# Agno

## Docs

- [Approvals](https://docs.agno.com/agent-os/approvals/overview.md): Manage approval workflows for agents and teams via the AgentOS Control Panel.
- [Background Hooks](https://docs.agno.com/agent-os/background-tasks/overview.md): Run agent hooks as non-blocking background tasks in AgentOS
- [A2A Client](https://docs.agno.com/agent-os/client/a2a-client.md): Connect to any A2A-compatible agent server
- [AgentOS Client](https://docs.agno.com/agent-os/client/agentos-client.md): Connect to Agno AgentOS instances via REST API
- [Clients](https://docs.agno.com/agent-os/client/overview.md): Python clients for connecting to agent servers
- [AgentOS Configuration](https://docs.agno.com/agent-os/config.md): Customize your AgentOS instance with custom configuration
- [Connect Your AgentOS](https://docs.agno.com/agent-os/connect-your-os.md): Connect your AgentOS to the control plane for monitoring and management.
- [AgentOS Control Plane](https://docs.agno.com/agent-os/control-plane.md): A web interface for testing, monitoring, and managing your multi-agent system.
- [Overriding Routes](https://docs.agno.com/agent-os/custom-fastapi/override-routes.md): Learn how to override AgentOS routes with your own custom routes when conflicts occur
- [Bring Your Own FastAPI App](https://docs.agno.com/agent-os/custom-fastapi/overview.md): Integrate your own FastAPI app with AgentOS.
- [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
- [Interfaces](https://docs.agno.com/agent-os/interfaces/overview.md): Expose Agno agents through various communication protocols and platforms
- [Slack](https://docs.agno.com/agent-os/interfaces/slack/introduction.md): Host agents as Slack Applications.
- [Telegram](https://docs.agno.com/agent-os/interfaces/telegram/introduction.md): Expose agents, teams, or workflows as Telegram bots with webhook endpoints.
- [WhatsApp](https://docs.agno.com/agent-os/interfaces/whatsapp/introduction.md): Host agents as WhatsApp applications
- [What is AgentOS?](https://docs.agno.com/agent-os/introduction.md): The production runtime and control plane for multi-agent systems.
- [Filter Knowledge](https://docs.agno.com/agent-os/knowledge/filter-knowledge.md): Use filter expressions through the Agno API for precise knowledge base filtering.
- [Manage Knowledge](https://docs.agno.com/agent-os/knowledge/manage-knowledge.md): Attach Knowledge to your AgentOS instance
- [Custom Lifespan](https://docs.agno.com/agent-os/lifespan.md): Customize the lifespan of your AgentOS app to handle startup and shutdown logic.
- [AgentOS as MCP Server](https://docs.agno.com/agent-os/mcp/mcp.md): Learn how and why to expose your AgentOS as an MCP server
- [MCPTools within AgentOS](https://docs.agno.com/agent-os/mcp/tools.md): Learn how to use MCPTools in the Agents, Teams and Workflows within your AgentOS
- [Custom Middleware](https://docs.agno.com/agent-os/middleware/custom.md): Create custom middleware for rate limiting, logging, security, and monitoring in AgentOS
- [JWT Middleware](https://docs.agno.com/agent-os/middleware/jwt.md): Secure your AgentOS application with JWT token validation and RBAC
- [AgentOS Middleware](https://docs.agno.com/agent-os/middleware/overview.md): Add authentication, logging, monitoring, and security features to your AgentOS application using middleware
- [Overview](https://docs.agno.com/agent-os/overview.md): The production runtime and control plane for your agentic systems.
- [AgentOS Gateway](https://docs.agno.com/agent-os/remote-execution/gateway.md): Create a unified API gateway for multiple AgentOS instances
- [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
- [Run Your AgentOS](https://docs.agno.com/agent-os/run-your-os.md): Run a local AgentOS in 20 lines of code.
- [Scheduler](https://docs.agno.com/agent-os/scheduler/overview.md): Deploy and manage scheduled execution for agents and workflows via AgentOS cron jobs.
- [AgentOS Security](https://docs.agno.com/agent-os/security/overview.md): Secure your AgentOS with authentication and authorization.
- [Role-Based Access Control (RBAC)](https://docs.agno.com/agent-os/security/rbac.md): Secure your AgentOS with fine-grained permissions.
- [Agents](https://docs.agno.com/agent-os/studio/agents.md): Build and configure agents 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.
- [Overview](https://docs.agno.com/agent-os/studio/introduction.md): A visual editor in AgentOS to build Agents, Teams, and Workflows.
- [Registry](https://docs.agno.com/agent-os/studio/registry.md): Register tools, models, databases, and schemas for use 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.
- [Filter Options](https://docs.agno.com/agent-os/tracing/filter-options.md): Filter and search traces using structured queries, time ranges, and view toggles.
- [Tracing](https://docs.agno.com/agent-os/tracing/overview.md): Configure tracing for your agents, teams, and workflows 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.
- [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.
- [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.
- [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 Hooks (Global)](https://docs.agno.com/agent-os/usage/background-hooks-global.md): Run all agent hooks as background tasks using AgentOS
- [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 perform basic operations
- [Knowledge Search](https://docs.agno.com/agent-os/usage/client/knowledge-search.md): Search and manage knowledge base content
- [Memory Operations](https://docs.agno.com/agent-os/usage/client/memory-operations.md): Create, update, list, and delete user memories
- [Running Agents](https://docs.agno.com/agent-os/usage/client/run-agents.md): Execute agent runs with streaming and non-streaming 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
- [Database Migrations](https://docs.agno.com/agent-os/usage/database-migrations.md): Migrate your AgentOS database schema.
- [AgentOS Demo](https://docs.agno.com/agent-os/usage/demo.md): AgentOS demo with agents and teams
- [AgentOS Configuration](https://docs.agno.com/agent-os/usage/extra-configuration.md): Passing extra configuration to your AgentOS
- [Human-in-the-Loop Example](https://docs.agno.com/agent-os/usage/hitl.md): AgentOS with tools requiring user confirmation
- [Agent with Tools](https://docs.agno.com/agent-os/usage/interfaces/a2a/agent-with-tools.md): Investment analyst agent with financial tools and web interface
- [Basic](https://docs.agno.com/agent-os/usage/interfaces/a2a/basic.md): Create a basic AI agent with A2A interface
- [Research Team](https://docs.agno.com/agent-os/usage/interfaces/a2a/team.md): Multi-agent research team with specialized roles and web interface
- [Agent with Tools](https://docs.agno.com/agent-os/usage/interfaces/ag-ui/agent-with-tools.md): Investment analyst agent with financial tools and web interface
- [Basic](https://docs.agno.com/agent-os/usage/interfaces/ag-ui/basic.md): Create a basic AI agent with ChatGPT-like web interface
- [Research Team](https://docs.agno.com/agent-os/usage/interfaces/ag-ui/team.md): Multi-agent research team with specialized roles and web interface
- [Slack Agent with User Memory](https://docs.agno.com/agent-os/usage/interfaces/slack/agent-with-user-memory.md): Personalized Slack agent that remembers user information and preferences
- [Basic Slack Agent](https://docs.agno.com/agent-os/usage/interfaces/slack/basic.md): Create a basic AI agent that integrates with Slack for conversations
- [Slack Reasoning Finance Agent](https://docs.agno.com/agent-os/usage/interfaces/slack/reasoning-agent.md): Slack agent with advanced reasoning and financial analysis capabilities
- [Slack Research Workflow](https://docs.agno.com/agent-os/usage/interfaces/slack/research-workflow.md): Integrate a research and writing workflow with Slack for structured AI-powered content creation
- [Telegram Agent with Media](https://docs.agno.com/agent-os/usage/interfaces/telegram/agent-with-media.md): DALL-E image generation and ElevenLabs TTS on Telegram
- [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
- [Basic Telegram Agent](https://docs.agno.com/agent-os/usage/interfaces/telegram/basic.md): Gemini agent with session persistence on Telegram
- [Multiple Instances](https://docs.agno.com/agent-os/usage/interfaces/telegram/multiple-instances.md): Multiple Telegram bots on a single AgentOS server
- [Telegram Reasoning Agent](https://docs.agno.com/agent-os/usage/interfaces/telegram/reasoning-agent.md): ReasoningTools + DuckDuckGo search 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
- [Streaming Workflow](https://docs.agno.com/agent-os/usage/interfaces/telegram/streaming-workflow.md): Research + Write workflow with live step progress on Telegram
- [Multi-Agent Telegram Team](https://docs.agno.com/agent-os/usage/interfaces/telegram/team.md): Researcher + Writer team coordinating on Telegram
- [Telegram Workflow](https://docs.agno.com/agent-os/usage/interfaces/telegram/workflow.md): Draft + Edit two-step workflow on Telegram
- [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 WhatsApp Agent](https://docs.agno.com/agent-os/usage/interfaces/whatsapp/basic.md): Create a basic AI agent that integrates with WhatsApp Business API
- [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 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
- [Enable AgentOS MCP](https://docs.agno.com/agent-os/usage/mcp/enable-mcp-example.md): Complete AgentOS setup with MCP support enabled
- [AgentOS with MCPTools](https://docs.agno.com/agent-os/usage/mcp/mcp-tools-example.md): Complete AgentOS setup with MCPTools enabled on agents
- [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
- [Custom Middleware](https://docs.agno.com/agent-os/usage/middleware/custom-middleware.md): AgentOS with custom middleware for rate limiting, logging, and monitoring
- [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
- [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
- [Advanced Scopes](https://docs.agno.com/agent-os/usage/rbac/advanced-scopes.md)
- [Basic RBAC](https://docs.agno.com/agent-os/usage/rbac/basic.md)
- [Custom Scope Mappings](https://docs.agno.com/agent-os/usage/rbac/custom-scope-mappings.md)
- [Per-Agent Permissions](https://docs.agno.com/agent-os/usage/rbac/per-agent-permissions.md)
- [AgentOS Gateway](https://docs.agno.com/agent-os/usage/remote-execution/gateway.md): Create a unified API gateway for multiple AgentOS instances
- [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
- [Using the API](https://docs.agno.com/agent-os/using-the-api.md): Call the AgentOS API to run agents, teams, and workflows.
- [Building Agents](https://docs.agno.com/agents/building-agents.md): Start simple: a model, tools, and instructions.
- [Debugging Agents](https://docs.agno.com/agents/debugging-agents.md): Inspect execution flow, tool calls, and intermediate steps.
- [What are Agents?](https://docs.agno.com/agents/overview.md): Agents are AI programs that use tools to accomplish tasks.
- [Running Agents](https://docs.agno.com/agents/running-agents.md): Run agents and process their output.
- [Agent with Followup Suggestions](https://docs.agno.com/agents/usage/agent-with-followup-suggestions.md): Generate actionable followup prompts after every agent response.
- [Agent with Knowledge](https://docs.agno.com/agents/usage/agent-with-knowledge.md): Give your agent a searchable knowledge base (Agentic RAG).
- [Agent with Memory](https://docs.agno.com/agents/usage/agent-with-memory.md): Store user preferences that persist across conversations.
- [Agent with Storage](https://docs.agno.com/agents/usage/agent-with-storage.md): Persist conversation history across runs.
- [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 Tools](https://docs.agno.com/agents/usage/agent-with-tools.md): Give your agent tools to interact with external services.
- [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.
- [Providing Datetime](https://docs.agno.com/context/agent/datetime-instructions.md)
- [Dynamic Instructions](https://docs.agno.com/context/agent/dynamic-instructions.md)
- [Few-Shot Learning](https://docs.agno.com/context/agent/few-shot-learning.md)
- [Managing Tool Calls](https://docs.agno.com/context/agent/filter-tool-calls-from-history.md)
- [Basic Instructions](https://docs.agno.com/context/agent/instructions.md)
- [Instructions via Function](https://docs.agno.com/context/agent/instructions-via-function.md)
- [Providing Location](https://docs.agno.com/context/agent/location-instructions.md)
- [Context Engineering](https://docs.agno.com/context/agent/overview.md): Configure system messages, instructions, and context for agents.
- [Context Engineering](https://docs.agno.com/context/overview.md): Design and control the information sent to language models to guide their behavior.
- [Managing Tool Calls](https://docs.agno.com/context/team/filter-tool-calls-from-history.md)
- [Context Engineering](https://docs.agno.com/context/team/overview.md): Configure system messages, instructions, and context for teams.
- [What is Culture?](https://docs.agno.com/culture/overview.md): Enable your agents to share universal knowledge, principles, and best practices that compound across all interactions.
- [Custom Logging](https://docs.agno.com/custom-logging.md): Configure custom loggers and formatters for your Agno setup.
- [Chat History](https://docs.agno.com/database/chat-history.md): Include previous messages in context for multi-turn conversations.
- [Database](https://docs.agno.com/database/overview.md): Give your agents persistent storage for sessions, context, memory and knowledge.
- [Async MongoDB](https://docs.agno.com/database/providers/async-mongo/overview.md): Use MongoDB asynchronously for agent session storage.
- [Async MongoDB for Agent](https://docs.agno.com/database/providers/async-mongo/usage/async-mongodb-for-agent.md)
- [Async MongoDB for Team](https://docs.agno.com/database/providers/async-mongo/usage/async-mongodb-for-team.md)
- [Async MongoDB for Workflow](https://docs.agno.com/database/providers/async-mongo/usage/async-mongodb-for-workflow.md)
- [Async MySQL](https://docs.agno.com/database/providers/async-mysql/overview.md): Use MySQL asynchronously for agent session storage.
- [Async MySQL for Agent](https://docs.agno.com/database/providers/async-mysql/usage/async-mysql-for-agent.md)
- [Async MySQL for Team](https://docs.agno.com/database/providers/async-mysql/usage/async-mysql-for-team.md)
- [Async MySQL for Workflows](https://docs.agno.com/database/providers/async-mysql/usage/async-mysql-for-workflow.md)
- [Async PostgreSQL](https://docs.agno.com/database/providers/async-postgres/overview.md): Use PostgreSQL asynchronously for agent session storage.
- [Async Postgres for Agent](https://docs.agno.com/database/providers/async-postgres/usage/async-postgres-for-agent.md)
- [Async Postgres for Team](https://docs.agno.com/database/providers/async-postgres/usage/async-postgres-for-team.md)
- [Async Postgres for Workflows](https://docs.agno.com/database/providers/async-postgres/usage/async-postgres-for-workflow.md)
- [Async SQLite](https://docs.agno.com/database/providers/async-sqlite/overview.md): Use SQLite asynchronously for agent session storage.
- [Async Sqlite for Agent](https://docs.agno.com/database/providers/async-sqlite/usage/async-sqlite-for-agent.md)
- [Async Sqlite for Team](https://docs.agno.com/database/providers/async-sqlite/usage/async-sqlite-for-team.md)
- [Async SQLite for Workflow](https://docs.agno.com/database/providers/async-sqlite/usage/async-sqlite-for-workflow.md)
- [DynamoDB](https://docs.agno.com/database/providers/dynamodb/overview.md): Use DynamoDB for agent session storage and persistence.
- [DynamoDB for Agent](https://docs.agno.com/database/providers/dynamodb/usage/dynamodb-for-agent.md)
- [DynamoDB for Team](https://docs.agno.com/database/providers/dynamodb/usage/dynamodb-for-team.md)
- [DynamoDB Workflow Storage](https://docs.agno.com/database/providers/dynamodb/usage/dynamodb-for-workflow.md)
- [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)
- [Firestore for Team](https://docs.agno.com/database/providers/firestore/usage/firestore-for-team.md)
- [Firestore for Workflows](https://docs.agno.com/database/providers/firestore/usage/firestore-for-workflow.md)
- [JSON files as 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.
- [Google Cloud Storage for Agent](https://docs.agno.com/database/providers/gcs/usage/gcs-for-agent.md)
- [GCS for Team](https://docs.agno.com/database/providers/gcs/usage/gcs-for-team.md)
- [GCS for Workflows](https://docs.agno.com/database/providers/gcs/usage/gcs-for-workflow.md)
- [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)
- [In-Memory Storage for Teams](https://docs.agno.com/database/providers/in-memory/usage/in-memory-for-team.md)
- [In-Memory Storage for Workflows](https://docs.agno.com/database/providers/in-memory/usage/in-memory-for-workflow.md)
- [JSON Files as Database](https://docs.agno.com/database/providers/json/overview.md): Use local JSON files for simple agent session storage.
- [JSON for Agent](https://docs.agno.com/database/providers/json/usage/json-for-agent.md)
- [JSON for Team](https://docs.agno.com/database/providers/json/usage/json-for-team.md)
- [JSON for Workflows](https://docs.agno.com/database/providers/json/usage/json-for-workflow.md)
- [MongoDB Database](https://docs.agno.com/database/providers/mongo/overview.md): Use MongoDB for agent session storage and persistence.
- [MongoDB for Agent](https://docs.agno.com/database/providers/mongo/usage/mongodb-for-agent.md)
- [MongoDB for Team](https://docs.agno.com/database/providers/mongo/usage/mongodb-for-team.md)
- [MongoDB for Workflow](https://docs.agno.com/database/providers/mongo/usage/mongodb-for-workflow.md)
- [MySQL](https://docs.agno.com/database/providers/mysql/overview.md): Use MySQL for agent session storage and persistence.
- [MySQL for Agent](https://docs.agno.com/database/providers/mysql/usage/mysql-for-agent.md)
- [MySQL for Team](https://docs.agno.com/database/providers/mysql/usage/mysql-for-team.md)
- [MySQL Workflow Storage](https://docs.agno.com/database/providers/mysql/usage/mysql-for-workflow.md)
- [Neon](https://docs.agno.com/database/providers/neon/overview.md): Use Neon serverless PostgreSQL for agent session storage.
- [Database Index](https://docs.agno.com/database/providers/overview.md): Index of all databases supported by Agno.
- [PostgreSQL](https://docs.agno.com/database/providers/postgres/overview.md): Use PostgreSQL for agent session storage and persistence.
- [Postgres for Agent](https://docs.agno.com/database/providers/postgres/usage/postgres-for-agent.md)
- [Postgres for Team](https://docs.agno.com/database/providers/postgres/usage/postgres-for-team.md)
- [Postgres for Workflows](https://docs.agno.com/database/providers/postgres/usage/postgres-for-workflow.md)
- [Redis](https://docs.agno.com/database/providers/redis/overview.md): Use Redis for agent session storage and persistence.
- [Redis for Agent](https://docs.agno.com/database/providers/redis/usage/redis-for-agent.md)
- [Redis for Team](https://docs.agno.com/database/providers/redis/usage/redis-for-team.md)
- [Redis for Workflows](https://docs.agno.com/database/providers/redis/usage/redis-for-workflow.md)
- [Selecting Custom Table Names](https://docs.agno.com/database/providers/selecting-tables.md)
- [Singlestore](https://docs.agno.com/database/providers/singlestore/overview.md): Use SingleStore for agent session storage and persistence.
- [Singlestore for Agent](https://docs.agno.com/database/providers/singlestore/usage/singlestore-for-agent.md)
- [Singlestore for Team](https://docs.agno.com/database/providers/singlestore/usage/singlestore-for-team.md)
- [Singlestore for Workflow](https://docs.agno.com/database/providers/singlestore/usage/singlestore-for-workflow.md)
- [SQLite](https://docs.agno.com/database/providers/sqlite/overview.md): Use SQLite for local agent session storage.
- [Sqlite for Agent](https://docs.agno.com/database/providers/sqlite/usage/sqlite-for-agent.md)
- [Sqlite for Team](https://docs.agno.com/database/providers/sqlite/usage/sqlite-for-team.md)
- [SQLite for Workflow](https://docs.agno.com/database/providers/sqlite/usage/sqlite-for-workflow.md)
- [Supabase](https://docs.agno.com/database/providers/supabase/overview.md): Use Supabase PostgreSQL for agent session storage.
- [SurreabDB](https://docs.agno.com/database/providers/surrealdb/overview.md): Use SurrealDB for agent session storage.
- [SurrealDB for Agent](https://docs.agno.com/database/providers/surrealdb/usage/surrealdb-for-agent.md)
- [SurrealDB for Team](https://docs.agno.com/database/providers/surrealdb/usage/surrealdb-for-team.md)
- [SurrealDB for Workflow](https://docs.agno.com/database/providers/surrealdb/usage/surrealdb-for-workflow.md)
- [Session Storage](https://docs.agno.com/database/session-storage.md): Store and retrieve agent sessions from your database.
- [Access Dependencies in Tool](https://docs.agno.com/dependencies/agent/access-dependencies-in-tool.md): This example demonstrates how tools can access dependencies passed to the agent, allowing tools to utilize dynamic context like user profiles and current time information for enhanced functionality.
- [Add Dependencies to Agent Run](https://docs.agno.com/dependencies/agent/add-dependencies-run.md): This example demonstrates how to inject dependencies into agent runs, allowing the agent to access dynamic context like user profiles and current time information for personalized responses.
- [Add Dependencies to Agent Context](https://docs.agno.com/dependencies/agent/add-dependencies-to-context.md): This example demonstrates how to create a context-aware agent that can access real-time HackerNews data through dependency injection, enabling the agent to provide current information.
- [Dependencies with Agents](https://docs.agno.com/dependencies/agent/overview.md): Inject variables into agent context with dependencies.
- [Dependencies](https://docs.agno.com/dependencies/overview.md): Inject variables into agent and team context with dependencies.
- [Access Dependencies in Team Tool](https://docs.agno.com/dependencies/team/access-dependencies-in-tool.md): This example demonstrates how team tools can access dependencies passed to the team, allowing tools to utilize dynamic context like team metrics and current time information while team members collaborate with shared data sources.
- [Adding Dependencies to Team Run](https://docs.agno.com/dependencies/team/add-dependencies-run.md): This example demonstrates how to add dependencies to a specific team run. Dependencies are functions that provide contextual information (like user profiles and current context) that get passed to the team during execution for personalized responses.
- [Adding Dependencies to Team Context](https://docs.agno.com/dependencies/team/add-dependencies-to-context.md): This example demonstrates how to add dependencies directly to the team context. Unlike adding dependencies per run, this approach makes the dependency functions available to all team runs by default, providing consistent access to contextual information across all interactions.
- [Dependencies with Teams](https://docs.agno.com/dependencies/team/overview.md): Inject variables into team context with dependencies.
- [Using Reference Dependencies in Team Instructions](https://docs.agno.com/dependencies/team/reference-dependencies.md): This example demonstrates how to use reference dependencies by defining them in the team constructor and referencing them directly in team instructions. This approach allows dependencies to be automatically injected into the team's context and referenced using template variables in instructions.
- [Apps](https://docs.agno.com/deploy/apps.md): Agents, teams, and workflows for your AgentOS deployment.
- [Code Review Agent](https://docs.agno.com/deploy/apps/agents/code-review.md): Review pull requests with context-aware suggestions and best practice enforcement.
- [Contract Review Agent](https://docs.agno.com/deploy/apps/agents/contract-review.md): Analyze legal documents, extract key terms, and flag risks.
- [Customer Support Agent](https://docs.agno.com/deploy/apps/agents/customer-support.md): Resolve support tickets with knowledge retrieval and smart escalation.
- [Document Summarizer](https://docs.agno.com/deploy/apps/agents/document-summarizer.md): Structured document summaries with key points, entity extraction, and action item detection from PDFs, text, and URLs.
- [Inbox Agent](https://docs.agno.com/deploy/apps/agents/inbox-agent.md): Gmail assistant that triages emails, drafts replies, and flags urgent items with safety controls.
- [Invoice Analyst](https://docs.agno.com/deploy/apps/agents/invoice-extractor.md): Vision-based invoice data extraction from PDFs and images with validation and confidence scoring.
- [Internal Knowledge Agent](https://docs.agno.com/deploy/apps/agents/knowledge-agent.md): RAG-powered agent for answering questions from company docs with source citations.
- [Research Agent](https://docs.agno.com/deploy/apps/agents/research-agent.md): Autonomous web research agent using Parallel API with multi-source synthesis and citation tracking.
- [Social Media Analyst](https://docs.agno.com/deploy/apps/agents/social-media-analyst.md): Brand intelligence agent analyzing X (Twitter) sentiment with engagement metrics and brand health scoring.
- [Text-to-SQL Agent](https://docs.agno.com/deploy/apps/agents/text-to-sql.md): Self-learning SQL agent that queries databases with knowledge-based query generation and self-improving query storage.
- [Content Production Team](https://docs.agno.com/deploy/apps/teams/content-team.md): Multi-agent team with Writer, Editor, SEO Optimizer, and Publisher working together.
- [Competitor Tracker](https://docs.agno.com/deploy/apps/workflows/competitor-tracker.md): Monitor competitor content, pricing, and features. Surface changes automatically.
- [Lead Enrichment](https://docs.agno.com/deploy/apps/workflows/lead-enrichment.md): Enrich CRM contacts with LinkedIn and company data automatically.
- [Meeting to Tasks](https://docs.agno.com/deploy/apps/workflows/meeting-to-tasks.md): Extract action items from meeting recordings and create Linear issues automatically.
- [Sales Call Analyzer](https://docs.agno.com/deploy/apps/workflows/sales-call-analyzer.md): Transcribe sales calls, extract insights, and score conversations.
- [Interfaces](https://docs.agno.com/deploy/interfaces.md): Connect your agents to Slack, Discord, WhatsApp, Telegram, and MCP.
- [Overview](https://docs.agno.com/deploy/interfaces/ag-ui/overview.md)
- [Discord Bot](https://docs.agno.com/deploy/interfaces/discord/overview.md): Create a Discord bot powered by Agno agents for community support, moderation, or custom commands.
- [Overview](https://docs.agno.com/deploy/interfaces/mcp/overview.md)
- [Slack Bot](https://docs.agno.com/deploy/interfaces/slack/overview.md): Build a Slack bot that responds to messages, joins channels, and executes commands using agents.
- [Telegram Bot](https://docs.agno.com/deploy/interfaces/telegram/overview.md): Deploy agents on Telegram for direct and group chat interactions.
- [WhatsApp Bot](https://docs.agno.com/deploy/interfaces/whatsapp/overview.md): Deploy agents on WhatsApp Business for customer-facing interactions.
- [Deploy AgentOS](https://docs.agno.com/deploy/introduction.md): Deploy AgentOS to your cloud platform of choice.
- [Templates](https://docs.agno.com/deploy/templates.md): Production-ready codebases for deploying AgentOS to your infrastructure.
- [CI/CD Automation](https://docs.agno.com/deploy/templates/aws/configure/ci-cd.md): Automate builds with GitHub Actions
- [Code Quality](https://docs.agno.com/deploy/templates/aws/configure/code-quality.md): Format and validate your code before committing
- [Database Setup](https://docs.agno.com/deploy/templates/aws/configure/database.md): Configure RDS PostgreSQL for agent memory and knowledge
- [Database Tables](https://docs.agno.com/deploy/templates/aws/configure/database-tables.md)
- [Development Application](https://docs.agno.com/deploy/templates/aws/configure/development-app.md)
- [Persistent Storage with EFS](https://docs.agno.com/deploy/templates/aws/configure/efs.md): Use Amazon EFS for data that survives container restarts
- [Environment Variables](https://docs.agno.com/deploy/templates/aws/configure/env-vars.md): Reference for app environment configuration
- [Format & Validate](https://docs.agno.com/deploy/templates/aws/configure/format-and-validate.md)
- [Create Git Repo](https://docs.agno.com/deploy/templates/aws/configure/git-repo.md)
- [Infra Settings](https://docs.agno.com/deploy/templates/aws/configure/infra-settings.md)
- [Install & Setup](https://docs.agno.com/deploy/templates/aws/configure/install.md)
- [Local Development](https://docs.agno.com/deploy/templates/aws/configure/local.md): Build and run your application locally with Docker
- [Setup infra for new users](https://docs.agno.com/deploy/templates/aws/configure/new-users.md)
- [Customize AgentOS on AWS](https://docs.agno.com/deploy/templates/aws/configure/overview.md): Iterate, customize, and manage your AgentOS AWS template for production.
- [Production Application](https://docs.agno.com/deploy/templates/aws/configure/production-app.md)
- [Add Python Libraries](https://docs.agno.com/deploy/templates/aws/configure/python-packages.md)
- [Secrets & API Keys](https://docs.agno.com/deploy/templates/aws/configure/secrets.md): Configure API keys and database credentials for your deployment
- [SSH Access](https://docs.agno.com/deploy/templates/aws/configure/ssh-access.md)
- [Deploy to AWS](https://docs.agno.com/deploy/templates/aws/deploy.md): Deploy AgentOS to AWS with ECS Fargate, RDS PostgreSQL, and a public endpoint.
- [Connect to Control Plane](https://docs.agno.com/deploy/templates/aws/go-live/connect.md): Connect your AgentOS to os.agno.com
- [Add HTTPS](https://docs.agno.com/deploy/templates/aws/go-live/https.md): Add a custom domain and SSL certificate
- [Production Deployment](https://docs.agno.com/deploy/templates/aws/go-live/updates.md): Deploy your application to AWS with ECS Fargate
- [Verify Your Deployment](https://docs.agno.com/deploy/templates/aws/go-live/verify.md): Confirm your AWS deployment is working
- [Monitoring AgentOS on AWS](https://docs.agno.com/deploy/templates/aws/manage/monitoring.md): View logs and monitor your AWS deployment
- [Troubleshooting AgentOS on AWS](https://docs.agno.com/deploy/templates/aws/manage/troubleshooting.md): Common AWS deployment errors and solutions
- [AWS Reference](https://docs.agno.com/deploy/templates/aws/reference.md): Manage, customize, and troubleshoot your AWS deployment.
- [Dash](https://docs.agno.com/deploy/templates/dash/overview.md): Self-learning data agent that grounds answers in 6 layers of context.
- [Deploy with Docker](https://docs.agno.com/deploy/templates/docker/deploy.md): Run AgentOS locally with Docker and PostgreSQL, then deploy to any cloud.
- [Docker Reference](https://docs.agno.com/deploy/templates/docker/reference.md): Manage, customize, and troubleshoot your Docker deployment.
- [Gcode](https://docs.agno.com/deploy/templates/gcode/overview.md): Self-improving coding agent that operates inside a persistent workspace.
- [Deploy to Railway](https://docs.agno.com/deploy/templates/railway/deploy.md): Deploy AgentOS to Railway with PostgreSQL, automatic HTTPS, and a public domain.
- [Railway Reference](https://docs.agno.com/deploy/templates/railway/reference.md): Manage, customize, and troubleshoot your Railway deployment.
- [Overview](https://docs.agno.com/deploy/templates/scout/overview.md)
- [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.
- [Async Accuracy Evaluation](https://docs.agno.com/evals/accuracy/usage/accuracy-async.md): Example showing how to run accuracy evaluations asynchronously for better performance.
- [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 the accuracy of an Agno Agent's response with a given answer.
- [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.
- [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.
- [Basic Accuracy](https://docs.agno.com/evals/accuracy/usage/basic.md): Example showing how to check how complete, correct and accurate an Agno Agent's response is.
- [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.
- [Async Agent as Judge](https://docs.agno.com/evals/agent-as-judge/usage/agent-as-judge-async.md): Asynchronous evaluation with Agent as Judge
- [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
- [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
- [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
- [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 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
- [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
- [What is Evals](https://docs.agno.com/evals/overview.md): Evals is a way to measure the quality of your Agents and Teams.<br/> Agno provides multiple dimensions for evaluating Agents.
- [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 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.
- [Async Performance Evaluation](https://docs.agno.com/evals/performance/usage/performance-async.md): Example showing how to run performance evaluations on async functions.
- [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.
- [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 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.
- [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 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 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.
- [Reliability Evals](https://docs.agno.com/evals/reliability/overview.md): Reliability evals measure how well your Agents and Teams handle tool calls and error scenarios.
- [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.
- [Team Reliability with Stock Tools](https://docs.agno.com/evals/reliability/usage/reliability-team-advanced.md): Example showing how to evaluate team reliability with real-world tools like stock price lookup.
- [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.
- [Agents](https://docs.agno.com/examples/agent-os/advanced-demo/agents.md): Demonstrates  agents.
- [AgentOS Demo](https://docs.agno.com/examples/agent-os/advanced-demo/demo.md): Set the OS_SECURITY_KEY environment variable to your OS security key to enable authentication.
- [File Output](https://docs.agno.com/examples/agent-os/advanced-demo/file-output.md): Demonstrates file output.
- [This example shows how to run an Agent using our MCP integration in the Agno OS.](https://docs.agno.com/examples/agent-os/advanced-demo/mcp-demo.md): Run an agent with MCP tool integration in AgentOS.
- [Multiple Knowledge Bases](https://docs.agno.com/examples/agent-os/advanced-demo/multiple-knowledge-bases.md): Demonstrates multiple knowledge bases.
- [Advanced Demo](https://docs.agno.com/examples/agent-os/advanced-demo/overview.md): Examples for `advanced_demo` in AgentOS.
- [Advanced Demo Reasoning Demo](https://docs.agno.com/examples/agent-os/advanced-demo/reasoning-demo.md): Use LanceDB as the vector database and store embeddings in the `agno_docs` table.
- [Example showing a reasoning Agent in the AgentOS.](https://docs.agno.com/examples/agent-os/advanced-demo/reasoning-model.md): You can interact with the Agent as normally. It will reason before providing a final answer.
- [Teams](https://docs.agno.com/examples/agent-os/advanced-demo/teams.md): Demonstrates  teams.
- [Teams Demo](https://docs.agno.com/examples/agent-os/advanced-demo/teams-demo.md): Demonstrates teams demo.
- [Agno Agent](https://docs.agno.com/examples/agent-os/agno-agent.md): Demonstrates agno agent.
- [Example: Per-Hook Background Control with AgentAsJudgeEval in AgentOS](https://docs.agno.com/examples/agent-os/background-tasks/background-evals-example.md): Fine-grained control over which hooks run in the background per endpoint.
- [Example: Using Background Post-Hooks in AgentOS](https://docs.agno.com/examples/agent-os/background-tasks/background-hooks-decorator.md): This example demonstrates how to run post-hooks as FastAPI background tasks, making them completely non-blocking.
- [Example: Using Background Post-Hooks in AgentOS](https://docs.agno.com/examples/agent-os/background-tasks/background-hooks-example.md): This example demonstrates how to run post-hooks as FastAPI background tasks, making them completely non-blocking.
- [Example: Background Hooks with Teams in AgentOS](https://docs.agno.com/examples/agent-os/background-tasks/background-hooks-team.md): This example demonstrates how to use background hooks with a Team.
- [Example: Background Hooks with Workflows in AgentOS](https://docs.agno.com/examples/agent-os/background-tasks/background-hooks-workflow.md): This example demonstrates how to use background hooks with a Workflow.
- [Example: Background Output Evaluation with Agent-as-Judge](https://docs.agno.com/examples/agent-os/background-tasks/background-output-evaluation.md): This example demonstrates how to use a validator agent to evaluate the main agent's output as a background task.
- [Background Tasks Evals Demo](https://docs.agno.com/examples/agent-os/background-tasks/evals-demo.md): Setting up and running an eval for our agent.
- [Background Tasks](https://docs.agno.com/examples/agent-os/background-tasks/overview.md): Examples for `background_tasks` in AgentOS.
- [Minimal example for AgentOS.](https://docs.agno.com/examples/agent-os/basic.md): Setup basic agents, teams and workflows.
- [Basic A2A Messaging with A2AClient](https://docs.agno.com/examples/agent-os/client-a2a/basic-messaging.md): This example demonstrates simple message sending with user identification using the A2A protocol.
- [Connect Agno A2AClient to Google ADK A2A Server.](https://docs.agno.com/examples/agent-os/client-a2a/connect-to-google-adk.md): This example demonstrates cross-framework A2A communication: Agno client -> Google ADK server.
- [Error Handling with A2AClient](https://docs.agno.com/examples/agent-os/client-a2a/error-handling.md): This example demonstrates how to handle various error scenarios when using the A2A protocol.
- [Multi-Turn Conversations with A2AClient](https://docs.agno.com/examples/agent-os/client-a2a/multi-turn.md): This example demonstrates how to maintain conversation context across multiple messages using the A2A protocol.
- [Agno AgentOS A2A Server for testing A2AClient.](https://docs.agno.com/examples/agent-os/client-a2a/servers/agno-server.md): This server uses Agno's AgentOS to create an A2A-compatible agent that can be tested with A2AClient.
- [Google ADK A2A Server for testing A2AClient.](https://docs.agno.com/examples/agent-os/client-a2a/servers/google-adk-server.md): This server uses Google's Agent Development Kit (ADK) to create an A2A-compatible.
- [Servers](https://docs.agno.com/examples/agent-os/client-a2a/servers/overview.md): Examples for `client_a2a/servers` in AgentOS.
- [Streaming A2A Messages with A2AClient](https://docs.agno.com/examples/agent-os/client-a2a/streaming.md): This example demonstrates real-time streaming responses using the A2A protocol.
- [Basic AgentOSClient Example](https://docs.agno.com/examples/agent-os/client/basic-client.md): This example demonstrates how to use AgentOSClient to connect to a remote AgentOS instance and perform basic operations.
- [Knowledge Search with AgentOSClient](https://docs.agno.com/examples/agent-os/client/knowledge-search.md): This example demonstrates how to search the knowledge base using AgentOSClient.
- [Memory Operations with AgentOSClient](https://docs.agno.com/examples/agent-os/client/memory-operations.md): This example demonstrates how to manage user memories using AgentOSClient.
- [Client](https://docs.agno.com/examples/agent-os/client/overview.md): Examples for `client` in AgentOS.
