvector-backed Agno documentation knowledge, a focused RAG assistant, and a current web-and-finance researcher. - [Create the Showcase Finance Team](https://docs.agno.com/examples/agent-os/showcase/-teams.md): Combine Sage's current research with a focused market-data specialist. - [Showcase Demo](https://docs.agno.com/examples/agent-os/showcase/demo.md): Load Agno documentation into pgvector, run and store one real AccuracyEval, then serve two Agents and one finance Team with authentication and tracing. - [Platform Ops Agent](https://docs.agno.com/examples/agent-os/agentos-tools/platform-ops-agent.md): Give an agent a read-only ops view of the AgentOS it runs on with AgentOSTools. - [Agent OS](https://docs.agno.com/examples/agent-os/overview.md): Top-level AgentOS quickstart and entrypoint examples. - [Hello AgentOS](https://docs.agno.com/examples/agent-os/basic.md): Serve one SQLite-backed documentation agent with MCP tools through AgentOS. - [Advanced Demo](https://docs.agno.com/examples/agent-os/advanced-demo/overview.md): Find current examples for knowledge management and composing agents, teams, and workflows on AgentOS. - [Multiple Knowledge Bases](https://docs.agno.com/examples/agent-os/advanced-demo/multiple-knowledge-bases.md): Migrate the legacy multiple-knowledge-base route to current AgentOS knowledge examples. - [Background Tasks](https://docs.agno.com/examples/agent-os/background-tasks/overview.md): Choose background hooks, durable execution, or shared event transport. - [Durable Continue](https://docs.agno.com/examples/agent-os/background-tasks/durable-continue.md): Durable continuation legs: HITL pause/continue that survives crashes. - [Durable Queue](https://docs.agno.com/examples/agent-os/background-tasks/durable-queue.md): AgentOS with a durable job queue: accepted background runs survive crashes. - [Redis Event Stream](https://docs.agno.com/examples/agent-os/background-tasks/redis-event-stream.md): AgentOS with Redis-coordinated background runs (multi-container ready). - [Client](https://docs.agno.com/examples/agent-os/client/overview.md): Connect to AgentOS and manage runs, sessions, knowledge, and evals. - [Knowledge Search with AgentOSClient](https://docs.agno.com/examples/agent-os/client/knowledge-search.md): Migrate the legacy client knowledge-search route to the current AgentOSClient knowledge example. - [Workflow SSE Reconnection](https://docs.agno.com/examples/agent-os/client/workflow-sse-reconnect.md): Current alternatives for the retired Workflow SSE Reconnection example. - [Servers](https://docs.agno.com/examples/agent-os/client-a2a/servers/overview.md): Serve Agno and Google ADK agents over A2A. - [Basic A2A Messaging](https://docs.agno.com/examples/agent-os/client-a2a/basic-messaging.md): Migrate the legacy A2A client route to the current AgentOS server and client examples. - [Connect Agno A2AClient to Google ADK A2A Server](https://docs.agno.com/examples/agent-os/client-a2a/connect-to-google-adk.md): Current alternatives for the retired Connect Agno A2AClient to Google ADK A2A Server example. - [Client A2A](https://docs.agno.com/examples/agent-os/client-a2a/overview.md): Message A2A agents and preserve conversation context. - [Customize](https://docs.agno.com/examples/agent-os/customize/overview.md): Customize AgentOS application routes, middleware, dependencies, lifecycle, and response handling. - [Mount AgentOS on an existing FastAPI application](https://docs.agno.com/examples/agent-os/customize/basic.md): Pass an existing FastAPI instance through ``base_app`` so its routes and the AgentOS routes share one process. - [Configure CORS and a shared AgentOS security key](https://docs.agno.com/examples/agent-os/customize/cors-and-security-key.md): ``cors_allowed_origins`` replaces AgentOS's default allowed origins. - [Stream a custom tool event through AgentOS](https://docs.agno.com/examples/agent-os/customize/custom-events.md): An async tool yields a ``CustomEvent`` populated from request session state. - [Add rate-limit and request-log middleware to AgentOS](https://docs.agno.com/examples/agent-os/customize/custom-middleware.md): Custom Starlette middleware can wrap the FastAPI app returned by ``AgentOS.get_app()``. - [Pass per-request dependencies through AgentOS](https://docs.agno.com/examples/agent-os/customize/dependencies.md): AgentOS accepts a JSON ``dependencies`` form field on run requests and passes the values into the agent's instruction templates. - [Update AgentOS from a custom application lifespan](https://docs.agno.com/examples/agent-os/customize/lifespan.md): AgentOS detects a lifespan parameter named ``agent_os`` and injects the active instance. - [Observe AgentOS run responses without private response classes](https://docs.agno.com/examples/agent-os/customize/response-middleware.md): This middleware captures non-streaming JSON bodies and streaming SSE content for ``POST .../runs`` requests carrying ``X-APP-UUID``. - [Choose which application owns conflicting routes](https://docs.agno.com/examples/agent-os/customize/route-conflicts.md): An existing FastAPI app defines ``/`` and ``/health``, which AgentOS also provides. - [Surreal DB](https://docs.agno.com/examples/agent-os/dbs/surreal-db/overview.md): Current AgentOS SurrealDB database example. - [Example showing how to use AgentOS with Valkey as the database](https://docs.agno.com/examples/agent-os/dbs/valkey-db.md): Current alternatives for the retired Example showing how to use AgentOS with Valkey as the database example. - [DBs](https://docs.agno.com/examples/agent-os/dbs/overview.md): Database backends for AgentOS agents, teams, workflows, and session storage. - [Factories](https://docs.agno.com/examples/agent-os/factories/overview.md): Build agents, teams, and workflows from validated request context. - [Per-tenant Agent Factory](https://docs.agno.com/examples/agent-os/factories/tenant-agent-factory.md): Migrate the legacy tenant-agent factory route to the current Agno v3 factory example. - [Factory with Input Schema](https://docs.agno.com/examples/agent-os/factories/input-schema-factory.md): Client-controlled agent parameters validated against a Pydantic schema before the factory runs. - [JWT-Driven Factory](https://docs.agno.com/examples/agent-os/factories/jwt-role-factory.md): Migrate the legacy JWT factory route to the current Agno v3 RBAC example. - [Factory with HITL Tool](https://docs.agno.com/examples/agent-os/factories/hitl-factory.md): Migrate the legacy factory HITL route to current Agno v3 factory and approval examples. - [Build an Agent per Request](https://docs.agno.com/examples/agent-os/factories/basic.md): Register an AgentFactory that creates a fresh tenant-aware Agent for every run. - [Validate Factory Input](https://docs.agno.com/examples/agent-os/factories/with-input-schema.md): Attach a Pydantic schema to an AgentFactory, send typed factory_input through the run API, and observe invalid input fail with HTTP 400 before construction. - [Grant Factory Tools from Trusted JWT Claims](https://docs.agno.com/examples/agent-os/factories/with-jwt-rbac.md): Grant factory-agent tools from verified JWT role claims and return 403 for unsupported roles. - [Choose Model Configuration from a Trusted Tier](https://docs.agno.com/examples/agent-os/factories/tiered-model.md): Use a verified subscription claim to choose low or high reasoning effort for the approved gpt-5.5 model without trusting client-controlled factory input. - [Build a Team per Request](https://docs.agno.com/examples/agent-os/factories/team-factory.md): Register a TeamFactory that creates a fresh two-role support Team with tenant-specific instructions for every Team run. - [Build a Workflow per Request](https://docs.agno.com/examples/agent-os/factories/workflow-factory.md): Register a WorkflowFactory that creates a fresh tenant-aware draft-and-edit pipeline for every Workflow run. - [Use Sync and Async Factories Together](https://docs.agno.com/examples/agent-os/factories/async-factory.md): Register one synchronous and one asynchronous AgentFactory on the same AgentOS and call both through the same async-aware REST resolver. - [Tiered Team Factory](https://docs.agno.com/examples/agent-os/factories/team/tiered-team-factory.md): Current alternatives for the retired Tiered Team Factory example. - [Tiered Workflow Factory](https://docs.agno.com/examples/agent-os/factories/workflow/tiered-workflow-factory.md): Current alternatives for the retired Tiered Workflow Factory example. - [Integrations](https://docs.agno.com/examples/agent-os/integrations/overview.md): Connect AgentOS to LangGraph and remote AgentOS or A2A components. - [Interfaces](https://docs.agno.com/examples/agent-os/interfaces/overview.md): AgentOS interface examples: expose agents and teams over Slack, Telegram, WhatsApp, AG-UI and A2A. - [Slack](https://docs.agno.com/examples/agent-os/interfaces/slack/overview.md): Current AgentOS Slack examples for bots, tools, streaming, teams, memory, workflows, and human approval. - [Reasoning Agent](https://docs.agno.com/examples/agent-os/interfaces/slack/reasoning-agent.md): Current alternatives for the retired Reasoning Agent example. - [Multimodal Team](https://docs.agno.com/examples/agent-os/interfaces/slack/multimodal-team.md): Current alternatives for the retired Multimodal Team example. - [WhatsApp](https://docs.agno.com/examples/agent-os/interfaces/whatsapp/overview.md): Serve chat, media, interactive replies, reasoning, and multiple phone numbers through WhatsApp. - [Agent With User Memory](https://docs.agno.com/examples/agent-os/interfaces/whatsapp/agent-with-user-memory.md): Current alternatives for the retired Agent With User Memory example. - [Image Generation on WhatsApp](https://docs.agno.com/examples/agent-os/interfaces/whatsapp/image-generation-model.md): Migrate the legacy image-generation route to the current AgentOS WhatsApp media example. - [Show Agent Reasoning on WhatsApp](https://docs.agno.com/examples/agent-os/interfaces/whatsapp/reasoning-agent.md): Run an OpenAI finance agent with ReasoningTools and YFinance on WhatsApp with visible reasoning. - [Deep Research Agent](https://docs.agno.com/examples/agent-os/interfaces/whatsapp/deep-research.md): Current alternatives for the retired Deep Research Agent example. - [Multimodal Team](https://docs.agno.com/examples/agent-os/interfaces/whatsapp/multimodal-team.md): Current alternatives for the retired Multimodal Team example. - [Multimodal Workflow](https://docs.agno.com/examples/agent-os/interfaces/whatsapp/multimodal-workflow.md): Current alternatives for the retired Multimodal Workflow example. - [Support Team](https://docs.agno.com/examples/agent-os/interfaces/whatsapp/support-team.md): Current alternatives for the retired Support Team example. - [A2A](https://docs.agno.com/examples/agent-os/interfaces/a2a/overview.md): Serve AgentOS entities over A2A and call them with the first-party client. - [Reasoning Agent](https://docs.agno.com/examples/agent-os/interfaces/a2a/reasoning-agent.md): Current alternatives for the retired Reasoning Agent example. - [Structured Output](https://docs.agno.com/examples/agent-os/interfaces/a2a/structured-output.md): Current alternatives for the retired Structured Output example. - [Multi-Agent A2A](https://docs.agno.com/examples/agent-os/interfaces/a2a/multi-agent-a2a/overview.md): Run weather and accommodation specialists behind a trip-planning agent. - [AG-UI](https://docs.agno.com/examples/agent-os/interfaces/agui/overview.md): Serve agents and teams to AG-UI clients with tools, media, state, and approval events. - [AG-UI Showcase](https://docs.agno.com/examples/agent-os/interfaces/agui/showcase.md): Current alternatives for the retired AG-UI Showcase example. - [Tool Based Generative UI: Dojo Demo](https://docs.agno.com/examples/agent-os/interfaces/agui/tool-based-generative-ui.md): Migrate the legacy AG-UI generative-interface route to the current agent-with-tools example. - [Telegram Agent with User Memory](https://docs.agno.com/examples/agent-os/interfaces/telegram/agent-with-user-memory.md): Current alternatives for the retired Telegram Agent with User Memory example. - [Telegram Reasoning Agent](https://docs.agno.com/examples/agent-os/interfaces/telegram/reasoning-agent.md): Current alternatives for the retired Telegram Reasoning Agent example. - [Streaming Workflow on Telegram](https://docs.agno.com/examples/agent-os/interfaces/telegram/streaming-workflow.md): Current alternatives for the retired Streaming Workflow on Telegram example. - [Telegram Team Agent](https://docs.agno.com/examples/agent-os/interfaces/telegram/team.md): Current alternatives for the retired Telegram Team Agent example. - [Telegram Workflow Agent](https://docs.agno.com/examples/agent-os/interfaces/telegram/workflow.md): Current alternatives for the retired Telegram Workflow Agent example. - [Knowledge](https://docs.agno.com/examples/agent-os/knowledge/overview.md): Manage AgentOS knowledge and publish documentation pages. - [Serve Knowledge with AgentOS](https://docs.agno.com/examples/agent-os/knowledge/basic.md): Serve one local knowledge base through an AgentOS and share the same instance with an agent so successfully processed uploads are available for search. - [Manage Knowledge over REST](https://docs.agno.com/examples/agent-os/knowledge/rest-api-knowledge.md): Exercise the complete AgentOS knowledge-content lifecycle over raw HTTP: upload, poll processing, list, semantic search, delete, and verify deletion. - [AgentOS Excel Analyst](https://docs.agno.com/examples/agent-os/knowledge/agentos-excel-analyst.md): Current alternatives for the retired AgentOS Excel Analyst example. - [AgentOS Docling Markdown Analyst](https://docs.agno.com/examples/agent-os/knowledge/agentos-docling-markdown-analyst.md): Current alternatives for the retired AgentOS Docling Markdown Analyst example. - [MCP Demo](https://docs.agno.com/examples/agent-os/mcp-demo/overview.md): Expose AgentOS agents and custom tools through MCP with OAuth, dynamic headers, and managed MCPTools lifespans. - [MCP Tools Advanced Example](https://docs.agno.com/examples/agent-os/mcp-demo/mcp-tools-advanced-example.md): Current alternatives for the retired MCP Tools Advanced Example example. - [MCP Tools Existing Lifespan](https://docs.agno.com/examples/agent-os/mcp-demo/mcp-tools-existing-lifespan.md): Current alternatives for the retired MCP Tools Existing Lifespan example. - [Media Storage](https://docs.agno.com/examples/agent-os/media-storage/overview.md): Serve and delete AgentOS media held in external storage. - [S3 Media Storage](https://docs.agno.com/examples/agent-os/media-storage/s3.md): Offload attached and generated media to S3 and store references after successful uploads. - [GCS Media Storage](https://docs.agno.com/examples/agent-os/media-storage/gcs.md): Offload attached and generated media to Google Cloud Storage and store references after successful uploads. - [Reading and Deleting Session Media](https://docs.agno.com/examples/agent-os/media-storage/delete.md): Read session media through AgentOS, then delete the session and request a best-effort object sweep. - [Middleware](https://docs.agno.com/examples/agent-os/middleware/overview.md): AgentOS middleware examples for authentication, request context, rate limiting, and custom request handling. - [Guardrails Demo](https://docs.agno.com/examples/agent-os/middleware/guardrails-demo.md): Current alternatives for the retired Guardrails Demo example. - [OS Config](https://docs.agno.com/examples/agent-os/os-config/overview.md): Configure AgentOS in Python or YAML, including manifests and database-domain metadata. - [Configure AgentOS with YAML](https://docs.agno.com/examples/agent-os/os-config/yaml-config.md): Load AgentOS UI metadata and database domains from YAML, then verify the rendered configuration. - [Configure AgentOS in Python](https://docs.agno.com/examples/agent-os/os-config/config-basics.md): Attach UI metadata and database-domain labels to an AgentOSConfig, then fetch GET /config to inspect the rendered control-plane configuration. - [Symmetric](https://docs.agno.com/examples/agent-os/rbac/symmetric/overview.md): HS256 AgentOS RBAC examples for scopes, custom mappings, and user isolation. - [Asymmetric](https://docs.agno.com/examples/agent-os/rbac/asymmetric/overview.md): RS256 AgentOS RBAC examples for generated keys, custom scope mappings, and WorkOS-issued tokens. - [RBAC](https://docs.agno.com/examples/agent-os/rbac/overview.md): JWT-based AgentOS RBAC examples for symmetric and asymmetric keys, scope mapping, and user isolation. - [Remote](https://docs.agno.com/examples/agent-os/remote/overview.md): Connect AgentOS to remote agents, teams, workflows, A2A endpoints, and gateway instances. - [Call and stream an Agent on another AgentOS](https://docs.agno.com/examples/agent-os/remote/remote-agent.md): Call an agent hosted on another AgentOS with RemoteAgent, in both single-shot and streaming mode. - [Remote Team and Workflow](https://docs.agno.com/examples/agent-os/remote/remote-team-and-workflow.md): RemoteTeam and RemoteWorkflow use the native AgentOS protocol while returning typed Agno Team and Workflow outputs to the caller. - [Call Agno and Google ADK Agents through A2A](https://docs.agno.com/examples/agent-os/remote/remote-via-a2a.md): The Agno peer uses entity-scoped REST routes while Google ADK uses JSON-RPC at the server root. - [Compose AgentOS and A2A RemoteAgents in one Team](https://docs.agno.com/examples/agent-os/remote/remote-as-team-member.md): A local Team can coordinate remote members reached through different protocols. - [Serve one AgentOS gateway over local and remote components](https://docs.agno.com/examples/agent-os/remote/gateway.md): The gateway registers a local Agent alongside native AgentOS, Agno A2A REST, and Google ADK JSON-RPC components. - [Authenticate a RemoteAgent run with a Bearer credential](https://docs.agno.com/examples/agent-os/remote/remote-auth.md): Restart the native AgentOS backend with OS_SECURITY_KEY set, then pass that same value through RemoteAgent.arun(auth_token=...). - [Serve an Agno Agent through the A2A REST interface](https://docs.agno.com/examples/agent-os/remote/servers/a2a-server.md): This server is the first-party A2A peer used by RemoteAgent. - [Serve a Google ADK Agent through A2A JSON-RPC](https://docs.agno.com/examples/agent-os/remote/servers/adk-server.md): Google ADK exposes one Agent as a standard A2A JSON-RPC application. - [Serve Agents, a Team, and a Workflow for Remote* examples](https://docs.agno.com/examples/agent-os/remote/servers/agentos-server.md): This AgentOS is the native-protocol backend for the remote curriculum. - [Scheduler](https://docs.agno.com/examples/agent-os/scheduler/overview.md): Run schedules in AgentOS, manage them through REST or Python, and expose scheduling tools to an agent. - [Schedule Management via REST API](https://docs.agno.com/examples/agent-os/scheduler/schedule-management.md): Migrate the legacy schedule-management route to the current AgentOS scheduler examples. - [Schedule Validation and Error Handling](https://docs.agno.com/examples/agent-os/scheduler/schedule-validation.md): Migrate the legacy schedule-validation route to the current AgentOS scheduler example. - [Give an Agent Scheduler Tools](https://docs.agno.com/examples/agent-os/scheduler/scheduler-tools-agent.md): Give an AgentOS agent SchedulerTools to create, inspect, enable or disable, and delete cron schedules and view run history. - [Run in AgentOS](https://docs.agno.com/examples/agent-os/scheduler/run-in-agentos.md): Serve a Postgres-backed AgentOS scheduler, seed one minute schedule before startup, and observe a naturally claimed run appear in persisted history. - [Manage Schedules over REST](https://docs.agno.com/examples/agent-os/scheduler/rest-api.md): Use raw HTTP to create, list, read, update, enable, disable, trigger, page through run history, and delete a schedule served by 01_run_in_agentos.py. - [Manage Schedules with Python](https://docs.agno.com/examples/agent-os/scheduler/manage-with-python.md): Use synchronous and genuinely asynchronous Postgres adapters with ScheduleManager, including safe cron updates, paging, and validation errors. - [Schemas](https://docs.agno.com/examples/agent-os/schemas/overview.md): Validate agent, team, factory, and workflow inputs and outputs. - [Agent Input And Output Schemas](https://docs.agno.com/examples/agent-os/schemas/agent-schemas.md): Current alternatives for the retired Agent Input And Output Schemas example. - [Team Input And Output Schemas](https://docs.agno.com/examples/agent-os/schemas/team-schemas.md): Current alternatives for the retired Team Input And Output Schemas example. - [Scripts](https://docs.agno.com/examples/agent-os/skills/sample-skills/system-info/scripts/overview.md): Run the system-info sample skill's scripts for JSON system details and directory listings. - [Get System Info](https://docs.agno.com/examples/agent-os/skills/sample-skills/system-info/scripts/get-system-info.md): Print OS, Python version, and host details as JSON from a system-info skill script. - [Print a sorted directory inventory as JSON](https://docs.agno.com/examples/agent-os/skills/sample-skills/system-info/scripts/list-directory.md): List directory entries with type and size as JSON from a system-info skill script. - [System Info](https://docs.agno.com/examples/agent-os/skills/sample-skills/system-info/overview.md): Examples for System Info. - [Sample Skills](https://docs.agno.com/examples/agent-os/skills/sample-skills/overview.md): Examples for Sample Skills. - [Skills](https://docs.agno.com/examples/agent-os/skills/overview.md): Load local skills into an AgentOS agent, including sample system-information scripts. - [Serve an Agent with a Local Skill](https://docs.agno.com/examples/agent-os/skills/basic.md): Load a skill from disk, expose its instructions and scripts to an Agent, and prove a real script execution through the AgentOS run API. - [Tracing](https://docs.agno.com/examples/agent-os/tracing/overview.md): OpenTelemetry tracing for AgentOS agents, teams, and workflows, including filtering, metrics, trace trees, and ClickHouse storage. - [Basic Agent with MongoDB](https://docs.agno.com/examples/agent-os/tracing/dbs/basic-agent-with-mongodb.md): Current alternatives for the retired Basic Agent with MongoDB example. - [DBs](https://docs.agno.com/examples/agent-os/tracing/dbs/overview.md): Route AgentOS traces to a dedicated ClickHouse database. - [Workflow](https://docs.agno.com/examples/agent-os/workflow/overview.md): Browse AgentOS workflow examples for steps, conditions, loops, routers, parallel branches, and custom function executors. - [Customer Research Workflow Parallel](https://docs.agno.com/examples/agent-os/workflow/customer-research-workflow-parallel.md): Migrate the removed customer-research workflow route to a supported Agno v3 parallel workflow. - [Workflow With Parallel And Custom Function Step Stream](https://docs.agno.com/examples/agent-os/workflow/workflow-with-parallel-and-custom-function-step-stream.md): Current alternatives for the retired Workflow With Parallel And Custom Function Step Stream example. - [Data Labeling](https://docs.agno.com/examples/agent-os/google/gemini-3/data-labeling.md): Current alternatives for the retired Data Labeling example. - [Write an audit approval after an ephemeral HITL decision](https://docs.agno.com/examples/agent-os/human-in-the-loop/audit-record.md): ``@approval(type="audit")`` does not replace a HITL flag. - [Confirm an ephemeral AgentOS tool call](https://docs.agno.com/examples/agent-os/human-in-the-loop/basic.md): Pause an agent run with ``@tool(requires_confirmation=True)``, inspect the pending tool returned by AgentOS, confirm it, and send the updated tool to the nested ``continue`` route. - [Return an externally executed tool result to AgentOS](https://docs.agno.com/examples/agent-os/human-in-the-loop/external-execution.md): A member agent requests ``send_email``, but ``external_execution=True`` keeps the side effect outside the AgentOS process. - [Place persistent approval tools on a team leader or member](https://docs.agno.com/examples/agent-os/human-in-the-loop/team-approval.md): AgentOS propagates approval pauses differently depending on where the tool lives. - [Resolve multi-round user input for an AgentOS team](https://docs.agno.com/examples/agent-os/human-in-the-loop/user-input.md): A member agent collects a traveller name and trip preferences in two separate tool calls. - [Resolve a persistent AgentOS approval record](https://docs.agno.com/examples/agent-os/human-in-the-loop/with-approval-record.md): ``@approval(type="required")`` creates a database-backed approval record in addition to pausing the run. - [Resolve three workflow HITL pauses through AgentOS](https://docs.agno.com/examples/agent-os/human-in-the-loop/workflow-hitl.md): One OS-served workflow pauses before a sensitive step, pauses again to collect step input, then pauses after producing a draft so a human can review its output. - [Step-Level User Input HITL Example](https://docs.agno.com/examples/agent-os/human-in-the-loop/workflow/step-user-input.md): Pause a workflow to collect user input using Step parameters directly (without the @pause decorator). - [Serve Agent Learning with AgentOS](https://docs.agno.com/examples/agent-os/learnings/learnings-with-agentos.md): Serve a learning-enabled agent and the shared database that backs both its automatic user learning and the AgentOS /learnings CRUD routes. - [Read Agent Learning over REST](https://docs.agno.com/examples/agent-os/learnings/rest-api-learnings.md): Create, list, update, and delete learning records through the AgentOS /learnings REST endpoints. - [Public pages](https://docs.agno.com/examples/agent-os/public-pages/overview.md): Build public chat and MCP over a published documentation corpus. - [Public page-backed chat](https://docs.agno.com/examples/agent-os/public-pages/public-pages.md): Serve documentation chat and explicit MCP tools with a protected indexing workflow. - [Read-only page commands](https://docs.agno.com/examples/agent-os/public-pages/page-filesystem.md): Explore published pages through bounded filesystem-style commands. - [Public team](https://docs.agno.com/examples/agent-os/public-pages/public-team.md): Expose one selected team while keeping its member routes private. - [Reasoning](https://docs.agno.com/examples/reasoning/overview.md): Examples of native reasoning models, reasoning tools, and reasoning in agents and teams. - [Agents](https://docs.agno.com/examples/reasoning/agents/overview.md): Task examples with a separate DeepSeek reasoning stage and an OpenAI response. - [Treaty Of Versailles Analysis](https://docs.agno.com/examples/reasoning/agents/analyse-treaty-of-versailles.md): Analyze the Treaty of Versailles with a DeepSeek reasoning stage and an OpenAI response. - [Fibonacci Script Planning](https://docs.agno.com/examples/reasoning/agents/fibonacci.md): Plan a Fibonacci script with a DeepSeek reasoning stage and an OpenAI response. - [Reasoning Finance Agent](https://docs.agno.com/examples/reasoning/agents/finance-agent.md): Prepare a financial report