- [Running Agents with AgentOSClient](https://docs.agno.com/examples/agent-os/client/run-agents.md): This example demonstrates how to execute agent runs using AgentOSClient, including both streaming and non-streaming responses.
- [Running Evaluations with AgentOSClient](https://docs.agno.com/examples/agent-os/client/run-evals.md): This example demonstrates how to run and manage evaluations using AgentOSClient.
- [Running Teams with AgentOSClient](https://docs.agno.com/examples/agent-os/client/run-teams.md): This example demonstrates how to execute team runs using AgentOSClient, including both streaming and non-streaming responses.
- [Running Workflows with AgentOSClient](https://docs.agno.com/examples/agent-os/client/run-workflows.md): This example demonstrates how to execute workflow runs using AgentOSClient, including both streaming and non-streaming responses.
- [AgentOS Server for Cookbook Client Examples](https://docs.agno.com/examples/agent-os/client/server.md): Database Configuration.
- [Session Management with AgentOSClient](https://docs.agno.com/examples/agent-os/client/session-management.md): This example demonstrates how to manage sessions using AgentOSClient.
- [Uploading Content to Knowledge Base with AgentOSClient](https://docs.agno.com/examples/agent-os/client/upload-content.md): This example demonstrates how to upload documents and content to the knowledge base using AgentOSClient.
- [Example AgentOS app with a custom FastAPI app with basic routes.](https://docs.agno.com/examples/agent-os/customize/custom-fastapi-app.md): You can also run this using the FastAPI cli (uv pip install fastapi["standard"]).
- [Example AgentOS app with a custom health endpoint.](https://docs.agno.com/examples/agent-os/customize/custom-health-endpoint.md): This example demonstrates how to add a custom health endpoint to your AgentOS app.
- [Example AgentOS app where the agent has a custom lifespan.](https://docs.agno.com/examples/agent-os/customize/custom-lifespan.md): Setup basic agents, teams and workflows.
- [Example for AgentOS to show how to generate custom events.](https://docs.agno.com/examples/agent-os/customize/handle-custom-events.md): You can yield custom events from your own tools. These events will be handled internally as an Agno event, and you will be able to access it in the same way you would ...
- [Example AgentOS app with a custom FastAPI app with conflicting routes.](https://docs.agno.com/examples/agent-os/customize/override-routes.md): This example demonstrates the `on_route_conflict="preserve_base_app"` functionality which allows your.
- [Customize](https://docs.agno.com/examples/agent-os/customize/overview.md): Examples for `customize` in AgentOS.
- [Example for AgentOS to show how to pass dependencies to an agent.](https://docs.agno.com/examples/agent-os/customize/pass-dependencies-to-agent.md): Setup basic agents, teams and workflows.
- [Update From Lifespan](https://docs.agno.com/examples/agent-os/customize/update-from-lifespan.md): Demonstrates update from lifespan.
- [AgentOS Demo](https://docs.agno.com/examples/agent-os/dbs/agentos-default-db.md): Set the OS_SECURITY_KEY environment variable to your OS security key to enable authentication.
- [Example showing how to use AgentOS with a DynamoDB database](https://docs.agno.com/examples/agent-os/dbs/dynamo.md): Use DynamoDB as the database backend for AgentOS.
- [Example showing how to use AgentOS with a Firestore database](https://docs.agno.com/examples/agent-os/dbs/firestore.md): Setup the Firestore database.
- [Example showing how to use AgentOS with JSON files hosted in GCS as database.](https://docs.agno.com/examples/agent-os/dbs/gcs-json.md): Use Google Cloud Storage JSON as the database backend for AgentOS.
- [Example showing how to use AgentOS with JSON files as database](https://docs.agno.com/examples/agent-os/dbs/json-db.md): Setup the JSON database.
- [Mongo Database Backend](https://docs.agno.com/examples/agent-os/dbs/mongo.md): Demonstrates AgentOS with MongoDB storage using both sync and async setups.
- [MySQL Database Backend](https://docs.agno.com/examples/agent-os/dbs/mysql.md): Demonstrates AgentOS with MySQL storage using both sync and async setups.
- [Example showing how to use AgentOS with Neon as our database provider](https://docs.agno.com/examples/agent-os/dbs/neon.md): Setup a basic agent and a basic team.
- [Postgres Database Backend](https://docs.agno.com/examples/agent-os/dbs/postgres.md): Demonstrates AgentOS with PostgreSQL storage using both sync and async setups.
- [Example showing how to use AgentOS with Redis as database](https://docs.agno.com/examples/agent-os/dbs/redis-db.md): Setup the Redis database.
- [Example showing how to use AgentOS with SingleStore as our database provider](https://docs.agno.com/examples/agent-os/dbs/singlestore.md): Setup the SingleStore database.
- [Example showing how to use AgentOS with a SQLite database](https://docs.agno.com/examples/agent-os/dbs/sqlite.md): Setup the SQLite database.
- [Example showing how to use AgentOS with Supabase as our database provider](https://docs.agno.com/examples/agent-os/dbs/supabase.md): Setup the Postgres database.
- [Example showing how to use AgentOS with SurrealDB as database](https://docs.agno.com/examples/agent-os/dbs/surreal.md): Setup the SurrealDB database.
- [Agents](https://docs.agno.com/examples/agent-os/dbs/surreal-db/agents.md)
- [Db](https://docs.agno.com/examples/agent-os/dbs/surreal-db/db.md): Demonstrates db.
- [Surreal Db](https://docs.agno.com/examples/agent-os/dbs/surreal-db/overview.md): Examples for `dbs/surreal_db` in AgentOS.
- [SurrealDB + AgentOS demo](https://docs.agno.com/examples/agent-os/dbs/surreal-db/run.md): Run AgentOS with SurrealDB as the storage backend.
- [Teams](https://docs.agno.com/examples/agent-os/dbs/surreal-db/teams.md): Demonstrates teams.
- [Workflows](https://docs.agno.com/examples/agent-os/dbs/surreal-db/workflows.md): Demonstrates workflows.
- [AgentOS Demo](https://docs.agno.com/examples/agent-os/demo.md): Set the OS_SECURITY_KEY environment variable to your OS security key to enable authentication.
- [Integrations](https://docs.agno.com/examples/agent-os/integrations/overview.md): Examples for `integrations` in AgentOS.
- [Example for AgentOS with Shopify tools.](https://docs.agno.com/examples/agent-os/integrations/shopify-demo.md): Prerequisites: - Set the following environment variables: - SHOPIFY_SHOP_NAME -> Your Shopify shop name, e.g.
- [Agent With Tools](https://docs.agno.com/examples/agent-os/interfaces/a2a/agent-with-tools.md): Demonstrates agent with tools.
- [Basic](https://docs.agno.com/examples/agent-os/interfaces/a2a/basic.md): Demonstrates basic.
- [Airbnb Agent](https://docs.agno.com/examples/agent-os/interfaces/a2a/multi-agent-a2a/airbnb-agent.md): Demonstrates airbnb agent.
- [Trip Planning A2A Client](https://docs.agno.com/examples/agent-os/interfaces/a2a/multi-agent-a2a/trip-planning-a2a-client.md): Demonstrates trip planning a2a client.
- [Weather Agent](https://docs.agno.com/examples/agent-os/interfaces/a2a/multi-agent-a2a/weather-agent.md): Demonstrates weather agent.
- [Reasoning Agent](https://docs.agno.com/examples/agent-os/interfaces/a2a/reasoning-agent.md): Demonstrates reasoning agent.
- [Research Team](https://docs.agno.com/examples/agent-os/interfaces/a2a/research-team.md): Demonstrates research team.
- [Structured Output](https://docs.agno.com/examples/agent-os/interfaces/a2a/structured-output.md): Demonstrates structured output.
- [Silent External Tools - Suppress verbose messages in frontends](https://docs.agno.com/examples/agent-os/interfaces/agui/agent-with-silent-tools.md): When using `external_execution=True`, the agent prints 'I have tools to execute...' messages.
- [Agent With Tools](https://docs.agno.com/examples/agent-os/interfaces/agui/agent-with-tools.md): Demonstrates agent with tools.
- [Basic](https://docs.agno.com/examples/agent-os/interfaces/agui/basic.md): Demonstrates basic.
- [Multiple Instances](https://docs.agno.com/examples/agent-os/interfaces/agui/multiple-instances.md): Demonstrates multiple instances.
- [Reasoning Agent](https://docs.agno.com/examples/agent-os/interfaces/agui/reasoning-agent.md): Demonstrates reasoning agent.
- [Research Team](https://docs.agno.com/examples/agent-os/interfaces/agui/research-team.md): Demonstrates research team.
- [Structured Output](https://docs.agno.com/examples/agent-os/interfaces/agui/structured-output.md): Demonstrates structured output.
- [AgentOS Demo](https://docs.agno.com/examples/agent-os/interfaces/all-interfaces.md): Prerequisites: uv pip install -U fastapi uvicorn sqlalchemy pgvector psycopg openai ddgs yfinance.
- [Agent With User Memory](https://docs.agno.com/examples/agent-os/interfaces/slack/agent-with-user-memory.md): Demonstrates agent with user memory.
- [Basic](https://docs.agno.com/examples/agent-os/interfaces/slack/basic.md): Demonstrates basic.
- [Basic Workflow](https://docs.agno.com/examples/agent-os/interfaces/slack/basic-workflow.md): Demonstrates basic workflow.
- [Channel Summarizer](https://docs.agno.com/examples/agent-os/interfaces/slack/channel-summarizer.md): Demonstrates channel summarizer.
- [File Analyst](https://docs.agno.com/examples/agent-os/interfaces/slack/file-analyst.md): Demonstrates file analyst.
- [Multiple Instances](https://docs.agno.com/examples/agent-os/interfaces/slack/multiple-instances.md): Demonstrates multiple instances.
- [Reasoning Agent](https://docs.agno.com/examples/agent-os/interfaces/slack/reasoning-agent.md): Demonstrates reasoning agent.
- [Research Assistant](https://docs.agno.com/examples/agent-os/interfaces/slack/research-assistant.md): Demonstrates research assistant.
- [Support Team](https://docs.agno.com/examples/agent-os/interfaces/slack/support-team.md): Demonstrates support team.
- [Agent With Media](https://docs.agno.com/examples/agent-os/interfaces/whatsapp/agent-with-media.md): Demonstrates agent with media.
- [Agent With User Memory](https://docs.agno.com/examples/agent-os/interfaces/whatsapp/agent-with-user-memory.md): Demonstrates agent with user memory.
- [Basic](https://docs.agno.com/examples/agent-os/interfaces/whatsapp/basic.md): Demonstrates basic.
- [Image Generation Model](https://docs.agno.com/examples/agent-os/interfaces/whatsapp/image-generation-model.md): Demonstrates image generation model.
- [Image Generation Tools](https://docs.agno.com/examples/agent-os/interfaces/whatsapp/image-generation-tools.md): Demonstrates image generation tools.
- [Multiple Instances](https://docs.agno.com/examples/agent-os/interfaces/whatsapp/multiple-instances.md): Demonstrates multiple instances.
- [Whatsapp](https://docs.agno.com/examples/agent-os/interfaces/whatsapp/overview.md): Examples for `interfaces/whatsapp` in AgentOS.
- [Reasoning Agent](https://docs.agno.com/examples/agent-os/interfaces/whatsapp/reasoning-agent.md): Demonstrates reasoning agent.
- [Agentos Excel Analyst](https://docs.agno.com/examples/agent-os/knowledge/agentos-excel-analyst.md): Demonstrates agentos excel analyst.
- [AgentOS Knowledge (Sync And Async)](https://docs.agno.com/examples/agent-os/knowledge/agentos-knowledge.md): Demonstrates AgentOS knowledge integration with both sync and async database setups.
- [Agno docs agent](https://docs.agno.com/examples/agent-os/knowledge/agno-docs-agent.md)
- [Knowledge](https://docs.agno.com/examples/agent-os/knowledge/overview.md): Examples for `knowledge` in AgentOS.
- [AgentOS with MCPTools using dynamic headers.](https://docs.agno.com/examples/agent-os/mcp-demo/dynamic-headers/client.md): This example shows how to pass user context to external MCP servers.
- [Simple MCP server that logs headers received from clients.](https://docs.agno.com/examples/agent-os/mcp-demo/dynamic-headers/server.md): Run with: python server.py.
- [Example AgentOS app with MCP enabled.](https://docs.agno.com/examples/agent-os/mcp-demo/enable-mcp-example.md): After starting this AgentOS app, you can test the MCP server with the test_client.py file.
- [Example AgentOS app where the agent has MCPTools.](https://docs.agno.com/examples/agent-os/mcp-demo/mcp-tools-advanced-example.md): AgentOS handles the lifespan of the MCPTools internally.
- [Example AgentOS app where the agent has MCPTools.](https://docs.agno.com/examples/agent-os/mcp-demo/mcp-tools-example.md): AgentOS handles the lifespan of the MCPTools internally.
- [Example AgentOS app where the agent has MCPTools.](https://docs.agno.com/examples/agent-os/mcp-demo/mcp-tools-existing-lifespan.md): AgentOS handles the lifespan of the MCPTools internally.
- [Mcp Demo](https://docs.agno.com/examples/agent-os/mcp-demo/overview.md): Examples for `mcp_demo` in AgentOS.
- [First run the AgentOS with enable_mcp=True](https://docs.agno.com/examples/agent-os/mcp-demo/test-client.md): This is the URL of the MCP server we want to use.
- [Agent Os With Custom Middleware](https://docs.agno.com/examples/agent-os/middleware/agent-os-with-custom-middleware.md): We add two middleware: - Rate Limiting: Limits requests per IP address - Request/Response Logging: Logs requests and responses.
- [This example demonstrates how to use our JWT middleware with AgentOS.](https://docs.agno.com/examples/agent-os/middleware/agent-os-with-jwt-middleware.md): The middleware extracts JWT claims and stores them in request.state for easy access.
- [Agent Os With Jwt Middleware Cookies](https://docs.agno.com/examples/agent-os/middleware/agent-os-with-jwt-middleware-cookies.md): This is useful for web applications that prefer to store JWT tokens in HTTP-only cookies for security.
- [Custom Fastapi App With Jwt Middleware](https://docs.agno.com/examples/agent-os/middleware/custom-fastapi-app-with-jwt-middleware.md): # Note: This example won't work with the AgentOS UI, because of the token validation mechanism in the JWT middleware.
- [Extract Content Middleware](https://docs.agno.com/examples/agent-os/middleware/extract-content-middleware.md): This example middleware can extract content from both streaming and non-streaming responses.
- [Example demonstrating how to use guardrails with an Agno Agent.](https://docs.agno.com/examples/agent-os/middleware/guardrails-demo.md): The AgentOS UI will show an error when the guardrail is triggered.
- [Middleware](https://docs.agno.com/examples/agent-os/middleware/overview.md): Examples for `middleware` in AgentOS.
- [Basic](https://docs.agno.com/examples/agent-os/os-config/basic.md): Demonstrates basic.
- [Os Config](https://docs.agno.com/examples/agent-os/os-config/overview.md): Examples for `os_config` in AgentOS.
- [Yaml Config](https://docs.agno.com/examples/agent-os/os-config/yaml-config.md): Demonstrates yaml config.
- [Agent Os](https://docs.agno.com/examples/agent-os/overview.md): Top-level AgentOS quickstart and entrypoint examples.
- [Basic RBAC Example with AgentOS (Asymmetric Keys)](https://docs.agno.com/examples/agent-os/rbac/asymmetric/basic.md): This example demonstrates how to enable RBAC (Role-Based Access Control)
- [Custom Scope Mappings Example](https://docs.agno.com/examples/agent-os/rbac/asymmetric/custom-scope-mappings.md): This example demonstrates how to define custom scope mappings for your AgentOS endpoints.
- [Symmetric Advanced Scopes](https://docs.agno.com/examples/agent-os/rbac/symmetric/advanced-scopes.md): This example demonstrates the AgentOS RBAC system with simplified scope format.
- [Per-Agent Permissions Example with AgentOS](https://docs.agno.com/examples/agent-os/rbac/symmetric/agent-permissions.md): This example demonstrates how to define per-agent permission scopes to control which users can run which specific agents.
- [Basic RBAC Example with AgentOS](https://docs.agno.com/examples/agent-os/rbac/symmetric/basic.md): This example demonstrates how to enable RBAC (Role-Based Access Control)
- [Custom Scope Mappings Example](https://docs.agno.com/examples/agent-os/rbac/symmetric/custom-scope-mappings.md): This example demonstrates how to define custom scope mappings for your AgentOS endpoints.
- [Symmetric](https://docs.agno.com/examples/agent-os/rbac/symmetric/overview.md): Examples for `rbac/symmetric` in AgentOS.
- [Basic RBAC Example with AgentOS](https://docs.agno.com/examples/agent-os/rbac/symmetric/with-cookie.md): This example demonstrates how to enable RBAC (Role-Based Access Control)
- [Google ADK A2A Server for Cookbook Examples.](https://docs.agno.com/examples/agent-os/remote/adk-server.md): Uses Google's ADK to create an A2A-compatible agent.
- [Remote Agent Os Gateway](https://docs.agno.com/examples/agent-os/remote/agent-os-gateway.md): Example showing how to use an AgentOS instance as a gateway to remote agents, teams and workflows.
- [Agno A2A Server for Cookbook Examples.](https://docs.agno.com/examples/agent-os/remote/agno-a2a-server.md): Agent 1: Assistant with calculator tools and knowledge base.
- [Remote](https://docs.agno.com/examples/agent-os/remote/overview.md): Examples for `remote` in AgentOS.
- [Example demonstrating how to connect to a remote Google ADK agent.](https://docs.agno.com/examples/agent-os/remote/remote-adk-agent.md): This example shows how to use RemoteAgent with the A2A protocol to connect to a Google ADK agent that's exposed via the A2A interface.
- [Examples demonstrating AgentOSRunner for remote execution.](https://docs.agno.com/examples/agent-os/remote/remote-agent.md): Run agents remotely using AgentOSRunner.
- [Example demonstrating how to connect to a remote Agno A2A agent.](https://docs.agno.com/examples/agent-os/remote/remote-agno-a2a-agent.md): This example shows how to use RemoteAgent with the A2A protocol to connect to an Agno agent that's exposed via the A2A interface.
- [Examples demonstrating AgentOSRunner for remote execution.](https://docs.agno.com/examples/agent-os/remote/remote-team.md): Run teams remotely using AgentOSRunner.
- [AgentOS Server for Cookbook Client Examples](https://docs.agno.com/examples/agent-os/remote/server.md): Database Configuration.
- [Async schedule management using the async ScheduleManager API.](https://docs.agno.com/examples/agent-os/scheduler/async-schedule.md): This example demonstrates.
- [Basic scheduled agent run.](https://docs.agno.com/examples/agent-os/scheduler/basic-schedule.md): Starts an AgentOS with the scheduler enabled.
- [Running the scheduler inside AgentOS with programmatic schedule creation](https://docs.agno.com/examples/agent-os/scheduler/demo.md): Run the scheduler inside AgentOS with programmatic schedule creation.
- [Multi-agent scheduling with different cron patterns and payloads.](https://docs.agno.com/examples/agent-os/scheduler/multi-agent-schedules.md): This example demonstrates.
- [Scheduler](https://docs.agno.com/examples/agent-os/scheduler/overview.md): Examples for `scheduler` in AgentOS.
- [Using the scheduler REST API endpoints directly.](https://docs.agno.com/examples/agent-os/scheduler/rest-api-schedules.md): This example demonstrates.
- [Viewing and analyzing schedule run history.](https://docs.agno.com/examples/agent-os/scheduler/run-history.md): This example demonstrates.
- [Schedule management via REST API.](https://docs.agno.com/examples/agent-os/scheduler/schedule-management.md): Demonstrates creating, listing, updating, enabling/disabling, manually triggering, and deleting schedules.
- [Schedule validation and error handling.](https://docs.agno.com/examples/agent-os/scheduler/schedule-validation.md): This example demonstrates.
- [Running the scheduler inside AgentOS with automatic polling](https://docs.agno.com/examples/agent-os/scheduler/scheduler-with-agentos.md): Run the scheduler inside AgentOS with automatic schedule discovery.
- [Scheduling teams and workflows (not just agents).](https://docs.agno.com/examples/agent-os/scheduler/team-workflow-schedules.md): This example demonstrates.
- [Agent Input And Output Schemas](https://docs.agno.com/examples/agent-os/schemas/agent-schemas.md): Demonstrates AgentOS agents that use input and output schemas.
- [Schemas](https://docs.agno.com/examples/agent-os/schemas/overview.md): Examples for `schemas` in AgentOS.
- [Team Input And Output Schemas](https://docs.agno.com/examples/agent-os/schemas/team-schemas.md): Demonstrates AgentOS teams that use input and output schemas.
- [Get basic system information.](https://docs.agno.com/examples/agent-os/skills/sample-skills/system-info/scripts/get-system-info.md): Retrieve basic system information via agent skill.
- [List files in a directory.](https://docs.agno.com/examples/agent-os/skills/sample-skills/system-info/scripts/list-directory.md): List directory contents via agent skill.
- [Scripts](https://docs.agno.com/examples/agent-os/skills/sample-skills/system-info/scripts/overview.md): Examples for `skills/sample_skills/system-info/scripts` in AgentOS.
- [Skills With Agentos](https://docs.agno.com/examples/agent-os/skills/skills-with-agentos.md): Demonstrates skills with agentos.
- [Agent with knowledge tracing](https://docs.agno.com/examples/agent-os/tracing/agent-with-knowledge-tracing.md)
- [04 Agent With Reasoning Tools Tracing](https://docs.agno.com/examples/agent-os/tracing/agent-with-reasoning-tools-tracing.md): Demonstrates 04 agent with reasoning tools tracing.
- [Traces with AgentOS](https://docs.agno.com/examples/agent-os/tracing/basic-agent-tracing.md): Requirements: uv pip install agno opentelemetry-api opentelemetry-sdk openinference-instrumentation-agno.
- [02 Basic Team Tracing](https://docs.agno.com/examples/agent-os/tracing/basic-team-tracing.md): Demonstrates 02 basic team tracing.
- [Traces with AgentOS](https://docs.agno.com/examples/agent-os/tracing/basic-workflow-tracing.md): Requirements: uv pip install agno opentelemetry-api opentelemetry-sdk openinference-instrumentation-agno.
- [Traces with AgentOS](https://docs.agno.com/examples/agent-os/tracing/dbs/basic-agent-with-mongodb.md): Requirements: uv pip install agno opentelemetry-api opentelemetry-sdk openinference-instrumentation-agno.
- [Traces with AgentOS](https://docs.agno.com/examples/agent-os/tracing/dbs/basic-agent-with-postgresdb.md): Requirements: uv pip install agno opentelemetry-api opentelemetry-sdk openinference-instrumentation-agno.
- [Traces with AgentOS using SqliteDb](https://docs.agno.com/examples/agent-os/tracing/dbs/basic-agent-with-sqlite.md): Requirements: uv pip install agno opentelemetry-api opentelemetry-sdk openinference-instrumentation-agno.
- [Tracing](https://docs.agno.com/examples/agent-os/tracing/overview.md): Examples for `tracing` in AgentOS.
- [Traces with AgentOS](https://docs.agno.com/examples/agent-os/tracing/tracing-with-multi-db-and-tracing-flag.md): Requirements: pip install agno opentelemetry-api opentelemetry-sdk openinference-instrumentation-agno.
- [Traces with AgentOS](https://docs.agno.com/examples/agent-os/tracing/tracing-with-multi-db-scenario.md): Requirements: pip install agno opentelemetry-api opentelemetry-sdk openinference-instrumentation-agno.
- [Basic Chat Workflow Agent](https://docs.agno.com/examples/agent-os/workflow/basic-chat-workflow-agent.md): Check if story is long enough to benefit from editing.
- [Basic Workflow](https://docs.agno.com/examples/agent-os/workflow/basic-workflow.md): Demonstrates basic workflow.
- [Basic Workflow Team](https://docs.agno.com/examples/agent-os/workflow/basic-workflow-team.md): Demonstrates basic workflow team.
- [Customer Research Workflow Parallel](https://docs.agno.com/examples/agent-os/workflow/customer-research-workflow-parallel.md): Demonstrates customer research workflow parallel.
- [Workflow](https://docs.agno.com/examples/agent-os/workflow/overview.md): Examples for `workflow` in AgentOS.
- [Workflow With Conditional](https://docs.agno.com/examples/agent-os/workflow/workflow-with-conditional.md): Demonstrates workflow with conditional.
- [Workflow With Custom Function Executors](https://docs.agno.com/examples/agent-os/workflow/workflow-with-custom-function.md): Demonstrates AgentOS workflows using both sync and streaming custom function steps.
- [Workflow With Custom Function Updating Session State](https://docs.agno.com/examples/agent-os/workflow/workflow-with-custom-function-updating-session-state.md): Demonstrates workflow with custom function updating session state.
- [Workflow With History](https://docs.agno.com/examples/agent-os/workflow/workflow-with-history.md): Demonstrates workflow with history.
- [Workflow With Input Schema](https://docs.agno.com/examples/agent-os/workflow/workflow-with-input-schema.md): Demonstrates workflow with input schema.
- [Workflow With Loop](https://docs.agno.com/examples/agent-os/workflow/workflow-with-loop.md): Demonstrates workflow with loop.
- [Workflow With Nested Steps](https://docs.agno.com/examples/agent-os/workflow/workflow-with-nested-steps.md): Demonstrates workflow with nested steps.
- [Workflow With Parallel](https://docs.agno.com/examples/agent-os/workflow/workflow-with-parallel.md): Demonstrates workflow with parallel.
- [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): Demonstrates workflow with parallel and custom function step stream.
- [Workflow With Router](https://docs.agno.com/examples/agent-os/workflow/workflow-with-router.md): Demonstrates workflow with router.
- [Workflow With Steps](https://docs.agno.com/examples/agent-os/workflow/workflow-with-steps.md): Demonstrates workflow with steps.
- [Advanced Compression](https://docs.agno.com/examples/agents/advanced/advanced-compression.md): This example shows how to set a context token based limit for tool call compression.
- [Agent Serialization](https://docs.agno.com/examples/agents/advanced/agent-serialization.md): Serialize and deserialize agents using to_dict/from_dict and save/load with a database.
- [03 Automatic Cultural Management](https://docs.agno.com/examples/agents/advanced/automatic-cultural-management.md): Automatically update cultural knowledge based on Agent interactions.
- [Example demonstrating background execution with polling and cancellation.](https://docs.agno.com/examples/agents/advanced/background-execution.md): Background execution allows you to start an agent run that returns immediately.
- [Example demonstrating background execution with structured output.](https://docs.agno.com/examples/agents/advanced/background-execution-structured.md): Combines background execution (non-blocking, async) with Pydantic output_schema so the completed run returns typed, structured data.
- [Basic Agent Events](https://docs.agno.com/examples/agents/advanced/basic-agent-events.md): Stream agent events including run lifecycle, tool calls, and content output.
- [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): Test script to verify compression events are working correctly.
- [Concurrent Execution](https://docs.agno.com/examples/agents/advanced/concurrent-execution.md): Run multiple agents concurrently using asyncio.gather for parallel execution.
- [01 Create Cultural Knowledge](https://docs.agno.com/examples/agents/advanced/create-cultural-knowledge.md): Create cultural knowledge to use with your Agents.
- [Example demonstrating a custom cancellation manager.](https://docs.agno.com/examples/agents/advanced/custom-cancellation-manager.md): Shows how to 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): Example showing how to use a custom logger with Agno.
- [Debug](https://docs.agno.com/examples/agents/advanced/debug.md): You can set the debug mode on the agent for all runs to have more verbose output.
- [04 Manually Add Culture](https://docs.agno.com/examples/agents/advanced/manually-add-culture.md): Manually add cultural knowledge to your Agents.
- [Metrics](https://docs.agno.com/examples/agents/advanced/metrics.md): Track and display agent performance metrics.
- [Advanced](https://docs.agno.com/examples/agents/advanced/overview.md): Advanced examples covering caching, compression, concurrency, events, retries, debugging, culture, and serialization.
- [Reasoning Agent Events](https://docs.agno.com/examples/agents/advanced/reasoning-agent-events.md): Stream agent events including run lifecycle, tool calls, and content output.
- [Retries](https://docs.agno.com/examples/agents/advanced/retries.md): Example demonstrating how to set up retries with an Agent.
- [Tool Call Compression](https://docs.agno.com/examples/agents/advanced/tool-call-compression.md): Compress tool call history to reduce context size.
- [02 Use Cultural Knowledge In Agent](https://docs.agno.com/examples/agents/advanced/use-cultural-knowledge-in-agent.md): Use cultural knowledge with your Agents.
- [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): Example showing @approval vs @approval(type='audit') in the same agent.
- [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).
- [Agent with Instructions](https://docs.agno.com/examples/agents/basics/agent-with-instructions.md): Quickstart
- [Agent with Tools](https://docs.agno.com/examples/agents/basics/agent-with-tools.md): Quickstart
- [Basic Agent](https://docs.agno.com/examples/agents/basics/basic-agent.md): Quickstart
- [Few Shot Learning](https://docs.agno.com/examples/agents/context-management/few-shot-learning.md): This example demonstrates how to 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.
- [Instructions](https://docs.agno.com/examples/agents/context-management/instructions.md): Manage agent instructions and context.
- [Instructions With State](https://docs.agno.com/examples/agents/context-management/instructions-with-state.md): Example demonstrating how to use a function as instructions for an agent.
- [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.
- [Dependencies In Context](https://docs.agno.com/examples/agents/dependencies/dependencies-in-context.md): Exclude discussion threads.
- [Dependencies In Tools](https://docs.agno.com/examples/agents/dependencies/dependencies-in-tools.md): Example showing how tools can access dependencies passed to the agent.
- [Dynamic Tools](https://docs.agno.com/examples/agents/dependencies/dynamic-tools.md): Add or remove tools dynamically based on runtime dependencies.
- [Custom Guardrail](https://docs.agno.com/examples/agents/guardrails/custom-guardrail.md): Build custom guardrails to validate agent behavior.
- [Openai Moderation](https://docs.agno.com/examples/agents/guardrails/openai-moderation.md): Example demonstrating how to use OpenAI moderation guardrails with Agno Agent.
- [Output Guardrail](https://docs.agno.com/examples/agents/guardrails/output-guardrail.md): Validate and constrain agent output with guardrails.
- [Guardrails](https://docs.agno.com/examples/agents/guardrails/overview.md): Examples for input/output safety checks and policy enforcement.
- [Pii Detection](https://docs.agno.com/examples/agents/guardrails/pii-detection.md): Example demonstrating how to use PII detection guardrails with Agno Agent.
- [Prompt Injection](https://docs.agno.com/examples/agents/guardrails/prompt-injection.md): Example demonstrating how to use checks with Agno Agent to implement guardrails.
- [Hooks](https://docs.agno.com/examples/agents/hooks/overview.md): Examples for pre-hooks, post-hooks, tool hooks, and stream lifecycle hooks.
- [Post Hook Output](https://docs.agno.com/examples/agents/hooks/post-hook-output.md): Example demonstrating output validation using post-hooks with Agno Agent.
- [Pre Hook Input](https://docs.agno.com/examples/agents/hooks/pre-hook-input.md): Example demonstrating how to use a pre_hook to perform comprehensive input validation for your Agno Agent.
- [Session State Hooks](https://docs.agno.com/examples/agents/hooks/session-state-hooks.md): Example demonstrating how to use a pre_hook to update the session_state.
- [Stream Hook](https://docs.agno.com/examples/agents/hooks/stream-hook.md): Example demonstrating sending a notification to the user after an agent generates a response.
- [Tool Hooks](https://docs.agno.com/examples/agents/hooks/tool-hooks.md): Use tool_hooks to add middleware that wraps every tool call.
- [Agentic User Input](https://docs.agno.com/examples/agents/human-in-the-loop/agentic-user-input.md): Human-in-the-Loop: Allowing users to provide input externally.
- [Confirmation Advanced](https://docs.agno.com/examples/agents/human-in-the-loop/confirmation-advanced.md): Human-in-the-Loop: Adding User Confirmation to Tool Calls.
- [Confirmation Required](https://docs.agno.com/examples/agents/human-in-the-loop/confirmation-required.md): Human-in-the-Loop (HITL): Adding User Confirmation to Tool Calls.
- [Confirmation Required MCP Toolkit](https://docs.agno.com/examples/agents/human-in-the-loop/confirmation-required-mcp-toolkit.md): Human-in-the-Loop: Adding User Confirmation to Tool Calls with MCP Servers.
- [Confirmation Toolkit](https://docs.agno.com/examples/agents/human-in-the-loop/confirmation-toolkit.md): Human-in-the-Loop: Adding User Confirmation to Tool Calls.
- [External Tool Execution](https://docs.agno.com/examples/agents/human-in-the-loop/external-tool-execution.md): Human-in-the-Loop: Execute a tool call outside of the agent.
- [Human In The Loop](https://docs.agno.com/examples/agents/human-in-the-loop/overview.md): Examples for confirmation flows, user input prompts, and external tool handling.
- [User Input Required](https://docs.agno.com/examples/agents/human-in-the-loop/user-input-required.md): Human-in-the-Loop: Allowing users to provide input externally.
- [Expected Output](https://docs.agno.com/examples/agents/input-output/expected-output.md): Guide agent responses using the expected_output parameter.
- [Input Formats](https://docs.agno.com/examples/agents/input-output/input-formats.md): Handle different input formats for agent requests.
- [Input Schema](https://docs.agno.com/examples/agents/input-output/input-schema.md): Pass a dict that matches the input schema.
- [Output Model](https://docs.agno.com/examples/agents/input-output/output-model.md): Use a separate output model to refine the main model's response.
- [Output Schema](https://docs.agno.com/examples/agents/input-output/output-schema.md): This example shows how to use the output_model parameter to specify the model that will be used to generate the final response.
- [Input Output](https://docs.agno.com/examples/agents/input-output/overview.md): Examples for input formats, validation schemas, streaming, and structured outputs.
- [Parser Model](https://docs.agno.com/examples/agents/input-output/parser-model.md): Get the response in a variable.
- [Response As Variable](https://docs.agno.com/examples/agents/input-output/response-as-variable.md): Capture agent responses as variables for downstream use.
- [Save To File](https://docs.agno.com/examples/agents/input-output/save-to-file.md): Save agent responses to a file automatically.
- [Streaming](https://docs.agno.com/examples/agents/input-output/streaming.md): Demonstrates streaming agent responses token by token.
- [Agentic Rag](https://docs.agno.com/examples/agents/knowledge/agentic-rag.md): Build an agentic RAG pipeline with autonomous retrieval.
- [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 with result reranking for better relevance.
- [Custom Retriever](https://docs.agno.com/examples/agents/knowledge/custom-retriever.md): Use knowledge_retriever to provide a custom retrieval function.
- [Knowledge Filters](https://docs.agno.com/examples/agents/knowledge/knowledge-filters.md): Filter knowledge base searches using static filters or agentic filters.
- [Knowledge](https://docs.agno.com/examples/agents/knowledge/overview.md): Examples for retrieval-augmented generation, knowledge filters, and custom retrievers.
- [Rag Custom Embeddings](https://docs.agno.com/examples/agents/knowledge/rag-custom-embeddings.md): This cookbook is an implementation of Agentic RAG using Sentence Transformer Reranker with multilingual data.
- [References Format](https://docs.agno.com/examples/agents/knowledge/references-format.md): Control how knowledge base references are formatted for the agent.
- [Traditional Rag](https://docs.agno.com/examples/agents/knowledge/traditional-rag.md): Standard RAG pipeline with vector search and retrieval.
- [Learning Machine](https://docs.agno.com/examples/agents/memory-and-learning/learning-machine.md): Create agents that learn and improve from interactions over time.
- [Memory Manager](https://docs.agno.com/examples/agents/memory-and-learning/memory-manager.md): Use a MemoryManager to give agents persistent memory across sessions.
- [Audio Input Output](https://docs.agno.com/examples/agents/multimodal/audio-input-output.md): Fetch the audio file and convert it to a base64 encoded string.
- [Audio Sentiment Analysis](https://docs.agno.com/examples/agents/multimodal/audio-sentiment-analysis.md): Give a sentiment analysis of this audio conversation. Use speaker A, speaker B to identify speakers.
- [Audio Streaming](https://docs.agno.com/examples/agents/multimodal/audio-streaming.md): Mono (Change to 2 if Stereo).
- [Audio To Text](https://docs.agno.com/examples/agents/multimodal/audio-to-text.md): Give a transcript of this audio conversation. Use speaker A, speaker B to identify speakers.
- [Image To Audio](https://docs.agno.com/examples/agents/multimodal/image-to-audio.md): Convert image descriptions to audio output.
- [Image To Image](https://docs.agno.com/examples/agents/multimodal/image-to-image.md): Transform images using agent-driven processing.
- [Image To Structured Output](https://docs.agno.com/examples/agents/multimodal/image-to-structured-output.md): Extract structured data from images.
- [Image To Text](https://docs.agno.com/examples/agents/multimodal/image-to-text.md): Image to Text Example.
- [Media Input For Tool](https://docs.agno.com/examples/agents/multimodal/media-input-for-tool.md): Example showing how tools can access media (images, videos, audio, files) passed to the agent.
- [Multimodal](https://docs.agno.com/examples/agents/multimodal/overview.md): Examples for image/audio/video processing patterns.
- [Video Caption](https://docs.agno.com/examples/agents/multimodal/video-caption.md): Generate captions from video content.