with a separate DeepSeek reasoning stage and finance tools. - [Decimal Comparison Reasoning](https://docs.agno.com/examples/reasoning/agents/is-9-11-bigger-than-9-9.md): Compare 9.11 and 9.9 using a DeepSeek reasoning stage before an OpenAI response. - [Future Life Storytelling](https://docs.agno.com/examples/reasoning/agents/life-in-500000-years.md): Write speculative fiction with a DeepSeek reasoning stage and an OpenAI response. - [Missionaries And Cannibals Puzzle](https://docs.agno.com/examples/reasoning/agents/logical-puzzle.md): Explore the missionaries and cannibals puzzle with a separate DeepSeek reasoning stage. - [Sum Of Odd Numbers Proof](https://docs.agno.com/examples/reasoning/agents/mathematical-proof.md): Request a proof about odd numbers with a DeepSeek reasoning stage and an OpenAI response. - [Mistral Reasoning COT](https://docs.agno.com/examples/reasoning/agents/mistral-reasoning-cot.md): Migration notice for the retired Mistral Reasoning COT example. - [Python 101 Curriculum Planning](https://docs.agno.com/examples/reasoning/agents/python-101-curriculum.md): Plan a Python curriculum with a DeepSeek reasoning stage and an OpenAI response. - [Scientific Abstract Critique](https://docs.agno.com/examples/reasoning/agents/scientific-research.md): Critique a scientific abstract with a DeepSeek reasoning stage and an OpenAI response. - [Ship Of Theseus Debate](https://docs.agno.com/examples/reasoning/agents/ship-of-theseus.md): Discuss the Ship of Theseus with a DeepSeek reasoning stage and an OpenAI response. - [Strawberry Letter Counting](https://docs.agno.com/examples/reasoning/agents/strawberry.md): Compare a plain OpenAI agent with an OpenAI agent using a DeepSeek reasoning stage for letter counting. - [Trolley Problem Analysis](https://docs.agno.com/examples/reasoning/agents/trolley-problem.md): Analyze the trolley problem with a DeepSeek reasoning stage and an OpenAI response. - [xAI Reasoning Effort](https://docs.agno.com/examples/reasoning/models/xai/reasoning-effort.md): Raise reasoning_effort on grok-3-mini-fast for a YFinance-backed stock report. - [Anthropic Async Reasoning Stream](https://docs.agno.com/examples/reasoning/models/anthropic/async-reasoning-stream.md): Stream Claude extended-thinking reasoning events asynchronously with stream_events. - [Anthropic Basic Reasoning](https://docs.agno.com/examples/reasoning/models/anthropic/basic-reasoning.md): Compare a plain Claude agent with one using extended thinking, then inspect reasoning_content. - [Anthropic Basic Reasoning Stream](https://docs.agno.com/examples/reasoning/models/anthropic/basic-reasoning-stream.md): Stream reasoning events from Claude with extended thinking enabled. - [Anthropic](https://docs.agno.com/examples/reasoning/models/anthropic/overview.md): Anthropic Claude reasoning examples and streaming demos. - [Azure AI Foundry Reasoning Model DeepSeek](https://docs.agno.com/examples/reasoning/models/azure-ai-foundry/reasoning-model-deepseek.md): Historical Foundry DeepSeek-R1 recipe with a current Azure OpenAI reasoning alternative. - [Reasoning Model Stream DeepSeek](https://docs.agno.com/examples/reasoning/models/azure-ai-foundry/reasoning-model-stream-deepseek.md): Historical Foundry DeepSeek-R1 recipe with a current Azure OpenAI reasoning alternative. - [Azure AI Foundry](https://docs.agno.com/examples/reasoning/models/azure-ai-foundry/overview.md): Historical Foundry DeepSeek-R1 recipes and current Azure OpenAI reasoning alternatives. - [Azure OpenAI Basic Reasoning Stream](https://docs.agno.com/examples/reasoning/models/azure-openai/basic-reasoning-stream.md): Adapt the archived GPT-4.1 stage to a native Azure OpenAI reasoning deployment. - [Azure OpenAI O3 Mini with Tools](https://docs.agno.com/examples/reasoning/models/azure-openai/o3-mini-with-tools.md): Pair o3-mini on Azure OpenAI with YFinance tools to write a stock comparison report. - [Azure OpenAI Reasoning Model GPT 4 1](https://docs.agno.com/examples/reasoning/models/azure-openai/reasoning-model-gpt-4-1.md): Adapt the archived GPT-4.1 stage to a native Azure OpenAI reasoning deployment. - [Azure OpenAI](https://docs.agno.com/examples/reasoning/models/azure-openai/overview.md): Azure OpenAI reasoning examples and tooling demos. - [Ethical Dilemma](https://docs.agno.com/examples/reasoning/models/deepseek/ethical-dilemma.md): Use a native DeepSeek reasoning stage before an OpenAI response to an ethical dilemma. - [Plan Itinerary](https://docs.agno.com/examples/reasoning/models/deepseek/plan-itinerary.md): Plan an itinerary using a native DeepSeek reasoning stage and an OpenAI response. - [DeepSeek](https://docs.agno.com/examples/reasoning/models/deepseek/overview.md): DeepSeek-specific reasoning model examples. - [Gemini Async Reasoning Stream](https://docs.agno.com/examples/reasoning/models/gemini/async-reasoning-stream.md): Stream available Gemini 3.7 Flash thought summaries from a separate reasoning stage. - [Gemini Basic Reasoning](https://docs.agno.com/examples/reasoning/models/gemini/basic-reasoning.md): Compare a single Gemini 3.7 Flash call with a separate reasoning stage and inspect returned thought summaries. - [Gemini Basic Reasoning Stream](https://docs.agno.com/examples/reasoning/models/gemini/basic-reasoning-stream.md): Stream available Gemini 3.7 Flash thought summaries from a separate reasoning stage. - [Gemini](https://docs.agno.com/examples/reasoning/models/gemini/overview.md): Google Gemini reasoning examples and streaming demos. - [GPT-OSS Plus Claude](https://docs.agno.com/examples/reasoning/models/groq/deepseek-plus-claude.md): Use GPT-OSS 120B on Groq as a separate stage before a Claude response. - [Fast Reasoning](https://docs.agno.com/examples/reasoning/models/groq/fast-reasoning.md): Time a GPT-OSS call and a separate DeepSeek-plus-Qwen pipeline on the same math prompt. - [9.11 or 9.9](https://docs.agno.com/examples/reasoning/models/groq/or-9-9.md): Use native DeepSeek for a separate reasoning stage before a Groq response to the decimal comparison. - [Groq](https://docs.agno.com/examples/reasoning/models/groq/overview.md): Groq reasoning and model-combination examples. - [Local Reasoning](https://docs.agno.com/examples/reasoning/models/ollama/local-reasoning.md): Run QwQ and DeepSeek-R1 reasoning models locally through Ollama on a math task. - [Ollama Reasoning Model DeepSeek](https://docs.agno.com/examples/reasoning/models/ollama/reasoning-model-deepseek.md): Pair a local Llama 3.2 agent with DeepSeek-R1 on Ollama as its reasoning model. - [Ollama](https://docs.agno.com/examples/reasoning/models/ollama/overview.md): Local Ollama reasoning examples. - [O3 Mini](https://docs.agno.com/examples/reasoning/models/openai/o3-mini.md): Stream o3-mini working through the trolley problem with an ASCII diagram. - [OpenAI O3 Mini with Tools](https://docs.agno.com/examples/reasoning/models/openai/o3-mini-with-tools.md): Give o3-mini web search tools to research and write a stock comparison report. - [OpenAI Reasoning Effort](https://docs.agno.com/examples/reasoning/models/openai/reasoning-effort.md): Set reasoning_effort to high on o3-mini for a tool-assisted stock report. - [OpenAI Reasoning Model GPT 4 1](https://docs.agno.com/examples/reasoning/models/openai/reasoning-model-gpt-4-1.md): Adapt the archived GPT-4.1 stage to a supported separate OpenAI reasoning model. - [Reasoning Stream](https://docs.agno.com/examples/reasoning/models/openai/reasoning-stream.md): Stream reasoning events from o3-mini on a history essay with stream_events enabled. - [Reasoning Summary](https://docs.agno.com/examples/reasoning/models/openai/reasoning-summary.md): Request an automatic reasoning summary from o4-mini while it writes a stock report. - [OpenAI](https://docs.agno.com/examples/reasoning/models/openai/overview.md): OpenAI reasoning model examples, including effort, stream, and summary modes. - [Vertex AI Basic Reasoning Stream](https://docs.agno.com/examples/reasoning/models/vertex-ai/basic-reasoning-stream.md): Stream Gemini 2.5 Flash reasoning on Vertex AI with a fixed 1,024-token thinking budget and thought summaries. - [Vertex AI](https://docs.agno.com/examples/reasoning/models/vertex-ai/overview.md): Vertex AI reasoning stream examples with Gemini. - [Models](https://docs.agno.com/examples/reasoning/models/overview.md): Provider-specific reasoning cookbook examples. - [Teams](https://docs.agno.com/examples/reasoning/teams/overview.md): Reasoning-oriented team orchestration examples. - [Knowledge Tool Team](https://docs.agno.com/examples/reasoning/teams/knowledge-tool-team.md): Give a team leader KnowledgeTools over a LanceDB knowledge base of a Paul Graham essay. - [Reasoning Finance Team](https://docs.agno.com/examples/reasoning/teams/reasoning-finance-team.md): Claude team leader uses ReasoningTools to coordinate web and finance agents on tariff analysis. - [Tools](https://docs.agno.com/examples/reasoning/tools/overview.md): Reasoning tools integrations across multiple providers. - [Azure OpenAI Reasoning Tools](https://docs.agno.com/examples/reasoning/tools/azure-openai-reasoning-tools.md): Azure OpenAI agent pairs ReasoningTools with web search to write an NVDA vs TSLA report. - [Capture Reasoning Content Knowledge Tools](https://docs.agno.com/examples/reasoning/tools/capture-reasoning-content-knowledge-tools.md): Capture available tool-generated reasoning from a completed run and its successful terminal streaming event. - [Capture Reasoning Content Reasoning Tools](https://docs.agno.com/examples/reasoning/tools/capture-reasoning-content-reasoning-tools.md): Capture available tool-generated reasoning from a completed run and its successful terminal streaming event. - [Cerebras Llama Reasoning Tools](https://docs.agno.com/examples/reasoning/tools/cerebras-llama-reasoning-tools.md): Use ReasoningTools with a Cerebras agent to work through the fox, chicken, and grain puzzle. - [Claude Reasoning Tools](https://docs.agno.com/examples/reasoning/tools/claude-reasoning-tools.md): Claude Sonnet combines ReasoningTools and web search for a semiconductor market analysis. - [Gemini Finance Agent](https://docs.agno.com/examples/reasoning/tools/gemini-finance-agent.md): Gemini agent uses ReasoningTools and YFinanceTools to compare NVDA and TSLA in a report. - [Gemini Reasoning Tools](https://docs.agno.com/examples/reasoning/tools/gemini-reasoning-tools.md): Gemini 2.5 Pro runs think and analyze steps with YFinanceTools for an NVDA vs TSLA report. - [Groq Llama Finance Agent](https://docs.agno.com/examples/reasoning/tools/groq-llama-finance-agent.md): Llama 4 Scout on Groq uses the think tool as a scratchpad while writing a finance report. - [IBM Watsonx Reasoning Tools](https://docs.agno.com/examples/reasoning/tools/ibm-watsonx-reasoning-tools.md): IBM WatsonX Llama agent applies ReasoningTools to a river-crossing logic puzzle. - [Knowledge Tools](https://docs.agno.com/examples/reasoning/tools/knowledge-tools.md): Agent thinks, searches, and analyzes over Agno docs stored in a LanceDB knowledge base. - [Llama Reasoning Tools](https://docs.agno.com/examples/reasoning/tools/llama-reasoning-tools.md): Llama 4 Maverick pairs ReasoningTools with YFinanceTools to report on the NVDA stock price. - [Memory Tools](https://docs.agno.com/examples/reasoning/tools/memory-tools.md): Trip-planner agent stores and recalls user memories in SQLite with MemoryTools. - [Ollama Reasoning Tools](https://docs.agno.com/examples/reasoning/tools/ollama-reasoning-tools.md): Local Llama 3.2 via Ollama uses ReasoningTools and web search for an NVDA vs TSLA report. - [OpenAI Reasoning Tools](https://docs.agno.com/examples/reasoning/tools/openai-reasoning-tools.md): GPT-5.6 Luna combines few-shot ReasoningTools with web search to compare NVDA and TSLA. - [Reasoning Tools](https://docs.agno.com/examples/reasoning/tools/reasoning-tools.md): GPT-5.6 Luna agent uses the think and analyze tools to solve a river-crossing logic puzzle. - [Vercel Reasoning Tools](https://docs.agno.com/examples/reasoning/tools/vercel-reasoning-tools.md): Vercel v0 model pairs ReasoningTools with web search to write a TSLA report. - [Workflow Tools](https://docs.agno.com/examples/reasoning/tools/workflow-tools.md): Agent drives a blog-post workflow through WorkflowTools with think and analyze steps. - [Evals](https://docs.agno.com/examples/evals/overview.md): Evaluate agents and teams for accuracy, model-judged quality, performance, reliability, and reusable suites. - [Accuracy](https://docs.agno.com/examples/evals/accuracy/overview.md): Accuracy examples evaluate how well responses match expected outputs. - [Comparison Accuracy Evaluation](https://docs.agno.com/examples/evals/accuracy/accuracy-9-11-bigger-or-9-99.md): Score an agent that must use CalculatorTools to answer whether 9.11 or 9.9 is bigger, with an o4-mini judge. - [Basic Accuracy Evaluation](https://docs.agno.com/examples/evals/accuracy/accuracy-basic.md): Run AccuracyEval with run() and arun() over a multi-step calculator prompt, using 1 and 3 iterations. - [Team Accuracy Evaluation](https://docs.agno.com/examples/evals/accuracy/accuracy-team.md): Score a two-agent English/Spanish routing Team on its refusal response to a French prompt. - [Given Answer Accuracy Evaluation](https://docs.agno.com/examples/evals/accuracy/accuracy-with-given-answer.md): Grade a pre-computed output string with AccuracyEval.run_with_output(), no agent execution required. - [Tool-Enabled Accuracy Evaluation](https://docs.agno.com/examples/evals/accuracy/accuracy-with-tools.md): Score a CalculatorTools-equipped agent on computing 10! against the expected 3628800. - [Accuracy Evaluation with Database Logging](https://docs.agno.com/examples/evals/accuracy/db-logging.md): Persist AccuracyEval runs to a PostgresDb eval_runs_cookbook table while scoring a calculator agent. - [Accuracy Evaluation with Custom Evaluator Agent](https://docs.agno.com/examples/evals/accuracy/evaluator-agent.md): Score an agent's step-by-step calculator math against an expected answer using a custom evaluator agent with an AccuracyAgentResponse output schema. - [Accuracy Eval Metrics](https://docs.agno.com/examples/evals/accuracy/accuracy-eval-metrics.md): Accumulate evaluator token usage into an agent's run_output via the run_metrics parameter of evaluate_answer, surfacing it under the eval_model key of metrics.details. - [Agent As Judge](https://docs.agno.com/examples/evals/agent-as-judge/overview.md): Agent-as-judge examples evaluate output quality with model-based scoring. - [Basic Agent-as-Judge Evaluation](https://docs.agno.com/examples/evals/agent-as-judge/agent-as-judge-basic.md): Run numeric-scored agent-as-judge evaluations synchronously against PostgresDb and asynchronously against AsyncSqliteDb, with an on_fail callback and stored eval runs. - [Batch Agent-as-Judge Evaluation](https://docs.agno.com/examples/evals/agent-as-judge/agent-as-judge-batch.md): Judge three pre-supplied customer-service cases with binary scoring, persist the evaluation runs in SqliteDb, and report the result pass rate. - [Binary Agent-as-Judge Evaluation](https://docs.agno.com/examples/evals/agent-as-judge/agent-as-judge-binary.md): Judge a customer-service agent reply as pass or fail against a professional-tone criterion using AgentAsJudgeEval backed by SqliteDb. - [Custom Evaluator Agent-as-Judge Evaluation](https://docs.agno.com/examples/evals/agent-as-judge/agent-as-judge-custom-evaluator.md): Score agent output with AgentAsJudgeEval using a strict custom evaluator_agent instead of the default judge. - [Post-Hook Agent-as-Judge Evaluation](https://docs.agno.com/examples/evals/agent-as-judge/agent-as-judge-post-hook.md): Attach AgentAsJudgeEval as an agent post_hook and read stored scores back from SqliteDb and AsyncSqliteDb. - [Team Agent-as-Judge Evaluation](https://docs.agno.com/examples/evals/agent-as-judge/agent-as-judge-team.md): Judge a researcher/writer Team response with binary AgentAsJudgeEval scoring persisted to SqliteDb. - [Team Post-Hook Agent-as-Judge Evaluation](https://docs.agno.com/examples/evals/agent-as-judge/agent-as-judge-team-post-hook.md): Register AgentAsJudgeEval in Team.post_hooks to score final-response quality automatically after each team run. - [Guideline-Based Agent-as-Judge Evaluation](https://docs.agno.com/examples/evals/agent-as-judge/agent-as-judge-with-guidelines.md): Steer numeric AgentAsJudgeEval scoring with additional_guidelines that require units, variant context, and technical completeness. - [Tool-Using Agent-as-Judge Evaluation](https://docs.agno.com/examples/evals/agent-as-judge/agent-as-judge-with-tools.md): Score a CalculatorTools agent's math answer on step-by-step clarity with numeric AgentAsJudgeEval. - [Agent-as-Judge Eval Metrics](https://docs.agno.com/examples/evals/agent-as-judge/agent-as-judge-eval-metrics.md): Attach AgentAsJudgeEval as an agent post_hook and read the evaluator's token usage from run_output.metrics.details['eval_model']. - [Performance](https://docs.agno.com/examples/evals/performance/overview.md): Performance examples benchmark runtime and memory impact for agents and teams. - [Async Function Performance Evaluation](https://docs.agno.com/examples/evals/performance/async-function.md): Benchmark an async agent.arun call over 10 iterations per metric with PerformanceEval.arun, printing runtime and memory results. - [AutoGen Instantiation Performance Evaluation](https://docs.agno.com/examples/evals/performance/comparison/autogen-instantiation.md): Measure AutoGen AssistantAgent instantiation over 1,000 iterations per metric with PerformanceEval, using a gpt-5.6-luna OpenAIChatCompletionClient and a weather tool. - [CrewAI Instantiation Performance Evaluation](https://docs.agno.com/examples/evals/performance/comparison/crewai-instantiation.md): Measure CrewAI Agent instantiation over 1,000 iterations per metric with PerformanceEval, using a gpt-5.6-luna llm and a decorated weather tool. - [LangGraph Instantiation Performance Evaluation](https://docs.agno.com/examples/evals/performance/comparison/langgraph-instantiation.md): Measure LangGraph create_react_agent instantiation over 1,000 iterations per metric with PerformanceEval, using ChatOpenAI gpt-5.6-luna and a weather tool. - [OpenAI Agents Instantiation Performance Evaluation](https://docs.agno.com/examples/evals/performance/comparison/openai-agents-instantiation.md): Benchmark 1,000 per-metric OpenAI Agents SDK agent constructions with a function tool using Agno's PerformanceEval. - [PydanticAI Instantiation Performance Evaluation](https://docs.agno.com/examples/evals/performance/comparison/pydantic-ai-instantiation.md): Benchmark 1,000 per-metric PydanticAI agent constructions, including an inline @agent.tool_plain weather tool, via Agno's PerformanceEval. - [Smolagents Instantiation Performance Evaluation](https://docs.agno.com/examples/evals/performance/comparison/smolagents-instantiation.md): Measure runtime and memory across 1,000 Smolagents ToolCallingAgent constructions per metric with PerformanceEval. - [Comparison](https://docs.agno.com/examples/evals/performance/comparison/overview.md): These benchmarks compare agent instantiation patterns across non-Agno frameworks. - [Performance Evaluation with Database Logging](https://docs.agno.com/examples/evals/performance/db-logging.md): Run a single-iteration agent run benchmark and persist its PerformanceEval results to the eval_runs_cookbook table in PostgresDb. - [Agent Instantiation Performance Evaluation](https://docs.agno.com/examples/evals/performance/instantiate-agent.md): Measure runtime and memory of 1,000 per-metric bare Agno Agent constructions with only a system message. - [Agent-with-Tool Instantiation Performance Evaluation](https://docs.agno.com/examples/evals/performance/instantiate-agent-with-tool.md): Measure runtime and memory of 1,000 per-metric Agno Agent constructions using OpenAIChat gpt-5.6-luna plus a weather tool. - [Team Instantiation Performance Evaluation](https://docs.agno.com/examples/evals/performance/instantiate-team.md): Benchmark Team construction cost over 1,000 iterations per metric with PerformanceEval. - [Memory Update Performance Evaluation](https://docs.agno.com/examples/evals/performance/response-with-memory-updates.md): Benchmark agent run latency with update_memory_on_run enabled against a SqliteDb over 5 iterations per metric. - [Storage-Backed Response Performance Evaluation](https://docs.agno.com/examples/evals/performance/response-with-storage.md): PerformanceEval invokes the benchmark function separately for runtime and memory measurement, producing four model requests total. - [Simple Response Performance Evaluation](https://docs.agno.com/examples/evals/performance/simple-response.md): Measure baseline response latency for a single GPT-5.2 prompt. - [Team Memory and Reasoning Performance Evaluation](https://docs.agno.com/examples/evals/performance/team-response-with-memory-and-reasoning.md): Track memory growth and top allocations for a PostgresDb-backed team using ReasoningTools across five concurrent users. - [Multi-User Team Memory Performance Evaluation](https://docs.agno.com/examples/evals/performance/team-response-with-memory-multi-user.md): Track memory growth for a PostgresDb-backed team answering five users concurrently, each in its own session. - [Simple Team Memory Performance Evaluation](https://docs.agno.com/examples/evals/performance/team-response-with-memory-simple.md): Benchmark memory growth across 5 async runs of a weather team with persistent memory and history in Postgres. - [Team](https://docs.agno.com/examples/evals/reliability/team/overview.md): These examples validate reliability for team-level tool usage and delegation. - [Team Reliability Evaluation for News Search](https://docs.agno.com/examples/evals/reliability/team/ai-news.md): Check whether a news-research team makes the expected delegation and web search tool calls. - [Reliability Evaluation with Database Logging](https://docs.agno.com/examples/evals/reliability/db-logging.md): Persist ReliabilityEval tool-call results to a Postgres eval_runs table via PostgresDb. - [Multiple Tool Call Reliability Evaluation](https://docs.agno.com/examples/evals/reliability/multiple-tool-calls/calculator.md): Assert an agent calls both multiply and exponentiate, then relax to subset matching with allow_additional_tool_calls. - [Multiple Tool Calls](https://docs.agno.com/examples/evals/reliability/multiple-tool-calls/overview.md): These examples validate reliability for multi-step tool workflows. - [Asynchronous Reliability Evaluation](https://docs.agno.com/examples/evals/reliability/reliability-async.md): Score a factorial tool call using ReliabilityEval.arun() driven by asyncio.run. - [Single Tool Call Reliability Evaluation](https://docs.agno.com/examples/evals/reliability/single-tool-calls/calculator.md): Assert a single expected tool call and validate its arguments with expected_tool_call_arguments. - [Single Tool Calls](https://docs.agno.com/examples/evals/reliability/single-tool-calls/overview.md): These examples validate reliability for one expected tool call. - [Reliability](https://docs.agno.com/examples/evals/reliability/overview.md): Reliability examples validate whether expected expected tool executions are recorded. - [Eval Suite](https://docs.agno.com/examples/evals/suite/suite-basic.md): Declare a few Cases and run them as a suite with the built-in CLI. - [Eval Suite: Team with Numeric Judge Scoring](https://docs.agno.com/examples/evals/suite/suite-team-scoring.md): Run eval Cases against a Team and grade each answer with a numeric 1-10 judge and threshold. - [Integrations](https://docs.agno.com/examples/integrations/overview.md): Integration examples showing how to connect Agno agents with external platforms and services. - [A2A](https://docs.agno.com/examples/integrations/a2a/overview.md): Examples for running Agno with the A2A protocol. - [Basic Agent](https://docs.agno.com/examples/integrations/a2a/basic-agent/overview.md): Serve an Agno agent over A2A and call it with the first-party client. - [Discord](https://docs.agno.com/examples/integrations/discord/overview.md): This module provides a Discord client implementation for Agno, allowing you to create AI-powered Discord bots using Agno's agent framework. - [Discord Agent With Media](https://docs.agno.com/examples/integrations/discord/agent-with-media.md): Current alternatives for the retired Discord Agent With Media example. - [Discord Agent With User Memory](https://docs.agno.com/examples/integrations/discord/agent-with-user-memory.md): Current alternatives for the retired Discord Agent With User Memory example. - [Basic](https://docs.agno.com/examples/integrations/discord/basic.md): Discord Bot Run an Agno agent as a Discord bot with DiscordClient. - [Memory](https://docs.agno.com/examples/integrations/memory/overview.md): Examples for connecting Agno agents to external memory services. - [Mem0 Integration](https://docs.agno.com/examples/integrations/memory/mem0-integration.md): Seed a Mem0 cloud memory client with user facts and inject the retrieved memories into an Agno agent's context via dependencies. - [Memori Integration](https://docs.agno.com/examples/integrations/memory/memori-integration.md): Register Memori against the agent's OpenAI client with a SQLite-backed store so preferences from earlier turns are recalled in later ones. - [Zep Integration](https://docs.agno.com/examples/integrations/memory/zep-integration.md): Write user messages through ZepTools, wait for Zep to sync, then pass the retrieved Zep context block into an Agno agent as a dependency. - [Workflows](https://docs.agno.com/examples/integrations/observability/workflows/overview.md): Examples for tracing Agno workflows. - [Arize Phoenix Workflow Via OpenInference](https://docs.agno.com/examples/integrations/observability/workflows/arize-phoenix-via-openinference-workflow.md): Trace a research-summarize-fact-check-write workflow to Arize Phoenix using phoenix.otel auto-instrumentation. - [Langfuse Workflows Via OpenInference](https://docs.agno.com/examples/integrations/observability/workflows/langfuse-via-openinference-workflows.md): Export OpenTelemetry spans from a conditional research workflow to Langfuse via the OpenInference Agno instrumentor. - [AgentOps Integration](https://docs.agno.com/examples/integrations/observability/agent-ops.md): Initialize AgentOps and auto-log an Agno agent's model calls. - [Arize Phoenix Project Routing](https://docs.agno.com/examples/integrations/observability/arize-phoenix-moving-traces-to-different-projects.md): Route traces from a stock agent and a search agent into separate Phoenix projects using dangerously_using_project. - [Arize Phoenix Via OpenInference](https://docs.agno.com/examples/integrations/observability/arize-phoenix-via-openinference.md): Trace a structured-output stock agent to Phoenix Cloud via phoenix.otel register with auto-instrumentation. - [Arize Phoenix Local Via OpenInference](https://docs.agno.com/examples/integrations/observability/arize-phoenix-via-openinference-local.md): Send stock-agent traces to a self-hosted Phoenix collector at localhost:6006 under a named project. - [Atla