- [Agents](https://docs.agno.com/examples/agents/overview.md): Practical examples for building agents with Agno, organized by feature area.
- [Basic Reasoning](https://docs.agno.com/examples/agents/reasoning/basic-reasoning.md): Add chain-of-thought reasoning capabilities to agents.
- [Reasoning With Model](https://docs.agno.com/examples/agents/reasoning/reasoning-with-model.md): Use a separate reasoning model with configurable step limits.
- [Basic Skills](https://docs.agno.com/examples/agents/skills/basic-skills.md): Basic Skills Example.
- [Check Style](https://docs.agno.com/examples/agents/skills/sample-skills/code-review/scripts/check-style.md): Check Python code for style issues.
- [Commit Message](https://docs.agno.com/examples/agents/skills/sample-skills/git-workflow/scripts/commit-message.md): Validate or generate conventional commit messages.
- [Agentic Session State](https://docs.agno.com/examples/agents/state-and-session/agentic-session-state.md): Required so the agent is aware of the session state.
- [Chat History](https://docs.agno.com/examples/agents/state-and-session/chat-history.md): Manage and access agent chat history.
- [Dynamic Session State](https://docs.agno.com/examples/agents/state-and-session/dynamic-session-state.md): Update session state dynamically during agent runs.
- [Last N Session Messages](https://docs.agno.com/examples/agents/state-and-session/last-n-session-messages.md): Remove the tmp db file before running the script.
- [State And Session](https://docs.agno.com/examples/agents/state-and-session/overview.md): Examples for session state management, chat history, and session persistence.
- [Persistent Session](https://docs.agno.com/examples/agents/state-and-session/persistent-session.md): Persistent Session Example.
- [Session Options](https://docs.agno.com/examples/agents/state-and-session/session-options.md): Simple example demonstrating store_history_messages option.
- [Session State Advanced](https://docs.agno.com/examples/agents/state-and-session/session-state-advanced.md): Define tools to manage our shopping list.
- [Session State Basic](https://docs.agno.com/examples/agents/state-and-session/session-state-basic.md): Create an Agent that maintains state.
- [Session State Events](https://docs.agno.com/examples/agents/state-and-session/session-state-events.md): Create an Agent that maintains state.
- [Session State Manual Update](https://docs.agno.com/examples/agents/state-and-session/session-state-manual-update.md): Create an Agent that maintains state.
- [Session State Multiple Users](https://docs.agno.com/examples/agents/state-and-session/session-state-multiple-users.md): This example demonstrates how to maintain state for each user in a multi-user environment.
- [Session Summary](https://docs.agno.com/examples/agents/state-and-session/session-summary.md): This example shows how to use the session summary to store the conversation summary.
- [Callable Tools Factory](https://docs.agno.com/examples/agents/tools/callable-tools.md): Pass a function as `tools` instead of a list.
- [Tools](https://docs.agno.com/examples/agents/tools/overview.md): Examples for callable tool factories, tool choice, and tool call limits.
- [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): This cookbook shows how to 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): Tool Choice Control.
- [Agentic Search over Knowledge](https://docs.agno.com/examples/basics/agent-search-over-knowledge.md): This example shows how to give an agent a searchable knowledge base.
- [Agent with Guardrails](https://docs.agno.com/examples/basics/agent-with-guardrails.md): This example shows how to add guardrails to your agent to validate input before processing.
- [Agent with Memory](https://docs.agno.com/examples/basics/agent-with-memory.md): This example shows how to give your agent memory of user preferences.
- [Agent with State Management](https://docs.agno.com/examples/basics/agent-with-state-management.md): This example shows how to give your agent persistent state that it can read and modify.
- [Agent with Storage](https://docs.agno.com/examples/basics/agent-with-storage.md): Building on the Finance Agent from 01, this example adds persistent storage.
- [Agent with Structured Output](https://docs.agno.com/examples/basics/agent-with-structured-output.md): This example shows how to get structured, typed responses from your agent.
- [Agent with Tools](https://docs.agno.com/examples/basics/agent-with-tools.md): Your first Agno agent: a data-driven financial analyst that retrieves market data, computes key metrics, and delivers concise insights.
- [Agent with Typed I/O](https://docs.agno.com/examples/basics/agent-with-typed-input-output.md): This example shows how to define both input and output schemas for your agent.
- [Custom Tool for Self-Learning](https://docs.agno.com/examples/basics/custom-tool-for-self-learning.md): This example shows how to write custom tools for your agent.
- [Human in the Loop](https://docs.agno.com/examples/basics/human-in-the-loop.md): This example shows how to require user confirmation before executing certain tools.
- [Multi-Agent Team](https://docs.agno.com/examples/basics/multi-agent-team.md): This example shows how to create a team of agents that work together.
- [Quickstart](https://docs.agno.com/examples/basics/overview.md): Build your first agent with tools, structured output, memory, knowledge, guardrails.
- [Agent OS - Web Interface for Your Agents](https://docs.agno.com/examples/basics/run.md): This file starts an Agent OS server that provides a web interface for all the agents, teams, and workflows in this Quick Start guide.
- [Sequential Workflow](https://docs.agno.com/examples/basics/sequential-workflow.md): This example shows how to create a workflow with sequential steps.
- [AgentOS Registry App](https://docs.agno.com/examples/components/agent-os-registry.md): Demonstrates configuring AgentOS with a Registry and serving the app.
- [AgentOS Registry Demo](https://docs.agno.com/examples/components/demo.md): Demonstrates using Registry with AgentOS for tools, functions, schemas, models, and vector database components.
- [Load Agent from Database](https://docs.agno.com/examples/components/get-agent.md): Demonstrates loading an agent from the database by ID and running it.
- [Load Team from Database](https://docs.agno.com/examples/components/get-team.md): Demonstrates loading a team from the database by ID and running it.
- [Load Workflow from Database](https://docs.agno.com/examples/components/get-workflow.md): Demonstrates loading a workflow from the database by ID and running it.
- [Components](https://docs.agno.com/examples/components/overview.md): This cookbook demonstrates how to save and load Agents, Teams, and Workflows to/from a database, enabling configuration-as-code patterns where your AI components can be versioned,.
- [Registry for Non-Serializable Components](https://docs.agno.com/examples/components/registry.md): Demonstrates using Registry to restore tools, models, and schemas when loading components from the database.
- [Save Agent to Database](https://docs.agno.com/examples/components/save-agent.md): Demonstrates creating an agent and saving it to the database.
- [Save Team to Database](https://docs.agno.com/examples/components/save-team.md): Demonstrates creating a team with member agents and saving it to the database.
- [Save Workflow to Database](https://docs.agno.com/examples/components/save-workflow.md): Demonstrates creating a workflow with multiple steps and saving it to the database.
- [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): Demonstrates creating a workflow with conditional steps, saving it to the database, and loading it back with a Registry.
- [Save Custom Executor Workflow Steps](https://docs.agno.com/examples/components/workflows/save-custom-steps.md): Demonstrates creating a workflow with custom executor steps, saving it to the database, and loading it back with a Registry.
- [Save Loop Workflow Steps](https://docs.agno.com/examples/components/workflows/save-loop-steps.md): Demonstrates creating a workflow with loop steps, saving it to the database, and loading it back with a Registry.
- [Save Parallel Workflow Steps](https://docs.agno.com/examples/components/workflows/save-parallel-steps.md): Demonstrates creating a workflow with parallel steps, saving it to the database, and loading it back.
- [Save Router Workflow Steps](https://docs.agno.com/examples/components/workflows/save-router-steps.md): Demonstrates creating a workflow with router steps, saving it to the database, and loading it back with a Registry.
- [Comparison Accuracy Evaluation](https://docs.agno.com/examples/evals/accuracy/accuracy-9-11-bigger-or-9-99.md): Demonstrates accuracy evaluation for numeric comparison tasks.
- [Basic Accuracy Evaluation](https://docs.agno.com/examples/evals/accuracy/accuracy-basic.md): Demonstrates synchronous and asynchronous accuracy evaluations.
- [Team Accuracy Evaluation](https://docs.agno.com/examples/evals/accuracy/accuracy-team.md): Demonstrates evaluating language routing accuracy for a team.
- [Given Answer Accuracy Evaluation](https://docs.agno.com/examples/evals/accuracy/accuracy-with-given-answer.md): Demonstrates accuracy evaluation for a provided answer string.
- [Tool-Enabled Accuracy Evaluation](https://docs.agno.com/examples/evals/accuracy/accuracy-with-tools.md): Demonstrates accuracy evaluation for an agent using calculator tools.
- [Accuracy Evaluation with Database Logging](https://docs.agno.com/examples/evals/accuracy/db-logging.md): Demonstrates storing accuracy evaluation results in PostgreSQL.
- [Accuracy Evaluation with Custom Evaluator Agent](https://docs.agno.com/examples/evals/accuracy/evaluator-agent.md): Demonstrates accuracy evaluation using a custom evaluator agent.
- [Accuracy](https://docs.agno.com/examples/evals/accuracy/overview.md): Accuracy examples evaluate how well responses match expected outputs.
- [Basic Agent-as-Judge Evaluation](https://docs.agno.com/examples/evals/agent-as-judge/agent-as-judge-basic.md): Demonstrates synchronous and asynchronous agent-as-judge evaluations.
- [Batch Agent-as-Judge Evaluation](https://docs.agno.com/examples/evals/agent-as-judge/agent-as-judge-batch.md): Demonstrates evaluating multiple cases in one run.
- [Binary Agent-as-Judge Evaluation](https://docs.agno.com/examples/evals/agent-as-judge/agent-as-judge-binary.md): Demonstrates pass/fail response quality evaluation.
- [Custom Evaluator Agent-as-Judge Evaluation](https://docs.agno.com/examples/evals/agent-as-judge/agent-as-judge-custom-evaluator.md): Demonstrates using a custom evaluator agent for judging.
- [Post-Hook Agent-as-Judge Evaluation](https://docs.agno.com/examples/evals/agent-as-judge/agent-as-judge-post-hook.md): Demonstrates synchronous and asynchronous post-hook judging.
- [Team Agent-as-Judge Evaluation](https://docs.agno.com/examples/evals/agent-as-judge/agent-as-judge-team.md): Demonstrates response quality evaluation for team outputs.
- [Team Post-Hook Agent-as-Judge Evaluation](https://docs.agno.com/examples/evals/agent-as-judge/agent-as-judge-team-post-hook.md): Demonstrates a post-hook judge running on team responses.
- [Guideline-Based Agent-as-Judge Evaluation](https://docs.agno.com/examples/evals/agent-as-judge/agent-as-judge-with-guidelines.md): Demonstrates agent-as-judge scoring with additional guidelines.
- [Tool-Using Agent-as-Judge Evaluation](https://docs.agno.com/examples/evals/agent-as-judge/agent-as-judge-with-tools.md): Demonstrates judging responses generated by an agent using tools.
- [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.
- [Evals](https://docs.agno.com/examples/evals/overview.md): This directory contains runnable examples for Agno evaluation patterns.
- [Async Function Performance Evaluation](https://docs.agno.com/examples/evals/performance/async-function.md): Demonstrates performance evaluation for an asynchronous function.
- [AutoGen Instantiation Performance Evaluation](https://docs.agno.com/examples/evals/performance/comparison/autogen-instantiation.md): Demonstrates agent instantiation benchmarking with AutoGen.
- [CrewAI Instantiation Performance Evaluation](https://docs.agno.com/examples/evals/performance/comparison/crewai-instantiation.md): Demonstrates agent instantiation benchmarking with CrewAI.
- [LangGraph Instantiation Performance Evaluation](https://docs.agno.com/examples/evals/performance/comparison/langgraph-instantiation.md): Demonstrates agent instantiation benchmarking with LangGraph.
- [OpenAI Agents Instantiation Performance Evaluation](https://docs.agno.com/examples/evals/performance/comparison/openai-agents-instantiation.md): Demonstrates agent instantiation benchmarking with OpenAI Agents SDK.
- [PydanticAI Instantiation Performance Evaluation](https://docs.agno.com/examples/evals/performance/comparison/pydantic-ai-instantiation.md): Demonstrates agent instantiation benchmarking with PydanticAI.
- [Smolagents Instantiation Performance Evaluation](https://docs.agno.com/examples/evals/performance/comparison/smolagents-instantiation.md): Demonstrates agent instantiation benchmarking with Smolagents.
- [Performance Evaluation with Database Logging](https://docs.agno.com/examples/evals/performance/db-logging.md): Demonstrates storing performance evaluation results in PostgreSQL.
- [Agent Instantiation Performance Evaluation](https://docs.agno.com/examples/evals/performance/instantiate-agent.md): Demonstrates measuring agent instantiation performance.
- [Agent-with-Tool Instantiation Performance Evaluation](https://docs.agno.com/examples/evals/performance/instantiate-agent-with-tool.md): Demonstrates measuring instantiation performance for a tooled agent.
- [Team Instantiation Performance Evaluation](https://docs.agno.com/examples/evals/performance/instantiate-team.md): Demonstrates measuring team instantiation performance.
- [Performance](https://docs.agno.com/examples/evals/performance/overview.md): Performance examples benchmark runtime and memory impact for agents and teams.
- [Memory Update Performance Evaluation](https://docs.agno.com/examples/evals/performance/response-with-memory-updates.md): Demonstrates measuring performance when memory updates are enabled.
- [Storage-Backed Response Performance Evaluation](https://docs.agno.com/examples/evals/performance/response-with-storage.md): Demonstrates measuring performance when storage-backed history is enabled.
- [Simple Response Performance Evaluation](https://docs.agno.com/examples/evals/performance/simple-response.md): Demonstrates baseline response performance for a single prompt.
- [Team Memory and Reasoning Performance Evaluation](https://docs.agno.com/examples/evals/performance/team-response-with-memory-and-reasoning.md): Demonstrates memory growth performance for a reasoning-enabled team.
- [Multi-User Team Memory Performance Evaluation](https://docs.agno.com/examples/evals/performance/team-response-with-memory-multi-user.md): Demonstrates concurrent team performance across multiple users with memory.
- [Simple Team Memory Performance Evaluation](https://docs.agno.com/examples/evals/performance/team-response-with-memory-simple.md): Demonstrates team response performance with memory enabled.
- [Reliability Evaluation with Database Logging](https://docs.agno.com/examples/evals/reliability/db-logging.md): Demonstrates storing reliability evaluation results in PostgreSQL.
- [Multiple Tool Call Reliability Evaluation](https://docs.agno.com/examples/evals/reliability/multiple-tool-calls/calculator.md): Demonstrates reliability checks for multiple expected tool calls.
- [Asynchronous Reliability Evaluation](https://docs.agno.com/examples/evals/reliability/reliability-async.md): Demonstrates running reliability checks with asynchronous evaluation.
- [Single Tool Call Reliability Evaluation](https://docs.agno.com/examples/evals/reliability/single-tool-calls/calculator.md): Demonstrates reliability checks for one expected tool call.
- [Team Reliability Evaluation for News Search](https://docs.agno.com/examples/evals/reliability/team/ai-news.md): Demonstrates tool-call reliability checks for a team workflow.
- [Team](https://docs.agno.com/examples/evals/reliability/team/overview.md): These examples validate reliability for team-level tool usage and delegation.
- [Basic A2A Server](https://docs.agno.com/examples/integrations/a2a/basic-agent/--main--.md): Starts a local A2A server backed by an Agno agent executor.
- [Basic A2A Agent Executor](https://docs.agno.com/examples/integrations/a2a/basic-agent/basic-agent.md): Implements an A2A executor that routes incoming text to an Agno agent.
- [Basic A2A Client](https://docs.agno.com/examples/integrations/a2a/basic-agent/client.md): Sends a message to the local A2A server and prints the JSON response.
- [A2A](https://docs.agno.com/examples/integrations/a2a/overview.md): Examples for running Agno with the A2A protocol.
- [Discord Agent With Media](https://docs.agno.com/examples/integrations/discord/agent-with-media.md): Runs a Discord bot that can analyze user-provided media.
- [Discord Agent With User Memory](https://docs.agno.com/examples/integrations/discord/agent-with-user-memory.md): Runs a Discord bot that combines web search with persistent user memory.
- [Basic Discord Agent](https://docs.agno.com/examples/integrations/discord/basic.md): Runs a simple Agno-powered Discord bot.
- [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.
- [Mem0 Integration](https://docs.agno.com/examples/integrations/memory/mem0-integration.md): Demonstrates using Mem0 as an external memory service for an Agno agent.
- [Memori Integration](https://docs.agno.com/examples/integrations/memory/memori-integration.md): Demonstrates conversational memory persistence with Memori and Agno.
- [Memory](https://docs.agno.com/examples/integrations/memory/overview.md): Examples for connecting Agno agents to external memory services.
- [Zep Integration](https://docs.agno.com/examples/integrations/memory/zep-integration.md): Demonstrates Zep-powered memory retrieval for an Agno agent.
- [AgentOps Integration](https://docs.agno.com/examples/integrations/observability/agent-ops.md): Demonstrates logging Agno model calls with AgentOps.
- [Arize Phoenix Project Routing](https://docs.agno.com/examples/integrations/observability/arize-phoenix-moving-traces-to-different-projects.md): Demonstrates sending traces from different agents to different Phoenix projects.
- [Arize Phoenix Via OpenInference](https://docs.agno.com/examples/integrations/observability/arize-phoenix-via-openinference.md): Demonstrates instrumenting an Agno agent with OpenInference and sending traces to Phoenix.
- [Arize Phoenix Local Via OpenInference](https://docs.agno.com/examples/integrations/observability/arize-phoenix-via-openinference-local.md): Demonstrates instrumenting an Agno agent and sending traces to a local Phoenix instance.
- [Atla Observability Integration](https://docs.agno.com/examples/integrations/observability/atla-op.md): Demonstrates adding Atla observability to an Agno agent.
- [Langfuse Via OpenInference](https://docs.agno.com/examples/integrations/observability/langfuse-via-openinference.md): Demonstrates instrumenting an Agno agent with OpenInference and sending traces to Langfuse.
- [Langfuse Via OpenInference With Response Model](https://docs.agno.com/examples/integrations/observability/langfuse-via-openinference-response-model.md): Demonstrates Langfuse tracing for an Agno agent that returns structured output.
- [Langfuse Via OpenLIT](https://docs.agno.com/examples/integrations/observability/langfuse-via-openlit.md): Demonstrates sending Agno traces to Langfuse through OpenLIT.
- [LangSmith Via OpenInference](https://docs.agno.com/examples/integrations/observability/langsmith-via-openinference.md): Demonstrates instrumenting an Agno agent with OpenInference and sending traces to LangSmith.
- [Langtrace Integration](https://docs.agno.com/examples/integrations/observability/langtrace-op.md): Demonstrates instrumenting an Agno agent with Langtrace.
- [LangWatch Integration](https://docs.agno.com/examples/integrations/observability/langwatch-op.md): Demonstrates instrumenting an Agno agent and sending traces to LangWatch.
- [Logfire Via OpenInference](https://docs.agno.com/examples/integrations/observability/logfire-via-openinference.md): Demonstrates instrumenting an Agno agent with OpenInference and sending traces to Logfire.
- [Maxim Integration](https://docs.agno.com/examples/integrations/observability/maxim-ops.md): Demonstrates using Maxim to trace and log Agno agent and team calls.
- [Opik Via OpenInference](https://docs.agno.com/examples/integrations/observability/opik-via-openinference.md): Demonstrates instrumenting Agno with OpenTelemetry and exporting traces to Opik.
- [Langfuse Team Tracing Via OpenInference](https://docs.agno.com/examples/integrations/observability/teams/langfuse-via-openinference-team.md): Demonstrates sync and async team tracing with Langfuse.
- [Trace To Database](https://docs.agno.com/examples/integrations/observability/trace-to-database.md): Demonstrates Agno's two-table trace design and how to inspect traces and spans.
- [Traceloop Integration](https://docs.agno.com/examples/integrations/observability/traceloop-op.md): Demonstrates wrapping Agno calls in Traceloop workflow spans.
- [Weave Integration](https://docs.agno.com/examples/integrations/observability/weave-op.md): Demonstrates logging Agno model calls with Weave.
- [Arize Phoenix Workflow Via OpenInference](https://docs.agno.com/examples/integrations/observability/workflows/arize-phoenix-via-openinference-workflow.md): Demonstrates tracing a multi-step Agno workflow in Arize Phoenix.
- [Langfuse Workflows Via OpenInference](https://docs.agno.com/examples/integrations/observability/workflows/langfuse-via-openinference-workflows.md): Demonstrates tracing a multi-step Agno workflow in Langfuse.
- [Workflows](https://docs.agno.com/examples/integrations/observability/workflows/overview.md): Examples for tracing Agno workflows.
- [Integrations](https://docs.agno.com/examples/integrations/overview.md): Integration examples showing how to connect Agno agents with external platforms and services.
- [Agentic Rag Infinity Reranker](https://docs.agno.com/examples/integrations/rag/agentic-rag-infinity-reranker.md): Demonstrates agentic RAG with an Infinity reranker backend (relocated integration example).
- [Agentic Rag With Lightrag](https://docs.agno.com/examples/integrations/rag/agentic-rag-with-lightrag.md): Demonstrates an agentic RAG flow backed by LightRAG (relocated integration example).
- [Local Rag Langchain Qdrant](https://docs.agno.com/examples/integrations/rag/local-rag-langchain-qdrant.md): Local RAG pipeline using LangChain and Qdrant.
- [Rag](https://docs.agno.com/examples/integrations/rag/overview.md): Examples for third-party RAG and retrieval-stack integrations.
- [SurrealDB Custom Memory Instructions](https://docs.agno.com/examples/integrations/surrealdb/custom-memory-instructions.md): Create Memory Managers.
- [SurrealDB Memory DB Tools Control](https://docs.agno.com/examples/integrations/surrealdb/db-tools-control.md): Give agents persistent memory across sessions using a MemoryManager.
- [SurrealDB Memory Creation](https://docs.agno.com/examples/integrations/surrealdb/memory-creation.md): Create Memory Manager.
- [SurrealDB Memory Search](https://docs.agno.com/examples/integrations/surrealdb/memory-search-surreal.md): Create Memory Manager.
- [Surrealdb](https://docs.agno.com/examples/integrations/surrealdb/overview.md): Examples showing SurrealDB as a backend for Agno memory management.
- [Standalone SurrealDB Memory Operations](https://docs.agno.com/examples/integrations/surrealdb/standalone-memory-surreal.md): Create Memory Manager.
- [Examples](https://docs.agno.com/examples/introduction.md): 2000+ examples covering 40+ models, 100+ tools and 18 vector databases.
- [Agentic Chunking](https://docs.agno.com/examples/knowledge/chunking/agentic-chunking.md): Run Agentic Chunking.
- [Code Chunking](https://docs.agno.com/examples/knowledge/chunking/code-chunking.md): Run Code Chunking.
- [Code Chunking Custom Tokenizer](https://docs.agno.com/examples/knowledge/chunking/code-chunking-custom-tokenizer.md): Run Code Chunking Custom Tokenizer.
- [Csv Row Chunking](https://docs.agno.com/examples/knowledge/chunking/csv-row-chunking.md): Run Csv Row Chunking.
- [Custom Strategy Example](https://docs.agno.com/examples/knowledge/chunking/custom-strategy-example.md): Run Custom Strategy Example.
- [Document Chunking](https://docs.agno.com/examples/knowledge/chunking/document-chunking.md): Run Document Chunking.
- [Fixed Size Chunking](https://docs.agno.com/examples/knowledge/chunking/fixed-size-chunking.md): Run Fixed Size Chunking.
- [Markdown Chunking Examples](https://docs.agno.com/examples/knowledge/chunking/markdown-chunking.md): This cookbook demonstrates different ways to use MarkdownChunking for splitting markdown documents based on heading structure.
- [Chunking](https://docs.agno.com/examples/knowledge/chunking/overview.md): Chunking breaks down large documents into manageable pieces for efficient knowledge retrieval and processing in databases.
- [Recursive Chunking](https://docs.agno.com/examples/knowledge/chunking/recursive-chunking.md): Run Recursive Chunking.
- [Semantic Chunking](https://docs.agno.com/examples/knowledge/chunking/semantic-chunking.md): Run Semantic Chunking.
- [Semantic Chunking Agno Embedder](https://docs.agno.com/examples/knowledge/chunking/semantic-chunking-agno-embedder.md): Run Semantic Chunking Agno Embedder.
- [Semantic Chunking Chonkie Embedder](https://docs.agno.com/examples/knowledge/chunking/semantic-chunking-chonkie-embedder.md): Run Semantic Chunking Chonkie Embedder.
- [Azure Blob Storage Content Source for Knowledge](https://docs.agno.com/examples/knowledge/cloud/azure-blob.md): Load files and folders from Azure Blob Storage containers into your Knowledge base.
- [Content Sources for Knowledge - DX Design](https://docs.agno.com/examples/knowledge/cloud/cloud-agentos.md): This cookbook demonstrates the API for adding content from various remote sources (S3, GCS, SharePoint, GitHub, etc.) to Knowledge.
- [GitHub Content Source for Knowledge](https://docs.agno.com/examples/knowledge/cloud/github.md): Load files and folders from GitHub repositories into your Knowledge base.
- [Cloud](https://docs.agno.com/examples/knowledge/cloud/overview.md): This directory contains Agno knowledge cookbook examples for cloud.
- [SharePoint Content Source for Knowledge](https://docs.agno.com/examples/knowledge/cloud/sharepoint.md): Load files and folders from SharePoint document libraries into your Knowledge base.
- [Async Retriever](https://docs.agno.com/examples/knowledge/custom-retriever/async-retriever.md): Run Async Retriever.
- [Custom Retriever](https://docs.agno.com/examples/knowledge/custom-retriever/overview.md): Custom retrievers provide complete control over how your agents find and process information from knowledge sources.
- [Retriever](https://docs.agno.com/examples/knowledge/custom-retriever/retriever.md): Run Retriever.
- [Example demonstrating custom knowledge retriever with runtime dependencies.](https://docs.agno.com/examples/knowledge/custom-retriever/retriever-with-dependencies.md): This cookbook shows how to access dependencies passed at runtime (e.g., via `agent.run(dependencies={...})`)
- [AWS Bedrock Embedder](https://docs.agno.com/examples/knowledge/embedders/aws-bedrock-embedder.md): Demonstrates Cohere v3 embeddings through AWS Bedrock and knowledge insertion.
- [AWS Bedrock Embedder v4](https://docs.agno.com/examples/knowledge/embedders/aws-bedrock-embedder-v4.md): Demonstrates Cohere v4 embeddings on AWS Bedrock with configurable dimensions.
- [Azure OpenAI Embedder](https://docs.agno.com/examples/knowledge/embedders/azure-embedder.md): Demonstrates Azure OpenAI embeddings and knowledge insertion, including a batching variant.
- [Cohere Embedder](https://docs.agno.com/examples/knowledge/embedders/cohere-embedder.md): Demonstrates Cohere embeddings and knowledge insertion, including a batching variant.
- [Fireworks Embedder](https://docs.agno.com/examples/knowledge/embedders/fireworks-embedder.md): Demonstrates Fireworks embeddings and knowledge insertion, including a batching variant.
- [Gemini Embedder](https://docs.agno.com/examples/knowledge/embedders/gemini-embedder.md): Demonstrates Gemini embeddings and knowledge insertion, including a batching variant.
- [Hugging Face Embedder](https://docs.agno.com/examples/knowledge/embedders/huggingface-embedder.md): Demonstrates Hugging Face custom embeddings and knowledge insertion.
- [Jina Embedder](https://docs.agno.com/examples/knowledge/embedders/jina-embedder.md): Demonstrates Jina embeddings, usage metadata retrieval, and a batching variant.
- [LangDB Embedder](https://docs.agno.com/examples/knowledge/embedders/langdb-embedder.md): Demonstrates LangDB embeddings and knowledge insertion.
- [Mistral Embedder](https://docs.agno.com/examples/knowledge/embedders/mistral-embedder.md): Demonstrates Mistral embeddings and knowledge insertion, including a batching variant.
- [Nebius Embedder](https://docs.agno.com/examples/knowledge/embedders/nebius-embedder.md): Demonstrates Nebius embeddings and knowledge insertion.
- [Ollama Embedder](https://docs.agno.com/examples/knowledge/embedders/ollama-embedder.md): Demonstrates Ollama embeddings and knowledge insertion.
- [OpenAI Embedder](https://docs.agno.com/examples/knowledge/embedders/openai-embedder.md): Demonstrates OpenAI embeddings and knowledge insertion, including a batching variant.
- [Embedders](https://docs.agno.com/examples/knowledge/embedders/overview.md): Embedders convert text into vector representations for semantic search and knowledge retrieval. Agno supports multiple embedding providers to fit different deployment needs.
- [FastEmbed Embedder](https://docs.agno.com/examples/knowledge/embedders/qdrant-fastembed.md): Demonstrates FastEmbed embeddings and knowledge insertion.
- [Sentence Transformer Embedder](https://docs.agno.com/examples/knowledge/embedders/sentence-transformer-embedder.md): Demonstrates sentence-transformer embeddings and knowledge insertion.
- [Together Embedder](https://docs.agno.com/examples/knowledge/embedders/together-embedder.md): Demonstrates Together embeddings and knowledge insertion.
- [vLLM Local Embedder](https://docs.agno.com/examples/knowledge/embedders/vllm-embedder-local.md): Demonstrates local vLLM embeddings and knowledge insertion with standard and batching modes.
- [vLLM Remote Embedder](https://docs.agno.com/examples/knowledge/embedders/vllm-embedder-remote.md): Demonstrates remote vLLM embeddings and knowledge insertion with optional batching.
- [VoyageAI Embedder](https://docs.agno.com/examples/knowledge/embedders/voyageai-embedder.md): Demonstrates VoyageAI embeddings and knowledge insertion, including a batching variant.
- [Agentic Filtering](https://docs.agno.com/examples/knowledge/filters/agentic-filtering.md): Run Agentic Filtering.
- [Agentic Filtering With Output Schema](https://docs.agno.com/examples/knowledge/filters/agentic-filtering-with-output-schema.md): Run Agentic Filtering With Output Schema.
- [Async Agentic Filtering](https://docs.agno.com/examples/knowledge/filters/async-agentic-filtering.md): Run Async Agentic Filtering.
- [Async Filtering](https://docs.agno.com/examples/knowledge/filters/async-filtering.md): Run Async Filtering.
- [Filtering](https://docs.agno.com/examples/knowledge/filters/filtering.md): Run Filtering.
- [Filtering On Load](https://docs.agno.com/examples/knowledge/filters/filtering-on-load.md): Run Filtering On Load.
- [This example demonstrates how to use knowledge filter expressions with agents.](https://docs.agno.com/examples/knowledge/filters/filtering-with-conditions-on-agent.md): Knowledge filters allow you to restrict knowledge searches to specific documents or metadata criteria, enabling personalized and contextual responses.
- [This example demonstrates how to use knowledge filter expressions with teams.](https://docs.agno.com/examples/knowledge/filters/filtering-with-conditions-on-team.md): Knowledge filters allow you to restrict knowledge searches to specific documents or metadata criteria, enabling personalized and contextual responses.
- [Filtering With Invalid Keys](https://docs.agno.com/examples/knowledge/filters/filtering-with-invalid-keys.md): Run Filtering With Invalid Keys.
- [Filtering Chroma Db](https://docs.agno.com/examples/knowledge/filters/vector-dbs/filtering-chroma-db.md): Run Filtering Chroma Db.
- [Filtering Lance Db](https://docs.agno.com/examples/knowledge/filters/vector-dbs/filtering-lance-db.md): Run Filtering Lance Db.
- [Filtering Milvus](https://docs.agno.com/examples/knowledge/filters/vector-dbs/filtering-milvus.md): Run Filtering Milvus.
- [Filtering Mongo Db](https://docs.agno.com/examples/knowledge/filters/vector-dbs/filtering-mongo-db.md): Run Filtering Mongo Db.
- [Filtering Pgvector](https://docs.agno.com/examples/knowledge/filters/vector-dbs/filtering-pgvector.md): Run Filtering Pgvector.
- [Filtering Pinecone](https://docs.agno.com/examples/knowledge/filters/vector-dbs/filtering-pinecone.md): Run Filtering Pinecone.
- [Filtering Qdrant Db](https://docs.agno.com/examples/knowledge/filters/vector-dbs/filtering-qdrant-db.md): Run Filtering Qdrant Db.
- [Filtering Surrealdb](https://docs.agno.com/examples/knowledge/filters/vector-dbs/filtering-surrealdb.md): Run Filtering Surrealdb.
- [Filtering Weaviate](https://docs.agno.com/examples/knowledge/filters/vector-dbs/filtering-weaviate.md): Run Filtering Weaviate.
- [Vector Dbs](https://docs.agno.com/examples/knowledge/filters/vector-dbs/overview.md): This directory contains Agno knowledge cookbook examples for vector_dbs.
- [Multiple Knowledge Instances in AgentOS](https://docs.agno.com/examples/knowledge/os/multiple-knowledge-instances.md): This cookbook demonstrates how to configure multiple Knowledge instances in AgentOS, each with isolated content.
- [Os](https://docs.agno.com/examples/knowledge/os/overview.md): Examples for Os.
- [Knowledge](https://docs.agno.com/examples/knowledge/overview.md): **Knowledge Base:** is information that the Agent can search to improve its responses. This directory contains a series of cookbooks that demonstrate how to build a knowledge base.
- [FileSystemKnowledge Example](https://docs.agno.com/examples/knowledge/protocol/file-system.md): Demonstrates using FileSystemKnowledge to let an agent search local files.
- [Protocol](https://docs.agno.com/examples/knowledge/protocol/overview.md): This directory contains Agno knowledge cookbook examples for protocol.
- [Batching](https://docs.agno.com/examples/knowledge/quickstart/batching.md): Demonstrates knowledge insertion with batch embeddings using sync and async APIs.
- [From GCS](https://docs.agno.com/examples/knowledge/quickstart/from-gcs.md): Demonstrates loading knowledge from GCS remote content using sync and async inserts.
- [From Multiple Sources](https://docs.agno.com/examples/knowledge/quickstart/from-multiple.md): Demonstrates loading knowledge from multiple paths and URLs using sync and async operations.
- [From Path](https://docs.agno.com/examples/knowledge/quickstart/from-path.md): Demonstrates loading knowledge from a local file path using sync and async inserts.
- [From S3](https://docs.agno.com/examples/knowledge/quickstart/from-s3.md): Demonstrates loading knowledge from S3 remote content using sync and async inserts.
- [From Topic](https://docs.agno.com/examples/knowledge/quickstart/from-topic.md): Demonstrates loading topics from Wikipedia and Arxiv using sync and async operations.
- [From URL](https://docs.agno.com/examples/knowledge/quickstart/from-url.md): Demonstrates loading knowledge from a URL using sync and async inserts.
- [From YouTube](https://docs.agno.com/examples/knowledge/quickstart/from-youtube.md): Demonstrates loading knowledge from a YouTube URL using sync and async inserts.
- [Include And Exclude Files](https://docs.agno.com/examples/knowledge/quickstart/include-exclude-files.md): Demonstrates include and exclude filters when loading directory content into knowledge.
- [Demonstrates knowledge isolation with isolate_vector_search flag.](https://docs.agno.com/examples/knowledge/quickstart/isolate-vector-search.md): When multiple Knowledge instances share the same vector database, you can use.
- [Knowledge Instructions](https://docs.agno.com/examples/knowledge/quickstart/knowledge-instructions.md): Demonstrates disabling automatic search-knowledge instructions on an agent.
- [Quickstart](https://docs.agno.com/examples/knowledge/quickstart/overview.md): This directory contains Agno knowledge cookbook examples for 01_quickstart.
- [Remove Content](https://docs.agno.com/examples/knowledge/quickstart/remove-content.md): Demonstrates removing knowledge content by id and clearing all content, with sync and async APIs.
- [Remove Vectors](https://docs.agno.com/examples/knowledge/quickstart/remove-vectors.md): Demonstrates removing vectors by metadata and by name using sync and async insert flows.
- [Skip If Exists](https://docs.agno.com/examples/knowledge/quickstart/skip-if-exists.md): Demonstrates skip-if-exists behavior for repeated knowledge inserts with sync and async APIs.
- [Skip If Exists With Contents DB](https://docs.agno.com/examples/knowledge/quickstart/skip-if-exists-contentsdb.md): Demonstrates adding existing vector content into a contents database using skip-if-exists.
- [Specify Reader](https://docs.agno.com/examples/knowledge/quickstart/specify-reader.md): Demonstrates setting a specific reader during knowledge insertion with sync and async APIs.
- [Text Content](https://docs.agno.com/examples/knowledge/quickstart/text-content.md): Demonstrates adding direct text content to knowledge using sync and async APIs.
- [Arxiv Reader](https://docs.agno.com/examples/knowledge/readers/arxiv-reader.md): Run Arxiv Reader.
- [Arxiv Reader Async](https://docs.agno.com/examples/knowledge/readers/arxiv-reader-async.md): Run Arxiv Reader Async.
- [Field Labeled CSV Reader](https://docs.agno.com/examples/knowledge/readers/csv-field-labeled-reader.md): Demonstrates field-labeled CSV ingestion for movie metadata.
- [Csv Reader](https://docs.agno.com/examples/knowledge/readers/csv-reader.md): Run Csv Reader.
- [Csv Reader Async](https://docs.agno.com/examples/knowledge/readers/csv-reader-async.md): Run Csv Reader Async.
- [Csv Reader Custom Encodings](https://docs.agno.com/examples/knowledge/readers/csv-reader-custom-encodings.md): Run Csv Reader Custom Encodings.
- [Csv Reader Url Async](https://docs.agno.com/examples/knowledge/readers/csv-reader-url-async.md): Run Csv Reader Url Async.
- [Doc Kb Async](https://docs.agno.com/examples/knowledge/readers/doc-kb-async.md): Run Doc Kb Async.
- [Docling Multiple Formats](https://docs.agno.com/examples/knowledge/readers/docling-multi-formats.md): Process multiple document formats with Docling Reader.
- [Docling Reader](https://docs.agno.com/examples/knowledge/readers/docling-reader.md): Process documents with Docling Reader.
- [Docling Reader Async](https://docs.agno.com/examples/knowledge/readers/docling-reader-async.md): Process documents asynchronously with Docling Reader.
- [Docling Reader URL](https://docs.agno.com/examples/knowledge/readers/docling-reader-url.md): Process documents from URLs with Docling Reader.
- [Excel Legacy Xls](https://docs.agno.com/examples/knowledge/readers/excel-legacy-xls.md): Run Excel Legacy Xls.
- [Excel Reader](https://docs.agno.com/examples/knowledge/readers/excel-reader.md): Run Excel Reader.
- [Firecrawl Reader](https://docs.agno.com/examples/knowledge/readers/firecrawl-reader.md): Run Firecrawl Reader.
- [Json Reader](https://docs.agno.com/examples/knowledge/readers/json-reader.md): Run Json Reader.
- [Markdown Reader Async](https://docs.agno.com/examples/knowledge/readers/markdown-reader-async.md): Run Markdown Reader Async.
- [Md Reader Async](https://docs.agno.com/examples/knowledge/readers/md-reader-async.md): Run Md Reader Async.
- [Readers](https://docs.agno.com/examples/knowledge/readers/overview.md): Readers transform raw data into structured, searchable knowledge for your agents. Agno supports multiple document types and data sources.
- [Pdf Reader Async](https://docs.agno.com/examples/knowledge/readers/pdf-reader-async.md): Run Pdf Reader Async.
- [Pdf Reader Password](https://docs.agno.com/examples/knowledge/readers/pdf-reader-password.md): Run Pdf Reader Password.
- [Pdf Reader Url Password](https://docs.agno.com/examples/knowledge/readers/pdf-reader-url-password.md): Run Pdf Reader Url Password.
- [Pptx Reader](https://docs.agno.com/examples/knowledge/readers/pptx-reader.md): Run Pptx Reader.
- [Pptx Reader Async](https://docs.agno.com/examples/knowledge/readers/pptx-reader-async.md): Run Pptx Reader Async.
- [Tavily Reader](https://docs.agno.com/examples/knowledge/readers/tavily-reader.md): Run Tavily Reader.
- [Tavily Reader Async](https://docs.agno.com/examples/knowledge/readers/tavily-reader-async.md): Run Tavily Reader Async.
- [Web Reader](https://docs.agno.com/examples/knowledge/readers/web-reader.md): Run Web Reader.
- [Web Search Reader](https://docs.agno.com/examples/knowledge/readers/web-search-reader.md): Run Web Search Reader.
- [Web Search Reader Async](https://docs.agno.com/examples/knowledge/readers/web-search-reader-async.md): Run Web Search Reader Async.
- [Website Reader](https://docs.agno.com/examples/knowledge/readers/website-reader.md): Run Website Reader.
- [Hybrid Search](https://docs.agno.com/examples/knowledge/search-type/hybrid-search.md): Run Hybrid Search.
- [Keyword Search](https://docs.agno.com/examples/knowledge/search-type/keyword-search.md): Run Keyword Search.
- [Search Type](https://docs.agno.com/examples/knowledge/search-type/overview.md): Search strategies determine how your agents find relevant information in knowledge bases using different algorithms and approaches.
- [Vector Search](https://docs.agno.com/examples/knowledge/search-type/vector-search.md): Run Vector Search.
- [Cassandra Database](https://docs.agno.com/examples/knowledge/vector-db/cassandra-db/cassandra-db.md): Demonstrates Cassandra-backed knowledge with sync, async, and async-batching flows.
- [Chroma Database](https://docs.agno.com/examples/knowledge/vector-db/chroma-db/chroma-db.md): Demonstrates Chroma-backed knowledge with sync, async, and async-batching flows.
- [Chroma db hybrid search](https://docs.agno.com/examples/knowledge/vector-db/chroma-db/chroma-db-hybrid-search.md)
- [ClickHouse Database](https://docs.agno.com/examples/knowledge/vector-db/clickhouse-db/clickhouse.md): Demonstrates ClickHouse-backed knowledge with sync, async, and async-batching flows.
- [Couchbase Vector DB Example](https://docs.agno.com/examples/knowledge/vector-db/couchbase-db/couchbase-db.md): Setup Couchbase Cluster (Local via Docker) and run the Couchbase vector database example.
- [LanceDB Database](https://docs.agno.com/examples/knowledge/vector-db/lance-db/lance-db.md): Demonstrates LanceDB-backed knowledge with sync and async-batching flows.
- [LanceDB Cloud connection test.](https://docs.agno.com/examples/knowledge/vector-db/lance-db/lance-db-cloud.md): Requires environment variables: - LANCE_DB_URI: LanceDB Cloud database URI (e.g.
- [LanceDB Hybrid Search](https://docs.agno.com/examples/knowledge/vector-db/lance-db/lance-db-hybrid-search.md): Demonstrates hybrid search with LanceDB.
- [LanceDB With Mistral Embedder](https://docs.agno.com/examples/knowledge/vector-db/lance-db/lance-db-with-mistral-embedder.md): Demonstrates LanceDB hybrid search with the Mistral embedder.
- [LangChain Vector DB](https://docs.agno.com/examples/knowledge/vector-db/langchain/langchain-db.md): Install dependencies: - uv pip install langchain langchain-community langchain-openai langchain-chroma agno.
- [LightRAG Vector DB](https://docs.agno.com/examples/knowledge/vector-db/lightrag/lightrag.md): Demonstrates LightRAG-backed knowledge and retrieval with references.
- [LlamaIndex Vector DB](https://docs.agno.com/examples/knowledge/vector-db/llamaindex-db/llamaindex-db.md): Install dependencies: - uv pip install llama-index-core llama-index-readers-file llama-index-embeddings-openai agno.
- [Milvus Database](https://docs.agno.com/examples/knowledge/vector-db/milvus-db/milvus-db.md): Demonstrates Milvus-backed knowledge with sync, async, and async-batching flows.
- [Milvus Hybrid Search](https://docs.agno.com/examples/knowledge/vector-db/milvus-db/milvus-db-hybrid-search.md): Demonstrates Milvus hybrid search with sync and async flows.
- [Milvus Range Search](https://docs.agno.com/examples/knowledge/vector-db/milvus-db/milvus-db-range-search.md): Demonstrates Milvus range-search parameters (`radius`, `range_filter`) in sync and async calls.
- [Cosmos MongoDB vCore](https://docs.agno.com/examples/knowledge/vector-db/mongo-db/cosmos-mongodb-vcore.md): Demonstrates Cosmos DB (MongoDB vCore compatibility) as a vector DB backend.
- [MongoDB Vector DB](https://docs.agno.com/examples/knowledge/vector-db/mongo-db/mongo-db.md): - Go to https://www.mongodb.com/cloud/atlas/register.
- [MongoDB Hybrid Search](https://docs.agno.com/examples/knowledge/vector-db/mongo-db/mongo-db-hybrid-search.md): Demonstrates MongoDB vector + keyword hybrid retrieval.
- [PgVector Database](https://docs.agno.com/examples/knowledge/vector-db/pgvector/pgvector-db.md): Demonstrates PgVector-backed knowledge with sync, async, and async-batching flows.
- [PgVector Hybrid Search](https://docs.agno.com/examples/knowledge/vector-db/pgvector/pgvector-hybrid-search.md): Demonstrates PgVector hybrid search with conversational memory.
- [AWS Bedrock Reranker Example with PgVector](https://docs.agno.com/examples/knowledge/vector-db/pgvector/pgvector-with-bedrock-reranker.md): Demonstrates AWS Bedrock rerankers with PgVector for retrieval augmented generation.
- [Pinecone Database](https://docs.agno.com/examples/knowledge/vector-db/pinecone-db/pinecone-db.md): Demonstrates Pinecone-backed knowledge with sync and async-batching flows.
- [Qdrant Database](https://docs.agno.com/examples/knowledge/vector-db/qdrant-db/qdrant-db.md): Demonstrates Qdrant-backed knowledge with sync, async, and async-batching flows.
- [Qdrant Hybrid Search](https://docs.agno.com/examples/knowledge/vector-db/qdrant-db/qdrant-db-hybrid-search.md): Demonstrates Qdrant hybrid retrieval in an interactive loop.
- [Redis Vector DB](https://docs.agno.com/examples/knowledge/vector-db/redis-db/redis-db.md): Demonstrates Redis-backed knowledge with sync and async flows.
- [Redis With Cohere Reranker](https://docs.agno.com/examples/knowledge/vector-db/redis-db/redis-db-with-cohere-reranker.md): Demonstrates Redis vector retrieval with Cohere reranking.
- [SingleStore Vector DB](https://docs.agno.com/examples/knowledge/vector-db/singlestore-db/singlestore-db.md): Run setup script: `./cookbook/scripts/run_singlestore.sh`.
- [SurrealDB Vector DB](https://docs.agno.com/examples/knowledge/vector-db/surrealdb/surreal-db.md): Run SurrealDB before running this example: `docker run --rm --pull always -p 8000:8000 surrealdb/surrealdb:latest start --user root --pass root`.
- [Upstash Vector DB](https://docs.agno.com/examples/knowledge/vector-db/upstash-db/upstash-db.md): Install dependency: - uv pip install upstash-vector.
- [Weaviate Db](https://docs.agno.com/examples/knowledge/vector-db/weaviate-db/overview.md): This directory contains Agno knowledge cookbook examples for weaviate_db.
- [Weaviate Vector DB](https://docs.agno.com/examples/knowledge/vector-db/weaviate-db/weaviate-db.md): Demonstrates Weaviate-backed knowledge with sync, async, and async-batching flows.
- [Weaviate Hybrid Search](https://docs.agno.com/examples/knowledge/vector-db/weaviate-db/weaviate-db-hybrid-search.md): Demonstrates Weaviate hybrid retrieval with interactive querying.
- [Weaviate Upsert](https://docs.agno.com/examples/knowledge/vector-db/weaviate-db/weaviate-db-upsert.md): Demonstrates repeated inserts with `skip_if_exists` in Weaviate.
- [Entity Memory: Always Mode](https://docs.agno.com/examples/learning/basics/a-entity-memory-always.md): Entity Memory stores knowledge about external things: - Companies, people, projects - Facts, events, relationships - Shared context across users.
- [Session Context: Summary Mode](https://docs.agno.com/examples/learning/basics/a-session-context-summary.md): Session Context tracks the current conversation's state: - What's been discussed - Key decisions made - Important context.
- [User Memory: Always Mode](https://docs.agno.com/examples/learning/basics/a-user-memory-always.md): User Memory captures unstructured observations about users.
- [User Profile: Always Mode](https://docs.agno.com/examples/learning/basics/a-user-profile-always.md): User Profile captures structured profile fields about users: - Name and preferred name - Custom profile fields (when using extended schemas).
- [Entity Memory: Agentic Mode](https://docs.agno.com/examples/learning/basics/b-entity-memory-agentic.md): Entity Memory stores knowledge about external things: - Companies, people, projects - Facts, events, relationships - Shared context across users.
- [Session Context: Planning Mode](https://docs.agno.com/examples/learning/basics/b-session-context-planning.md): Session Context tracks the current conversation's state: - What's been discussed - Current goals and their status - Active plans and progress.
- [User Memory: Agentic Mode](https://docs.agno.com/examples/learning/basics/b-user-memory-agentic.md): User Memory captures unstructured observations about users.
- [User Profile: Agentic Mode](https://docs.agno.com/examples/learning/basics/b-user-profile-agentic.md): User Profile captures structured profile fields about users: - Name and preferred name - Custom profile fields (when using extended schemas).
- [Learned Knowledge: Agentic Mode](https://docs.agno.com/examples/learning/basics/learned-knowledge.md): Learned Knowledge stores reusable insights that apply across users.
- [Basics](https://docs.agno.com/examples/learning/basics/overview.md): Core learning primitives and default patterns.
- [Custom Store: Database-Backed Example](https://docs.agno.com/examples/learning/custom-stores/custom-store-with-db.md): Shows how to create a custom learning store with database persistence.
- [Custom Store: Minimal Example](https://docs.agno.com/examples/learning/custom-stores/minimal-custom-store.md): Shows how to create a custom learning store by implementing the LearningStore protocol.
- [Custom Stores](https://docs.agno.com/examples/learning/custom-stores/overview.md): Custom learning store implementations and integration patterns.
- [Decision Logs: Basic Usage](https://docs.agno.com/examples/learning/decision-logs/basic-decision-log.md): This example demonstrates how to use DecisionLogStore to record and retrieve agent decisions.
- [Decision Logs: ALWAYS Mode (Automatic Logging)](https://docs.agno.com/examples/learning/decision-logs/decision-log-always.md): This example demonstrates automatic decision logging where tool calls are automatically recorded as decisions.
- [Decision Logs](https://docs.agno.com/examples/learning/decision-logs/overview.md): Examples for capturing and reviewing agent decision logs.
- [Entity Memory: Relationships (Deep Dive)](https://docs.agno.com/examples/learning/entity-memory/entity-relationships.md): Graph edges between entities.
- [Entity Memory: Facts and Events (Deep Dive)](https://docs.agno.com/examples/learning/entity-memory/facts-and-events.md): Semantic (facts) vs episodic (events) memory for entities.
- [Entity Memory](https://docs.agno.com/examples/learning/entity-memory/overview.md): Deep-dive examples for entity memory, facts, events, and relationships.
- [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](https://docs.agno.com/examples/learning/learned-knowledge/overview.md): Deep-dive examples for reusable learned knowledge.
- [Learned Knowledge: Propose Mode (Deep Dive)](https://docs.agno.com/examples/learning/learned-knowledge/propose-mode.md): Agent proposes learnings, user confirms before saving.
- [Learning](https://docs.agno.com/examples/learning/overview.md): A comprehensive guide to building agents that learn, adapt, and improve.
- [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.
- [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.
- [Quick Tests](https://docs.agno.com/examples/learning/quick-tests/overview.md): Quick validation scripts for critical learning paths.
- [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.
- [Quickstart](https://docs.agno.com/examples/learning/quickstart/overview.md): Quick start examples for enabling learning in an agent.
- [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: 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.
- [User Profile](https://docs.agno.com/examples/learning/user-profile/overview.md): Deep-dive examples focused on user profile extraction and schema control.
- [Agent With Persistent Memory](https://docs.agno.com/examples/memory/agent-with-memory.md): This example shows how to use persistent memory with an Agent.
- [Agentic Memory Management](https://docs.agno.com/examples/memory/agentic-memory.md): This example shows how to use agentic memory with an Agent.
- [Agents Sharing Memory](https://docs.agno.com/examples/memory/agents-share-memory.md): This example shows two agents sharing the same user memory.
- [Custom Memory Manager Configuration](https://docs.agno.com/examples/memory/custom-memory-manager.md): This example shows how to configure a MemoryManager separately from the Agent and apply custom memory capture instructions.
- [Custom Memory Capture Instructions](https://docs.agno.com/examples/memory/memory-manager/custom-memory-instructions.md): This example shows how to customize memory capture instructions and compare the results with a default memory manager.
- [Control Memory Database Tools](https://docs.agno.com/examples/memory/memory-manager/db-tools-control.md): This example demonstrates how to 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): This example shows how to 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): This example shows how to search user memories using different retrieval methods such as last_n, first_n, and agentic retrieval.
- [Memory Manager](https://docs.agno.com/examples/memory/memory-manager/overview.md): The Memory Manager handles user memory CRUD and retrieval operations.
- [Standalone Memory Manager CRUD](https://docs.agno.com/examples/memory/memory-manager/standalone-memory.md): This example shows how to add, get, delete, and replace user memories manually.
- [Memory Tools With Web Search](https://docs.agno.com/examples/memory/memory-tools.md): This example shows how to 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): This example demonstrates 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): This example runs multiple user conversations concurrently while persisting memory per user and session.
- [Custom Memory Optimization Strategy](https://docs.agno.com/examples/memory/optimize-memories/custom-memory-strategy.md): This example shows how to 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): This example demonstrates memory optimization using the summarize strategy, which combines all memories into one summary for token reduction.
- [Optimize Memories](https://docs.agno.com/examples/memory/optimize-memories/overview.md): This directory demonstrates memory optimization strategies.
- [Memory](https://docs.agno.com/examples/memory/overview.md): This section demonstrates how Agno agents persist and use user memories across runs, sessions, and agents.
- [Share Memory and History Between Agents](https://docs.agno.com/examples/memory/share-memory-and-history-between-agents.md): This example shows two agents sharing both conversation history and user memory through a common database, user ID, and session ID.
- [Aimlapi Basic](https://docs.agno.com/examples/models/aimlapi/basic.md): Cookbook example for `aimlapi/basic.py`.
- [Aimlapi Image Agent](https://docs.agno.com/examples/models/aimlapi/image-agent.md): Cookbook example for `aimlapi/image_agent.py`.
- [Aimlapi Image Agent Bytes](https://docs.agno.com/examples/models/aimlapi/image-agent-bytes.md): Cookbook example for `aimlapi/image_agent_bytes.py`.
- [Aimlapi Image Agent With Memory](https://docs.agno.com/examples/models/aimlapi/image-agent-with-memory.md): Cookbook example for `aimlapi/image_agent_with_memory.py`.
- [Aimlapi](https://docs.agno.com/examples/models/aimlapi/overview.md): Aimlapi model example.
- [Example demonstrating how to set up retries with AIMLAPI.](https://docs.agno.com/examples/models/aimlapi/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Aimlapi Structured Output](https://docs.agno.com/examples/models/aimlapi/structured-output.md): Cookbook example for `aimlapi/structured_output.py`.
- [Aimlapi Tool Use](https://docs.agno.com/examples/models/aimlapi/tool-use.md): Aimlapi model example.
- [Anthropic Basic](https://docs.agno.com/examples/models/anthropic/basic.md): Cookbook example for `anthropic/basic.py`.
- [Anthropic Basic With Timeout](https://docs.agno.com/examples/models/anthropic/basic-with-timeout.md): Cookbook example for `anthropic/basic_with_timeout.py`.
- [Example demonstrating how to use Anthropic beta features.](https://docs.agno.com/examples/models/anthropic/betas.md): Beta features are experimental capability extensions for Anthropic models.
- [Anthropic Code Execution](https://docs.agno.com/examples/models/anthropic/code-execution.md): Cookbook example for `anthropic/code_execution.py`.
- [Self-managed Context Management](https://docs.agno.com/examples/models/anthropic/context-management.md): This cookbook demonstrates Claude's context management feature for automatic tool result clearing.
- [Anthropic Csv Input](https://docs.agno.com/examples/models/anthropic/csv-input.md): Cookbook example for `anthropic/csv_input.py`.
- [Anthropic Db](https://docs.agno.com/examples/models/anthropic/db.md): Anthropic model example.
- [Anthropic Financial Analyst Thinking](https://docs.agno.com/examples/models/anthropic/financial-analyst-thinking.md): Cookbook example for `anthropic/financial_analyst_thinking.py`.
- [Anthropic Image Input Bytes](https://docs.agno.com/examples/models/anthropic/image-input-bytes.md): Cookbook example for `anthropic/image_input_bytes.py`.
- [Anthropic Image Input File Upload](https://docs.agno.com/examples/models/anthropic/image-input-file-upload.md): Download the file using the download_file function.
- [Anthropic Image Input Local File](https://docs.agno.com/examples/models/anthropic/image-input-local-file.md): Download the file using the download_file function.
- [Anthropic Image Input Url](https://docs.agno.com/examples/models/anthropic/image-input-url.md): Cookbook example for `anthropic/image_input_url.py`.
- [Anthropic Knowledge](https://docs.agno.com/examples/models/anthropic/knowledge.md): Add content to the knowledge.
- [Anthropic Mcp Connector](https://docs.agno.com/examples/models/anthropic/mcp-connector.md): Cookbook example for `anthropic/mcp_connector.py`.
- [This recipe shows how to use personalized memories and summaries in an agent.](https://docs.agno.com/examples/models/anthropic/memory.md): Agent memory and session persistence with Anthropic.
- [Anthropic Pdf Input Bytes](https://docs.agno.com/examples/models/anthropic/pdf-input-bytes.md): Cookbook example for `anthropic/pdf_input_bytes.py`.
- [Anthropic Pdf Input File Upload](https://docs.agno.com/examples/models/anthropic/pdf-input-file-upload.md): Download the file using the download_file function.
- [Anthropic Pdf Input Local](https://docs.agno.com/examples/models/anthropic/pdf-input-local.md): Cookbook example for `anthropic/pdf_input_local.py`.
- [Anthropic Pdf Input Url](https://docs.agno.com/examples/models/anthropic/pdf-input-url.md): Cookbook example for `anthropic/pdf_input_url.py`.
- [Anthropic Prompt Caching](https://docs.agno.com/examples/models/anthropic/prompt-caching.md): This can significantly reduce processing time and costs.
- [Anthropic Prompt Caching Extended](https://docs.agno.com/examples/models/anthropic/prompt-caching-extended.md): You can check more about extended prompt caching with Anthropic models here: https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching#1-hour-cache-duration-...
- [Example demonstrating how to set up retries with Anthropic Claude.](https://docs.agno.com/examples/models/anthropic/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Agno Agent with Word Document Skills.](https://docs.agno.com/examples/models/anthropic/skills/agent-with-documents.md): This cookbook demonstrates how to 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): This cookbook demonstrates how to 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): This cookbook demonstrates how to 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): This cookbook demonstrates how to create an agent with multiple Claude Agent Skills.
- [Skills](https://docs.agno.com/examples/models/anthropic/skills/overview.md): These skills use a **progressive disclosure** architecture - Claude first discovers which skills are relevant, then loads full instructions only when needed.
- [Anthropic Structured Output](https://docs.agno.com/examples/models/anthropic/structured-output.md): Cookbook example for `anthropic/structured_output.py`.
- [Example demonstrating strict tool use with Anthropic structured outputs.](https://docs.agno.com/examples/models/anthropic/structured-output-strict-tools.md): Strict tool use ensures that tool parameters strictly follow the input_schema.
- [Anthropic Thinking](https://docs.agno.com/examples/models/anthropic/thinking.md): Cookbook example for `anthropic/thinking.py`.
- [Anthropic Tool Use](https://docs.agno.com/examples/models/anthropic/tool-use.md): Anthropic model example.
- [Anthropic Web Fetch](https://docs.agno.com/examples/models/anthropic/web-fetch.md): Cookbook example for `anthropic/web_fetch.py`.
- [Anthropic Web Search](https://docs.agno.com/examples/models/anthropic/web-search.md): Cookbook example for `anthropic/web_search.py`.
- [Aws Basic](https://docs.agno.com/examples/models/aws/bedrock/basic.md): Cookbook example for `aws/bedrock/basic.py`.
- [Aws Image Agent Bytes](https://docs.agno.com/examples/models/aws/bedrock/image-agent-bytes.md): Cookbook example for `aws/bedrock/image_agent_bytes.py`.
- [Aws Pdf Agent Bytes](https://docs.agno.com/examples/models/aws/bedrock/pdf-agent-bytes.md): Cookbook example for `aws/bedrock/pdf_agent_bytes.py`.
- [Aws Structured Output](https://docs.agno.com/examples/models/aws/bedrock/structured-output.md): Cookbook example for `aws/bedrock/structured_output.py`.
- [Bedrock Tool Use](https://docs.agno.com/examples/models/aws/bedrock/tool-use.md): Aws model example.
- [Aws Basic](https://docs.agno.com/examples/models/aws/claude/basic.md): Cookbook example for `aws/claude/basic.py`.
- [Claude Db](https://docs.agno.com/examples/models/aws/claude/db.md): Aws model example.
- [Aws Image Agent](https://docs.agno.com/examples/models/aws/claude/image-agent.md): Cookbook example for `aws/claude/image_agent.py`.
- [Claude Knowledge](https://docs.agno.com/examples/models/aws/claude/knowledge.md): Add content to the knowledge.
- [Aws Structured Output](https://docs.agno.com/examples/models/aws/claude/structured-output.md): Cookbook example for `aws/claude/structured_output.py`.
- [Claude Tool Use](https://docs.agno.com/examples/models/aws/claude/tool-use.md): Aws model example.
- [Aws](https://docs.agno.com/examples/models/aws/overview.md): Cookbook examples for `cookbook/90_models/aws`.
- [Example demonstrating how to set up retries with AWS Bedrock.](https://docs.agno.com/examples/models/aws/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Azure Basic](https://docs.agno.com/examples/models/azure/ai-foundry/basic.md): Cookbook example for `azure/ai_foundry/basic.py`.
- [Ai Foundry Db](https://docs.agno.com/examples/models/azure/ai-foundry/db.md): Azure model example.
- [Azure Demo Cohere](https://docs.agno.com/examples/models/azure/ai-foundry/demo-cohere.md): Cookbook example for `azure/ai_foundry/demo_cohere.py`.
- [Azure Demo Mistral](https://docs.agno.com/examples/models/azure/ai-foundry/demo-mistral.md): Cookbook example for `azure/ai_foundry/demo_mistral.py`.
- [Azure Image Agent](https://docs.agno.com/examples/models/azure/ai-foundry/image-agent.md): Cookbook example for `azure/ai_foundry/image_agent.py`.
- [Azure Image Agent Bytes](https://docs.agno.com/examples/models/azure/ai-foundry/image-agent-bytes.md): Cookbook example for `azure/ai_foundry/image_agent_bytes.py`.
- [Ai Foundry Knowledge](https://docs.agno.com/examples/models/azure/ai-foundry/knowledge.md): Add content to the knowledge.
- [Azure Structured Output](https://docs.agno.com/examples/models/azure/ai-foundry/structured-output.md): Cookbook example for `azure/ai_foundry/structured_output.py`.
- [Ai Foundry Tool Use](https://docs.agno.com/examples/models/azure/ai-foundry/tool-use.md): Azure model example.
- [Azure Basic](https://docs.agno.com/examples/models/azure/openai/basic.md): Cookbook example for `azure/openai/basic.py`.
- [Openai Db](https://docs.agno.com/examples/models/azure/openai/db.md): Azure model example.
- [Openai Knowledge](https://docs.agno.com/examples/models/azure/openai/knowledge.md): Add content to the knowledge.
- [Azure Structured Output](https://docs.agno.com/examples/models/azure/openai/structured-output.md): Cookbook example for `azure/openai/structured_output.py`.
- [Openai Tool Use](https://docs.agno.com/examples/models/azure/openai/tool-use.md): Azure model example.
- [Azure](https://docs.agno.com/examples/models/azure/overview.md): Cookbook examples for `cookbook/90_models/azure`.
- [Example demonstrating how to set up retries with Azure AI Foundry.](https://docs.agno.com/examples/models/azure/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Cerebras Openai Basic](https://docs.agno.com/examples/models/cerebras-openai/basic.md): Cookbook example for `cerebras_openai/basic.py`.
- [Cerebras Openai Db](https://docs.agno.com/examples/models/cerebras-openai/db.md): Cerebras Openai model example.
- [Cerebras Openai Knowledge](https://docs.agno.com/examples/models/cerebras-openai/knowledge.md): Add content to the knowledge.
- [Cerebras Openai Oss Gpt](https://docs.agno.com/examples/models/cerebras-openai/oss-gpt.md): Cookbook example for `cerebras_openai/oss_gpt.py`.
- [Cerebras Openai](https://docs.agno.com/examples/models/cerebras-openai/overview.md): Cookbook examples for `cookbook/90_models/cerebras_openai`.
- [Cerebras Openai Structured Output](https://docs.agno.com/examples/models/cerebras-openai/structured-output.md): Cookbook example for `cerebras_openai/structured_output.py`.
- [Cerebras Openai Tool Use](https://docs.agno.com/examples/models/cerebras-openai/tool-use.md): Cookbook example for `cerebras_openai/tool_use.py`.
- [Cerebras Basic](https://docs.agno.com/examples/models/cerebras/basic.md): Cookbook example for `cerebras/basic.py`.
- [Cerebras Db](https://docs.agno.com/examples/models/cerebras/db.md): Cerebras model example.
- [Cerebras Knowledge](https://docs.agno.com/examples/models/cerebras/knowledge.md): Add content to the knowledge.
- [Cerebras Oss Gpt](https://docs.agno.com/examples/models/cerebras/oss-gpt.md): Cookbook example for `cerebras/oss_gpt.py`.
- [Cerebras](https://docs.agno.com/examples/models/cerebras/overview.md): Cookbook examples for `cookbook/90_models/cerebras`.
- [Example demonstrating how to set up retries with Cerebras.](https://docs.agno.com/examples/models/cerebras/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Cerebras Structured Output](https://docs.agno.com/examples/models/cerebras/structured-output.md): Cookbook example for `cerebras/structured_output.py`.
- [Cerebras Tool Use](https://docs.agno.com/examples/models/cerebras/tool-use.md): Cookbook example for `cerebras/tool_use.py`.
- [Global HTTP Client Customization (Cookbook)](https://docs.agno.com/examples/models/clients/http-client-caching.md): Demonstrates how to define a single global `httpx.Client`.
- [Clients](https://docs.agno.com/examples/models/clients/overview.md): Cookbook examples for `cookbook/90_models/clients`.
- [Cohere Basic](https://docs.agno.com/examples/models/cohere/basic.md): Cookbook example for `cohere/basic.py`.
- [Cohere Db](https://docs.agno.com/examples/models/cohere/db.md): Cohere model example.
- [Cohere Image Agent](https://docs.agno.com/examples/models/cohere/image-agent.md): Cookbook example for `cohere/image_agent.py`.
- [Cohere Image Agent Bytes](https://docs.agno.com/examples/models/cohere/image-agent-bytes.md): Cookbook example for `cohere/image_agent_bytes.py`.
- [Cohere Image Agent Local File](https://docs.agno.com/examples/models/cohere/image-agent-local-file.md): Cookbook example for `cohere/image_agent_local_file.py`.
- [Cohere Knowledge](https://docs.agno.com/examples/models/cohere/knowledge.md): Add content to the knowledge.
- [This recipe shows how to use personalized memories and summaries in an agent.](https://docs.agno.com/examples/models/cohere/memory.md): Agent memory and session persistence with Cohere.
- [Cohere](https://docs.agno.com/examples/models/cohere/overview.md): Cohere model example.
- [Example demonstrating how to set up retries with Cohere.](https://docs.agno.com/examples/models/cohere/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Cohere Structured Output](https://docs.agno.com/examples/models/cohere/structured-output.md): Cookbook example for `cohere/structured_output.py`.
- [Cohere Tool Use](https://docs.agno.com/examples/models/cohere/tool-use.md): Cohere model example.
- [Cometapi Basic](https://docs.agno.com/examples/models/cometapi/basic.md): Cookbook example for `cometapi/basic.py`.
- [Image analysis example using CometAPI with vision models.](https://docs.agno.com/examples/models/cometapi/image-agent.md): Use a vision-capable model from CometAPI.
- [Image analysis with memory example using CometAPI.](https://docs.agno.com/examples/models/cometapi/image-agent-with-memory.md): GPT-4o has vision capabilities.
- [Example showcasing different models available through CometAPI.](https://docs.agno.com/examples/models/cometapi/multi-model.md): Test different model categories.
- [Cometapi](https://docs.agno.com/examples/models/cometapi/overview.md): This cookbook demonstrates how to use CometAPI with the Agno framework. CometAPI provides unified access to multiple LLM providers (GPT, Claude, Gemini, DeepSeek, Qwen, and more) t.
- [Example demonstrating how to set up retries with CometAPI.](https://docs.agno.com/examples/models/cometapi/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Cometapi Structured Output](https://docs.agno.com/examples/models/cometapi/structured-output.md): Cookbook example for `cometapi/structured_output.py`.
- [Cometapi Tool Use](https://docs.agno.com/examples/models/cometapi/tool-use.md): Cookbook example for `cometapi/tool_use.py`.
- [Dashscope Basic](https://docs.agno.com/examples/models/dashscope/basic.md): Cookbook example for `dashscope/basic.py`.
- [Dashscope Image Agent](https://docs.agno.com/examples/models/dashscope/image-agent.md): Cookbook example for `dashscope/image_agent.py`.
- [Dashscope Image Agent Bytes](https://docs.agno.com/examples/models/dashscope/image-agent-bytes.md): Cookbook example for `dashscope/image_agent_bytes.py`.
- [Dashscope Knowledge Tools](https://docs.agno.com/examples/models/dashscope/knowledge-tools.md): Dashscope model example.
- [Dashscope](https://docs.agno.com/examples/models/dashscope/overview.md): Dashscope model example.
- [Example demonstrating how to set up retries with DashScope.](https://docs.agno.com/examples/models/dashscope/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Dashscope Structured Output](https://docs.agno.com/examples/models/dashscope/structured-output.md): Cookbook example for `dashscope/structured_output.py`.
- [Dashscope Thinking Agent](https://docs.agno.com/examples/models/dashscope/thinking-agent.md): Cookbook example for `dashscope/thinking_agent.py`.
- [Dashscope Tool Use](https://docs.agno.com/examples/models/dashscope/tool-use.md): Cookbook example for `dashscope/tool_use.py`.
- [Deepinfra Basic](https://docs.agno.com/examples/models/deepinfra/basic.md): Cookbook example for `deepinfra/basic.py`.
- [Deepinfra Json Output](https://docs.agno.com/examples/models/deepinfra/json-output.md): Cookbook example for `deepinfra/json_output.py`.
- [Deepinfra](https://docs.agno.com/examples/models/deepinfra/overview.md): Deepinfra model example.
- [Example demonstrating how to set up retries with DeepInfra.](https://docs.agno.com/examples/models/deepinfra/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Deepinfra Tool Use](https://docs.agno.com/examples/models/deepinfra/tool-use.md): Deepinfra model example.
- [Deepseek Basic](https://docs.agno.com/examples/models/deepseek/basic.md): Cookbook example for `deepseek/basic.py`.
- [Deepseek](https://docs.agno.com/examples/models/deepseek/overview.md): Deepseek model example.
- [Deepseek Reasoning Agent](https://docs.agno.com/examples/models/deepseek/reasoning-agent.md): Cookbook example for `deepseek/reasoning_agent.py`.
- [Example demonstrating how to set up retries with DeepSeek.](https://docs.agno.com/examples/models/deepseek/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Deepseek Structured Output](https://docs.agno.com/examples/models/deepseek/structured-output.md): Cookbook example for `deepseek/structured_output.py`.
- [Deepseek Thinking Tool Calls](https://docs.agno.com/examples/models/deepseek/thinking-tool-calls.md): Deepseek model example.
- [Deepseek Tool Use](https://docs.agno.com/examples/models/deepseek/tool-use.md): Deepseek model example.
- [Fireworks Basic](https://docs.agno.com/examples/models/fireworks/basic.md): Cookbook example for `fireworks/basic.py`.
- [Fireworks](https://docs.agno.com/examples/models/fireworks/overview.md): Fireworks model example.
- [Example demonstrating how to set up retries with Fireworks.](https://docs.agno.com/examples/models/fireworks/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Fireworks Structured Output](https://docs.agno.com/examples/models/fireworks/structured-output.md): Cookbook example for `fireworks/structured_output.py`.
- [Fireworks Tool Use](https://docs.agno.com/examples/models/fireworks/tool-use.md): Fireworks model example.
- [An example of how to use the thinking budget parameter with the Gemini model.](https://docs.agno.com/examples/models/google/gemini/agent-with-thinking-budget.md): This requires `google-genai > 1.10.0`.
- [Google Audio Input Bytes Content](https://docs.agno.com/examples/models/google/gemini/audio-input-bytes-content.md): Cookbook example for `google/gemini/audio_input_bytes_content.py`.
- [Google Audio Input File Upload](https://docs.agno.com/examples/models/google/gemini/audio-input-file-upload.md): Cookbook example for `google/gemini/audio_input_file_upload.py`.
- [Google Audio Input Local File Upload](https://docs.agno.com/examples/models/google/gemini/audio-input-local-file-upload.md): Cookbook example for `google/gemini/audio_input_local_file_upload.py`.
- [Google Basic](https://docs.agno.com/examples/models/google/gemini/basic.md): Cookbook example for `google/gemini/basic.py`.
- [Gemini Db](https://docs.agno.com/examples/models/google/gemini/db.md): Google model example.
- [Example: Analyze files from public HTTPS URLs.](https://docs.agno.com/examples/models/google/gemini/external-url-input.md): The Gemini API now supports external HTTPS URLs (up to 100MB).
- [Google File Search Advanced](https://docs.agno.com/examples/models/google/gemini/file-search-advanced.md): Cookbook example for `google/gemini/file_search_advanced.py`.
- [Google File Search Basic](https://docs.agno.com/examples/models/google/gemini/file-search-basic.md): Cookbook example for `google/gemini/file_search_basic.py`.
- [Google File Search Rag Pipeline](https://docs.agno.com/examples/models/google/gemini/file-search-rag-pipeline.md): Cookbook example for `google/gemini/file_search_rag_pipeline.py`.