Observability Integration](https://docs.agno.com/examples/integrations/observability/atla-op.md): Historical Atla Insights instrumentation example, retained after the service shutdown. - [Langfuse Via OpenInference](https://docs.agno.com/examples/integrations/observability/langfuse-via-openinference.md): Export Agno agent spans to Langfuse over OTLP HTTP with base64 basic-auth headers and AgnoInstrumentor. - [Langfuse Via OpenInference With Response Model](https://docs.agno.com/examples/integrations/observability/langfuse-via-openinference-response-model.md): Trace a YFinance stock-price agent with a Pydantic output_schema to Langfuse via the OpenInference Agno instrumentor and OTLP HTTP export. - [Langfuse Via OpenLIT](https://docs.agno.com/examples/integrations/observability/langfuse-via-openlit.md): Export traces from a web-search agent to Langfuse by wiring an OTLP HTTP span exporter into OpenLIT's global tracer with batching disabled. - [LangSmith Via OpenInference](https://docs.agno.com/examples/integrations/observability/langsmith-via-openinference.md): Send spans from a web-search stock-news agent to the LangSmith EU OTLP endpoint using the OpenInference Agno instrumentor with API-key and project headers. - [Langtrace Integration](https://docs.agno.com/examples/integrations/observability/langtrace-op.md): Auto-instrument a YFinance stock-price agent with langtrace.init() from the Langtrace Python SDK. - [LangWatch Integration](https://docs.agno.com/examples/integrations/observability/langwatch-op.md): Register the OpenInference AgnoInstrumentor through langwatch.setup() so a YFinance stock-price agent reports spans to LangWatch. - [Latitude Via OpenInference](https://docs.agno.com/examples/integrations/observability/latitude-via-openinference.md): Stream an async YFinance stock-price agent and export its OpenInference spans to Latitude's OTLP ingest endpoint via bearer-token and project headers. - [Logfire Via OpenInference](https://docs.agno.com/examples/integrations/observability/logfire-via-openinference.md): Streams a YFinance stock agent's spans to Logfire over OTLP HTTP using the OpenInference Agno instrumentor. - [Maxim Integration](https://docs.agno.com/examples/integrations/observability/maxim-ops.md): Trace delegated agent calls in an interactive web-search and YFinance team with Maxim. - [Opik Via OpenInference](https://docs.agno.com/examples/integrations/observability/opik-via-openinference.md): Exports agent, model, and tool spans with custom trace attributes to Opik via an OTLP HTTP span processor. - [The Context Company](https://docs.agno.com/examples/integrations/observability/the-context-company.md): Demonstrates instrumenting an Agno agent with The Context Company. - [Langfuse Team Tracing Via OpenInference](https://docs.agno.com/examples/integrations/observability/teams/langfuse-via-openinference-team.md): Traces a two-agent market-data and news team to Langfuse over OTLP with a run_mode switch between sync and async runs. - [Teams](https://docs.agno.com/examples/integrations/observability/teams/overview.md): Examples for tracing multi-agent teams. - [Trace To Database](https://docs.agno.com/examples/integrations/observability/trace-to-database.md): Persists agent spans to SQLite with setup_tracing, then queries db.get_trace and db.get_spans to print the span tree and attributes. - [Traceloop Integration](https://docs.agno.com/examples/integrations/observability/traceloop-op.md): Wraps an agent run in a Traceloop @workflow-decorated function so the run appears under a parent workflow span. - [Weave Integration](https://docs.agno.com/examples/integrations/observability/weave-op.md): Wrap an Agno agent run in a @weave.op() decorator to log calls to a Weave project. - [MLflow Via Autolog](https://docs.agno.com/examples/integrations/observability/mlflow-via-autolog.md): Enable mlflow.agno.autolog() against a local MLflow tracking server to capture traces from a YFinance stock agent. - [MLflow Via OpenInference](https://docs.agno.com/examples/integrations/observability/mlflow-via-openinference.md): Send OpenInference spans from an async YFinance agent to an MLflow tracking server over the OTLP HTTP trace endpoint. - [Observability](https://docs.agno.com/examples/integrations/observability/overview.md): Observability examples for tracing and monitoring Agno agents, teams, and workflows. - [SurrealDB](https://docs.agno.com/examples/integrations/surrealdb/overview.md): Examples showing SurrealDB as a backend for Agno memory management. - [SurrealDB Custom Memory Instructions](https://docs.agno.com/examples/integrations/surrealdb/custom-memory-instructions.md): Scope what MemoryManager captures with custom instructions, storing memories in SurrealDB. - [SurrealDB Memory DB Tools Control](https://docs.agno.com/examples/integrations/surrealdb/db-tools-control.md): Control memory writes with MemoryManager's add_memories and update_memories flags, backed by SurrealDB. - [SurrealDB Memory Creation](https://docs.agno.com/examples/integrations/surrealdb/memory-creation.md): Create user memories from raw text and message history with MemoryManager on SurrealDB. - [SurrealDB Memory Search](https://docs.agno.com/examples/integrations/surrealdb/memory-search-surreal.md): Search SurrealDB memories with last_n, first_n, and agentic retrieval methods. - [Standalone SurrealDB Memory Operations](https://docs.agno.com/examples/integrations/surrealdb/standalone-memory-surreal.md): Add, delete, and replace user memories directly in SurrealDB with a standalone MemoryManager. - [Parallel Quickstart - Web Research Agent](https://docs.agno.com/examples/integrations/parallel/quickstart.md): The smallest possible Parallel-powered agent: give an Agent the Parallel Search API and ask it something that needs fresh information from the web. - [Parallel Extract - Clean Content From URLs](https://docs.agno.com/examples/integrations/parallel/extract-content.md): The Extract API turns specific URLs into clean, structured text - handling JavaScript-heavy pages and PDFs - so your agent can read sources you already have in hand instead of searching for them. - [Parallel Deep Research - Cited Reports With the Task API](https://docs.agno.com/examples/integrations/parallel/deep-research.md): The Task API runs deep, multi-step research and returns an answer with a "basis": the citations and confidence behind the findings. - [Parallel Research Assistant - Persistent, Multi-API Agent](https://docs.agno.com/examples/integrations/parallel/research-assistant.md): Persistent Parallel research agent combining Search, Extract, and Task APIs with SQLite-backed sessions, history, and user memory across follow-up turns. - [Web + Knowledge - Live Search Meets Your Own Documents](https://docs.agno.com/examples/integrations/parallel/web-plus-knowledge.md): Agent that routes between a local ChromaDB knowledge base (hybrid search, OpenAI embeddings) and Parallel live web search depending on whether the question needs internal or current information. - [Research Team - Coordinated, Parallel-Powered Agents](https://docs.agno.com/examples/integrations/parallel/research-team.md): Agno Team pairing a Parallel Search/Extract web researcher with a Task-API deep researcher, whose lead synthesizes one cited answer. - [Research Workflow](https://docs.agno.com/examples/integrations/parallel/research-workflow.md): Run source gathering and cited-brief writing as two defined workflow steps persisted to SQLite. - [Competitive Intelligence Monitor - Track Changes Over Time](https://docs.agno.com/examples/integrations/parallel/competitive-intel-monitor.md): Agent using the Parallel Monitor API at a 1-day frequency to create topic monitors, list active ones, and report detected change events with sources. - [AgentOS Research App - Deploy Your Parallel Agent](https://docs.agno.com/examples/integrations/parallel/agent-os-app.md): Serve a Parallel-powered research agent through the AgentOS REST API. - [Components](https://docs.agno.com/examples/components/overview.md): Save and load Agents, Teams, and Workflows to and from a database, with a Registry for restoring tools, models, and schemas. - [AgentOS Registry App](https://docs.agno.com/examples/components/agent-os-registry.md): Serve an AgentOS app whose Registry pre-registers DuckDuckGo and calculator tools, a custom function, and OpenAI/Claude/Azure models. - [AgentOS Registry Demo](https://docs.agno.com/examples/components/demo.md): Register tools, custom functions, Pydantic schemas, models, and a PgVector vector DB in an AgentOS Registry. - [Load Agent from Database](https://docs.agno.com/examples/components/get-agent.md): Fetch a stored agent from PostgresDb with get_agent_by_id and run it, with get_agents shown for listing all. - [Load Team from Database](https://docs.agno.com/examples/components/get-team.md): Fetch a stored team from PostgresDb with get_team_by_id and stream its response, with get_teams shown for listing all. - [Load Workflow from Database](https://docs.agno.com/examples/components/get-workflow.md): Fetch a stored workflow from PostgresDb with get_workflow_by_id and run it, with get_workflows shown for listing all. - [Registry for Non-Serializable Components](https://docs.agno.com/examples/components/registry.md): Register tools, models, dbs, and Pydantic schemas in a Registry so saved agents can be rehydrated from Postgres. - [Save Agent to Database](https://docs.agno.com/examples/components/save-agent.md): Persist an agent to PostgresDb with agent.save(), which returns a new version number on each save. - [Save Team to Database](https://docs.agno.com/examples/components/save-team.md): Persist a two-member Team (researcher and writer) to PostgresDb with team.save(), returning a new version each time. - [Save Workflow to Database](https://docs.agno.com/examples/components/save-workflow.md): Persist a two-step content workflow to PostgresDb with workflow.save(db=db), which versions the workflow on each save. - [Workflows](https://docs.agno.com/examples/components/workflows/overview.md): Examples for saving and loading workflows with advanced step types. - [Save Conditional Workflow Steps](https://docs.agno.com/examples/components/workflows/save-conditional-steps.md): Save a workflow whose Condition evaluator is a plain function, then reload it from Postgres by registering that function in a Registry. - [Save Custom Executor Workflow Steps](https://docs.agno.com/examples/components/workflows/save-custom-steps.md): Register a custom executor function in a Registry so a saved workflow's non-agent step resolves on load from Postgres. - [Save Loop Workflow Steps](https://docs.agno.com/examples/components/workflows/save-loop-steps.md): Persist a Loop step whose end_condition function is restored from a Registry, iterating HackerNews and web research up to 3 times before summarizing. - [Save Parallel Workflow Steps](https://docs.agno.com/examples/components/workflows/save-parallel-steps.md): Save and reload a parallel research workflow, with an explicit model and Registry to restore its tools. - [Save Router Workflow Steps](https://docs.agno.com/examples/components/workflows/save-router-steps.md): Persist a Router whose keyword-based selector function is restored from a Registry, dispatching topics to HackerNews or web research before summarizing. - [Registry Agents in Workflow](https://docs.agno.com/examples/components/workflows/registry-agents-in-workflow.md): Expose code-defined agents through the AgentOS registry and resolve unpinned workflow references by stable ID. - [Save HITL Condition, Loop, and Router Steps](https://docs.agno.com/examples/components/workflows/save-hitl-condition-loop-router.md): Round-trip requires_confirmation, on_reject, and route-selection settings on Condition, Loop, and Router through workflow save/load, then drive the pauses interactively. - [Save HITL Confirmation Workflow Steps](https://docs.agno.com/examples/components/workflows/save-hitl-confirmation-steps.md): Save a workflow whose ProcessData step requires confirmation, reload it via get_workflow_by_id with a Registry, and confirm or skip the paused step at run time. - [Save HITL User Input Workflow Steps](https://docs.agno.com/examples/components/workflows/save-hitl-user-input-steps.md): Persist a workflow whose step declares a UserInputField schema (tone, length, include_examples), reload it from Postgres, and fill the paused step's fields interactively. - [Auto-Populating the Registry from Agents, Teams, and Workflows](https://docs.agno.com/examples/components/auto-populate-registry.md): AgentOS automatically discovers the components (models, tools, databases, and vector databases) used anywhere inside its agents, teams, and workflows and adds them to the registry. - [Serving an AgentOS with an Auto-Populated Registry](https://docs.agno.com/examples/components/auto-populate-registry-os.md): Serve an AgentOS with no explicit registry and read the auto-discovered models, tools, and dbs from the GET /registry endpoint. - [Per-User Component Isolation](https://docs.agno.com/examples/components/user-isolation-os.md): Keep component drafts private to their owner, share published components, and restrict writes by owner. ## Optional - [Reference](https://docs.agno.com/reference/agents/agent.md) - [RemoteAgent](https://docs.agno.com/reference/agents/remote-agent.md): Execute agents hosted on a remote AgentOS instance. - [RunOutput](https://docs.agno.com/reference/agents/run-response.md): Attributes of RunOutput and the events streamed during an agent