- [In this example, we upload a text file to Google and then create a cache.](https://docs.agno.com/examples/models/google/gemini/file-upload-with-cache.md): This greatly saves on tokens during normal prompting.
- [Example: Analyze files directly from Google Cloud Storage (GCS).](https://docs.agno.com/examples/models/google/gemini/gcs-file-input.md): The Gemini API now supports GCS URIs natively (up to 2GB).
- [Async example using Gemini with tool calls.](https://docs.agno.com/examples/models/google/gemini/gemini-2-to-3.md): Create a new agent with Gemini 3 Pro and re-use the history from the previous session.
- [Async example using Gemini with tool calls.](https://docs.agno.com/examples/models/google/gemini/gemini-3-pro.md): Non-streaming response.
- [Async example using Gemini with tool calls.](https://docs.agno.com/examples/models/google/gemini/gemini-3-pro-thinking-level.md): Demonstrate async Gemini usage with tool calls and thinking.
- [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): Cookbook example for `google/gemini/image_editing.py`.
- [Google Image Generation](https://docs.agno.com/examples/models/google/gemini/image-generation.md): Cookbook example for `google/gemini/image_generation.py`.
- [Google Image Input](https://docs.agno.com/examples/models/google/gemini/image-input.md): Cookbook example for `google/gemini/image_input.py`.
- [Google Image Input File Upload](https://docs.agno.com/examples/models/google/gemini/image-input-file-upload.md): Cookbook example for `google/gemini/image_input_file_upload.py`.
- [Example: Using the GeminiTools Toolkit for Image Generation](https://docs.agno.com/examples/models/google/gemini/imagen-tool.md): Make sure you have set the GOOGLE_API_KEY environment variable.
- [Example: Using the GeminiTools Toolkit for Image Generation](https://docs.agno.com/examples/models/google/gemini/imagen-tool-advanced.md): An Agent using the Gemini image generation tool.
- [Gemini Knowledge](https://docs.agno.com/examples/models/google/gemini/knowledge.md): Add content to the knowledge.
- [Gemini Pdf Input File Upload](https://docs.agno.com/examples/models/google/gemini/pdf-input-file-upload.md): Note: If the size of the file is greater than 20MB, and a file path is provided, the file automatically gets uploaded to Google GenAI.
- [Google Pdf Input Local](https://docs.agno.com/examples/models/google/gemini/pdf-input-local.md): Cookbook example for `google/gemini/pdf_input_local.py`.
- [Google Pdf Input Url](https://docs.agno.com/examples/models/google/gemini/pdf-input-url.md): Cookbook example for `google/gemini/pdf_input_url.py`.
- [Example demonstrating how to set up retries with Google Gemini.](https://docs.agno.com/examples/models/google/gemini/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Example: Analyze files from AWS S3 using pre-signed URLs.](https://docs.agno.com/examples/models/google/gemini/s3-url-file-input.md): The Gemini API now supports external HTTPS URLs (up to 100MB).
- [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.
- [Gemini Storage And Memory](https://docs.agno.com/examples/models/google/gemini/storage-and-memory.md): Comment out after first run.
- [Google Structured Output](https://docs.agno.com/examples/models/google/gemini/structured-output.md): Cookbook example for `google/gemini/structured_output.py`.
- [Google Text To Speech](https://docs.agno.com/examples/models/google/gemini/text-to-speech.md): Cookbook example for `google/gemini/text_to_speech.py`.
- [Google Thinking Agent](https://docs.agno.com/examples/models/google/gemini/thinking-agent.md): Cookbook example for `google/gemini/thinking_agent.py`.
- [Gemini Tool Use](https://docs.agno.com/examples/models/google/gemini/tool-use.md): Google model example.
- [Gemini Url Context](https://docs.agno.com/examples/models/google/gemini/url-context.md): Google model example.
- [Combine URL context with Google Search for comprehensive web analysis.](https://docs.agno.com/examples/models/google/gemini/url-context-with-search.md): Combine URL context with Google Search for comprehensive web research.
- [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.
- [Gemini Vertexai](https://docs.agno.com/examples/models/google/gemini/vertexai.md): Google model example.
- [Google Vertexai With Credentials](https://docs.agno.com/examples/models/google/gemini/vertexai-with-credentials.md): Cookbook example for `google/gemini/vertexai_with_credentials.py`.
- [Google Video Input Bytes Content](https://docs.agno.com/examples/models/google/gemini/video-input-bytes-content.md): Cookbook example for `google/gemini/video_input_bytes_content.py`.
- [Google Video Input File Upload](https://docs.agno.com/examples/models/google/gemini/video-input-file-upload.md): Cookbook example for `google/gemini/video_input_file_upload.py`.
- [Google Video Input Local File Upload](https://docs.agno.com/examples/models/google/gemini/video-input-local-file-upload.md): Cookbook example for `google/gemini/video_input_local_file_upload.py`.
- [Google Video Input Youtube](https://docs.agno.com/examples/models/google/gemini/video-input-youtube.md): Cookbook example for `google/gemini/video_input_youtube.py`.
- [Google](https://docs.agno.com/examples/models/google/overview.md): Cookbook examples for `cookbook/90_models/google`.
- [Groq Agent Team](https://docs.agno.com/examples/models/groq/agent-team.md): Cookbook example for `groq/agent_team.py`.
- [Groq Basic](https://docs.agno.com/examples/models/groq/basic.md): Cookbook example for `groq/basic.py`.
- [Groq Browser Search](https://docs.agno.com/examples/models/groq/browser-search.md): Cookbook example for `groq/browser_search.py`.
- [Groq Db](https://docs.agno.com/examples/models/groq/db.md): Groq model example.
- [Groq Deep Knowledge](https://docs.agno.com/examples/models/groq/deep-knowledge.md): This agent performs iterative searches through its knowledge base, breaking down complex.
- [Groq Image Agent](https://docs.agno.com/examples/models/groq/image-agent.md): Cookbook example for `groq/image_agent.py`.
- [Groq Knowledge](https://docs.agno.com/examples/models/groq/knowledge.md): Add content to the knowledge.
- [Groq Metrics](https://docs.agno.com/examples/models/groq/metrics.md): Cookbook example for `groq/metrics.py`.
- [Groq Reasoning Agent](https://docs.agno.com/examples/models/groq/reasoning-agent.md): Cookbook example for `groq/reasoning_agent.py`.
- [Groq Basic](https://docs.agno.com/examples/models/groq/reasoning/basic.md): Cookbook example for `groq/reasoning/basic.py`.
- [Reasoning Demo Deepseek Qwen](https://docs.agno.com/examples/models/groq/reasoning/demo-deepseek-qwen.md): Groq model example.
- [Reasoning Demo Qwen 2 5 32b](https://docs.agno.com/examples/models/groq/reasoning/demo-qwen-2-5-32b.md): Add content to the knowledge.
- [Groq Finance Agent](https://docs.agno.com/examples/models/groq/reasoning/finance-agent.md): Cookbook example for `groq/reasoning/finance_agent.py`.
- [Reasoning](https://docs.agno.com/examples/models/groq/reasoning/overview.md): Cookbook examples for `cookbook/90_models/groq/reasoning`.
- [Groq Research Agent Exa](https://docs.agno.com/examples/models/groq/research-agent-exa.md): Engaging Report Title.
- [Groq Research Agent Seltz](https://docs.agno.com/examples/models/groq/research-agent-seltz.md): Engaging Report Title.
- [Example demonstrating how to set up retries with Groq.](https://docs.agno.com/examples/models/groq/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Groq Structured Output](https://docs.agno.com/examples/models/groq/structured-output.md): Cookbook example for `groq/structured_output.py`.
- [Please install dependencies using:](https://docs.agno.com/examples/models/groq/tool-use.md): Please install dependencies using:.
- [Groq Transcription Agent](https://docs.agno.com/examples/models/groq/transcription-agent.md): Groq model example.
- [Groq Translation Agent](https://docs.agno.com/examples/models/groq/translation-agent.md): Cookbook example for `groq/translation_agent.py`.
- [Huggingface Basic](https://docs.agno.com/examples/models/huggingface/basic.md): Cookbook example for `huggingface/basic.py`.
- [Huggingface Llama Essay Writer](https://docs.agno.com/examples/models/huggingface/llama-essay-writer.md): Cookbook example for `huggingface/llama_essay_writer.py`.
- [Huggingface](https://docs.agno.com/examples/models/huggingface/overview.md): Examples for HuggingFace model integration.
- [Example demonstrating how to set up retries with Hugging Face.](https://docs.agno.com/examples/models/huggingface/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Huggingface Tool Use](https://docs.agno.com/examples/models/huggingface/tool-use.md): Cookbook example for `huggingface/tool_use.py`.
- [Example demonstrating how to set up retries with IBM WatsonX.](https://docs.agno.com/examples/models/ibm/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Ibm Basic](https://docs.agno.com/examples/models/ibm/watsonx/basic.md): Cookbook example for `ibm/watsonx/basic.py`.
- [Watsonx Db](https://docs.agno.com/examples/models/ibm/watsonx/db.md): Ibm model example.
- [Ibm Image Agent Bytes](https://docs.agno.com/examples/models/ibm/watsonx/image-agent-bytes.md): Cookbook example for `ibm/watsonx/image_agent_bytes.py`.
- [Watsonx Knowledge](https://docs.agno.com/examples/models/ibm/watsonx/knowledge.md): Add content to the knowledge.
- [Watsonx](https://docs.agno.com/examples/models/ibm/watsonx/overview.md): Ibm model example.
- [Ibm Structured Output](https://docs.agno.com/examples/models/ibm/watsonx/structured-output.md): Cookbook example for `ibm/watsonx/structured_output.py`.
- [Watsonx Tool Use](https://docs.agno.com/examples/models/ibm/watsonx/tool-use.md): Ibm model example.
- [Internlm](https://docs.agno.com/examples/models/internlm/overview.md): Cookbook examples for `cookbook/90_models/internlm`.
- [Example demonstrating how to set up retries with InternLM.](https://docs.agno.com/examples/models/internlm/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Langdb Agent](https://docs.agno.com/examples/models/langdb/agent.md): Get the response in a variable.
- [Langdb Basic](https://docs.agno.com/examples/models/langdb/basic.md): Cookbook example for `langdb/basic.py`.
- [Langdb Data Analyst](https://docs.agno.com/examples/models/langdb/data-analyst.md): Langdb model example.
- [Langdb Finance Agent](https://docs.agno.com/examples/models/langdb/finance-agent.md): Langdb model example.
- [Langdb](https://docs.agno.com/examples/models/langdb/overview.md): Langdb model example.
- [Example demonstrating how to set up retries with LangDB.](https://docs.agno.com/examples/models/langdb/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Langdb Structured Output](https://docs.agno.com/examples/models/langdb/structured-output.md): Cookbook example for `langdb/structured_output.py`.
- [Langdb Web Search](https://docs.agno.com/examples/models/langdb/web-search.md): Langdb model example.
- [Please first install litellm[proxy] by running: uv pip install 'litellm[proxy]](https://docs.agno.com/examples/models/litellm-openai/audio-input-agent.md): Process audio input with an agent using LiteLLM and OpenAI.
- [Litellm Openai Basic](https://docs.agno.com/examples/models/litellm-openai/basic.md): Cookbook example for `litellm_openai/basic.py`.
- [Litellm Openai](https://docs.agno.com/examples/models/litellm-openai/overview.md): Examples for LiteLLM with OpenAI-compatible models.
- [Litellm Openai Tool Use](https://docs.agno.com/examples/models/litellm-openai/tool-use.md): Litellm Openai model example.
- [Litellm Audio Input Agent](https://docs.agno.com/examples/models/litellm/audio-input-agent.md): Cookbook example for `litellm/audio_input_agent.py`.
- [Litellm Basic](https://docs.agno.com/examples/models/litellm/basic.md): Cookbook example for `litellm/basic.py`.
- [Litellm Basic Gpt](https://docs.agno.com/examples/models/litellm/basic-gpt.md): Cookbook example for `litellm/basic_gpt.py`.
- [Litellm Db](https://docs.agno.com/examples/models/litellm/db.md): Add storage to the Agent.
- [Litellm Image Agent](https://docs.agno.com/examples/models/litellm/image-agent.md): Cookbook example for `litellm/image_agent.py`.
- [Litellm Image Agent Bytes](https://docs.agno.com/examples/models/litellm/image-agent-bytes.md): Cookbook example for `litellm/image_agent_bytes.py`.
- [Litellm Knowledge](https://docs.agno.com/examples/models/litellm/knowledge.md): Cookbook example for `litellm/knowledge.py`.
- [Litellm Memory](https://docs.agno.com/examples/models/litellm/memory.md): Cookbook example for `litellm/memory.py`.
- [Litellm Metrics](https://docs.agno.com/examples/models/litellm/metrics.md): Cookbook example for `litellm/metrics.py`.
- [Litellm](https://docs.agno.com/examples/models/litellm/overview.md): Litellm model example.
- [Litellm Pdf Input Bytes](https://docs.agno.com/examples/models/litellm/pdf-input-bytes.md): Cookbook example for `litellm/pdf_input_bytes.py`.
- [Litellm Pdf Input Local](https://docs.agno.com/examples/models/litellm/pdf-input-local.md): Cookbook example for `litellm/pdf_input_local.py`.
- [Litellm Pdf Input Url](https://docs.agno.com/examples/models/litellm/pdf-input-url.md): Cookbook example for `litellm/pdf_input_url.py`.
- [LiteLLM Reasoning Agent Example](https://docs.agno.com/examples/models/litellm/reasoning-agent.md): This example demonstrates using reasoning models through LiteLLM.
- [Example demonstrating how to set up retries with LiteLLM.](https://docs.agno.com/examples/models/litellm/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Litellm Structured Output](https://docs.agno.com/examples/models/litellm/structured-output.md): Cookbook example for `litellm/structured_output.py`.
- [Litellm Tool Use](https://docs.agno.com/examples/models/litellm/tool-use.md): Cookbook example for `litellm/tool_use.py`.
- [Llama Cpp Basic](https://docs.agno.com/examples/models/llama-cpp/basic.md): Cookbook example for `llama_cpp/basic.py`.
- [Llama Cpp](https://docs.agno.com/examples/models/llama-cpp/overview.md): Run your chat model using Llama CPP. For the examples below make sure to download `ggml-org/gpt-oss-20b-GGUF`. Please also make sure that the model is reachable at `http://127.0.0.
- [Example demonstrating how to set up retries with llama.cpp.](https://docs.agno.com/examples/models/llama-cpp/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Llama Cpp Structured Output](https://docs.agno.com/examples/models/llama-cpp/structured-output.md): Cookbook example for `llama_cpp/structured_output.py`.
- [Llama Cpp Tool Use](https://docs.agno.com/examples/models/llama-cpp/tool-use.md): Llama Cpp model example.
- [Lmstudio Basic](https://docs.agno.com/examples/models/lmstudio/basic.md): Cookbook example for `lmstudio/basic.py`.
- [Lmstudio Db](https://docs.agno.com/examples/models/lmstudio/db.md): Lmstudio model example.
- [Lmstudio Image Agent](https://docs.agno.com/examples/models/lmstudio/image-agent.md): Cookbook example for `lmstudio/image_agent.py`.
- [Lmstudio Knowledge](https://docs.agno.com/examples/models/lmstudio/knowledge.md): Add content to the knowledge.
- [This recipe shows how to use personalized memories and summaries in an agent.](https://docs.agno.com/examples/models/lmstudio/memory.md): Agent memory and session persistence with Lmstudio.
- [Lmstudio](https://docs.agno.com/examples/models/lmstudio/overview.md): Run your chat model using LMStudio. For the examples below make sure to get `qwen2.5-7b-instruct-1m`. Please also make sure that the status is set to `Running` and the model is rea.
- [Example demonstrating how to set up retries with LM Studio.](https://docs.agno.com/examples/models/lmstudio/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Lmstudio Structured Output](https://docs.agno.com/examples/models/lmstudio/structured-output.md): Cookbook example for `lmstudio/structured_output.py`.
- [Lmstudio Tool Use](https://docs.agno.com/examples/models/lmstudio/tool-use.md): Lmstudio model example.
- [Meta Basic](https://docs.agno.com/examples/models/meta/llama-openai/basic.md): Cookbook example for `meta/llama_openai/basic.py`.
- [Meta Image Input Bytes](https://docs.agno.com/examples/models/meta/llama-openai/image-input-bytes.md): Cookbook example for `meta/llama_openai/image_input_bytes.py`.
- [Meta Image Input File](https://docs.agno.com/examples/models/meta/llama-openai/image-input-file.md): Cookbook example for `meta/llama_openai/image_input_file.py`.
- [Llama Openai Knowledge](https://docs.agno.com/examples/models/meta/llama-openai/knowledge.md): Add content to the knowledge.
- [This recipe shows how to use personalized memories and summaries in an agent.](https://docs.agno.com/examples/models/meta/llama-openai/memory.md): Agent memory and session persistence with Llama Openai.
- [Meta Metrics](https://docs.agno.com/examples/models/meta/llama-openai/metrics.md): Cookbook example for `meta/llama_openai/metrics.py`.
- [Llama Openai Storage](https://docs.agno.com/examples/models/meta/llama-openai/storage.md): Meta model example.
- [Meta Structured Output](https://docs.agno.com/examples/models/meta/llama-openai/structured-output.md): Cookbook example for `meta/llama_openai/structured_output.py`.
- [Llama Openai Tool Use](https://docs.agno.com/examples/models/meta/llama-openai/tool-use.md): Meta model example.
- [Llama Async Knowledge](https://docs.agno.com/examples/models/meta/llama/async-knowledge.md): Add content to the knowledge.
- [Meta Basic](https://docs.agno.com/examples/models/meta/llama/basic.md): Cookbook example for `meta/llama/basic.py`.
- [Llama Db](https://docs.agno.com/examples/models/meta/llama/db.md): Meta model example.
- [Meta Image Input Bytes](https://docs.agno.com/examples/models/meta/llama/image-input-bytes.md): Cookbook example for `meta/llama/image_input_bytes.py`.
- [Meta Image Input File](https://docs.agno.com/examples/models/meta/llama/image-input-file.md): Cookbook example for `meta/llama/image_input_file.py`.
- [Llama Knowledge](https://docs.agno.com/examples/models/meta/llama/knowledge.md): Add content to the knowledge.
- [This recipe shows how to use personalized memories and summaries in an agent.](https://docs.agno.com/examples/models/meta/llama/memory.md): Agent memory and session persistence with Llama.
- [Meta Metrics](https://docs.agno.com/examples/models/meta/llama/metrics.md): Cookbook example for `meta/llama/metrics.py`.
- [Meta Structured Output](https://docs.agno.com/examples/models/meta/llama/structured-output.md): Cookbook example for `meta/llama/structured_output.py`.
- [Llama Tool Use](https://docs.agno.com/examples/models/meta/llama/tool-use.md): Meta model example.
- [Meta](https://docs.agno.com/examples/models/meta/overview.md): Meta model example.
- [Meta Retry](https://docs.agno.com/examples/models/meta/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Mistral Basic](https://docs.agno.com/examples/models/mistral/basic.md): Cookbook example for `mistral/basic.py`.
- [Mistral Image Bytes Input Agent](https://docs.agno.com/examples/models/mistral/image-bytes-input-agent.md): Cookbook example for `mistral/image_bytes_input_agent.py`.
- [Mistral Image Compare Agent](https://docs.agno.com/examples/models/mistral/image-compare-agent.md): Cookbook example for `mistral/image_compare_agent.py`.
- [Mistral Image File Input Agent](https://docs.agno.com/examples/models/mistral/image-file-input-agent.md): Cookbook example for `mistral/image_file_input_agent.py`.
- [Mistral Image Ocr With Structured Output](https://docs.agno.com/examples/models/mistral/image-ocr-with-structured-output.md): Cookbook example for `mistral/image_ocr_with_structured_output.py`.
- [This agent transcribes an old written document from an image.](https://docs.agno.com/examples/models/mistral/image-transcribe-document-agent.md): Transcribe handwritten or old documents from images using Mistral.
- [This recipe shows how to use personalized memories and summaries in an agent.](https://docs.agno.com/examples/models/mistral/memory.md): Agent memory and session persistence with Mistral.
- [Mistral Small](https://docs.agno.com/examples/models/mistral/mistral-small.md): Mistral model example.
- [Mistral](https://docs.agno.com/examples/models/mistral/overview.md): Mistral model example.
- [Example demonstrating how to set up retries with Mistral.](https://docs.agno.com/examples/models/mistral/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Mistral Structured Output](https://docs.agno.com/examples/models/mistral/structured-output.md): Cookbook example for `mistral/structured_output.py`.
- [Mistral Structured Output With Tool Use](https://docs.agno.com/examples/models/mistral/structured-output-with-tool-use.md): Cookbook example for `mistral/structured_output_with_tool_use.py`.
- [Mistral Tool Use](https://docs.agno.com/examples/models/mistral/tool-use.md): Mistral model example.
- [Moonshot Basic](https://docs.agno.com/examples/models/moonshot/basic.md): Cookbook example for `moonshot/basic.py`.
- [Moonshot](https://docs.agno.com/examples/models/moonshot/overview.md): Cookbook examples for `cookbook/90_models/moonshot`.
- [Moonshot Tool Use](https://docs.agno.com/examples/models/moonshot/tool-use.md): Cookbook example for `moonshot/tool_use.py`.
- [N1N Basic](https://docs.agno.com/examples/models/n1n/basic.md): Cookbook example for `n1n/basic.py`.
- [N1N](https://docs.agno.com/examples/models/n1n/overview.md): Cookbook examples for `cookbook/90_models/n1n`.
- [N1N Tool Use](https://docs.agno.com/examples/models/n1n/tool-use.md): Cookbook example for `n1n/tool_use.py`.
- [Nebius Basic](https://docs.agno.com/examples/models/nebius/basic.md): Cookbook example for `nebius/basic.py`.
- [Nebius Db](https://docs.agno.com/examples/models/nebius/db.md): Nebius model example.
- [Nebius Knowledge](https://docs.agno.com/examples/models/nebius/knowledge.md): Add content to the knowledge.
- [Nebius](https://docs.agno.com/examples/models/nebius/overview.md): Cookbook examples for `cookbook/90_models/nebius`.
- [Example demonstrating how to set up retries with Nebius.](https://docs.agno.com/examples/models/nebius/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Nebius Structured Output](https://docs.agno.com/examples/models/nebius/structured-output.md): Cookbook example for `nebius/structured_output.py`.
- [Nebius Tool Use](https://docs.agno.com/examples/models/nebius/tool-use.md): Cookbook example for `nebius/tool_use.py`.
- [Neosantara Basic](https://docs.agno.com/examples/models/neosantara/basic.md): Cookbook example for `neosantara/basic.py`.
- [Neosantara](https://docs.agno.com/examples/models/neosantara/overview.md): Neosantara model example.
- [Neosantara Structured Output](https://docs.agno.com/examples/models/neosantara/structured-output.md): Cookbook example for `neosantara/structured_output.py`.
- [Neosantara Tool Use](https://docs.agno.com/examples/models/neosantara/tool-use.md): Cookbook example for `neosantara/tool_use.py`.
- [Nexus Basic](https://docs.agno.com/examples/models/nexus/basic.md): Cookbook example for `nexus/basic.py`.
- [Nexus](https://docs.agno.com/examples/models/nexus/overview.md): Nexus model example.
- [Example demonstrating how to set up retries with Nexus.](https://docs.agno.com/examples/models/nexus/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Nexus Tool Use](https://docs.agno.com/examples/models/nexus/tool-use.md): Nexus model example.
- [Nvidia Basic](https://docs.agno.com/examples/models/nvidia/basic.md): Cookbook example for `nvidia/basic.py`.
- [Nvidia](https://docs.agno.com/examples/models/nvidia/overview.md): Nvidia model example.
- [Example demonstrating how to set up retries with NVIDIA.](https://docs.agno.com/examples/models/nvidia/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Nvidia Tool Use](https://docs.agno.com/examples/models/nvidia/tool-use.md): Nvidia model example.
- [Ollama Basic](https://docs.agno.com/examples/models/ollama/chat/basic.md): Cookbook example for `ollama/chat/basic.py`.
- [Chat Db](https://docs.agno.com/examples/models/ollama/chat/db.md): Ollama model example.
- [Ollama Demo Deepseek R1](https://docs.agno.com/examples/models/ollama/chat/demo-deepseek-r1.md): Cookbook example for `ollama/chat/demo_deepseek_r1.py`.
- [Ollama Demo Gemma](https://docs.agno.com/examples/models/ollama/chat/demo-gemma.md): Cookbook example for `ollama/chat/demo_gemma.py`.
- [Ollama Demo Phi4](https://docs.agno.com/examples/models/ollama/chat/demo-phi4.md): Cookbook example for `ollama/chat/demo_phi4.py`.
- [Ollama Demo Qwen](https://docs.agno.com/examples/models/ollama/chat/demo-qwen.md): Cookbook example for `ollama/chat/demo_qwen.py`.
- [Ollama Image Agent](https://docs.agno.com/examples/models/ollama/chat/image-agent.md): Cookbook example for `ollama/chat/image_agent.py`.
- [Chat Knowledge](https://docs.agno.com/examples/models/ollama/chat/knowledge.md): Add content to the knowledge.
- [This recipe shows how to use personalized memories and summaries in an agent.](https://docs.agno.com/examples/models/ollama/chat/memory.md): Agent memory and session persistence with Chat.
- [Chat Ollama Cloud](https://docs.agno.com/examples/models/ollama/chat/ollama-cloud.md): Chat Ollama Cloud.
- [Ollama Reasoning Agent](https://docs.agno.com/examples/models/ollama/chat/reasoning-agent.md): Cookbook example for `ollama/chat/reasoning_agent.py`.
- [Example demonstrating how to set up retries with Ollama.](https://docs.agno.com/examples/models/ollama/chat/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Chat Set Client](https://docs.agno.com/examples/models/ollama/chat/set-client.md): Print the response in the terminal.
- [Ollama Set Temperature](https://docs.agno.com/examples/models/ollama/chat/set-temperature.md): Cookbook example for `ollama/chat/set_temperature.py`.
- [Ollama Structured Output](https://docs.agno.com/examples/models/ollama/chat/structured-output.md): Cookbook example for `ollama/chat/structured_output.py`.
- [Chat Tool Use](https://docs.agno.com/examples/models/ollama/chat/tool-use.md): Ollama model example.
- [Basic example using Ollama with the OpenAI Responses API.](https://docs.agno.com/examples/models/ollama/responses/basic.md): This uses Ollama's OpenAI-compatible /v1/responses endpoint, which was added in Ollama v0.13.3.
- [Responses](https://docs.agno.com/examples/models/ollama/responses/overview.md): Cookbook examples for `cookbook/90_models/ollama/responses`.
- [Structured output example using Ollama with the OpenAI Responses API.](https://docs.agno.com/examples/models/ollama/responses/structured-output.md): This demonstrates using Pydantic models for structured output with Ollama's Responses API endpoint.
- [Tool use example using Ollama with the OpenAI Responses API.](https://docs.agno.com/examples/models/ollama/responses/tool-use.md): This demonstrates using tools with Ollama's Responses API endpoint.
- [Test script to verify memory events are working correctly.](https://docs.agno.com/examples/models/openai/chat/access-memories-in-memory-completed-event.md): Access stored memories from the memory completed event callback.
- [Openai Agent Flex Tier](https://docs.agno.com/examples/models/openai/chat/agent-flex-tier.md): Cookbook example for `openai/chat/agent_flex_tier.py`.
- [Openai Audio Input Agent](https://docs.agno.com/examples/models/openai/chat/audio-input-agent.md): Cookbook example for `openai/chat/audio_input_agent.py`.
- [Openai Audio Input And Output Multi Turn](https://docs.agno.com/examples/models/openai/chat/audio-input-and-output-multi-turn.md): Cookbook example for `openai/chat/audio_input_and_output_multi_turn.py`.
- [Openai Audio Input Local File Upload](https://docs.agno.com/examples/models/openai/chat/audio-input-local-file-upload.md): Cookbook example for `openai/chat/audio_input_local_file_upload.py`.
- [Openai Audio Output Agent](https://docs.agno.com/examples/models/openai/chat/audio-output-agent.md): Cookbook example for `openai/chat/audio_output_agent.py`.
- [Openai Audio Output Stream](https://docs.agno.com/examples/models/openai/chat/audio-output-stream.md): Cookbook example for `openai/chat/audio_output_stream.py`.
- [Openai Basic](https://docs.agno.com/examples/models/openai/chat/basic.md): Cookbook example for `openai/chat/basic.py`.
- [Openai Basic Stream Metrics](https://docs.agno.com/examples/models/openai/chat/basic-stream-metrics.md): Cookbook example for `openai/chat/basic_stream_metrics.py`.
- [This example shows how to use a custom role map with the OpenAIChat class.](https://docs.agno.com/examples/models/openai/chat/custom-role-map.md): This is useful when using a custom model that doesn't support the default role map.
- [Chat Db](https://docs.agno.com/examples/models/openai/chat/db.md): Openai model example.
- [Openai Generate Images](https://docs.agno.com/examples/models/openai/chat/generate-images.md): Cookbook example for `openai/chat/generate_images.py`.
- [Openai Image Agent](https://docs.agno.com/examples/models/openai/chat/image-agent.md): Cookbook example for `openai/chat/image_agent.py`.
- [Openai Image Agent Bytes](https://docs.agno.com/examples/models/openai/chat/image-agent-bytes.md): Cookbook example for `openai/chat/image_agent_bytes.py`.
- [Openai Image Agent With Memory](https://docs.agno.com/examples/models/openai/chat/image-agent-with-memory.md): Cookbook example for `openai/chat/image_agent_with_memory.py`.
- [Chat Knowledge](https://docs.agno.com/examples/models/openai/chat/knowledge.md): Add content to the knowledge.
- [This recipe shows how to use personalized memories and summaries in an agent.](https://docs.agno.com/examples/models/openai/chat/memory.md): Agent memory and session persistence with Chat.
- [Openai Metrics](https://docs.agno.com/examples/models/openai/chat/metrics.md): Cookbook example for `openai/chat/metrics.py`.
- [Chat Pdf Input File Upload](https://docs.agno.com/examples/models/openai/chat/pdf-input-file-upload.md): Pass the local PDF file path directly; the client will inline small files or upload large files automatically.
- [Openai Pdf Input Local](https://docs.agno.com/examples/models/openai/chat/pdf-input-local.md): Cookbook example for `openai/chat/pdf_input_local.py`.
- [Openai Pdf Input Url](https://docs.agno.com/examples/models/openai/chat/pdf-input-url.md): Cookbook example for `openai/chat/pdf_input_url.py`.
- [Openai Reasoning O3 Mini](https://docs.agno.com/examples/models/openai/chat/reasoning-o3-mini.md): Cookbook example for `openai/chat/reasoning_o3_mini.py`.
- [Example demonstrating how to set up retries with OpenAI Chat.](https://docs.agno.com/examples/models/openai/chat/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Openai Structured Output](https://docs.agno.com/examples/models/openai/chat/structured-output.md): Cookbook example for `openai/chat/structured_output.py`.
- [Example: Using the OpenAITools Toolkit for Text-to-Speech](https://docs.agno.com/examples/models/openai/chat/text-to-speech-agent.md): This script demonstrates how to use an agent to generate speech from a given text input and optionally save it to a specified audio file.
- [Chat Tool Use](https://docs.agno.com/examples/models/openai/chat/tool-use.md): Openai model example.
- [Openai Verbosity Control](https://docs.agno.com/examples/models/openai/chat/verbosity-control.md): Cookbook example for `openai/chat/verbosity_control.py`.
- [Openai With Retries](https://docs.agno.com/examples/models/openai/chat/with-retries.md): Cookbook example for `openai/chat/with_retries.py`.
- [Openai Agent Flex Tier](https://docs.agno.com/examples/models/openai/responses/agent-flex-tier.md): Cookbook example for `openai/responses/agent_flex_tier.py`.
- [Openai Basic](https://docs.agno.com/examples/models/openai/responses/basic.md): Cookbook example for `openai/responses/basic.py`.
- [Responses Db](https://docs.agno.com/examples/models/openai/responses/db.md): Openai model example.
- [Openai Deep Research Agent](https://docs.agno.com/examples/models/openai/responses/deep-research-agent.md): Cookbook example for `openai/responses/deep_research_agent.py`.
- [Openai Image Agent](https://docs.agno.com/examples/models/openai/responses/image-agent.md): Cookbook example for `openai/responses/image_agent.py`.
- [Openai Image Agent Bytes](https://docs.agno.com/examples/models/openai/responses/image-agent-bytes.md): Cookbook example for `openai/responses/image_agent_bytes.py`.
- [Openai Image Agent With Memory](https://docs.agno.com/examples/models/openai/responses/image-agent-with-memory.md): Cookbook example for `openai/responses/image_agent_with_memory.py`.
- [Example: Using the OpenAITools Toolkit for Image Generation](https://docs.agno.com/examples/models/openai/responses/image-generation-agent.md): This script demonstrates how to use the `OpenAITools` toolkit, which includes a tool for generating images using OpenAI's DALL-E within an Agno Agent.
- [Responses Knowledge](https://docs.agno.com/examples/models/openai/responses/knowledge.md): Add content to the knowledge.
- [This recipe shows how to use personalized memories and summaries in an agent.](https://docs.agno.com/examples/models/openai/responses/memory.md): Agent memory and session persistence with Responses.
- [Responses](https://docs.agno.com/examples/models/openai/responses/overview.md): Cookbook examples for `cookbook/90_models/openai/responses`.
- [Openai Pdf Input Local](https://docs.agno.com/examples/models/openai/responses/pdf-input-local.md): Cookbook example for `openai/responses/pdf_input_local.py`.
- [Openai Pdf Input Url](https://docs.agno.com/examples/models/openai/responses/pdf-input-url.md): Cookbook example for `openai/responses/pdf_input_url.py`.
- [Openai Reasoning O3 Mini](https://docs.agno.com/examples/models/openai/responses/reasoning-o3-mini.md): Cookbook example for `openai/responses/reasoning_o3_mini.py`.
- [Openai Structured Output](https://docs.agno.com/examples/models/openai/responses/structured-output.md): Cookbook example for `openai/responses/structured_output.py`.
- [Openai Structured Output With Tools](https://docs.agno.com/examples/models/openai/responses/structured-output-with-tools.md): Cookbook example for `openai/responses/structured_output_with_tools.py`.
- [Responses Tool Use](https://docs.agno.com/examples/models/openai/responses/tool-use.md): Openai model example.
- [Openai Tool Use Gpt 5](https://docs.agno.com/examples/models/openai/responses/tool-use-gpt-5.md): Cookbook example for `openai/responses/tool_use_gpt_5.py`.
- [Openai Tool Use O3](https://docs.agno.com/examples/models/openai/responses/tool-use-o3.md): Cookbook example for `openai/responses/tool_use_o3.py`.
- [Openai Verbosity Control](https://docs.agno.com/examples/models/openai/responses/verbosity-control.md): Cookbook example for `openai/responses/verbosity_control.py`.
- [Openai Websearch Builtin Tool](https://docs.agno.com/examples/models/openai/responses/websearch-builtin-tool.md): Cookbook example for `openai/responses/websearch_builtin_tool.py`.
- [Responses Zdr Reasoning Agent](https://docs.agno.com/examples/models/openai/responses/zdr-reasoning-agent.md): Read more about ZDR mode here: https://openai.com/enterprise-privacy/.
- [Basic](https://docs.agno.com/examples/models/openrouter/chat/basic.md): Cookbook example for `openrouter/chat/basic.py`.
- [This example demonstrates how to use dynamic model router with OpenRouter.](https://docs.agno.com/examples/models/openrouter/chat/dynamic-model-router.md): Dynamic models provide automatic failover when the primary model encounters: - Rate limits - Timeouts - Unavailability - Model overload.
- [Chat](https://docs.agno.com/examples/models/openrouter/chat/overview.md): Send messages to multiple models in parallel.
- [Retry](https://docs.agno.com/examples/models/openrouter/chat/retry.md): Set up retries with OpenRouter.
- [Structured Output](https://docs.agno.com/examples/models/openrouter/chat/structured-output.md): Using structured output with OpenRouter's Chat API.
- [Tools](https://docs.agno.com/examples/models/openrouter/chat/tool-use.md): Use of tools with OpenRouter's Chat API.
- [Basic Usage](https://docs.agno.com/examples/models/openrouter/responses/basic.md): OpenRouter's Responses API (Beta) basic usage.
- [Fallback Routing](https://docs.agno.com/examples/models/openrouter/responses/fallback.md): Use fallback models with OpenRouter's dynamic model routing.
- [Responses](https://docs.agno.com/examples/models/openrouter/responses/overview.md): Access multiple AI models through a unified, stateless API.
- [Streaming](https://docs.agno.com/examples/models/openrouter/responses/stream.md): Use streaming responses from OpenRouter's Responses API.
- [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): Use of tools with OpenRouter's Responses API.
- [Models](https://docs.agno.com/examples/models/overview.md): Examples for all supported LLM providers in Agno.
- [Perplexity Basic](https://docs.agno.com/examples/models/perplexity/basic.md): Cookbook example for `perplexity/basic.py`.
- [Perplexity Knowledge](https://docs.agno.com/examples/models/perplexity/knowledge.md): Add content to the knowledge.
- [This recipe shows how to use personalized memories and summaries in an agent.](https://docs.agno.com/examples/models/perplexity/memory.md): Agent memory and session persistence with Perplexity.