run. - [AgentSession](https://docs.agno.com/reference/agents/session.md): Attributes and methods of AgentSession, the session object stored in the database for agent runs. - [Team](https://docs.agno.com/reference/teams/team.md): Constructor parameters and methods for the Team class. - [RemoteTeam](https://docs.agno.com/reference/teams/remote-team.md): Execute teams hosted on a remote AgentOS instance. - [TeamRunOutput](https://docs.agno.com/reference/teams/team-response.md): Attributes of TeamRunOutput and the events streamed during a team run. - [Team Session](https://docs.agno.com/reference/teams/session.md): Attributes and methods of TeamSession, the session object stored in the database for team runs. - [Workflow](https://docs.agno.com/reference/workflows/workflow.md): Constructor parameters and core execution, persistence, and session methods for the Workflow class. - [RemoteWorkflow](https://docs.agno.com/reference/workflows/remote-workflow.md): Execute workflows hosted on a remote AgentOS or A2A server. - [Step](https://docs.agno.com/reference/workflows/step.md): Constructor parameters for the workflow Step class, including retry and HITL options. - [StepInput](https://docs.agno.com/reference/workflows/step_input.md): Fields and helper methods on the StepInput object passed to each workflow step. - [StepOutput](https://docs.agno.com/reference/workflows/step_output.md): Fields returned by workflow step execution. - [Conditional Steps](https://docs.agno.com/reference/workflows/conditional-steps.md): Constructor parameters for the workflow Condition class, which branches execution based on an evaluator. - [Parallel Steps](https://docs.agno.com/reference/workflows/parallel-steps.md): Constructor parameters for the workflow Parallel class, which runs steps concurrently. - [Router Steps](https://docs.agno.com/reference/workflows/router-steps.md): Constructor parameters for the workflow Router class, which dynamically selects steps to execute. - [Loop Steps](https://docs.agno.com/reference/workflows/loop-steps.md): Parameter reference for the Loop step class, which repeats a sequence of steps until max_iterations or an end_condition is met. - [Steps](https://docs.agno.com/reference/workflows/steps-step.md): Parameter reference for the Steps class, which groups a list of steps into a single sequential pipeline. - [WorkflowRunOutput](https://docs.agno.com/reference/workflows/run-output.md): Attributes of WorkflowRunOutput, the events streamed during a workflow run, and workflow metrics. - [WorkflowSession](https://docs.agno.com/reference/workflows/session.md): Attributes and methods of WorkflowSession, the session object stored in the database for workflow runs. - [AgentOS](https://docs.agno.com/reference/agent-os/agent-os.md): Parameter and method reference for the AgentOS class that serves agents, teams, and workflows as a FastAPI app. - [AgentOSConfig](https://docs.agno.com/reference/agent-os/configuration.md): Reference for AgentOSConfig, the YAML and Python schema for AgentOS UI, chat, and domain-page settings. - [AuthorizationConfig](https://docs.agno.com/reference/agent-os/authorization-config.md): Parameter reference for AuthorizationConfig, which configures JWT verification for AgentOS RBAC authorization. - [AuthMiddleware](https://docs.agno.com/reference/agent-os/jwt-middleware.md): Parameter reference for AuthMiddleware (aliased as JWTMiddleware), the AgentOS authentication and RBAC middleware. - [Factories](https://docs.agno.com/reference/agent-os/factories.md): Registered callables that produce a fresh Agent, Team, or Workflow per request. - [AgentOSClient](https://docs.agno.com/reference/clients/agentos-client.md): Python client for interacting with AgentOS API endpoints - [A2AClient](https://docs.agno.com/reference/clients/a2a-client.md): Python client for communicating with A2A-compatible agent servers - [RunContext](https://docs.agno.com/reference/run/run-context.md): Per-run state passed to hooks and tools: IDs, session state, dependencies, and resolved tools. - [Metrics](https://docs.agno.com/reference/run/metrics.md): Token usage and timing for agent, team, and workflow runs, messages, sessions, and tool calls. - [Reasoning Reference](https://docs.agno.com/reference/reasoning/reasoning.md): Data structures, toolkit parameters, and events shared by Reasoning Models, Reasoning Tools, and Reasoning Agents. - [SessionSummaryManager](https://docs.agno.com/reference/session/summary_manager.md): Attributes and methods of SessionSummaryManager, which generates session summaries with optional topic extraction. - [Memory Manager](https://docs.agno.com/reference/memory/memory.md): Create, search, update, and delete long-term user memories with MemoryManager. - [CompressionManager](https://docs.agno.com/reference/compression/compression-manager.md): CompressionManager attributes and methods for compressing tool call results. - [PostgresDb](https://docs.agno.com/reference/storage/postgres.md): Store sessions, memories, metrics, knowledge, evals, and traces in PostgreSQL. - [SqliteDb](https://docs.agno.com/reference/storage/sqlite.md): Store sessions, memories, metrics, knowledge, evals, and traces in SQLite. - [MongoDb](https://docs.agno.com/reference/storage/mongodb.md): Store sessions, memories, metrics, knowledge, evals, and traces in MongoDB. - [InMemoryDb](https://docs.agno.com/reference/storage/in_memory.md): Ephemeral in-process storage for sessions, memories, metrics, evals, and knowledge. - [MySQLDb](https://docs.agno.com/reference/storage/mysql.md): Store AgentOS sessions, memory, knowledge, evals, and traces in MySQL. - [RedisDb](https://docs.agno.com/reference/storage/redis.md): Store AgentOS sessions, memory, knowledge, evals, and traces in Redis. - [ValkeyDb](https://docs.agno.com/reference/storage/valkey.md): ValkeyDb parameters for storing sessions and other AgentOS data in Valkey. - [DynamoDb](https://docs.agno.com/reference/storage/dynamodb.md): Store AgentOS sessions, memory, knowledge, evals, and traces in Amazon DynamoDB. - [SingleStoreDb](https://docs.agno.com/reference/storage/singlestore.md): Store AgentOS sessions, memory, knowledge, evals, and traces in SingleStore. - [SurrealDb](https://docs.agno.com/reference/storage/surrealdb.md): Store AgentOS sessions, memory, knowledge, evals, and traces in SurrealDB. - [FirestoreDb](https://docs.agno.com/reference/storage/firestore.md): Store AgentOS sessions, memory, knowledge, evals, and traces in Google Firestore. - [JsonDb](https://docs.agno.com/reference/storage/json.md): Store sessions, memories, metrics, knowledge, evals, and traces in local JSON files. - [GcsJsonDb](https://docs.agno.com/reference/storage/gcs.md): Store sessions, memories, metrics, knowledge, evals, and traces as JSON files in Google Cloud Storage. - [MigrationManager](https://docs.agno.com/reference/storage/migrations.md): API reference for the MigrationManager class used to handle database migrations. - [Local Media Storage](https://docs.agno.com/reference/media-storage/local.md): LocalMediaStorage stores agent media on the local filesystem. - [S3 Media Storage](https://docs.agno.com/reference/media-storage/s3.md): S3MediaStorage stores agent media in an S3 bucket. - [GCS Media Storage](https://docs.agno.com/reference/media-storage/gcs.md): GCSMediaStorage stores agent media in a Google Cloud Storage bucket. - [MediaReference](https://docs.agno.com/reference/media-storage/media-reference.md): MediaReference is the pointer stored in the database in place of media bytes. - [Trace](https://docs.agno.com/reference/tracing/trace.md): Trace attributes, methods, and how to query stored traces from the database. - [Span](https://docs.agno.com/reference/tracing/span.md): Span attributes, common span names, and OpenTelemetry attribute conventions. - [Pre-hooks](https://docs.agno.com/reference/hooks/pre-hooks.md): Parameters automatically injected into pre-hook functions during an Agent or Team run. - [Post-hooks](https://docs.agno.com/reference/hooks/post-hooks.md): Parameters automatically injected into post-hook functions during an Agent or Team run. - [@hook Decorator](https://docs.agno.com/reference/hooks/hook-decorator.md): Configure a hook to run as a non-blocking background task with @hook(run_in_background=True). - [BaseGuardrail](https://docs.agno.com/reference/hooks/base-guardrail.md): Abstract base class for custom guardrails. Implement check and async_check to validate run input. - [OpenAIModerationGuardrail](https://docs.agno.com/reference/hooks/openai-moderation-guardrail.md): Guardrail that flags or blocks input using OpenAI's moderation API. - [PIIDetectionGuardrail](https://docs.agno.com/reference/hooks/pii-guardrail.md): Guardrail that detects and optionally masks PII such as SSNs, credit cards, emails, and phone numbers. - [PromptInjectionGuardrail](https://docs.agno.com/reference/hooks/prompt-injection-guardrail.md): PromptInjectionGuardrail parameters and the default prompt injection patterns it checks for. - [AccuracyEval](https://docs.agno.com/reference/evals/accuracy.md): AccuracyEval parameters, run methods, and AccuracyResult fields. - [AgentAsJudgeEval](https://docs.agno.com/reference/evals/agent-as-judge.md): AgentAsJudgeEval parameters, binary and numeric scoring, and AgentAsJudgeResult fields. - [PerformanceEval](https://docs.agno.com/reference/evals/performance.md): PerformanceEval parameters and PerformanceResult runtime and memory statistics. - [ReliabilityEval](https://docs.agno.com/reference/evals/reliability.md): ReliabilityEval parameters, tool call and argument checks, and ReliabilityResult fields. - [Eval Suite](https://docs.agno.com/reference/evals/suite.md): Case, CaseResult, JudgeMode, SuiteResult, the run_cases runners, and the cli exit code contract. - [Model](https://docs.agno.com/reference/models/model.md): Parameters on the base Model class shared by every Agno model provider, including caching and retry behavior. - [OpenAI](https://docs.agno.com/reference/models/openai.md): Configure OpenAIChat, Agno's model class for OpenAI's Chat Completions API. - [AI/ML API](https://docs.agno.com/reference/models/aimlapi.md): Configure models served through the AI/ML API OpenAI-compatible endpoint. - [Ollama](https://docs.agno.com/reference/models/ollama.md): Configure the Ollama model class for local Ollama servers or Ollama Cloud. - [Ollama Responses](https://docs.agno.com/reference/models/ollama-responses.md): Configure OllamaResponses, Agno's model class for Ollama's OpenAI-compatible Responses API. - [Claude](https://docs.agno.com/reference/models/anthropic.md): Configure the Claude model class, Agno's wrapper for Anthropic's Messages API. - [Cohere](https://docs.agno.com/reference/models/cohere.md): Parameter reference for the Cohere model class, covering request, client, and retry settings. - [DeepInfra](https://docs.agno.com/reference/models/deepinfra.md): Parameter reference for the DeepInfra model class, which extends OpenAILike. - [DeepSeek](https://docs.agno.com/reference/models/deepseek.md): Parameter reference for the DeepSeek model class, including thinking mode controls. - [Gemini](https://docs.agno.com/reference/models/gemini.md): Parameter reference for the Gemini model class, covering Google AI Studio and Vertex AI configuration. - [Groq](https://docs.agno.com/reference/models/groq.md): Parameter reference for the Groq model class, covering request and client settings. - [HuggingFace](https://docs.agno.com/reference/models/huggingface.md): Parameter reference for the HuggingFace model class, covering Hugging Face Hub Inference settings. - [Meta](https://docs.agno.com/reference/models/meta.md): Configure Llama, Agno's model class for Meta's Llama API. - [Mistral](https://docs.agno.com/reference/models/mistral.md): Configure MistralChat, Agno's model class for Mistral's language models. - [Nebius](https://docs.agno.com/reference/models/nebius.md): Configure Nebius, Agno's model class for Nebius Token Factory models. - [N1N](https://docs.agno.com/reference/models/n1n.md): Access n1n.ai models through Agno's OpenAI-compatible N1N model class. - [Nvidia](https://docs.agno.com/reference/models/nvidia.md): Configure Nvidia, Agno's model class for NVIDIA's language models. - [Perplexity](https://docs.agno.com/reference/models/perplexity.md): Configure Perplexity, Agno's model class for Perplexity's language models. - [Requesty](https://docs.agno.com/reference/models/requesty.md): Parameter reference for the Requesty model class, which extends OpenAILike to route requests through Requesty AI. - [Together](https://docs.agno.com/reference/models/together.md): Parameter reference for the Together model class, which extends OpenAILike to connect to Together's language models. - [xAI](https://docs.agno.com/reference/models/xai.md): Parameter reference for the xAI model class, which extends OpenAILike to connect to xAI's language models. - [Vercel v0](https://docs.agno.com/reference/models/vercel.md): Parameter reference for the V0 model class, which extends OpenAILike to connect to Vercel's v0 API. - [AWS Bedrock](https://docs.agno.com/reference/models/bedrock.md): Configuration reference for AwsBedrock, Agno's model class for models hosted on AWS Bedrock. - [AWS Bedrock Claude](https://docs.agno.com/reference/models/bedrock-claude.md): Configuration reference for the Bedrock Claude model class, Agno's wrapper for Anthropic Claude models hosted on AWS Bedrock. - [Azure AI Foundry](https://docs.agno.com/reference/models/azure.md): Configure AzureAIFoundry, Agno's model class for Azure AI Foundry-hosted models. - [Azure