- [Perplexity](https://docs.agno.com/examples/models/perplexity/overview.md): Perplexity model example.
- [Example demonstrating how to set up retries with Perplexity.](https://docs.agno.com/examples/models/perplexity/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Perplexity Structured Output](https://docs.agno.com/examples/models/perplexity/structured-output.md): Cookbook example for `perplexity/structured_output.py`.
- [Perplexity Web Search](https://docs.agno.com/examples/models/perplexity/web-search.md): Cookbook example for `perplexity/web_search.py`.
- [Portkey Basic](https://docs.agno.com/examples/models/portkey/basic.md): Cookbook example for `portkey/basic.py`.
- [Portkey](https://docs.agno.com/examples/models/portkey/overview.md): Portkey model example.
- [Example demonstrating how to set up retries with Portkey.](https://docs.agno.com/examples/models/portkey/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Portkey Structured Output](https://docs.agno.com/examples/models/portkey/structured-output.md): Cookbook example for `portkey/structured_output.py`.
- [Portkey Tool Use](https://docs.agno.com/examples/models/portkey/tool-use.md): Cookbook example for `portkey/tool_use.py`.
- [Requesty Basic](https://docs.agno.com/examples/models/requesty/basic.md): Cookbook example for `requesty/basic.py`.
- [Requesty](https://docs.agno.com/examples/models/requesty/overview.md): Requesty AI is an LLM gateway with AI governance. See their [website](https://www.requesty.ai) for more information.
- [Example demonstrating how to set up retries with Requesty.](https://docs.agno.com/examples/models/requesty/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Requesty Structured Output](https://docs.agno.com/examples/models/requesty/structured-output.md): Cookbook example for `requesty/structured_output.py`.
- [Requesty Tool Use](https://docs.agno.com/examples/models/requesty/tool-use.md): Requesty model example.
- [Sambanova Basic](https://docs.agno.com/examples/models/sambanova/basic.md): Cookbook example for `sambanova/basic.py`.
- [Sambanova](https://docs.agno.com/examples/models/sambanova/overview.md): Sambanova model example.
- [Example demonstrating how to set up retries with SambaNova.](https://docs.agno.com/examples/models/sambanova/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Siliconflow Basic](https://docs.agno.com/examples/models/siliconflow/basic.md): Cookbook example for `siliconflow/basic.py`.
- [Siliconflow](https://docs.agno.com/examples/models/siliconflow/overview.md): Examples for SiliconFlow model integration.
- [Example demonstrating how to set up retries with SiliconFlow.](https://docs.agno.com/examples/models/siliconflow/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Siliconflow Structured Output](https://docs.agno.com/examples/models/siliconflow/structured-output.md): Cookbook example for `siliconflow/structured_output.py`.
- [Siliconflow Tool Use](https://docs.agno.com/examples/models/siliconflow/tool-use.md): Siliconflow model example.
- [Together Basic](https://docs.agno.com/examples/models/together/basic.md): Cookbook example for `together/basic.py`.
- [Together Image Agent](https://docs.agno.com/examples/models/together/image-agent.md): Cookbook example for `together/image_agent.py`.
- [Together Image Agent Bytes](https://docs.agno.com/examples/models/together/image-agent-bytes.md): Cookbook example for `together/image_agent_bytes.py`.
- [Together Image Agent With Memory](https://docs.agno.com/examples/models/together/image-agent-with-memory.md): Cookbook example for `together/image_agent_with_memory.py`.
- [Together](https://docs.agno.com/examples/models/together/overview.md): Together model example.
- [Together Reasoning Agent](https://docs.agno.com/examples/models/together/reasoning-agent.md): Cookbook example for `together/reasoning_agent.py`.
- [Example demonstrating how to set up retries with Together.](https://docs.agno.com/examples/models/together/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Together Structured Output](https://docs.agno.com/examples/models/together/structured-output.md): Cookbook example for `together/structured_output.py`.
- [Together Tool Use](https://docs.agno.com/examples/models/together/tool-use.md): Together model example.
- [Vercel Basic](https://docs.agno.com/examples/models/vercel/basic.md): Cookbook example for `vercel/basic.py`.
- [Vercel Image Agent](https://docs.agno.com/examples/models/vercel/image-agent.md): Cookbook example for `vercel/image_agent.py`.
- [Vercel Knowledge](https://docs.agno.com/examples/models/vercel/knowledge.md): Add content to the knowledge.
- [Vercel](https://docs.agno.com/examples/models/vercel/overview.md): Vercel model example.
- [Example demonstrating how to set up retries with Vercel AI.](https://docs.agno.com/examples/models/vercel/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Build a Web Search Agent using xAI.](https://docs.agno.com/examples/models/vercel/tool-use.md): Build a web search agent using Vercel AI SDK.
- [Vertexai Basic](https://docs.agno.com/examples/models/vertexai/claude/basic.md): Cookbook example for `vertexai/claude/basic.py`.
- [Vertexai Basic With Timeout](https://docs.agno.com/examples/models/vertexai/claude/basic-with-timeout.md): Cookbook example for `vertexai/claude/basic_with_timeout.py`.
- [Example demonstrating how to use Anthropic beta features.](https://docs.agno.com/examples/models/vertexai/claude/betas.md): Beta features are experimental capability extensions for Anthropic models.
- [Claude Db](https://docs.agno.com/examples/models/vertexai/claude/db.md): Vertexai model example.
- [Vertexai Image Input Bytes](https://docs.agno.com/examples/models/vertexai/claude/image-input-bytes.md): Cookbook example for `vertexai/claude/image_input_bytes.py`.
- [Vertexai Image Input Url](https://docs.agno.com/examples/models/vertexai/claude/image-input-url.md): Cookbook example for `vertexai/claude/image_input_url.py`.
- [Claude Knowledge](https://docs.agno.com/examples/models/vertexai/claude/knowledge.md): Add content to the knowledge.
- [This recipe shows how to use personalized memories and summaries in an agent.](https://docs.agno.com/examples/models/vertexai/claude/memory.md): Agent memory and session persistence with Claude.
- [Vertexai Pdf Input Bytes](https://docs.agno.com/examples/models/vertexai/claude/pdf-input-bytes.md): Cookbook example for `vertexai/claude/pdf_input_bytes.py`.
- [Vertexai Pdf Input Local](https://docs.agno.com/examples/models/vertexai/claude/pdf-input-local.md): Cookbook example for `vertexai/claude/pdf_input_local.py`.
- [Claude Prompt Caching](https://docs.agno.com/examples/models/vertexai/claude/prompt-caching.md): This can significantly reduce processing time and costs.
- [Vertexai Structured Output](https://docs.agno.com/examples/models/vertexai/claude/structured-output.md): Cookbook example for `vertexai/claude/structured_output.py`.
- [Vertexai Thinking](https://docs.agno.com/examples/models/vertexai/claude/thinking.md): Cookbook example for `vertexai/claude/thinking.py`.
- [Claude Tool Use](https://docs.agno.com/examples/models/vertexai/claude/tool-use.md): Vertexai model example.
- [Vertexai](https://docs.agno.com/examples/models/vertexai/overview.md): Cookbook examples for `cookbook/90_models/vertexai`.
- [Example demonstrating how to set up retries with Vertex AI.](https://docs.agno.com/examples/models/vertexai/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Vllm Basic](https://docs.agno.com/examples/models/vllm/basic.md): Cookbook example for `vllm/basic.py`.
- [Code generation example with DeepSeek-Coder.](https://docs.agno.com/examples/models/vllm/code-generation.md): Run vLLM model: vllm serve deepseek-ai/deepseek-coder-6.7b-instruct  --dtype float32  --tool-call-parser pythonic.
- [Vllm Db](https://docs.agno.com/examples/models/vllm/db.md): Vllm model example.
- [Personalized memory and session summaries with vLLM.](https://docs.agno.com/examples/models/vllm/memory.md): Prerequisites: 1.
- [Vllm](https://docs.agno.com/examples/models/vllm/overview.md): vLLM is a fast and easy-to-use library for running LLM models locally.
- [Example demonstrating how to set up retries with vLLM.](https://docs.agno.com/examples/models/vllm/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Vllm Structured Output](https://docs.agno.com/examples/models/vllm/structured-output.md): Cookbook example for `vllm/structured_output.py`.
- [Build a Web Search Agent using xAI.](https://docs.agno.com/examples/models/vllm/tool-use.md): Build a web search agent using vLLM.
- [Xai Basic](https://docs.agno.com/examples/models/xai/basic.md): Cookbook example for `xai/basic.py`.
- [Finance Agent - Your Personal Market Analyst!](https://docs.agno.com/examples/models/xai/finance-agent.md): This example shows how to create a sophisticated financial analyst that provides comprehensive market insights using real-time data.
- [Xai Image Agent](https://docs.agno.com/examples/models/xai/image-agent.md): Cookbook example for `xai/image_agent.py`.
- [Xai Image Agent Bytes](https://docs.agno.com/examples/models/xai/image-agent-bytes.md): Cookbook example for `xai/image_agent_bytes.py`.
- [Xai Image Agent With Memory](https://docs.agno.com/examples/models/xai/image-agent-with-memory.md): Cookbook example for `xai/image_agent_with_memory.py`.
- [Xai Live Search Agent](https://docs.agno.com/examples/models/xai/live-search-agent.md): Cookbook example for `xai/live_search_agent.py`.
- [Xai Live Search Agent Stream](https://docs.agno.com/examples/models/xai/live-search-agent-stream.md): Cookbook example for `xai/live_search_agent_stream.py`.
- [Xai](https://docs.agno.com/examples/models/xai/overview.md): Xai model example.
- [Xai Reasoning Agent](https://docs.agno.com/examples/models/xai/reasoning-agent.md): Cookbook example for `xai/reasoning_agent.py`.
- [Example demonstrating how to set up retries with xAI.](https://docs.agno.com/examples/models/xai/retry.md): We will use a deliberately wrong model ID, to trigger retries.
- [Xai Structured Output](https://docs.agno.com/examples/models/xai/structured-output.md): Cookbook example for `xai/structured_output.py`.
- [Build a Web Search Agent using xAI.](https://docs.agno.com/examples/models/xai/tool-use.md): Build a web search agent using xAI Grok.
- [Treaty Of Versailles Analysis](https://docs.agno.com/examples/reasoning/agents/analyse-treaty-of-versailles.md): Demonstrates built-in and DeepSeek-backed reasoning for historical analysis.
- [Capture Reasoning Content](https://docs.agno.com/examples/reasoning/agents/capture-reasoning-content-default-cot.md): Demonstrates how to inspect reasoning_content in streaming and non-streaming runs.
- [Cerebras Default COT Fallback](https://docs.agno.com/examples/reasoning/agents/cerebras-llama-default-cot.md): Demonstrates default chain-of-thought behavior with a Cerebras model.
- [OpenAI Default Chain Of Thought](https://docs.agno.com/examples/reasoning/agents/default-chain-of-thought.md): Demonstrates fallback chain-of-thought and built-in reasoning in one script.
- [Fibonacci Script Planning](https://docs.agno.com/examples/reasoning/agents/fibonacci.md): Demonstrates built-in and DeepSeek-backed reasoning for coding guidance.
- [Reasoning Finance Agent](https://docs.agno.com/examples/reasoning/agents/finance-agent.md): Demonstrates built-in and DeepSeek-backed reasoning for financial reporting.
- [WatsonX Default COT Fallback](https://docs.agno.com/examples/reasoning/agents/ibm-watsonx-default-cot.md): Demonstrates default chain-of-thought behavior with an IBM WatsonX model.
- [Decimal Comparison Reasoning](https://docs.agno.com/examples/reasoning/agents/is-9-11-bigger-than-9-9.md): Demonstrates regular, built-in, and DeepSeek-backed reasoning for 9.11 vs 9.9.
- [Future Life Storytelling](https://docs.agno.com/examples/reasoning/agents/life-in-500000-years.md): Demonstrates built-in and DeepSeek-backed reasoning for speculative writing.
- [Missionaries And Cannibals Puzzle](https://docs.agno.com/examples/reasoning/agents/logical-puzzle.md): Demonstrates built-in and DeepSeek-backed reasoning for logic puzzle solving.
- [Sum Of Odd Numbers Proof](https://docs.agno.com/examples/reasoning/agents/mathematical-proof.md): Demonstrates built-in and DeepSeek-backed reasoning for mathematical proofs.
- [Mistral Reasoning COT](https://docs.agno.com/examples/reasoning/agents/mistral-reasoning-cot.md): Demonstrates built-in chain-of-thought reasoning with Mistral.
- [Agents](https://docs.agno.com/examples/reasoning/agents/overview.md): Reasoning agent examples, including built-in COT and DeepSeek reasoning-model comparisons.
- [Python 101 Curriculum Planning](https://docs.agno.com/examples/reasoning/agents/python-101-curriculum.md): Demonstrates built-in and DeepSeek-backed reasoning for curriculum design.
- [Scientific Abstract Critique](https://docs.agno.com/examples/reasoning/agents/scientific-research.md): Demonstrates built-in and DeepSeek-backed reasoning for methodology critique.
- [Ship Of Theseus Debate](https://docs.agno.com/examples/reasoning/agents/ship-of-theseus.md): Demonstrates built-in and DeepSeek-backed reasoning for philosophical analysis.
- [Strawberry Letter Counting](https://docs.agno.com/examples/reasoning/agents/strawberry.md): Demonstrates regular, built-in, and DeepSeek-backed reasoning for counting tasks.
- [Trolley Problem Analysis](https://docs.agno.com/examples/reasoning/agents/trolley-problem.md): Demonstrates built-in and DeepSeek-backed reasoning for ethical analysis.
- [Async Reasoning Stream](https://docs.agno.com/examples/reasoning/models/anthropic/async-reasoning-stream.md): Demonstrates this reasoning cookbook example.
- [Basic Reasoning](https://docs.agno.com/examples/reasoning/models/anthropic/basic-reasoning.md): Demonstrates this reasoning cookbook example.
- [Basic Reasoning Stream](https://docs.agno.com/examples/reasoning/models/anthropic/basic-reasoning-stream.md): Demonstrates this reasoning cookbook example.
- [Reasoning Model Deepseek](https://docs.agno.com/examples/reasoning/models/azure-ai-foundry/reasoning-model-deepseek.md): Demonstrates this reasoning cookbook example.
- [Reasoning Model Stream Deepseek](https://docs.agno.com/examples/reasoning/models/azure-ai-foundry/reasoning-model-stream-deepseek.md): Demonstrates this reasoning cookbook example.
- [Basic Reasoning Stream](https://docs.agno.com/examples/reasoning/models/azure-openai/basic-reasoning-stream.md): Demonstrates this reasoning cookbook example.
- [O3 Mini With Tools](https://docs.agno.com/examples/reasoning/models/azure-openai/o3-mini-with-tools.md): Demonstrates this reasoning cookbook example.
- [Reasoning Model Gpt 4 1](https://docs.agno.com/examples/reasoning/models/azure-openai/reasoning-model-gpt-4-1.md): Demonstrates this reasoning cookbook example.
- [Ethical Dilemma](https://docs.agno.com/examples/reasoning/models/deepseek/ethical-dilemma.md): Demonstrates this reasoning cookbook example.
- [Plan Itinerary](https://docs.agno.com/examples/reasoning/models/deepseek/plan-itinerary.md): Demonstrates this reasoning cookbook example.
- [Async Reasoning Stream](https://docs.agno.com/examples/reasoning/models/gemini/async-reasoning-stream.md): Demonstrates this reasoning cookbook example.
- [Basic Reasoning](https://docs.agno.com/examples/reasoning/models/gemini/basic-reasoning.md): Demonstrates this reasoning cookbook example.
- [Basic Reasoning Stream](https://docs.agno.com/examples/reasoning/models/gemini/basic-reasoning-stream.md): Demonstrates this reasoning cookbook example.
- [Deepseek Plus Claude](https://docs.agno.com/examples/reasoning/models/groq/deepseek-plus-claude.md): Demonstrates this reasoning cookbook example.
- [Fast Reasoning](https://docs.agno.com/examples/reasoning/models/groq/fast-reasoning.md): Demonstrates this reasoning cookbook example.
- [9 11 Or 9 9](https://docs.agno.com/examples/reasoning/models/groq/or-9-9.md): Demonstrates this reasoning cookbook example.
- [Local Reasoning](https://docs.agno.com/examples/reasoning/models/ollama/local-reasoning.md): Demonstrates this reasoning cookbook example.
- [Reasoning Model Deepseek](https://docs.agno.com/examples/reasoning/models/ollama/reasoning-model-deepseek.md): Demonstrates this reasoning cookbook example.
- [O3 Mini](https://docs.agno.com/examples/reasoning/models/openai/o3-mini.md): Demonstrates this reasoning cookbook example.
- [O3 Mini With Tools](https://docs.agno.com/examples/reasoning/models/openai/o3-mini-with-tools.md): Demonstrates this reasoning cookbook example.
- [Reasoning Effort](https://docs.agno.com/examples/reasoning/models/openai/reasoning-effort.md): Demonstrates this reasoning cookbook example.
- [Reasoning Model Gpt 4 1](https://docs.agno.com/examples/reasoning/models/openai/reasoning-model-gpt-4-1.md): Demonstrates this reasoning cookbook example.
- [Reasoning Stream](https://docs.agno.com/examples/reasoning/models/openai/reasoning-stream.md): Demonstrates this reasoning cookbook example.
- [Reasoning Summary](https://docs.agno.com/examples/reasoning/models/openai/reasoning-summary.md): Demonstrates this reasoning cookbook example.
- [Basic Reasoning Stream](https://docs.agno.com/examples/reasoning/models/vertex-ai/basic-reasoning-stream.md): Demonstrates this reasoning cookbook example.
- [Xai](https://docs.agno.com/examples/reasoning/models/xai/overview.md): xAI reasoning examples.
- [Reasoning Effort](https://docs.agno.com/examples/reasoning/models/xai/reasoning-effort.md): Demonstrates this reasoning cookbook example.
- [Reasoning](https://docs.agno.com/examples/reasoning/overview.md): Reasoning gives Agents the ability to “think” before responding and “analyze” the results of their actions (i.e. tool calls), greatly improving the Agents’ ability to solve problem.
- [Finance Team Chain Of Thought](https://docs.agno.com/examples/reasoning/teams/finance-team-chain-of-thought.md): Demonstrates this reasoning cookbook example.
- [Knowledge Tool Team](https://docs.agno.com/examples/reasoning/teams/knowledge-tool-team.md): Demonstrates this reasoning cookbook example.
- [Teams](https://docs.agno.com/examples/reasoning/teams/overview.md): Reasoning-oriented team orchestration examples.
- [Reasoning Finance Team](https://docs.agno.com/examples/reasoning/teams/reasoning-finance-team.md): Demonstrates this reasoning cookbook example.
- [Azure Openai Reasoning Tools](https://docs.agno.com/examples/reasoning/tools/azure-openai-reasoning-tools.md): Demonstrates this reasoning cookbook example.
- [Capture Reasoning Content Knowledge Tools](https://docs.agno.com/examples/reasoning/tools/capture-reasoning-content-knowledge-tools.md): Demonstrates this reasoning cookbook example.
- [Capture Reasoning Content Reasoning Tools](https://docs.agno.com/examples/reasoning/tools/capture-reasoning-content-reasoning-tools.md): Demonstrates this reasoning cookbook example.
- [Cerebras Llama Reasoning Tools](https://docs.agno.com/examples/reasoning/tools/cerebras-llama-reasoning-tools.md): Demonstrates this reasoning cookbook example.
- [Claude Reasoning Tools](https://docs.agno.com/examples/reasoning/tools/claude-reasoning-tools.md): Demonstrates this reasoning cookbook example.
- [Gemini Finance Agent](https://docs.agno.com/examples/reasoning/tools/gemini-finance-agent.md): Demonstrates this reasoning cookbook example.
- [Gemini Reasoning Tools](https://docs.agno.com/examples/reasoning/tools/gemini-reasoning-tools.md): Demonstrates this reasoning cookbook example.
- [Groq Llama Finance Agent](https://docs.agno.com/examples/reasoning/tools/groq-llama-finance-agent.md): Demonstrates this reasoning cookbook example.
- [Ibm Watsonx Reasoning Tools](https://docs.agno.com/examples/reasoning/tools/ibm-watsonx-reasoning-tools.md): Demonstrates this reasoning cookbook example.
- [Knowledge Tools](https://docs.agno.com/examples/reasoning/tools/knowledge-tools.md): Demonstrates this reasoning cookbook example.
- [Llama Reasoning Tools](https://docs.agno.com/examples/reasoning/tools/llama-reasoning-tools.md): Demonstrates this reasoning cookbook example.
- [Memory Tools](https://docs.agno.com/examples/reasoning/tools/memory-tools.md): Demonstrates this reasoning cookbook example.
- [Ollama Reasoning Tools](https://docs.agno.com/examples/reasoning/tools/ollama-reasoning-tools.md): Demonstrates this reasoning cookbook example.
- [Openai Reasoning Tools](https://docs.agno.com/examples/reasoning/tools/openai-reasoning-tools.md): Demonstrates this reasoning cookbook example.
- [Tools](https://docs.agno.com/examples/reasoning/tools/overview.md): Reasoning tools integrations across multiple providers.
- [Reasoning Tools](https://docs.agno.com/examples/reasoning/tools/reasoning-tools.md): Demonstrates this reasoning cookbook example.
- [Vercel Reasoning Tools](https://docs.agno.com/examples/reasoning/tools/vercel-reasoning-tools.md): Demonstrates this reasoning cookbook example.
- [Workflow Tools](https://docs.agno.com/examples/reasoning/tools/workflow-tools.md): Demonstrates this reasoning cookbook example.
- [Use DynamoDb as the database for an agent.](https://docs.agno.com/examples/storage/dynamodb/dynamo-for-agent.md): Use DynamoDB as the storage backend for an agent.
- [Use DynamoDb as the database for a team.](https://docs.agno.com/examples/storage/dynamodb/dynamo-for-team.md): Use DynamoDB as the storage backend for a team.
- [Dynamodb](https://docs.agno.com/examples/storage/dynamodb/overview.md): Examples demonstrating AWS DynamoDB integration with Agno agents.
- [Multi-User Multi-Session](https://docs.agno.com/examples/storage/examples/multi-user-multi-session.md): Demonstrates handling multiple users and sessions with SQLite-backed agent storage.
- [Examples](https://docs.agno.com/examples/storage/examples/overview.md): Patterns and examples for database integration with Agno.
- [Use SQLite as the database for an Agent, selecting custom names for the tables.](https://docs.agno.com/examples/storage/examples/selecting-tables.md): Configure custom table names for agent storage.
- [This recipe shows how to store agent sessions in a Firestore database.](https://docs.agno.com/examples/storage/firestore/firestore-for-agent.md): Use Google Firestore as the storage backend for an agent.
- [Firestore](https://docs.agno.com/examples/storage/firestore/overview.md): Examples demonstrating Google Cloud Firestore integration with Agno agents.
- [GCS JSON Storage for Agent](https://docs.agno.com/examples/storage/gcs/gcs-json-for-agent.md): Demonstrates using GcsJsonDb as the session storage backend for an Agno agent.
- [Gcs](https://docs.agno.com/examples/storage/gcs/overview.md): Examples demonstrating Google Cloud Storage (GCS) integration with Agno agents using JSON blob storage.
- [In Memory Storage For Agent](https://docs.agno.com/examples/storage/in-memory/in-memory-storage-for-agent.md): The Agent sessions will now be stored in the in-memory database.
- [In Memory Storage For Team](https://docs.agno.com/examples/storage/in-memory/in-memory-storage-for-team.md): 2.
- [Use JSON files as the database for a Workflow.](https://docs.agno.com/examples/storage/in-memory/in-memory-storage-for-workflow.md): Useful for simple demos where performance is not critical.
- [In Memory](https://docs.agno.com/examples/storage/in-memory/overview.md): This directory contains examples demonstrating how to use `InMemoryDb` with Agno agents, workflows, and teams.
- [Use JSON files as the database for an Agent.](https://docs.agno.com/examples/storage/json-db/json-for-agent.md): Useful for simple demos where performance is not critical.
- [Use JSON files as the database for a Team.](https://docs.agno.com/examples/storage/json-db/json-for-team.md): Useful for simple demos where performance is not critical.
- [Use JSON files as the database for a Workflow.](https://docs.agno.com/examples/storage/json-db/json-for-workflows.md): Useful for simple demos where performance is not critical.
- [Json Db](https://docs.agno.com/examples/storage/json-db/overview.md): Examples demonstrating JSON file-based storage 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): Async MongoDB storage for agent sessions and history.
- [Use AsyncMongoDb as the database for a team.](https://docs.agno.com/examples/storage/mongo/async-mongo/async-mongodb-for-team.md): Store team sessions and memory in AsyncMongoDb.
- [Use AsyncMongoDb as the database for a workflow.](https://docs.agno.com/examples/storage/mongo/async-mongo/async-mongodb-for-workflow.md): Persist workflow state and intermediate outputs with AsyncMongoDb.
- [Async MongoDB](https://docs.agno.com/examples/storage/mongo/async-mongo/overview.md): Examples demonstrating AsyncMongoDb integration with Agno agents, teams, and workflows.
- [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): Use MongoDb as the database for a team.
- [Mongo](https://docs.agno.com/examples/storage/mongo/overview.md): Examples demonstrating MongoDB integration with Agno agents and teams.
- [Use MySQL as the database for an agent.](https://docs.agno.com/examples/storage/mysql/mysql-for-agent.md): MySQL storage backend for agent sessions.
- [Use MySQL as the database for a team.](https://docs.agno.com/examples/storage/mysql/mysql-for-team.md): MySQL storage backend for team sessions.
- [Mysql](https://docs.agno.com/examples/storage/mysql/overview.md): Examples demonstrating MySQL database integration with Agno agents, teams, and workflows.
- [Storage](https://docs.agno.com/examples/storage/overview.md): This directory contains examples demonstrating how to integrate various databases with Agno agents, teams, and workflows for persistent storage.
- [Postgres](https://docs.agno.com/examples/storage/postgres/overview.md): Examples demonstrating PostgreSQL database integration with Agno agents, teams, and workflows.
- [Use Postgres as the database for an agent.](https://docs.agno.com/examples/storage/postgres/postgres-for-agent.md): Postgres storage backend for agent sessions.
- [Postgres For Team](https://docs.agno.com/examples/storage/postgres/postgres-for-team.md): 2.
- [Postgres Storage for Workflow](https://docs.agno.com/examples/storage/postgres/postgres-for-workflow.md): Demonstrates using PostgresDb as the session storage backend for a workflow.
- [Redis](https://docs.agno.com/examples/storage/redis/overview.md): Examples demonstrating Redis integration with Agno agents, teams, and workflows.
- [Example showing how to use Redis as the database for an 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): 2.
- [Redis Storage for Workflow](https://docs.agno.com/examples/storage/redis/redis-for-workflow.md): Demonstrates using RedisDb 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): SingleStore storage backend for agent sessions.
- [Singlestore For Team](https://docs.agno.com/examples/storage/singlestore/singlestore-for-team.md): 2.
- [Sqlite](https://docs.agno.com/examples/storage/sqlite/overview.md): Examples demonstrating SQLite database integration with Agno agents, teams, and workflows.
- [Use SQLite as the database for an Agent.](https://docs.agno.com/examples/storage/sqlite/sqlite-for-agent.md): SQLite storage backend for agent sessions.
- [Sqlite For Team](https://docs.agno.com/examples/storage/sqlite/sqlite-for-team.md): 2.
- [SQLite Storage for Workflow](https://docs.agno.com/examples/storage/sqlite/sqlite-for-workflow.md): Demonstrates using SqliteDb as the session storage backend for a workflow.
- [Surrealdb](https://docs.agno.com/examples/storage/surrealdb/overview.md): Examples demonstrating SurrealDB integration with Agno agents, teams, and workflows.
- [Run SurrealDB in a container before running this script](https://docs.agno.com/examples/storage/surrealdb/surrealdb-for-agent.md): docker run --rm --pull always -p 8000:8000 surrealdb/surrealdb:latest start --user root --pass root.
- [Run SurrealDB in a container before running this script](https://docs.agno.com/examples/storage/surrealdb/surrealdb-for-team.md): docker run --rm --pull always -p 8000:8000 surrealdb/surrealdb:latest start --user root --pass root.
- [Run SurrealDB in a container before running this script](https://docs.agno.com/examples/storage/surrealdb/surrealdb-for-workflow.md): docker run --rm --pull always -p 8000:8000 surrealdb/surrealdb:latest start --user root --pass root.
- [Basic Coordination](https://docs.agno.com/examples/teams/basics/basic-coordination.md): Demonstrates coordinated team workflows for both sync and async execution patterns.
- [Broadcast Mode](https://docs.agno.com/examples/teams/basics/broadcast-mode.md): Demonstrates delegating the same task to all members using TeamMode.broadcast.
- [Concurrent Member Agents](https://docs.agno.com/examples/teams/basics/concurrent-member-agents.md): Demonstrates concurrent delegation to team members with streamed member events.
- [Delegate To All Members](https://docs.agno.com/examples/teams/basics/delegate-to-all-members.md): Demonstrates collaborative team execution with delegate-to-all behavior.
- [History Of Members](https://docs.agno.com/examples/teams/basics/history-of-members.md): Demonstrates member-level history where each member tracks its own prior context.
- [Nested Teams](https://docs.agno.com/examples/teams/basics/nested-teams.md): Demonstrates using teams as members in a higher-level coordinating team.
- [Quickstart](https://docs.agno.com/examples/teams/basics/overview.md): Examples for team workflows.
- [Respond Directly Router Team](https://docs.agno.com/examples/teams/basics/respond-directly-router-team.md): Demonstrates routing multilingual requests to specialized members with direct responses.
- [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.
- [Share Member Interactions](https://docs.agno.com/examples/teams/basics/share-member-interactions.md): Demonstrates sharing interactions among team members during execution.
- [Task Mode](https://docs.agno.com/examples/teams/basics/task-mode.md): Demonstrates autonomous task decomposition and execution using TeamMode.tasks.
- [Team History](https://docs.agno.com/examples/teams/basics/team-history.md): Demonstrates sharing team history with member agents across a session.
- [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.
- [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.
- [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.
- [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.
- [OpenAI Moderation](https://docs.agno.com/examples/teams/guardrails/openai-moderation.md): Demonstrates OpenAI moderation guardrails for team inputs.
- [Guardrails](https://docs.agno.com/examples/teams/guardrails/overview.md): Examples for team workflows in guardrails.
- [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.
- [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.
- [Reject Member Tool Call](https://docs.agno.com/examples/teams/human-in-the-loop/confirmation-rejected.md): Handle rejection of a tool call by a team member agent.
- [Streaming: Reject Member Tool Call.](https://docs.agno.com/examples/teams/human-in-the-loop/confirmation-rejected-stream.md): Handle rejection of a tool call by member agent in streaming mode.
- [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.
- [Async: Confirm Member Agent Tool](https://docs.agno.com/examples/teams/human-in-the-loop/confirmation-required-async.md): Same as confirmation_required.py but uses async run/continue_run.
- [Async Streaming: Confirm Member Agent Tool](https://docs.agno.com/examples/teams/human-in-the-loop/confirmation-required-async-stream.md): Same as confirmation_required_stream.py but uses async run/continue_run.
- [Streaming: Confirm Member Agent Tool](https://docs.agno.com/examples/teams/human-in-the-loop/confirmation-required-stream.md): This example demonstrates how a team pauses when a member agent's tool requires human confirmation in streaming mode.
- [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.
- [Streaming with External Execution of Tool](https://docs.agno.com/examples/teams/human-in-the-loop/external-tool-execution-stream.md): This example demonstrates how a team pauses when a member agent's tool requires external execution in streaming mode.
- [Human In The Loop](https://docs.agno.com/examples/teams/human-in-the-loop/overview.md): Examples for team workflows in human_in_the_loop.
- [Confirm Team Tool ](https://docs.agno.com/examples/teams/human-in-the-loop/team-tool-confirmation.md): This example demonstrates HITL for tools provided directly to the Team (not to member agents).
- [Streaming: Confirm Team Tool](https://docs.agno.com/examples/teams/human-in-the-loop/team-tool-confirmation-stream.md): This example demonstrates HITL for tools provided directly to the Team (not to member agents) in streaming mode.
- [Team Tool with User Input](https://docs.agno.com/examples/teams/human-in-the-loop/user-input-required.md): Demonstrates collecting required user input during paused team tool execution.
- [Streaming: Member Tool with User Input](https://docs.agno.com/examples/teams/human-in-the-loop/user-input-required-stream.md): This example demonstrates how a team pauses when a member agent's tool.
- [Knowledge](https://docs.agno.com/examples/teams/knowledge/overview.md): Examples for team workflows in knowledge.
- [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.
- [Learning](https://docs.agno.com/examples/teams/learning/overview.md): Examples demonstrating team learning capabilities in Agno. Teams can automatically capture user profiles, memories, entity information, session context, learned knowledge, and deci.
- [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.
- [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.
- [Team Metrics](https://docs.agno.com/examples/teams/metrics/team-metrics.md): Demonstrates retrieving team, session, and member-level execution metrics.
- [Basic Broadcast Mode Example](https://docs.agno.com/examples/teams/modes/broadcast/basic.md): Demonstrates `mode=broadcast` where the team leader sends the same task.
- [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): Demonstrates broadcast mode for gathering information from multiple sources simultaneously.
- [Basic Coordinate Mode Example](https://docs.agno.com/examples/teams/modes/coordinate/basic.md): Demonstrates the default `mode=coordinate` where the team leader: 1.
- [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.
- [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.
- [Basic Tasks Mode Example](https://docs.agno.com/examples/teams/modes/tasks/basic.md): Demonstrates `mode=tasks` where the team leader autonomously: 1.
- [Tasks with Dependencies Example](https://docs.agno.com/examples/teams/modes/tasks/dependencies.md): Demonstrates task mode with dependency chains.
- [Tasks](https://docs.agno.com/examples/teams/modes/tasks/overview.md): Examples for Tasks.
- [Parallel Tasks Execution Example](https://docs.agno.com/examples/teams/modes/tasks/parallel.md): Demonstrates task mode with parallel execution.
- [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): Demonstrates collaborative prompt optimization and DALL-E 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.
- [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.
- [Teams](https://docs.agno.com/examples/teams/overview.md): Cookbooks for building multi-agent teams in Agno.
- [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.
- [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.
- [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.
- [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.
- [Search Session History](https://docs.agno.com/examples/teams/session/search-session-history.md): Demonstrates searching prior sessions with user-scoped history access.
- [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.
- [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.
- [State](https://docs.agno.com/examples/teams/state/overview.md): Examples for team workflows in 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.
- [Team Events](https://docs.agno.com/examples/teams/streaming/team-events.md): Demonstrates monitoring team and member events in sync-like and async event streams.
- [Team Streaming](https://docs.agno.com/examples/teams/streaming/team-streaming.md): Demonstrates sync and async streaming responses from a team.
- [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.
- [JSON Schema Output](https://docs.agno.com/examples/teams/structured-input-output/json-schema-output.md): Demonstrates provider-native JSON schema output for team responses.
- [Output Model](https://docs.agno.com/examples/teams/structured-input-output/output-model.md): Demonstrates setting a dedicated model for final team response generation.
- [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 Input Output](https://docs.agno.com/examples/teams/structured-input-output/overview.md): Examples for team workflows in structured_input_output.
- [Parser Model](https://docs.agno.com/examples/teams/structured-input-output/parser-model.md): Demonstrates parser-model assisted team output parsing into rich schemas.
- [Pydantic Input](https://docs.agno.com/examples/teams/structured-input-output/pydantic-input.md): Demonstrates passing validated Pydantic models as team inputs.
- [Pydantic Output](https://docs.agno.com/examples/teams/structured-input-output/pydantic-output.md): Demonstrates team-level typed output using Pydantic schemas.
- [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.
- [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.
- [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): Demonstrates a team in `mode=tasks` where the team leader autonomously: 1.
- [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.
- [Task Mode](https://docs.agno.com/examples/teams/task-mode/overview.md): Cookbook example.
- [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.
- [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.
- [Tools](https://docs.agno.com/examples/teams/tools/overview.md): Examples for team workflows in tools.
- [Tool Hooks](https://docs.agno.com/examples/teams/tools/tool-hooks.md): Demonstrates team/member tool hooks for logging delegation and tool execution timing.
- [AgentQL](https://docs.agno.com/examples/tools/agentql-tools.md): Use AgentQL tools with Agno agents.
- [Airflow](https://docs.agno.com/examples/tools/airflow-tools.md): Use Airflow Tools with an Agno Agent.
- [Apify](https://docs.agno.com/examples/tools/apify-tools.md): Use Apify tools with Agno Agents for data extraction.
- [ArXiv](https://docs.agno.com/examples/tools/arxiv-tools.md): Use Arxiv tools with Agno Agents.
- [AWS Lambda](https://docs.agno.com/examples/tools/aws-lambda-tools.md): Use AWS Lambda tools with Agno agents.
- [AWS SES](https://docs.agno.com/examples/tools/aws-ses-tools.md): Use AWS Simple Email Service (SES) with Agno Agents.
- [Baidu Search](https://docs.agno.com/examples/tools/baidusearch-tools.md): Use Baidu Search with Agno Agents.
- [Bitbucket](https://docs.agno.com/examples/tools/bitbucket-tools.md): Use Bitbucket with Agno Agents.