OpenAI](https://docs.agno.com/reference/models/azure-open-ai.md): Configure AzureOpenAI, Agno's model class for Azure-hosted OpenAI models. - [Fireworks](https://docs.agno.com/reference/models/fireworks.md): Configure Fireworks, Agno's model class for Fireworks-hosted language models. - [OpenRouter](https://docs.agno.com/reference/models/openrouter.md): Configure OpenRouter, Agno's model class for unified access to language models via OpenRouter's Chat Completions API. - [OpenRouter Responses](https://docs.agno.com/reference/models/openrouter-responses.md): Configure OpenRouterResponses, Agno's model class for OpenRouter's OpenAI-compatible Responses API. - [SambaNova](https://docs.agno.com/reference/models/sambanova.md): Configure Sambanova, Agno's model class for SambaNova-hosted language models. - [InternLM](https://docs.agno.com/reference/models/internlm.md): Configuration reference for InternLM, which extends OpenAILike to connect to InternLM's language models. - [OpenAI Like](https://docs.agno.com/reference/models/openai-like.md): Configuration reference for OpenAILike, the base class for providers exposing an OpenAI-compatible Chat Completions API. - [Open Responses](https://docs.agno.com/reference/models/open-responses.md): Configuration reference for OpenResponses, the base class for providers implementing the Open Responses API specification. - [IBM WatsonX](https://docs.agno.com/reference/models/ibm-watsonx.md): Configuration reference for WatsonX, which connects to IBM's foundation models through the ibm-watsonx-ai client. - [Tool Decorator](https://docs.agno.com/reference/tools/decorator.md): Reference for the @tool decorator. - [Toolkit](https://docs.agno.com/reference/tools/toolkit.md): Reference for the Toolkit class. - [RetryAgentRun](https://docs.agno.com/reference/tools/retry-agent-run.md): API reference for the RetryAgentRun exception used to provide feedback to the model within the tool call loop. - [StopAgentRun](https://docs.agno.com/reference/tools/stop-agent-run.md): API reference for the StopAgentRun exception used to exit the tool call loop and complete the agent run. - [Knowledge](https://docs.agno.com/reference/knowledge/knowledge.md): Parameter reference for the Knowledge class: vector database, contents database, readers, and search options. - [Published Pages](https://docs.agno.com/reference/knowledge/pages.md): Knowledge page methods, immutable results, configuration, and errors. - [Azure OpenAI](https://docs.agno.com/reference/knowledge/embedder/azure-openai.md): Parameter reference for AzureOpenAIEmbedder: deployment, API version, endpoint, and batching options. - [Cohere](https://docs.agno.com/reference/knowledge/embedder/cohere.md): Parameter reference for CohereEmbedder: model ID, input type, embedding types, and batching options. - [FastEmbed](https://docs.agno.com/reference/knowledge/embedder/fastembed.md): Parameter reference for FastEmbedEmbedder. Defaults to the BAAI/bge-small-en-v1.5 model with 384 dimensions. - [Fireworks](https://docs.agno.com/reference/knowledge/embedder/fireworks.md): Parameters for FireworksEmbedder, which embeds documents through Fireworks AI's OpenAI-compatible embeddings API. - [Gemini](https://docs.agno.com/reference/knowledge/embedder/gemini.md): Parameters for GeminiEmbedder, which embeds documents with Gemini embedding models via the Gemini API or Vertex AI. - [Hugging Face](https://docs.agno.com/reference/knowledge/embedder/huggingface.md): Parameters for HuggingfaceCustomEmbedder, which embeds documents through Hugging Face's InferenceClient. - [Mistral](https://docs.agno.com/reference/knowledge/embedder/mistral.md): Parameters for MistralEmbedder, which embeds documents with Mistral AI embedding models. - [Nebius](https://docs.agno.com/reference/knowledge/embedder/nebius.md): Parameters for NebiusEmbedder, an OpenAI-compatible embedder for Nebius Token Factory embedding models. - [Ollama](https://docs.agno.com/reference/knowledge/embedder/ollama.md): Parameters for OllamaEmbedder, which embeds documents with models served by an Ollama instance. - [OpenAI](https://docs.agno.com/reference/knowledge/embedder/openai.md): Parameter reference for OpenAIEmbedder, which embeds text with OpenAI's text-embedding-3 models. - [Sentence Transformer](https://docs.agno.com/reference/knowledge/embedder/sentence-transformer.md): Parameter reference for SentenceTransformerEmbedder, which runs open-source sentence-transformers models locally. - [Together](https://docs.agno.com/reference/knowledge/embedder/together.md): Parameter reference for TogetherEmbedder, an OpenAIEmbedder subclass pointed at Together AI's OpenAI-compatible API. - [vLLM](https://docs.agno.com/reference/knowledge/embedder/vllm.md): Parameter reference for VLLMEmbedder, which embeds locally with a vLLM engine or remotely via an OpenAI-compatible server. - [VoyageAI](https://docs.agno.com/reference/knowledge/embedder/voyageai.md): Parameter reference for VoyageAIEmbedder, which embeds text with VoyageAI embedding models via the voyageai client. - [Reader](https://docs.agno.com/reference/knowledge/reader/base.md): Parameter reference for the Reader base class shared by all Agno readers. - [Arxiv Reader](https://docs.agno.com/reference/knowledge/reader/arxiv.md): Parameters and methods of ArxivReader, which searches arXiv and reads paper summaries into documents. - [CSV Reader](https://docs.agno.com/reference/knowledge/reader/csv.md): Parameters and methods of CSVReader, which reads CSV files into documents. - [Field Labeled CSV Reader](https://docs.agno.com/reference/knowledge/reader/field-labeled-csv.md): Parameters and methods of FieldLabeledCSVReader, which converts each CSV row into a field-labeled document. - [Docx Reader](https://docs.agno.com/reference/knowledge/reader/docx.md): Parameters and methods of DocxReader, which reads DOCX files into documents. - [JSON Reader](https://docs.agno.com/reference/knowledge/reader/json.md): Parameters and methods of JSONReader, which reads JSON files into documents. - [PDF Reader](https://docs.agno.com/reference/knowledge/reader/pdf.md): Parameters and methods of PDFReader, which reads PDF files into documents. - [PPTX Reader](https://docs.agno.com/reference/knowledge/reader/pptx.md): Parameters and methods of PPTXReader, which reads PowerPoint (.pptx) files into documents. - [Text Reader](https://docs.agno.com/reference/knowledge/reader/text.md): Parameters and methods of TextReader, which reads text files into documents. - [Website Reader](https://docs.agno.com/reference/knowledge/reader/website.md): Parameters and methods of WebsiteReader, which crawls websites and reads pages into documents. - [YouTube Reader](https://docs.agno.com/reference/knowledge/reader/youtube.md): Parameters and methods of YouTubeReader, which reads YouTube video transcripts into documents. - [Firecrawl Reader](https://docs.agno.com/reference/knowledge/reader/firecrawl.md): Parameters and methods of FirecrawlReader, which scrapes or crawls websites into documents using Firecrawl. - [Wikipedia Reader](https://docs.agno.com/reference/knowledge/reader/wikipedia.md): Parameters and methods of WikipediaReader, which reads Wikipedia article summaries into documents. - [Web Search Reader](https://docs.agno.com/reference/knowledge/reader/web-search.md): WebSearchReader uses DDGS to search the web and reads result pages into documents. - [Cohere Reranker](https://docs.agno.com/reference/knowledge/reranker/cohere.md): CohereReranker reorders retrieved documents by relevance using Cohere rerank models. - [Fixed Size Chunking](https://docs.agno.com/reference/knowledge/chunking/fixed-size.md): Split documents into character-limited chunks with configurable overlap. - [Agentic Chunking](https://docs.agno.com/reference/knowledge/chunking/agentic.md): AgenticChunking uses a model to pick natural breakpoints when splitting documents. - [Semantic Chunking](https://docs.agno.com/reference/knowledge/chunking/semantic.md): SemanticChunking splits documents at semantic boundaries using embeddings via the Chonkie library. - [Recursive Chunking](https://docs.agno.com/reference/knowledge/chunking/recursive.md): RecursiveChunking splits documents at natural break points like newlines and sentence endings. - [Code Chunking](https://docs.agno.com/reference/knowledge/chunking/code.md): CodeChunking splits code at AST boundaries like functions and classes using the Chonkie library. - [Document Chunking](https://docs.agno.com/reference/knowledge/chunking/document.md): DocumentChunking splits documents on structural boundaries like paragraphs and sections. - [CSV Row Chunking](https://docs.agno.com/reference/knowledge/chunking/csv-row.md): RowChunking splits text into one chunk per physical line. - [Markdown Chunking](https://docs.agno.com/reference/knowledge/chunking/markdown.md): MarkdownChunking splits markdown on structure like headings, paragraphs, and sections. - [GCS Content](https://docs.agno.com/reference/knowledge/remote-content/gcs-content.md): GCSContent adds content from a Google Cloud Storage bucket to your knowledge base. - [S3 Content](https://docs.agno.com/reference/knowledge/remote-content/s3-content.md): S3Content adds content from an S3 bucket to your knowledge base. - [AgentOS API Overview](https://docs.agno.com/reference-api/overview.md): REST endpoints for running agents, teams, and workflows and managing sessions, memory, knowledge, and evals on an AgentOS instance. - [Fetch Stored Media for a Session](https://docs.agno.com/reference-api/schema/media/fetch-stored-media-for-a-session.md) - [Health Check](https://docs.agno.com/reference-api/schema/health/health-check.md) - [Get Os Info](https://docs.agno.com/reference-api/schema/core/get-os-info.md) - [Get Os Configuration](https://docs.agno.com/reference-api/schema/core/get-os-configuration.md) - [List Agent Runs](https://docs.agno.com/reference-api/schema/agents/list-agent-runs.md) - [Create Agent Run](https://docs.agno.com/reference-api/schema/agents/create-agent-run.md) - [Cancel Agent Run](https://docs.agno.com/reference-api/schema/agents/cancel-agent-run.md) - [Continue Agent Run](https://docs.agno.com/reference-api/schema/agents/continue-agent-run.md) - [Fork Agent Session](https://docs.agno.com/reference-api/schema/agents/fork-agent-session.md) - [List All Agents](https://docs.agno.com/reference-api/schema/agents/list-all-agents.md) - [Get Agent Details](https://docs.agno.com/reference-api/schema/agents/get-agent-details.md) - [Get Agent Run](https://docs.agno.com/reference-api/schema/agents/get-agent-run.md) - [List Agent Run Checkpoints](https://docs.agno.com/reference-api/schema/agents/list-agent-run-checkpoints.md) - [Get Agent Run Checkpoint Snapshot](https://docs.agno.com/reference-api/schema/agents/get-agent-run-checkpoint-snapshot.md) - [Resume Agent Run Stream](https://docs.agno.com/reference-api/schema/agents/resume-agent-run-stream.md) - [List Team Runs](https://docs.agno.com/reference-api/schema/teams/list-team-runs.md) - [Create Team Run](https://docs.agno.com/reference-api/schema/teams/create-team-run.md) - [Cancel Team Run](https://docs.agno.com/reference-api/schema/teams/cancel-team-run.md) - [Resume Team Run Stream](https://docs.agno.com/reference-api/schema/teams/resume-team-run-stream.md) - [Continue Team Run](https://docs.agno.com/reference-api/schema/teams/continue-team-run.md) - [Fork Team Session](https://docs.agno.com/reference-api/schema/teams/fork-team-session.md) - [List All Teams](https://docs.agno.com/reference-api/schema/teams/list-all-teams.md) - [Get Team Details](https://docs.agno.com/reference-api/schema/teams/get-team-details.md) - [Get Team Run](https://docs.agno.com/reference-api/schema/teams/get-team-run.md) - [List Team Run Checkpoints](https://docs.agno.com/reference-api/schema/teams/list-team-run-checkpoints.md) - [Get Team Run Checkpoint Snapshot](https://docs.agno.com/reference-api/schema/teams/get-team-run-checkpoint-snapshot.md) - [List All Workflows](https://docs.agno.com/reference-api/schema/workflows/list-all-workflows.md) - [Get Workflow Details](https://docs.agno.com/reference-api/schema/workflows/get-workflow-details.md) - [Execute Workflow](https://docs.agno.com/reference-api/schema/workflows/execute-workflow.md) - [List Workflow Runs](https://docs.agno.com/reference-api/schema/workflows/list-workflow-runs.md) - [Continue Workflow Run](https://docs.agno.com/reference-api/schema/workflows/continue-workflow-run.md) - [Cancel Workflow Run](https://docs.agno.com/reference-api/schema/workflows/cancel-workflow-run.md) - [Resume Workflow Run Stream](https://docs.agno.com/reference-api/schema/workflows/resume-workflow-run-stream.md) - [Get Workflow Run](https://docs.agno.com/reference-api/schema/workflows/get-workflow-run.md) - [Slack Events](https://docs.agno.com/reference-api/schema/slack/slack-events.md) - [Slack Interactions](https://docs.agno.com/reference-api/schema/slack/slack-interactions.md) - [Status](https://docs.agno.com/reference-api/schema/telegram/status.md) - [Webhook](https://docs.agno.com/reference-api/schema/telegram/webhook.md) - [Status](https://docs.agno.com/reference-api/schema/whatsapp/status.md) - [Verify Webhook](https://docs.agno.com/reference-api/schema/whatsapp/verify-webhook.md) - [Webhook](https://docs.agno.com/reference-api/schema/whatsapp/webhook.md) - [Run Agent](https://docs.agno.com/reference-api/schema/agui/run-agent.md) - [Get Status](https://docs.agno.com/reference-api/schema/agui/get-status.md) - [Get Agent Card](https://docs.agno.com/reference-api/schema/a2a/get-agent-card.md) - [Run Message Agent](https://docs.agno.com/reference-api/schema/a2a/run-message-agent.md) - [Get