- [Brandfetch](https://docs.agno.com/examples/tools/brandfetch-tools.md): Use Brandfetch Tools with Agno Agents
- [Brave Search](https://docs.agno.com/examples/tools/bravesearch-tools.md): Use Brave Search tools with Agno Agents.
- [Brightdata](https://docs.agno.com/examples/tools/brightdata-tools.md): Use Brightdata tools with Agno Agents.
- [Browserbase](https://docs.agno.com/examples/tools/browserbase-tools.md): Use Browserbase Tools with Agno Agents.
- [Cal.com](https://docs.agno.com/examples/tools/calcom-tools.md): Use Calcom scheduling with Agno Agents.
- [Calculator](https://docs.agno.com/examples/tools/calculator-tools.md): Use Calculator with Agno Agents.
- [Cartesia](https://docs.agno.com/examples/tools/cartesia-tools.md): Use Cartesia with Agno Agents.
- [ClickUp](https://docs.agno.com/examples/tools/clickup-tools.md): Use ClickUp with Agno Agents.
- [Composio](https://docs.agno.com/examples/tools/composio-tools.md): Use Composio with Agno agents.
- [Confluence](https://docs.agno.com/examples/tools/confluence-tools.md): Use Confluence with Agno agents.
- [Crawl4AI](https://docs.agno.com/examples/tools/crawl4ai-tools.md): Use Crawl4AI with Agno agents.
- [CSV](https://docs.agno.com/examples/tools/csv-tools.md): Use CSV Tools with Agno agents.
- [Custom API](https://docs.agno.com/examples/tools/custom-api-tools.md): Use custom api tools with Agno agents.
- [Custom Events](https://docs.agno.com/examples/tools/custom-tool-events.md): Use custom events with Agno agents.
- [Custom Tools](https://docs.agno.com/examples/tools/custom-tools.md): Use custom tools with Agno agents.
- [Dalle](https://docs.agno.com/examples/tools/dalle-tools.md): Use Dalle with Agno agents.
- [Daytona](https://docs.agno.com/examples/tools/daytona-tools.md): Use Dayton with Agno agents.
- [Desi Vocal](https://docs.agno.com/examples/tools/desi-vocal-tools.md): Use Desi vocal tool with Agno agents.
- [Discord](https://docs.agno.com/examples/tools/discord-tools.md): Use Discord with Agno agents.
- [Docker](https://docs.agno.com/examples/tools/docker-tools.md): Use Docker with Agno agents.
- [Duckdb](https://docs.agno.com/examples/tools/duckdb-tools.md): Use Duckdb with Agno agents.
- [Duckduckgo](https://docs.agno.com/examples/tools/duckduckgo-tools.md): Demonstrates duckduckgo tools.
- [E2B](https://docs.agno.com/examples/tools/e2b-tools.md): Use E2B tools with Agno agents.
- [Elevenlabs](https://docs.agno.com/examples/tools/elevenlabs-tools.md): Use Elevenlabs with Agno agents.
- [Email](https://docs.agno.com/examples/tools/email-tools.md): Use Email tools with Agno agents
- [EVM](https://docs.agno.com/examples/tools/evm-tools.md): Use Ethereum Virtual Machine (EVM) with Agno agents.
- [Exa](https://docs.agno.com/examples/tools/exa-tools.md): Use Exa with Agno agents.
- [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](https://docs.agno.com/examples/tools/exceptions/retry-tool-call.md): Handle tool failures with `RetryAgentRun`.
- [Post-Hook Retry](https://docs.agno.com/examples/tools/exceptions/retry-tool-call-from-post-hook.md): Automatically retry a tool call using a post-hook with `RetryAgentRun`.
- [Stop Agent](https://docs.agno.com/examples/tools/exceptions/stop-agent-exception.md): Handle exceptions with `StopAgentRun`.
- [Fal](https://docs.agno.com/examples/tools/fal-tools.md): Use Fal with Agno agents.
- [File Generation](https://docs.agno.com/examples/tools/file-generation-tools.md): Use File Generation Tools with Agno agents.
- [File Management](https://docs.agno.com/examples/tools/file-tools.md): Use file management with Agno agents.
- [Financial Datasets](https://docs.agno.com/examples/tools/financial-datasets-tools.md): Use financial datasets with Agno agents
- [Firecrawl](https://docs.agno.com/examples/tools/firecrawl-tools.md): Use Firecrawl with Agno agents.
- [Giphy](https://docs.agno.com/examples/tools/giphy-tools.md): Use Giphy tools with Agno agents.
- [GitHub](https://docs.agno.com/examples/tools/github-tools.md): Use GitHub with Agno agents.
- [Gmail](https://docs.agno.com/examples/tools/gmail-tools.md): Use Gmail with Agno agents.
- [Google BigQuery](https://docs.agno.com/examples/tools/google-bigquery-tools.md): Use BigQuery with Agno agents.
- [Google Drive](https://docs.agno.com/examples/tools/google-drive.md): Use Google Drive with Agno agents.
- [Google Maps](https://docs.agno.com/examples/tools/google-maps-tools.md): Use Google Maps with Agno agents.
- [Google Calendar](https://docs.agno.com/examples/tools/googlecalendar-tools.md): Use Google Calendar with Agno agents.
- [Google Sheets](https://docs.agno.com/examples/tools/googlesheets-tools.md): Use Google Sheets with Agno agents.
- [Hackernews](https://docs.agno.com/examples/tools/hackernews-tools.md): Use Hackernews with Agno agents.
- [Jina Reader](https://docs.agno.com/examples/tools/jinareader-tools.md): Use Jina Reader with Agno agents.
- [Jira](https://docs.agno.com/examples/tools/jira-tools.md): Use Jira with Agno agents.
- [Knowledge](https://docs.agno.com/examples/tools/knowledge-tool.md): Use Knowledge tool with Agno agents.
- [Linear](https://docs.agno.com/examples/tools/linear-tools.md): Use Linear with Agno agents.
- [Linkup](https://docs.agno.com/examples/tools/linkup-tools.md): Use Linkup tools with Agno agents.
- [Lumalabs](https://docs.agno.com/examples/tools/lumalabs-tools.md): Use Lumalabs tools with Agno agents.
- [MCP](https://docs.agno.com/examples/tools/mcp-tools.md): Use MCP tools with Agno agents.
- [Agno MCP](https://docs.agno.com/examples/tools/mcp/agno-mcp.md): Demo: Agno MCP Capability.
- [MCP Airbnb Agent](https://docs.agno.com/examples/tools/mcp/airbnb.md): Creates an agent that uses MCP and Gemini 2.5 Pro to search for Airbnb listings.
- [MCP Brave Agent](https://docs.agno.com/examples/tools/mcp/brave.md): Creates 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): This example uses the MCP GitHub Agent.
- [Dynamic Headers](https://docs.agno.com/examples/tools/mcp/dynamic-headers/client.md): Agent with MCP tools using dynamic headers.
- [Overview](https://docs.agno.com/examples/tools/mcp/dynamic-headers/overview.md): Dynamically send information to the MCP server via HTTP headers.
- [Server](https://docs.agno.com/examples/tools/mcp/dynamic-headers/server.md): MCP server that reads dynamic HTTP headers from requests and uses them to personalize responses.
- [MCP Filesystem Agent](https://docs.agno.com/examples/tools/mcp/filesystem.md): Filesystem agent that uses MCP to explore, analyze, and provides files and directories insights.
- [GibsonAI MCP Server](https://docs.agno.com/examples/tools/mcp/gibsonai.md): This example shows how to connect a local GibsonAI MCP to Agno agent.
- [MCP GitHub Agent](https://docs.agno.com/examples/tools/mcp/github.md): This example shows how to create a GitHub agent that uses MCP to explore, analyze, and provide insights about GitHub repositories.
- [MCP Graphiti Agent](https://docs.agno.com/examples/tools/mcp/graphiti.md): This example demonstrates how to use Agno's MCP integration together with Graphiti, to build a personal diary assistant.
- [Groq and MCP Agents](https://docs.agno.com/examples/tools/mcp/groq-mcp.md): This example demonstrates how to create a high-performance filesystem agent by combining Groq and MCP.
- [Include Exclude Tools](https://docs.agno.com/examples/tools/mcp/include-exclude-tools.md)
- [Include Tools](https://docs.agno.com/examples/tools/mcp/include-tools.md): Demonstrates include tools.
- [Client](https://docs.agno.com/examples/tools/mcp/local-server/client.md): Demonstrates client.
- [Overview](https://docs.agno.com/examples/tools/mcp/local-server/overview.md): Cookbook examples for this tools subsection.
- [Server](https://docs.agno.com/examples/tools/mcp/local-server/server.md): Demonstrates Server.
- [Connect MCP Toolbox](https://docs.agno.com/examples/tools/mcp/mcp-toolbox-demo/agent.md): Connect to an MCP toolbox server for database operations.
- [AgentOS](https://docs.agno.com/examples/tools/mcp/mcp-toolbox-demo/agent-os.md): Demonstrates AgentOS.
- [Hotel Management Typesafe](https://docs.agno.com/examples/tools/mcp/mcp-toolbox-demo/hotel-management-typesafe.md): Demonstrates hotel management typesafe.
- [Hotel Search and Booking](https://docs.agno.com/examples/tools/mcp/mcp-toolbox-demo/hotel-management-workflows.md): Use database to demo sequential workflow for hotel search and booking.
- [Overview](https://docs.agno.com/examples/tools/mcp/mcp-toolbox-demo/overview.md): This demo showcases how to set up and run an Agno Agent that can interact with a PostgreSQL database through the [MCP Toolbox for Databases](https://googleapis.github.io/genai-tool).
- [Mcp Toolbox For Db](https://docs.agno.com/examples/tools/mcp/mcp-toolbox-for-db.md): Enable debug mode for verbose agent output on all runs or individual runs.
- [Mem0 MCP](https://docs.agno.com/examples/tools/mcp/mem0.md): This example demonstrates how to 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): Demonstrates multiiple MCP servers in a single agent.
- [MCP Server Failure](https://docs.agno.com/examples/tools/mcp/multiple-servers-allow-partial-failure.md): Connecting to MCP servers that are not always available or prone to failure.
- [Notion MCP Agent](https://docs.agno.com/examples/tools/mcp/notion-mcp-agent.md): Use the Agno MCP tools to interact with Notion workspace.
- [Overview](https://docs.agno.com/examples/tools/mcp/overview.md): Enable Agno agents to interact with external systems via MCP interface.
- [Oxylabs](https://docs.agno.com/examples/tools/mcp/oxylabs.md): Demonstrates Oxylabs.
- [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.
- [Pipedream MCP Server](https://docs.agno.com/examples/tools/mcp/pipedream-auth.md): Authenticate and Use Pipedream MCP Server.
- [Pipedream (Google Calendar MCP)](https://docs.agno.com/examples/tools/mcp/pipedream-google-calendar.md): Use Pipedream MCP servers (in this case the Google Calendar one) with Agno Agents.
- [Pipedream (LinkedIn MCP)](https://docs.agno.com/examples/tools/mcp/pipedream-linkedin.md): This example shows how to use Pipedream MCP servers (in this case the LinkedIn one) with Agno Agents.
- [Pipedream (Slack MCP)](https://docs.agno.com/examples/tools/mcp/pipedream-slack.md): This example shows how to use Pipedream MCP servers (in this case the Slack one) with Agno Agents.
- [Qdrant](https://docs.agno.com/examples/tools/mcp/qdrant.md): Demonstrates qdrant.
- [Sequential Thinking](https://docs.agno.com/examples/tools/mcp/sequential-thinking.md): Use multiple MCP servers in a single agent.
- [Client](https://docs.agno.com/examples/tools/mcp/sse-transport/client.md): Connect to MCP servers that use the SSE transport via Agno MCPTools.
- [Overview](https://docs.agno.com/examples/tools/mcp/sse-transport/overview.md): Use `MCPTool` utility with an MCP server via SSE transport.
- [Server](https://docs.agno.com/examples/tools/mcp/sse-transport/server.md): Run an MCP server using SSE transport.
- [Stagehand MCP Agent](https://docs.agno.com/examples/tools/mcp/stagehand.md): Use Agno's agent to create a Hacker News content using the Stagehand MCP server.
- [Client](https://docs.agno.com/examples/tools/mcp/streamable-http-transport/client.md): Connect to MCP servers that use streamable HTTP with Agno MCPTools
- [Overview](https://docs.agno.com/examples/tools/mcp/streamable-http-transport/overview.md): Use the `MCPTool` utility with an MCP server via streamable HTTP.
- [Server](https://docs.agno.com/examples/tools/mcp/streamable-http-transport/server.md): Run an MCP server using Streamable HTTP transport.
- [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 create projects, database schemas, edge functions, and more.
- [Tool Name Prefix](https://docs.agno.com/examples/tools/mcp/tool-name-prefix.md): This is useful to avoid name collisions with other tools, especially when using multiple MCP servers.
- [Mem0](https://docs.agno.com/examples/tools/mem0-tools.md): Use Mem0 tool with Agno agents.
- [MLX Transcribe](https://docs.agno.com/examples/tools/mlx-transcribe-tools.md): Use MLX Trascribe tool with Agno agents.
- [Models Lab Tools](https://docs.agno.com/examples/tools/models-lab-tools.md): Tool integration example.
- [Azure OpenaAI](https://docs.agno.com/examples/tools/models/azure-openai-tools.md): Use Azure OpenAI tools in Agno agents.
- [Gemini Image Generation](https://docs.agno.com/examples/tools/models/gemini-image-generation.md): Use Gemini for generating images in Agno agents.
- [Gemini Video Generation](https://docs.agno.com/examples/tools/models/gemini-video-generation.md): Use Gemini for video generation in Agno agents.
- [Morph](https://docs.agno.com/examples/tools/models/morph.md): Use Morph with Agno agents.
- [Nebius](https://docs.agno.com/examples/tools/models/nebius-tools.md): Use Nebius for image generation with Agno agents.
- [OpenAI](https://docs.agno.com/examples/tools/models/openai-tools.md): Use OpenAI with Agno agents.
- [Moviepy Video](https://docs.agno.com/examples/tools/moviepy-video-tools.md): Moviepy Video tools with Agno agents.
- [Multi-tools Usage](https://docs.agno.com/examples/tools/multiple-tools.md): Use multiple tools with Agno agents.
- [Nano Banana](https://docs.agno.com/examples/tools/nano-banana-tools.md): Use Nano Banana tool with Agno agents.
- [Neo4j](https://docs.agno.com/examples/tools/neo4j-tools.md): Use Neo4j tools with Agno agents.
- [Newspaper](https://docs.agno.com/examples/tools/newspaper-tools.md): Use Newspaper tools with Agno agents.
- [Newspaper4K](https://docs.agno.com/examples/tools/newspaper4k-tools.md): Use Newspaper4k tools with Agno agents.
- [Notion](https://docs.agno.com/examples/tools/notion-tools.md): Use Notion tools with Agno agents.
- [OpenBB](https://docs.agno.com/examples/tools/openbb-tools.md): Use OpenBB with Agno agents.
- [Opencv tools](https://docs.agno.com/examples/tools/opencv-tools.md): Use OpenCV with Agno agents.
- [OpenWeather](https://docs.agno.com/examples/tools/openweather-tools.md): Use OpenWeather with Agno agents.
- [Add Tool After Initialization](https://docs.agno.com/examples/tools/other/add-tool-after-initialization.md): Demonstrates add tool after initialization.
- [Cache Tool Calls](https://docs.agno.com/examples/tools/other/cache-tool-calls.md): Demonstrates cache tool calls.
- [Complex Input Types](https://docs.agno.com/examples/tools/other/complex-input-types.md): Use complex input types with tools.
- [HITL](https://docs.agno.com/examples/tools/other/human-in-the-loop.md): This example shows how to implement human-in-the-loop functionality in your Agno tool calls.
- [Include Exclude Tools](https://docs.agno.com/examples/tools/other/include-exclude-tools.md): Demonstrates include exclude tools.
- [Include/Exclude Tools (Custom Toolkit)](https://docs.agno.com/examples/tools/other/include-exclude-tools-custom-toolkit.md): Demonstrates include exclude tools custom toolkit.
- [Session State Tool](https://docs.agno.com/examples/tools/other/session-state-tool.md): Read and write agent session state from tools.
- [Stop After Tool Call](https://docs.agno.com/examples/tools/other/stop-after-tool-call.md): Demonstrates stop after tool call.
- [Stop After Tool Call Dual Inheritance](https://docs.agno.com/examples/tools/other/stop-after-tool-call-dual-inheritance.md): Using stop_after_tool_call_tools works in Toolkit class with multiple inheritance.
- [Stop After Tool Call In Toolkit](https://docs.agno.com/examples/tools/other/stop-after-tool-call-in-toolkit.md): This demonstrates using stop_after_tool_call_tools without the @tool decorator.
- [Tools](https://docs.agno.com/examples/tools/overview.md): Examples for using and creating tools in Agno.
- [Oxylabs](https://docs.agno.com/examples/tools/oxylabs-tools.md): Use Oxylabs with Agno agents.
- [Pandas tools](https://docs.agno.com/examples/tools/pandas-tools.md): Use Pandas with Agno agents.
- [Parallel](https://docs.agno.com/examples/tools/parallel-tools.md): Use Parallel tools with Agno agents.
- [Postgres](https://docs.agno.com/examples/tools/postgres-tools.md): Use Postgres with Agno agents.
- [PubMed](https://docs.agno.com/examples/tools/pubmed-tools.md): Use PubMed tools with Agno agents.
- [Python Function](https://docs.agno.com/examples/tools/python-function-as-tool.md): Use Python Function as a tool with Agno agents.
- [Python](https://docs.agno.com/examples/tools/python-tools.md): Use Python tools as tools with Agno agents.
- [Reddit](https://docs.agno.com/examples/tools/reddit-tools.md): Use Reddit tools with Agno agents.
- [Amazon Redshift](https://docs.agno.com/examples/tools/redshift-tools.md): Use Amazon Redshift tools with Agno agents.
- [Replicate](https://docs.agno.com/examples/tools/replicate-tools.md): Use Replicate tools with Agno agents.
- [Resend](https://docs.agno.com/examples/tools/resend-tools.md): Use Resend tools with Agno agents.
- [ScrapeGraph](https://docs.agno.com/examples/tools/scrapegraph-tools.md): Use ScrapeGraph tools with Agno agents.
- [SearXNG](https://docs.agno.com/examples/tools/searxng-tools.md): Use SearXNG with Agno agents.
- [Seltz](https://docs.agno.com/examples/tools/seltz-tools.md): Use Seltz tools in Agno agents.
- [SERP API](https://docs.agno.com/examples/tools/serpapi-tools.md): Use SERP API with Agno agents.
- [Serper](https://docs.agno.com/examples/tools/serper-tools.md): Use Serper tools with Agno agents.
- [Shell](https://docs.agno.com/examples/tools/shell-tools.md): Use shell tools with Agno agents.
- [Shopify](https://docs.agno.com/examples/tools/shopify-tools.md): Use Shopify tools with an Agno Agent.
- [Slack Tools](https://docs.agno.com/examples/tools/slack-tools.md): Use Slack tools with Agno agents.
- [Sleep](https://docs.agno.com/examples/tools/sleep-tools.md): Use Sleep tools with Agno agents.
- [Spider](https://docs.agno.com/examples/tools/spider-tools.md): Use Spider tools with Agno agents.
- [Spotify](https://docs.agno.com/examples/tools/spotify-tools.md): Use SpotifyTools with Agno agents.
- [SQL](https://docs.agno.com/examples/tools/sql-tools.md): Use SQL tools with Agno agents.
- [Tavily](https://docs.agno.com/examples/tools/tavily-tools.md): Use Tavily tools with Agno agents.
- [Telegram](https://docs.agno.com/examples/tools/telegram-tools.md): Use Telegram tools with Agno agents.
- [Todoist](https://docs.agno.com/examples/tools/todoist-tools.md): Use Todoist tools with Agno agents.
- [Async Tool Decorator](https://docs.agno.com/examples/tools/tool-decorator/async-tool-decorator.md): Define async tools with @tool and stream results via AsyncIterator.
- [Cache Tool Calls](https://docs.agno.com/examples/tools/tool-decorator/cache-tool-calls.md): Cache tool results with `cache_results` to avoid repeat calls.
- [Overview](https://docs.agno.com/examples/tools/tool-decorator/overview.md): Use Tool Decorator with Agno agents.
- [Stop After Tool Call](https://docs.agno.com/examples/tools/tool-decorator/stop-after-tool-call.md): Stop agent execution immediately after a tool call completes.
- [Tool Decorator](https://docs.agno.com/examples/tools/tool-decorator/tool-decorator.md): Define tool decorator with @tool, including sync and async examples.
- [Tool Decorator On Class Method](https://docs.agno.com/examples/tools/tool-decorator/tool-decorator-on-class-method.md): Use @tool on toolkit class methods, including generators.
- [Tool decorator with hook](https://docs.agno.com/examples/tools/tool-decorator/tool-decorator-with-hook.md): Attach execution hooks to a tool via the decorator pattern.
- [Tool Decorator with Instructions](https://docs.agno.com/examples/tools/tool-decorator/tool-decorator-with-instructions.md): Add tool instructions to guide model usage and output.
- [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): Use Pre and Post Hooks in Agno agents.
- [ Tool Hook](https://docs.agno.com/examples/tools/tool-hooks/tool-hook.md): Define custom logic to intercept and log tool executions.
- [Tool Hooks in Toolkit](https://docs.agno.com/examples/tools/tool-hooks/tool-hook-in-toolkit.md): Apply custom validation logic to tools within a toolkit.
- [Tool Hooks in Toolkit with State](https://docs.agno.com/examples/tools/tool-hooks/tool-hook-in-toolkit-with-state.md): Manage tool arguments dynamically using shared agent session state.
- [Nested Tool Hooks in Toolkit with State](https://docs.agno.com/examples/tools/tool-hooks/tool-hook-in-toolkit-with-state-nested.md): Manage nested tool execution logic using agent session state.
- [Nested Tool Hooks in Toolkit](https://docs.agno.com/examples/tools/tool-hooks/tool-hooks-in-toolkit-nested.md): Use of multiple hooks for complex validation and result sanitization.
- [Trafilatura](https://docs.agno.com/examples/tools/trafilatura-tools.md): Use Trafilatura tools with Agno agents.
- [Trello](https://docs.agno.com/examples/tools/trello-tools.md): Use Trello tool with Agno agents.
- [Twilio](https://docs.agno.com/examples/tools/twilio-tools.md): Use Twilio with Agno agents.
- [Unsplash](https://docs.agno.com/examples/tools/unsplash-tools.md): Use Unsplash tools with Agno agents.
- [Valyu](https://docs.agno.com/examples/tools/valyu-tools.md): Use Valyu with Agno agents.
- [Visualization Tools](https://docs.agno.com/examples/tools/visualization-tools.md): Use visualization with Agno agents.
- [Web](https://docs.agno.com/examples/tools/web-tools.md): Use Web tools with Agno agents.
- [Webbrowser ](https://docs.agno.com/examples/tools/webbrowser-tools.md): Use Webbrowser tool with Agno agents.
- [Webex](https://docs.agno.com/examples/tools/webex-tools.md): Use Webex with Agno agents.
- [Websearch](https://docs.agno.com/examples/tools/websearch-tools.md): Enable Websearch tools with Agno agents.
- [Website](https://docs.agno.com/examples/tools/website-tools.md): Use Website tools with Agno agents.
- [Website Knowledge](https://docs.agno.com/examples/tools/website-tools-knowledge.md): Use Website tools to update knowledge in Agno agents.
- [WhatsApp](https://docs.agno.com/examples/tools/whatsapp-tools.md): Use WhatsApp with Agno agents.
- [Wikipedia](https://docs.agno.com/examples/tools/wikipedia-tools.md): Use WhatsApp with Agno agents.
- [X](https://docs.agno.com/examples/tools/x-tools.md): Use X with Agno agents.
- [YFinance](https://docs.agno.com/examples/tools/yfinance-tools.md): Use YFinance tools with Agno agents.
- [Youtube](https://docs.agno.com/examples/tools/youtube-tools.md): Use Youtube with Agno agents.
- [Zendesk](https://docs.agno.com/examples/tools/zendesk-tools.md): Use Zendesk with Agno agents.
- [Zep](https://docs.agno.com/examples/tools/zep-tools.md): Use Zep with Agno agents.
- [Zoom](https://docs.agno.com/examples/tools/zoom-tools.md): Use Zoom tools with an Agno agents.
- [Background Poll](https://docs.agno.com/examples/workflows/advanced-concepts/background-execution/background-poll.md): Demonstrates running a workflow in async background mode and polling run status until completion.
- [Background Execution WebSocket Client](https://docs.agno.com/examples/workflows/advanced-concepts/background-execution/websocket-client.md): Demonstrates 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): Demonstrates running background workflows and streaming workflow events over WebSocket.
- [Early Stop Basic](https://docs.agno.com/examples/workflows/advanced-concepts/early-stopping/early-stop-basic.md): Demonstrates 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): Demonstrates stopping an entire workflow from inside a `Condition` branch.
- [Early Stop Loop](https://docs.agno.com/examples/workflows/advanced-concepts/early-stopping/early-stop-loop.md): Demonstrates stopping a looped workflow early using a safety-check step.
- [Early Stop Parallel](https://docs.agno.com/examples/workflows/advanced-concepts/early-stopping/early-stop-parallel.md): Demonstrates stopping the workflow from within a step running inside a `Parallel` block.
- [Prompt Injection Guardrail](https://docs.agno.com/examples/workflows/advanced-concepts/guardrails/prompt-injection.md): Demonstrates a workflow that blocks prompt-injection attempts before downstream processing.
- [Continuous Execution](https://docs.agno.com/examples/workflows/advanced-concepts/history/continuous-execution.md): Demonstrates single-step conversational execution with workflow history available to the step agent.
- [History In Function](https://docs.agno.com/examples/workflows/advanced-concepts/history/history-in-function.md): Demonstrates reading workflow history inside a custom function step for strategic content planning.
- [Intent Routing With History](https://docs.agno.com/examples/workflows/advanced-concepts/history/intent-routing-with-history.md): Demonstrates simple intent routing where all specialist steps share workflow history for context continuity.
- [Step History](https://docs.agno.com/examples/workflows/advanced-concepts/history/step-history.md): Demonstrates workflow-level and step-level history controls for conversation-aware content workflows.
- [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.
- [Access Previous Outputs](https://docs.agno.com/examples/workflows/advanced-concepts/previous-step-outputs/access-previous-outputs.md): Demonstrates accessing output from multiple prior steps using both named steps and implicit step keys.
- [Cancel Run](https://docs.agno.com/examples/workflows/advanced-concepts/run-control/cancel-run.md): Demonstrates starting a workflow run in one thread and cancelling it from another.
- [Workflow Deep Copy](https://docs.agno.com/examples/workflows/advanced-concepts/run-control/deep-copy.md): Demonstrates creating isolated workflow copies with `deep_copy(update=...)`.
- [Event Storage](https://docs.agno.com/examples/workflows/advanced-concepts/run-control/event-storage.md): Demonstrates storing workflow events while skipping selected high-volume events.
- [Executor Events](https://docs.agno.com/examples/workflows/advanced-concepts/run-control/executor-events.md): Demonstrates filtering internal executor events during streamed workflow runs.
- [Workflow Metrics](https://docs.agno.com/examples/workflows/advanced-concepts/run-control/metrics.md): Demonstrates reading workflow and step-level metrics from `WorkflowRunOutput`.
- [Remote Workflow](https://docs.agno.com/examples/workflows/advanced-concepts/run-control/remote-workflow.md): Demonstrates executing a workflow hosted on a remote server using `RemoteWorkflow`.
- [Workflow CLI](https://docs.agno.com/examples/workflows/advanced-concepts/run-control/workflow-cli.md): Demonstrates using `Workflow.cli_app()` for interactive command-line workflow runs.
- [Workflow Serialization](https://docs.agno.com/examples/workflows/advanced-concepts/run-control/workflow-serialization.md): Demonstrates `to_dict()`, `save()`, and `load()` for workflow persistence.
- [Rename Session](https://docs.agno.com/examples/workflows/advanced-concepts/session-state/rename-session.md): Demonstrates auto-generating a workflow session name after a run.
- [State In Condition](https://docs.agno.com/examples/workflows/advanced-concepts/session-state/state-in-condition.md): Demonstrates using workflow session state in a `Condition` evaluator and executor functions.
- [State In Function](https://docs.agno.com/examples/workflows/advanced-concepts/session-state/state-in-function.md): Demonstrates reading and mutating workflow session state inside custom function executors.
- [State In Router](https://docs.agno.com/examples/workflows/advanced-concepts/session-state/state-in-router.md): Demonstrates router selectors that use and update workflow session state for adaptive routing.
- [State With Agent](https://docs.agno.com/examples/workflows/advanced-concepts/session-state/state-with-agent.md): Demonstrates sharing mutable workflow session state across agent tool calls.
- [State With Team](https://docs.agno.com/examples/workflows/advanced-concepts/session-state/state-with-team.md): Demonstrates shared session state across team and agent steps for project-step lifecycle management.
- [Image Input](https://docs.agno.com/examples/workflows/advanced-concepts/structured-io/image-input.md): Demonstrates passing image media into workflow runs and chaining analysis with follow-up research.
- [Input Schema](https://docs.agno.com/examples/workflows/advanced-concepts/structured-io/input-schema.md): Demonstrates workflow-level `input_schema` validation with structured and invalid input examples.
- [Pydantic Input](https://docs.agno.com/examples/workflows/advanced-concepts/structured-io/pydantic-input.md): Demonstrates passing a Pydantic model instance directly as workflow input.
- [Structured IO Agent](https://docs.agno.com/examples/workflows/advanced-concepts/structured-io/structured-io-agent.md): Demonstrates structured output schemas at each agent step in a multi-step workflow.
- [Structured IO Function](https://docs.agno.com/examples/workflows/advanced-concepts/structured-io/structured-io-function.md): Demonstrates custom function steps in structured workflows, including string and BaseModel outputs.
- [Structured IO Team](https://docs.agno.com/examples/workflows/advanced-concepts/structured-io/structured-io-team.md): Demonstrates structured output schemas at each team step in a multi-step workflow.
- [Workflow Tools](https://docs.agno.com/examples/workflows/advanced-concepts/tools/workflow-tools.md): Demonstrates exposing a workflow as a tool that another agent can execute.
- [Basic Workflow Agent](https://docs.agno.com/examples/workflows/advanced-concepts/workflow-agent/basic-workflow-agent.md): Demonstrates using `WorkflowAgent` to decide when to execute workflow steps versus answer from history.
- [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.
- [Workflow Agent With Condition](https://docs.agno.com/examples/workflows/advanced-concepts/workflow-agent/workflow-agent-with-condition.md): Demonstrates using `WorkflowAgent` together with a conditional step in the workflow graph.
- [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.
- [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): Demonstrates how to 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.
- [Step With Function](https://docs.agno.com/examples/workflows/basic-workflows/step-with-function/overview.md): Runnable workflow examples under: cookbook/04_workflows/01_basic_workflows/02_step_with_function.
- [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.
- [CEL: 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.
- [CEL: Basic](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.
- [CEL: Previous Step](https://docs.agno.com/examples/workflows/cel-expressions/condition/cel-previous-step.md): Runs a classifier step first, then uses previous_step_content.contains()
- [CEL: Previous Step 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.
- [CEL Session State](https://docs.agno.com/examples/workflows/cel-expressions/condition/cel-session-state.md): Uses session_state.retry_count to implement retry logic.
- [CEL: Compound Exit](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.
- [CEL: Content Keyword](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.
- [CEL: Iteration Limit.](https://docs.agno.com/examples/workflows/cel-expressions/loop/cel-iteration-limit.md): Uses current_iteration to stop after a specific number of iterations, independent of max_iterations.
- [CEL: Step Output Check](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.
- [CEL: Additional Data Route](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.
- [CEL: Previous Step Route](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.
- [CEL: Session State Route](https://docs.agno.com/examples/workflows/cel-expressions/router/cel-session-state-route.md): Uses session_state.preferred_handler to persist routing preferences across workflow runs.
- [CEL: Ternary Operator](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.
- [CEL: Step Choices](https://docs.agno.com/examples/workflows/cel-expressions/router/cel-using-step-choices.md): Uses step_choices[0], step_choices[1], etc.
- [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): Demonstrates nested lists in router choices, which are converted into sequential `Steps` containers.
- [Conditional Branching](https://docs.agno.com/examples/workflows/conditional-branching/overview.md): Runnable workflow examples under: cookbook/04_workflows/05_conditional_branching.
- [Router Basic](https://docs.agno.com/examples/workflows/conditional-branching/router-basic.md): Demonstrates topic-based routing between specialized research steps before content publishing.
- [Router With Loop](https://docs.agno.com/examples/workflows/conditional-branching/router-with-loop.md): Demonstrates router-based selection between simple web research and iterative loop-based deep tech research.
- [Selector Media Pipeline](https://docs.agno.com/examples/workflows/conditional-branching/selector-media-pipeline.md): Demonstrates routing between image and video generation pipelines using a router selector.
- [Selector Types](https://docs.agno.com/examples/workflows/conditional-branching/selector-types.md): Demonstrates router selector flexibility across string, step object, list, and nested-choice return patterns.
- [Step Choices Parameter](https://docs.agno.com/examples/workflows/conditional-branching/step-choices-parameter.md): Demonstrates using `step_choices` in a router selector for dynamic step selection.
- [String Selector](https://docs.agno.com/examples/workflows/conditional-branching/string-selector.md): Demonstrates returning a step name string from a router selector.
- [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): Demonstrates multiple conditional branches executed in parallel before final synthesis steps.
- [Conditional Execution](https://docs.agno.com/examples/workflows/conditional-execution/overview.md): Runnable workflow examples under: cookbook/04_workflows/02_conditional_execution.
- [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.
- [Workflows](https://docs.agno.com/examples/workflows/overview.md): Runnable workflow examples under: cookbook/04_workflows.
- [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.
- [When to use a Workflow vs a Team in Agno](https://docs.agno.com/faq/When-to-use-a-Workflow-vs-a-Team-in-Agno.md)
- [AgentOS Connection Issues](https://docs.agno.com/faq/agentos-connection.md)
- [Connecting to Tableplus](https://docs.agno.com/faq/connecting-to-tableplus.md)
- [Could Not Connect To Docker](https://docs.agno.com/faq/could-not-connect-to-docker.md)
- [Setting Environment Variables](https://docs.agno.com/faq/environment-variables.md)
- [OpenAI Key Request While Using Other Models](https://docs.agno.com/faq/openai-key-request-for-other-models.md)
- [Authorization Failed - JWT Verification](https://docs.agno.com/faq/rbac-auth-failed.md)
- [Structured outputs](https://docs.agno.com/faq/structured-outputs.md)
- [How to Switch Between Different Models](https://docs.agno.com/faq/switching-models.md)
- [Tokens-per-minute rate limiting](https://docs.agno.com/faq/tpm-issues.md)
- [Your First Agent](https://docs.agno.com/first-agent.md): Build and run your first agent in 20 lines of code.
- [Getting Help](https://docs.agno.com/get-help.md): Connect with the Agno community, reach out to the team, build and share.
- [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/included/pii.md): Detect personally identifiable information in agent inputs.
- [Prompt Injection Guardrail](https://docs.agno.com/guardrails/included/prompt-injection.md): Detect prompt injection attempts in agent inputs.
- [Guardrails](https://docs.agno.com/guardrails/overview.md): Built-in safeguards for input validation, PII detection, and prompt injection defense.
- [OpenAI Moderation Guardrail](https://docs.agno.com/guardrails/usage/agent/openai-moderation.md): This example demonstrates how to use Agno's built-in OpenAI moderation guardrail to detect and block content that violates OpenAI's content policy.
- [PII Detection Guardrail](https://docs.agno.com/guardrails/usage/agent/pii-detection.md): This example demonstrates how to use Agno's built-in PII detection guardrail to protect sensitive data like SSNs, credit cards, emails, and phone numbers.
- [Prompt Injection Guardrail](https://docs.agno.com/guardrails/usage/agent/prompt-injection.md): This example demonstrates how to use Agno's built-in prompt injection guardrail to detect and stop prompt injection and jailbreak attempts.
- [OpenAI Moderation Guardrail for Teams](https://docs.agno.com/guardrails/usage/team/openai-moderation.md): This example demonstrates how to use Agno's built-in OpenAI moderation guardrail with a Team to detect and block policy violations.
- [PII Detection Guardrail for Teams](https://docs.agno.com/guardrails/usage/team/pii-detection.md): This example demonstrates how to use Agno's built-in PII detection guardrail with a Team to protect sensitive data.
- [Prompt Injection Guardrail for Teams](https://docs.agno.com/guardrails/usage/team/prompt-injection.md): This example demonstrates how to use Agno's built-in prompt injection guardrail with a Team to stop injection attempts.
- [Chat History](https://docs.agno.com/history/agent/chat-history.md)
- [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/overview.md): Persist and access conversation history for multi-turn interactions.
- [Member History](https://docs.agno.com/history/team/history-of-members.md)
- [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)
- [Share Member Interactions](https://docs.agno.com/history/team/share-member-interactions.md)
- [Team History](https://docs.agno.com/history/team/team-history.md)
- [Per-Step History](https://docs.agno.com/history/workflow/enable-history-for-step.md): This example demonstrates a workflow with history enabled for a specific step.
- [History in Functions](https://docs.agno.com/history/workflow/get-history-in-function.md): This example demonstrates how to get workflow history in a custom function.