Agent Task](https://docs.agno.com/reference-api/schema/a2a/get-agent-task.md) - [Cancel Agent Task](https://docs.agno.com/reference-api/schema/a2a/cancel-agent-task.md) - [Stream Message Agent](https://docs.agno.com/reference-api/schema/a2a/stream-message-agent.md) - [Get Team Card](https://docs.agno.com/reference-api/schema/a2a/get-team-card.md) - [Run Message Team](https://docs.agno.com/reference-api/schema/a2a/run-message-team.md) - [Get Team Task](https://docs.agno.com/reference-api/schema/a2a/get-team-task.md) - [Cancel Team Task](https://docs.agno.com/reference-api/schema/a2a/cancel-team-task.md) - [Stream Message Team](https://docs.agno.com/reference-api/schema/a2a/stream-message-team.md) - [Get Workflow Card](https://docs.agno.com/reference-api/schema/a2a/get-workflow-card.md) - [Run Message Workflow](https://docs.agno.com/reference-api/schema/a2a/run-message-workflow.md) - [Stream Message Workflow](https://docs.agno.com/reference-api/schema/a2a/stream-message-workflow.md) - [List Sessions](https://docs.agno.com/reference-api/schema/sessions/list-sessions.md) - [Create New Session](https://docs.agno.com/reference-api/schema/sessions/create-new-session.md) - [Delete Multiple Sessions](https://docs.agno.com/reference-api/schema/sessions/delete-multiple-sessions.md) - [Get Session By Id](https://docs.agno.com/reference-api/schema/sessions/get-session-by-id.md) - [Delete Session](https://docs.agno.com/reference-api/schema/sessions/delete-session.md) - [Update Session](https://docs.agno.com/reference-api/schema/sessions/update-session.md) - [Get Session Runs](https://docs.agno.com/reference-api/schema/sessions/get-session-runs.md) - [Get Run By Id](https://docs.agno.com/reference-api/schema/sessions/get-run-by-id.md) - [Rename Session](https://docs.agno.com/reference-api/schema/sessions/rename-session.md) - [List Memories](https://docs.agno.com/reference-api/schema/memory/list-memories.md) - [Create Memory](https://docs.agno.com/reference-api/schema/memory/create-memory.md) - [Delete Multiple Memories](https://docs.agno.com/reference-api/schema/memory/delete-multiple-memories.md) - [Get Memory By Id](https://docs.agno.com/reference-api/schema/memory/get-memory-by-id.md) - [Delete Memory](https://docs.agno.com/reference-api/schema/memory/delete-memory.md) - [Update Memory](https://docs.agno.com/reference-api/schema/memory/update-memory.md) - [Get Memory Topics](https://docs.agno.com/reference-api/schema/memory/get-memory-topics.md) - [Get User Memory Statistics](https://docs.agno.com/reference-api/schema/memory/get-user-memory-statistics.md) - [Optimize User Memories](https://docs.agno.com/reference-api/schema/memory/optimize-user-memories.md) - [List Learnings](https://docs.agno.com/reference-api/schema/learnings/list-learnings.md) - [Create Learning](https://docs.agno.com/reference-api/schema/learnings/create-learning.md) - [List Learning Users](https://docs.agno.com/reference-api/schema/learnings/list-learning-users.md) - [Delete Learning User](https://docs.agno.com/reference-api/schema/learnings/delete-learning-user.md) - [Get Learning](https://docs.agno.com/reference-api/schema/learnings/get-learning.md) - [Update Learning](https://docs.agno.com/reference-api/schema/learnings/update-learning.md) - [Delete Learning](https://docs.agno.com/reference-api/schema/learnings/delete-learning.md) - [List Evaluation Runs](https://docs.agno.com/reference-api/schema/evals/list-evaluation-runs.md) - [Execute Evaluation](https://docs.agno.com/reference-api/schema/evals/execute-evaluation.md) - [Delete Evaluation Runs](https://docs.agno.com/reference-api/schema/evals/delete-evaluation-runs.md) - [Get Evaluation Run](https://docs.agno.com/reference-api/schema/evals/get-evaluation-run.md) - [Update Evaluation Run](https://docs.agno.com/reference-api/schema/evals/update-evaluation-run.md) - [Get Agentos Metrics](https://docs.agno.com/reference-api/schema/metrics/get-agentos-metrics.md) - [Refresh Metrics](https://docs.agno.com/reference-api/schema/metrics/refresh-metrics.md) - [Get Metrics Refresh Status](https://docs.agno.com/reference-api/schema/metrics/get-metrics-refresh-status.md) - [Get Queue Stats](https://docs.agno.com/reference-api/schema/queue/get-queue-stats.md) - [List Queue Jobs](https://docs.agno.com/reference-api/schema/queue/list-queue-jobs.md) - [Get Queue Job](https://docs.agno.com/reference-api/schema/queue/get-queue-job.md) - [Requeue Queue Job](https://docs.agno.com/reference-api/schema/queue/requeue-queue-job.md) - [List Content](https://docs.agno.com/reference-api/schema/knowledge/list-content.md) - [Upload Content](https://docs.agno.com/reference-api/schema/knowledge/upload-content.md) - [Delete All Content](https://docs.agno.com/reference-api/schema/knowledge/delete-all-content.md) - [Upload Remote Content](https://docs.agno.com/reference-api/schema/knowledge/upload-remote-content.md) - [List Content Sources](https://docs.agno.com/reference-api/schema/knowledge/list-content-sources.md) - [List Files In Source](https://docs.agno.com/reference-api/schema/knowledge/list-files-in-source.md) - [Get Content By Id](https://docs.agno.com/reference-api/schema/knowledge/get-content-by-id.md) - [Delete Content By Id](https://docs.agno.com/reference-api/schema/knowledge/delete-content-by-id.md) - [Update Content](https://docs.agno.com/reference-api/schema/knowledge/update-content.md) - [Refresh Content](https://docs.agno.com/reference-api/schema/knowledge/refresh-content.md) - [Get Content Status](https://docs.agno.com/reference-api/schema/knowledge/get-content-status.md) - [Search Knowledge](https://docs.agno.com/reference-api/schema/knowledge/search-knowledge.md) - [Get Config](https://docs.agno.com/reference-api/schema/knowledge/get-config.md) - [List Traces](https://docs.agno.com/reference-api/schema/traces/list-traces.md) - [Get Trace Filter Schema](https://docs.agno.com/reference-api/schema/traces/get-trace-filter-schema.md) - [Get Trace Or Span Detail](https://docs.agno.com/reference-api/schema/traces/get-trace-or-span-detail.md) - [Get Trace Statistics By Session](https://docs.agno.com/reference-api/schema/traces/get-trace-statistics-by-session.md) - [Search Traces With Advanced Filters](https://docs.agno.com/reference-api/schema/traces/search-traces-with-advanced-filters.md) - [Migrate All Databases](https://docs.agno.com/reference-api/schema/database/migrate-all-databases.md) - [Migrate Database](https://docs.agno.com/reference-api/schema/database/migrate-database.md) - [List Components](https://docs.agno.com/reference-api/schema/components/list-components.md) - [Create Component](https://docs.agno.com/reference-api/schema/components/create-component.md) - [Get Component](https://docs.agno.com/reference-api/schema/components/get-component.md) - [Delete Component](https://docs.agno.com/reference-api/schema/components/delete-component.md) - [Restore Component](https://docs.agno.com/reference-api/schema/components/restore-component.md) - [Update Component](https://docs.agno.com/reference-api/schema/components/update-component.md) - [List Configs](https://docs.agno.com/reference-api/schema/components/list-configs.md) - [Create Config Version](https://docs.agno.com/reference-api/schema/components/create-config-version.md) - [Get Config Version](https://docs.agno.com/reference-api/schema/components/get-config-version.md) - [Delete Config Version](https://docs.agno.com/reference-api/schema/components/delete-config-version.md) - [Update Draft Config](https://docs.agno.com/reference-api/schema/components/update-draft-config.md) - [Get Current Config](https://docs.agno.com/reference-api/schema/components/get-current-config.md) - [Set Current Config Version](https://docs.agno.com/reference-api/schema/components/set-current-config-version.md) - [List Registry](https://docs.agno.com/reference-api/schema/registry/list-registry.md) - [List Schedules](https://docs.agno.com/reference-api/schema/schedules/list-schedules.md) - [Create Schedule](https://docs.agno.com/reference-api/schema/schedules/create-schedule.md) - [Get Schedule](https://docs.agno.com/reference-api/schema/schedules/get-schedule.md) - [Delete Schedule](https://docs.agno.com/reference-api/schema/schedules/delete-schedule.md) - [Update Schedule](https://docs.agno.com/reference-api/schema/schedules/update-schedule.md) - [Enable Schedule](https://docs.agno.com/reference-api/schema/schedules/enable-schedule.md) - [Disable Schedule](https://docs.agno.com/reference-api/schema/schedules/disable-schedule.md) - [Trigger Schedule](https://docs.agno.com/reference-api/schema/schedules/trigger-schedule.md) - [List Schedule Runs](https://docs.agno.com/reference-api/schema/schedules/list-schedule-runs.md) - [Get Schedule Run](https://docs.agno.com/reference-api/schema/schedules/get-schedule-run.md) - [List Approvals](https://docs.agno.com/reference-api/schema/approvals/list-approvals.md) - [Get Approval Count](https://docs.agno.com/reference-api/schema/approvals/get-approval-count.md) - [Get Approval](https://docs.agno.com/reference-api/schema/approvals/get-approval.md) - [Get Approval Status](https://docs.agno.com/reference-api/schema/approvals/get-approval-status.md) - [Delete Approval](https://docs.agno.com/reference-api/schema/approvals/delete-approval.md) - [Resolve Approval](https://docs.agno.com/reference-api/schema/approvals/resolve-approval.md) - [Create Service Account](https://docs.agno.com/reference-api/schema/service-accounts/create-service-account.md) - [List Service Accounts](https://docs.agno.com/reference-api/schema/service-accounts/list-service-accounts.md) - [Revoke Service Account](https://docs.agno.com/reference-api/schema/service-accounts/revoke-service-account.md) - [Agno CLI](https://docs.agno.com/reference/cli/agnoctl.md): Flags, environment variables, exit codes, and JSON output contracts for every agno CLI command. - [ag infra create](https://docs.agno.com/reference/agno-infra/cli/ws/create.md): Options for ag infra create: scaffold a new AgentOS codebase from a starter template. - [ag infra up](https://docs.agno.com/reference/agno-infra/cli/ws/up.md): Options for ag infra up: create resources for the active AgentOS codebase, filtered by env, infra, group, name, or type. - [ag infra down](https://docs.agno.com/reference/agno-infra/cli/ws/down.md): Options for ag infra down: delete resources for the active AgentOS codebase, filtered by env, infra, group, name, or type. - [ag infra restart](https://docs.agno.com/reference/agno-infra/cli/ws/restart.md): Options for ag infra restart: run ag infra down, wait 2 seconds, then ag infra up for the active AgentOS codebase. - [ag infra patch](https://docs.agno.com/reference/agno-infra/cli/ws/patch.md): Options for ag infra patch: update resources for the active AgentOS codebase, filtered by env, infra, group, name, or type. - [ag infra config](https://docs.agno.com/reference/agno-infra/cli/ws/config.md): Print the infrastructure root path from the active legacy configuration. - [ag infra delete](https://docs.agno.com/reference/agno-infra/cli/ws/delete.md): Deletes the AgentOS codebase record from Agno config without removing any files. - [Environment Variables](https://docs.agno.com/faq/environment-variables.md): Environment variables Agno reads: provider API keys, AgentOS security keys, CLI settings, and debug flags. - [ImportError - Missing Dependencies](https://docs.agno.com/faq/import-errors.md): Map common Agno ImportError messages to the pip extra that installs the missing dependency. - [Why does my agent use gpt-5.4?](https://docs.agno.com/faq/default-model.md): Agents without a model default to OpenAIResponses with gpt-5.4. Set model explicitly to change this. - [OpenAI Key Request While Using Other Models](https://docs.agno.com/faq/openai-key-request-for-other-models.md): Agno defaults to OpenAI for models and embedders. Set both explicitly to remove the OPENAI_API_KEY requirement. - [How to Switch Between Different Models](https://docs.agno.com/faq/switching-models.md): Switch models and supported providers within one agent session while preserving message and tool-call history. - [Structured outputs](https://docs.agno.com/faq/structured-outputs.md): Structured Outputs vs JSON mode: how Agno enforces output_schema and when to fall back to use_json_mode. - [Tokens-per-minute rate limiting](https://docs.agno.com/faq/tpm-issues.md): Retry rate-limited requests by setting retries, delay_between_retries, and exponential_backoff on the agent. - [When to use a Workflow vs a Team](https://docs.agno.com/faq/workflow-vs-team.md): Use a Workflow for code-defined execution and a Team for model-directed coordination. - [Where does the agno command come from?](https://docs.agno.com/faq/agno-cli.md): pip install agno 2.7+ puts the agno and agnoctl commands on your PATH; both run the same CLI. - [AgentOS Connection Issues](https://docs.agno.com/faq/agentos-connection.md): Fix browser-blocked connections to a local AgentOS, including local-network permissions, Brave shields, Safari restrictions, and tunneling workarounds. - [Authorization Failed - JWT Verification](https://docs.agno.com/faq/rbac-auth-failed.md): Diagnose AgentOS 'Authorization Failed' errors: algorithm mismatch, mangled PEM keys, missing scopes, and conflicting auth modes. - [Connecting to TablePlus](https://docs.agno.com/faq/connecting-to-tableplus.md): Inspect your pgvector container's sessions and knowledge tables with TablePlus. - [Could Not Connect To Docker](https://docs.agno.com/faq/could-not-connect-to-docker.md): Diagnose Docker daemon connection errors on macOS, Linux, and Windows.