- [Intent Routing](https://docs.agno.com/history/workflow/intent-routing-with-history.md): This example demonstrates how to use workflow history in intent routing.
- [Multi-Purpose CLI](https://docs.agno.com/history/workflow/multi-purpose-cli.md): This example demonstrates how to use workflow history in a multi purpose CLI.
- [Workflow History & Continuous Execution](https://docs.agno.com/history/workflow/overview.md): Build conversational workflows that maintain context across multiple executions, creating truly intelligent and natural interactions.
- [Single Step Workflow](https://docs.agno.com/history/workflow/single-step-continuous-execution-workflow.md): This example demonstrates a workflow with a single step that is executed continuously with access to workflow history.
- [Multi-Step Workflow](https://docs.agno.com/history/workflow/workflow-with-history-enabled-for-steps.md): This example demonstrates a workflow with history enabled for specific steps.
- [Approval](https://docs.agno.com/hitl/approval.md): Admin-mediated HITL workflows with persistent records and audit trails.
- [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.
- [Human-in-the-Loop (HITL)](https://docs.agno.com/hitl/overview.md): Control agent execution flow with human oversight and input.
- [Agentic User Input with Control Flow](https://docs.agno.com/hitl/usage/agentic-user-input.md): This example demonstrates how to use UserControlFlowTools to allow agents to dynamically request user input when they need additional information to complete tasks.
- [Tool Confirmation Required](https://docs.agno.com/hitl/usage/confirmation-required.md): This example demonstrates how to implement human-in-the-loop functionality by requiring user confirmation before executing sensitive tool operations.
- [Async Tool Confirmation Required](https://docs.agno.com/hitl/usage/confirmation-required-async.md): This example demonstrates how to implement human-in-the-loop functionality with async agents, requiring user confirmation before executing tool operations.
- [Confirmation Required with Mixed Tools](https://docs.agno.com/hitl/usage/confirmation-required-mixed-tools.md): This example demonstrates human-in-the-loop functionality where only some tools require user confirmation. The agent executes tools that don't require confirmation automatically and pauses only for tools that need approval.
- [Confirmation Required with Multiple Tools](https://docs.agno.com/hitl/usage/confirmation-required-multiple-tools.md): This example demonstrates human-in-the-loop functionality with multiple tools that require confirmation. It shows how to handle user confirmation during tool execution and gracefully cancel operations based on user choice.
- [Confirmation Required with Async Streaming](https://docs.agno.com/hitl/usage/confirmation-required-stream-async.md): This example demonstrates human-in-the-loop functionality with asynchronous streaming responses. It shows how to handle user confirmation during tool execution in an async environment while maintaining real-time streaming.
- [Confirmation Required with Toolkit](https://docs.agno.com/hitl/usage/confirmation-required-toolkit.md): This example demonstrates human-in-the-loop functionality using toolkit-based tools that require confirmation. It shows how to handle user confirmation when working with pre-built tool collections like YFinanceTools.
- [Confirmation Required with History](https://docs.agno.com/hitl/usage/confirmation-required-with-history.md): This example demonstrates human-in-the-loop functionality while maintaining conversation history. It shows how user confirmation works when the agent has access to previous conversation context.
- [Confirmation Required with Run ID](https://docs.agno.com/hitl/usage/confirmation-required-with-run-id.md): This example demonstrates human-in-the-loop functionality using specific run IDs for session management. It shows how to continue agent execution with updated tools using run identifiers.
- [External Tool Execution](https://docs.agno.com/hitl/usage/external-tool-execution.md): This example demonstrates how to execute tools outside of the agent using external tool execution. This pattern allows you to control tool execution externally while maintaining agent functionality.
- [External Tool Execution Async](https://docs.agno.com/hitl/usage/external-tool-execution-async.md): This example demonstrates how to execute tools outside of the agent using external tool execution in an asynchronous environment. This pattern allows you to control tool execution externally while maintaining agent functionality with async operations.
- [External Tool Execution Stream Async](https://docs.agno.com/hitl/usage/external-tool-execution-stream-async.md): This example demonstrates how to execute tools outside of the agent using external tool execution with async streaming responses. It shows how to handle external tool execution in an asynchronous environment while maintaining real-time streaming.
- [External Tool Execution Toolkit](https://docs.agno.com/hitl/usage/external-tool-execution-toolkit.md): This example demonstrates how to execute toolkit-based tools outside of the agent using external tool execution. It shows how to create a custom toolkit with tools that require external execution.
- [User Input Required for Tool Execution](https://docs.agno.com/hitl/usage/user-input-required.md): This example demonstrates how to create tools that require user input before execution, allowing for dynamic data collection during agent runs.
- [User Input Required All Fields](https://docs.agno.com/hitl/usage/user-input-required-all-fields.md): This example demonstrates how to use the requires_user_input parameter to collect input for all fields in a tool. It shows how to handle user input schema and collect values for each required field.
- [User Input Required Async](https://docs.agno.com/hitl/usage/user-input-required-async.md): This example demonstrates how to use the requires_user_input parameter with asynchronous operations. It shows how to collect specific user input fields in an async environment.
- [User Input Required Stream Async](https://docs.agno.com/hitl/usage/user-input-required-stream-async.md): This example demonstrates how to use the requires_user_input parameter with async streaming responses. It shows how to collect specific user input fields in an asynchronous environment while maintaining real-time streaming.
- [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.
- [Pre-hooks and Post-hooks](https://docs.agno.com/hooks/overview.md): Execute custom logic before and after agent runs with hooks.
- [Input Transformation Pre-Hook](https://docs.agno.com/hooks/usage/agent/input-transformation-pre-hook.md)
- [Input Validation Pre-Hook](https://docs.agno.com/hooks/usage/agent/input-validation-pre-hook.md)
- [Output Transformation Post-Hook](https://docs.agno.com/hooks/usage/agent/output-transformation-post-hook.md)
- [Output Validation Post-Hook](https://docs.agno.com/hooks/usage/agent/output-validation-post-hook.md)
- [Input Transformation Pre-Hook](https://docs.agno.com/hooks/usage/team/input-transformation-pre-hook.md)
- [Input Validation Pre-Hook](https://docs.agno.com/hooks/usage/team/input-validation-pre-hook.md)
- [Output Transformation Post-Hook](https://docs.agno.com/hooks/usage/team/output-transformation-post-hook.md)
- [Output Validation Post-Hook](https://docs.agno.com/hooks/usage/team/output-validation-post-hook.md)
- [Welcome to Agno](https://docs.agno.com/index.md): Build, run, and manage agentic software at scale.
- [Agno Infra](https://docs.agno.com/infra/overview.md)
- [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): Use secondary model, custom styling to refine the final output.
- [Input & Output](https://docs.agno.com/input-output/overview.md): Learn how to pass data to agents and handle their responses.
- [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 validated Pydantic object from agent instead of raw text.
- [Structured Output for Teams](https://docs.agno.com/input-output/structured-output/team.md): Get validated Pydantic object from team instead of raw text.
- [Discord Bot](https://docs.agno.com/integrations/discord/overview.md): Host agents as Discord Bots.
- [Agent with Media](https://docs.agno.com/integrations/discord/usage/agent-with-media.md)
- [Agent with User Memory](https://docs.agno.com/integrations/discord/usage/agent-with-user-memory.md)
- [Basic](https://docs.agno.com/integrations/discord/usage/basic.md)
- [AgentSystems Notary](https://docs.agno.com/integrations/governance/agentsystems-notary.md): Cryptographically verifiable audit trails for Agno applications.
- [Memori](https://docs.agno.com/integrations/memory/memori.md): Memori is an open-source memory layer for AI. It automatically captures conversations, extracts meaningful facts, and makes them searchable across entities, processes, and sessions.
- [Scenario Testing](https://docs.agno.com/integrations/testing/overview.md)
- [Scenario Testing](https://docs.agno.com/integrations/testing/usage/basic.md)
- [Introduction](https://docs.agno.com/introduction.md): **Build, run, and manage agentic software at scale.**
- [Agentic RAG with LanceDB](https://docs.agno.com/knowledge/agents/agentic-rag-lancedb.md)
- [Agentic RAG with PgVector](https://docs.agno.com/knowledge/agents/agentic-rag-pgvector.md)
- [Agents with Knowledge](https://docs.agno.com/knowledge/agents/overview.md): Understanding knowledge and how to use it with Agno agents
- [Traditional RAG with LanceDB](https://docs.agno.com/knowledge/agents/traditional-rag-lancedb.md)
- [Traditional RAG with PgVector](https://docs.agno.com/knowledge/agents/traditional-rag-pgvector.md)
- [Agentic Chunking](https://docs.agno.com/knowledge/concepts/chunking/agentic-chunking.md)
- [Code Chunking](https://docs.agno.com/knowledge/concepts/chunking/code-chunking.md)
- [CSV Row Chunking](https://docs.agno.com/knowledge/concepts/chunking/csv-row-chunking.md)
- [Custom Chunking](https://docs.agno.com/knowledge/concepts/chunking/custom-chunking.md)
- [Document Chunking](https://docs.agno.com/knowledge/concepts/chunking/document-chunking.md)
- [Fixed Size Chunking](https://docs.agno.com/knowledge/concepts/chunking/fixed-size-chunking.md)
- [Markdown Chunking](https://docs.agno.com/knowledge/concepts/chunking/markdown-chunking.md)
- [Chunking](https://docs.agno.com/knowledge/concepts/chunking/overview.md): Split documents into smaller pieces for effective vector search.
- [Recursive Chunking](https://docs.agno.com/knowledge/concepts/chunking/recursive-chunking.md)
- [Semantic Chunking](https://docs.agno.com/knowledge/concepts/chunking/semantic-chunking.md)
- [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.
- [Contents Database](https://docs.agno.com/knowledge/concepts/contents-db.md): Track and manage the content you've added to your knowledge base.
- [Cohere Embedder](https://docs.agno.com/knowledge/concepts/embedder/cohere/cohere-embedder.md)
- [Gemini Embedder](https://docs.agno.com/knowledge/concepts/embedder/gemini/gemini-embedder.md)
- [Mistral Embedder](https://docs.agno.com/knowledge/concepts/embedder/mistral/mistral-embedder.md)
- [Ollama Embedder](https://docs.agno.com/knowledge/concepts/embedder/ollama/ollama-embedder.md)
- [OpenAI Embedder](https://docs.agno.com/knowledge/concepts/embedder/openai/openai-embedder.md)
- [Embedders](https://docs.agno.com/knowledge/concepts/embedder/overview.md): Convert text into vector representations for semantic search.
- [VoyageAI Embedder](https://docs.agno.com/knowledge/concepts/embedder/voyageai/voyageai-embedder.md)
- [Filtering](https://docs.agno.com/knowledge/concepts/filters/overview.md): Filter knowledge searches by metadata for precise retrieval.
- [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.
- [Performance Tips](https://docs.agno.com/knowledge/concepts/performance-tips.md): Optimize knowledge base performance, search quality, and content loading speed.
- [CSV Reader](https://docs.agno.com/knowledge/concepts/readers/csv-reader.md)
- [Docling Reader](https://docs.agno.com/knowledge/concepts/readers/docling-reader.md)
- [JSON Reader](https://docs.agno.com/knowledge/concepts/readers/json-reader.md)
- [Markdown Reader](https://docs.agno.com/knowledge/concepts/readers/markdown-reader.md)
- [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)
- [Website Reader](https://docs.agno.com/knowledge/concepts/readers/website-reader.md)
- [YouTube Reader](https://docs.agno.com/knowledge/concepts/readers/youtube-reader.md)
- [Agentic RAG with Reranking](https://docs.agno.com/knowledge/concepts/search-and-retrieval/agentic-rag.md): Combine agentic search, hybrid retrieval, and reranking for high-quality responses.
- [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.
- [Hybrid Search](https://docs.agno.com/knowledge/concepts/search-and-retrieval/hybrid-search.md): Combine vector similarity with keyword matching for better retrieval accuracy.
- [Keyword Search](https://docs.agno.com/knowledge/concepts/search-and-retrieval/keyword-search.md): Find content using exact word and phrase matching.
- [Search & Retrieval](https://docs.agno.com/knowledge/concepts/search-and-retrieval/overview.md): How agents search knowledge bases to find relevant information.
- [Vector Search](https://docs.agno.com/knowledge/concepts/search-and-retrieval/vector-search.md): Find content by semantic meaning using vector similarity.
- [Vector Databases](https://docs.agno.com/knowledge/concepts/vector-db.md): Store embeddings and search for similar content.
- [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.
- [Distributed RAG with LanceDB](https://docs.agno.com/knowledge/teams/distributed-rag-lancedb.md)
- [Distributed RAG with PgVector](https://docs.agno.com/knowledge/teams/distributed-rag-pgvector.md)
- [Teams with Knowledge](https://docs.agno.com/knowledge/teams/overview.md): Use knowledge bases with teams.
- [Team with Knowledge Base](https://docs.agno.com/knowledge/teams/team-with-knowledge.md)
- [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.
- [Cassandra Vector Database](https://docs.agno.com/knowledge/vector-stores/cassandra/overview.md): Use Cassandra as a vector database for your Knowledge Base.
- [Cassandra Async](https://docs.agno.com/knowledge/vector-stores/cassandra/usage/async-cassandra-db.md)
- [Cassandra](https://docs.agno.com/knowledge/vector-stores/cassandra/usage/cassandra-db.md)
- [ChromaDB Vector Database](https://docs.agno.com/knowledge/vector-stores/chroma/overview.md): Use ChromaDB as a vector database for your Knowledge Base.
- [ChromaDB Async](https://docs.agno.com/knowledge/vector-stores/chroma/usage/async-chroma-db.md)
- [ChromaDB](https://docs.agno.com/knowledge/vector-stores/chroma/usage/chroma-db.md)
- [Chroma Hybrid Search](https://docs.agno.com/knowledge/vector-stores/chroma/usage/chroma-hybrid-search.md)
- [Clickhouse Vector Database](https://docs.agno.com/knowledge/vector-stores/clickhouse/overview.md): Use ClickHouse as a vector database for your Knowledge Base.
- [ClickHouse Async](https://docs.agno.com/knowledge/vector-stores/clickhouse/usage/async-clickhouse-db.md)
- [ClickHouse](https://docs.agno.com/knowledge/vector-stores/clickhouse/usage/clickhouse-db.md)
- [Couchbase Vector Database](https://docs.agno.com/knowledge/vector-stores/couchbase/overview.md): Use Couchbase as a vector database for your Knowledge Base.
- [Couchbase Async](https://docs.agno.com/knowledge/vector-stores/couchbase/usage/async-couchbase-db.md)
- [Couchbase](https://docs.agno.com/knowledge/vector-stores/couchbase/usage/couchbase-db.md)
- [Vector Store Index](https://docs.agno.com/knowledge/vector-stores/index.md): Index of all vector stores supported by Agno.
- [LanceDB Vector Database](https://docs.agno.com/knowledge/vector-stores/lancedb/overview.md): Use LanceDB as a vector database for your Knowledge Base.
- [LanceDB Async](https://docs.agno.com/knowledge/vector-stores/lancedb/usage/async-lance-db.md)
- [LanceDB](https://docs.agno.com/knowledge/vector-stores/lancedb/usage/lance-db.md)
- [LanceDB Hybrid Search](https://docs.agno.com/knowledge/vector-stores/lancedb/usage/lance-db-hybrid-search.md)
- [LangChain Vector Database](https://docs.agno.com/knowledge/vector-stores/langchain/overview.md): Use LangChain as a vector database for your Knowledge Base.
- [LangChain Async](https://docs.agno.com/knowledge/vector-stores/langchain/usage/async-langchain-db.md)
- [LangChain](https://docs.agno.com/knowledge/vector-stores/langchain/usage/langchain-db.md)
- [LightRAG Vector Database](https://docs.agno.com/knowledge/vector-stores/lightrag/overview.md): Use LightRAG as a vector database for your Knowledge Base.
- [LightRAG Async](https://docs.agno.com/knowledge/vector-stores/lightrag/usage/async-lightrag-db.md)
- [LightRAG](https://docs.agno.com/knowledge/vector-stores/lightrag/usage/lightrag-db.md)
- [LlamaIndex Vector Database](https://docs.agno.com/knowledge/vector-stores/llamaindex/overview.md): Use LlamaIndex as a vector database for your Knowledge Base.
- [LlamaIndex Async](https://docs.agno.com/knowledge/vector-stores/llamaindex/usage/async-llamaindex-db.md)
- [LlamaIndex](https://docs.agno.com/knowledge/vector-stores/llamaindex/usage/llamaindex-db.md)
- [Milvus Vector Database](https://docs.agno.com/knowledge/vector-stores/milvus/overview.md): Use Milvus as a vector database for your Knowledge Base.
- [Milvus Async](https://docs.agno.com/knowledge/vector-stores/milvus/usage/async-milvus-db.md)
- [Milvus Async Hybrid Search](https://docs.agno.com/knowledge/vector-stores/milvus/usage/async-milvus-db-hybrid-search.md)
- [Milvus](https://docs.agno.com/knowledge/vector-stores/milvus/usage/milvus-db.md)
- [Milvus Hybrid Search](https://docs.agno.com/knowledge/vector-stores/milvus/usage/milvus-db-hybrid-search.md)
- [MongoDB Vector Database](https://docs.agno.com/knowledge/vector-stores/mongodb/overview.md): Use MongoDB as a vector database for your Knowledge Base.
- [MongoDB Async](https://docs.agno.com/knowledge/vector-stores/mongodb/usage/async-mongo-db.md)
- [MongoDB](https://docs.agno.com/knowledge/vector-stores/mongodb/usage/mongo-db.md)
- [MongoDB Hybrid Search](https://docs.agno.com/knowledge/vector-stores/mongodb/usage/mongo-db-hybrid-search.md)
- [PgVector Vector Database](https://docs.agno.com/knowledge/vector-stores/pgvector/overview.md): Use PgVector as a vector database for your Knowledge Base.
- [PgVector Async](https://docs.agno.com/knowledge/vector-stores/pgvector/usage/async-pgvector-db.md)
- [PgVector](https://docs.agno.com/knowledge/vector-stores/pgvector/usage/pgvector-db.md)
- [PgVector Hybrid Search](https://docs.agno.com/knowledge/vector-stores/pgvector/usage/pgvector-hybrid-search.md)
- [Pinecone Vector Database](https://docs.agno.com/knowledge/vector-stores/pinecone/overview.md): Use Pinecone as a vector database for your Knowledge Base.
- [Pinecone Async](https://docs.agno.com/knowledge/vector-stores/pinecone/usage/async-pinecone-db.md)
- [Pinecone](https://docs.agno.com/knowledge/vector-stores/pinecone/usage/pinecone-db.md)
- [Qdrant Vector Database](https://docs.agno.com/knowledge/vector-stores/qdrant/overview.md): Use Qdrant as a vector database for your Knowledge Base.
- [Qdrant Async](https://docs.agno.com/knowledge/vector-stores/qdrant/usage/async-qdrant-db.md)
- [Qdrant](https://docs.agno.com/knowledge/vector-stores/qdrant/usage/qdrant-db.md)
- [Qdrant Hybrid Search](https://docs.agno.com/knowledge/vector-stores/qdrant/usage/qdrant-db-hybrid-search.md)
- [Redis Vector Database](https://docs.agno.com/knowledge/vector-stores/redis/overview.md): Use Redis as a vector database for your Knowledge Base.
- [Redis Async](https://docs.agno.com/knowledge/vector-stores/redis/usage/async-redis-db.md)
- [Redis](https://docs.agno.com/knowledge/vector-stores/redis/usage/redis-db.md)
- [SingleStore Vector Database](https://docs.agno.com/knowledge/vector-stores/singlestore/overview.md): Use SingleStore as a vector database for your Knowledge Base.
- [SingleStore Async](https://docs.agno.com/knowledge/vector-stores/singlestore/usage/async-singlestore-db.md)
- [SingleStore](https://docs.agno.com/knowledge/vector-stores/singlestore/usage/singlestore-db.md)
- [SurrealDB Vector Database](https://docs.agno.com/knowledge/vector-stores/surrealdb/overview.md): Use SurrealDB as a vector database for your Knowledge Base.
- [SurrealDB Async](https://docs.agno.com/knowledge/vector-stores/surrealdb/usage/async-surreal-db.md)
- [SurrealDB](https://docs.agno.com/knowledge/vector-stores/surrealdb/usage/surreal-db.md)
- [Upstash Vector Database](https://docs.agno.com/knowledge/vector-stores/upstash/overview.md): Use Upstash as a vector database for your Knowledge Base.
- [Upstash Async](https://docs.agno.com/knowledge/vector-stores/upstash/usage/async-upstash-db.md)
- [Upstash](https://docs.agno.com/knowledge/vector-stores/upstash/usage/upstash-db.md)
- [Weaviate Vector Database](https://docs.agno.com/knowledge/vector-stores/weaviate/overview.md): Use Weaviate as a vector database for your Knowledge Base.
- [Weaviate Async](https://docs.agno.com/knowledge/vector-stores/weaviate/usage/async-weaviate-db.md)
- [Weaviate](https://docs.agno.com/knowledge/vector-stores/weaviate/usage/weaviate-db.md)
- [Weaviate Hybrid Search](https://docs.agno.com/knowledge/vector-stores/weaviate/usage/weaviate-db-hybrid-search.md)
- [Custom Schemas](https://docs.agno.com/learning/custom-schemas.md): Extend stores with custom fields for your domain.
- [Learning Modes](https://docs.agno.com/learning/learning-modes.md): Control when and how agents learn.
- [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.
- [Decision Log](https://docs.agno.com/learning/stores/decision-log.md): Decisions with reasoning for auditing and learning.
- [Entity Memory](https://docs.agno.com/learning/stores/entity-memory.md): Facts about companies, projects, and people.
- [Learning Stores](https://docs.agno.com/learning/stores/intro.md): Each store captures a different type of knowledge.
- [Learned Knowledge](https://docs.agno.com/learning/stores/learned-knowledge.md): Insights that transfer across users.
- [Session Context](https://docs.agno.com/learning/stores/session-context.md): Goals, plans, and progress for active sessions.
- [User Memory](https://docs.agno.com/learning/stores/user-memory.md): Unstructured observations about users.
- [User Profile](https://docs.agno.com/learning/stores/user-profile.md): Structured facts about users.
- [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)
- [Share Memory between Agents](https://docs.agno.com/memory/agent/agents-share-memory.md)
- [Custom Memory Manager](https://docs.agno.com/memory/agent/custom-memory-manager.md)
- [Multi-user, Multi-session Chat](https://docs.agno.com/memory/agent/multi-user-multi-session-chat.md)
- [Multi-User, Multi-Session Chat Concurrently](https://docs.agno.com/memory/agent/multi-user-multi-session-chat-concurrent.md)
- [Agent Memory](https://docs.agno.com/memory/agent/overview.md): Memory gives an Agent the ability to recall information about the user.
- [Share Memory and History between Agents](https://docs.agno.com/memory/agent/share-memory-and-history-between-agents.md)
- [Production Best Practices](https://docs.agno.com/memory/best-practices.md): Avoid common pitfalls, optimize costs, and ensure reliable memory behavior in production.
- [What is Memory?](https://docs.agno.com/memory/overview.md): Give your agents the ability to remember user preferences, context, and past interactions for truly personalized experiences.
- [Teams with Memory](https://docs.agno.com/memory/team/overview.md): Use persistent memory with teams.
- [Team with Agentic Memory](https://docs.agno.com/memory/team/team-with-agentic-memory.md)
- [Team with Memory Manager](https://docs.agno.com/memory/team/team-with-memory-manager.md)
- [Custom Memory Instructions](https://docs.agno.com/memory/working-with-memories/custom-memory-instructions.md)
- [Memory Creation](https://docs.agno.com/memory/working-with-memories/memory-creation.md)
- [Memory Optimization](https://docs.agno.com/memory/working-with-memories/memory-optimization.md)
- [Memory Search](https://docs.agno.com/memory/working-with-memories/memory-search.md)
- [Memory with MongoDB](https://docs.agno.com/memory/working-with-memories/mongodb-memory.md)
- [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 with PostgreSQL](https://docs.agno.com/memory/working-with-memories/postgres-memory.md)
- [Memory with Redis](https://docs.agno.com/memory/working-with-memories/redis-memory.md)
- [Memory with SQLite](https://docs.agno.com/memory/working-with-memories/sqlite-memory.md)
- [Standalone Memory](https://docs.agno.com/memory/working-with-memories/standalone-memory.md)
- [Response Caching](https://docs.agno.com/models/cache-response.md): Cache model responses locally to reduce costs during development and testing.
- [Compatibility Overview](https://docs.agno.com/models/compatibility.md): Understand which features are supported across different model providers in Agno.
- [Model as String](https://docs.agno.com/models/model-as-string.md): Use the convenient provider:model_id string format to specify models without importing model classes.
- [What are Models?](https://docs.agno.com/models/overview.md): Language Models are machine-learning programs that are trained to understand natural language and code.
- [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)
- [Streaming Agent](https://docs.agno.com/models/providers/cloud/aws-bedrock/usage/basic-stream.md)
- [Agent with Image Input](https://docs.agno.com/models/providers/cloud/aws-bedrock/usage/image-agent.md)
- [Agent with Knowledge](https://docs.agno.com/models/providers/cloud/aws-bedrock/usage/knowledge.md)
- [Agent with Storage](https://docs.agno.com/models/providers/cloud/aws-bedrock/usage/storage.md)
- [Agent with Structured Outputs](https://docs.agno.com/models/providers/cloud/aws-bedrock/usage/structured-output.md)
- [Agent with Tools](https://docs.agno.com/models/providers/cloud/aws-bedrock/usage/tool-use.md)
- [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)
- [Streaming Agent](https://docs.agno.com/models/providers/cloud/aws-claude/usage/basic-stream.md)
- [Agent with Knowledge](https://docs.agno.com/models/providers/cloud/aws-claude/usage/knowledge.md)
- [Agent with Storage](https://docs.agno.com/models/providers/cloud/aws-claude/usage/storage.md)
- [Agent with Structured Outputs](https://docs.agno.com/models/providers/cloud/aws-claude/usage/structured-output.md)
- [Agent with Tools](https://docs.agno.com/models/providers/cloud/aws-claude/usage/tool-use.md)
- [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)
- [Basic Streaming](https://docs.agno.com/models/providers/cloud/azure-ai-foundry/usage/basic-stream.md)
- [Agent with Knowledge Base](https://docs.agno.com/models/providers/cloud/azure-ai-foundry/usage/knowledge.md)
- [Agent with Storage](https://docs.agno.com/models/providers/cloud/azure-ai-foundry/usage/storage.md)
- [Agent with Structured Outputs](https://docs.agno.com/models/providers/cloud/azure-ai-foundry/usage/structured-output.md)
- [Agent with Tools](https://docs.agno.com/models/providers/cloud/azure-ai-foundry/usage/tool-use.md)
- [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)
- [Basic Streaming](https://docs.agno.com/models/providers/cloud/azure-openai/usage/basic-stream.md)
- [Agent with Knowledge Base](https://docs.agno.com/models/providers/cloud/azure-openai/usage/knowledge.md)
- [Agent with Storage](https://docs.agno.com/models/providers/cloud/azure-openai/usage/storage.md)
- [Agent with Structured Outputs](https://docs.agno.com/models/providers/cloud/azure-openai/usage/structured-output.md)
- [Agent with Tools](https://docs.agno.com/models/providers/cloud/azure-openai/usage/tool-use.md)
- [IBM WatsonX](https://docs.agno.com/models/providers/cloud/ibm-watsonx/overview.md): Use IBM WatsonX foundation models with Agno agents.
- [Async Basic Agent](https://docs.agno.com/models/providers/cloud/ibm-watsonx/usage/async-basic.md)
- [Async Streaming Agent](https://docs.agno.com/models/providers/cloud/ibm-watsonx/usage/async-basic-stream.md)
- [Agent with Async Tool Usage](https://docs.agno.com/models/providers/cloud/ibm-watsonx/usage/async-tool-use.md)
- [Basic Agent](https://docs.agno.com/models/providers/cloud/ibm-watsonx/usage/basic.md)
- [Streaming Basic Agent](https://docs.agno.com/models/providers/cloud/ibm-watsonx/usage/basic-stream.md)
- [Image Agent](https://docs.agno.com/models/providers/cloud/ibm-watsonx/usage/image-agent-bytes.md)
- [RAG Agent](https://docs.agno.com/models/providers/cloud/ibm-watsonx/usage/knowledge.md)
- [Agent with Storage](https://docs.agno.com/models/providers/cloud/ibm-watsonx/usage/storage.md)
- [Agent with Structured Output](https://docs.agno.com/models/providers/cloud/ibm-watsonx/usage/structured-output.md)
- [Agent with Tools](https://docs.agno.com/models/providers/cloud/ibm-watsonx/usage/tool-use.md)
- [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)
- [Streaming Agent](https://docs.agno.com/models/providers/cloud/vertexai-claude/usage/basic-stream.md)
- [Image Input Bytes Content](https://docs.agno.com/models/providers/cloud/vertexai-claude/usage/image-input-bytes.md)
- [Image Input URL](https://docs.agno.com/models/providers/cloud/vertexai-claude/usage/image-input-url.md)
- [PDF Input Bytes Agent](https://docs.agno.com/models/providers/cloud/vertexai-claude/usage/pdf-input-bytes.md)
- [PDF Input Local Agent](https://docs.agno.com/models/providers/cloud/vertexai-claude/usage/pdf-input-local.md)
- [PDF Input URL Agent](https://docs.agno.com/models/providers/cloud/vertexai-claude/usage/pdf-input-url.md)
- [Agent with Structured Outputs](https://docs.agno.com/models/providers/cloud/vertexai-claude/usage/structured-output.md)
- [Agent with Tools](https://docs.agno.com/models/providers/cloud/vertexai-claude/usage/tool-use.md)
- [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 OpenAI](https://docs.agno.com/models/providers/gateways/cerebras-openai/overview.md): Use Cerebras via OpenAI-compatible interface in Agno agents.
- [Basic Agent](https://docs.agno.com/models/providers/gateways/cerebras-openai/usage/basic.md)
- [Streaming Agent](https://docs.agno.com/models/providers/gateways/cerebras-openai/usage/basic-stream.md)
- [Agent with Storage](https://docs.agno.com/models/providers/gateways/cerebras-openai/usage/knowledge.md)
- [Agent with Storage](https://docs.agno.com/models/providers/gateways/cerebras-openai/usage/storage.md)
- [Agent with Structured Outputs](https://docs.agno.com/models/providers/gateways/cerebras-openai/usage/structured-output.md)
- [Agent with Tools](https://docs.agno.com/models/providers/gateways/cerebras-openai/usage/tool-use.md)
- [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)
- [Streaming Agent](https://docs.agno.com/models/providers/gateways/cerebras/usage/basic-stream.md)
- [Agent with Knowledge Base](https://docs.agno.com/models/providers/gateways/cerebras/usage/knowledge.md)
- [Agent with Storage](https://docs.agno.com/models/providers/gateways/cerebras/usage/storage.md)
- [Agent with Structured Outputs](https://docs.agno.com/models/providers/gateways/cerebras/usage/structured-output.md)
- [Agent with Tools](https://docs.agno.com/models/providers/gateways/cerebras/usage/tool-use.md)
- [CometAPI](https://docs.agno.com/models/providers/gateways/cometapi/overview.md): Use CometAPI models with Agno agents.
- [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)
- [Streaming Agent](https://docs.agno.com/models/providers/gateways/deepinfra/usage/basic-stream.md)
- [Agent with Structured Outputs](https://docs.agno.com/models/providers/gateways/deepinfra/usage/structured-output.md)
- [Agent with Tools](https://docs.agno.com/models/providers/gateways/deepinfra/usage/tool-use.md)
- [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)
- [Streaming Agent](https://docs.agno.com/models/providers/gateways/fireworks/usage/basic-stream.md)
- [Agent with Structured Outputs](https://docs.agno.com/models/providers/gateways/fireworks/usage/structured-output.md)
- [Agent with Tools](https://docs.agno.com/models/providers/gateways/fireworks/usage/tool-use.md)
- [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)
- [Streaming Agent](https://docs.agno.com/models/providers/gateways/groq/usage/basic-stream.md)
- [Browser Search Agent](https://docs.agno.com/models/providers/gateways/groq/usage/browser-search.md)
- [Deep Knowledge Agent](https://docs.agno.com/models/providers/gateways/groq/usage/deep-knowledge.md)
- [Image Agent](https://docs.agno.com/models/providers/gateways/groq/usage/image-agent.md)
- [Agent with Knowledge](https://docs.agno.com/models/providers/gateways/groq/usage/knowledge.md)
- [Agent with Metrics](https://docs.agno.com/models/providers/gateways/groq/usage/metrics.md)
- [Reasoning Agent](https://docs.agno.com/models/providers/gateways/groq/usage/reasoning-agent.md)
- [Agent with Storage](https://docs.agno.com/models/providers/gateways/groq/usage/storage.md)
- [Agent with Structured Outputs](https://docs.agno.com/models/providers/gateways/groq/usage/structured-output.md)
- [Agent with Tools](https://docs.agno.com/models/providers/gateways/groq/usage/tool-use.md)
- [Transcription Agent](https://docs.agno.com/models/providers/gateways/groq/usage/transcription-agent.md)
- [Translation Agent](https://docs.agno.com/models/providers/gateways/groq/usage/translation-agent.md)
- [HuggingFace](https://docs.agno.com/models/providers/gateways/huggingface/overview.md): Use Hugging Face models with Agno agents.
- [Async Basic.Py](https://docs.agno.com/models/providers/gateways/huggingface/usage/async-basic.md)
- [Async Basic Stream.Py](https://docs.agno.com/models/providers/gateways/huggingface/usage/async-basic-stream.md)
- [Basic Agent](https://docs.agno.com/models/providers/gateways/huggingface/usage/basic.md)
- [Streaming Agent](https://docs.agno.com/models/providers/gateways/huggingface/usage/basic-stream.md)
- [Llama Essay Writer](https://docs.agno.com/models/providers/gateways/huggingface/usage/llama-essay-writer.md)
- [Tool Use](https://docs.agno.com/models/providers/gateways/huggingface/usage/tool-use.md)
- [LangDB](https://docs.agno.com/models/providers/gateways/langdb/overview.md)
- [Basic Agent](https://docs.agno.com/models/providers/gateways/langdb/usage/basic.md)
- [Basic Streaming Agent](https://docs.agno.com/models/providers/gateways/langdb/usage/basic-stream.md)
- [Data Analyst Agent](https://docs.agno.com/models/providers/gateways/langdb/usage/data-analyst.md)
- [Structured Output](https://docs.agno.com/models/providers/gateways/langdb/usage/structured-output.md)
- [Web Search Agent](https://docs.agno.com/models/providers/gateways/langdb/usage/tool-use.md)
- [LiteLLM OpenAI](https://docs.agno.com/models/providers/gateways/litellm-openai/overview.md): Use LiteLLM with Agno with an openai-compatible proxy server.
- [Basic Agent](https://docs.agno.com/models/providers/gateways/litellm-openai/usage/basic.md)
- [Streaming Agent](https://docs.agno.com/models/providers/gateways/litellm-openai/usage/basic-stream.md)
- [Agent with Tools](https://docs.agno.com/models/providers/gateways/litellm-openai/usage/tool-use.md)
- [LiteLLM](https://docs.agno.com/models/providers/gateways/litellm/overview.md): Integrate LiteLLM with Agno for a unified LLM experience.
- [Async Basic Agent](https://docs.agno.com/models/providers/gateways/litellm/usage/async-basic.md)
- [Async Basic Streaming Agent](https://docs.agno.com/models/providers/gateways/litellm/usage/async-basic-stream.md)
- [Async Tool Use](https://docs.agno.com/models/providers/gateways/litellm/usage/async-tool-use.md)
- [Audio Input Agent](https://docs.agno.com/models/providers/gateways/litellm/usage/audio-input-agent.md)
- [Basic Agent](https://docs.agno.com/models/providers/gateways/litellm/usage/basic.md)
- [Streaming Agent](https://docs.agno.com/models/providers/gateways/litellm/usage/basic-stream.md)
- [Agent with Knowledge](https://docs.agno.com/models/providers/gateways/litellm/usage/knowledge.md)
- [Agent with Storage](https://docs.agno.com/models/providers/gateways/litellm/usage/storage.md)
- [Agent with Structured Outputs](https://docs.agno.com/models/providers/gateways/litellm/usage/structured-output.md)
- [Agent with Tools](https://docs.agno.com/models/providers/gateways/litellm/usage/tool-use.md)
- [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)
- [Streaming Agent](https://docs.agno.com/models/providers/gateways/nebius/usage/basic-stream.md)
- [Agent with Knowledge](https://docs.agno.com/models/providers/gateways/nebius/usage/knowledge.md)
- [Agent with Storage](https://docs.agno.com/models/providers/gateways/nebius/usage/storage.md)
- [Agent with Structured Outputs](https://docs.agno.com/models/providers/gateways/nebius/usage/structured-output.md)
- [Agent with Tools](https://docs.agno.com/models/providers/gateways/nebius/usage/tool-use.md)
- [Neosantara](https://docs.agno.com/models/providers/gateways/neosantara/overview.md): Use Neosantara models in Agno agents.
- [Async Basic](https://docs.agno.com/models/providers/gateways/neosantara/usage/async-basic.md)
- [Async Basic Stream](https://docs.agno.com/models/providers/gateways/neosantara/usage/async-basic-stream.md)
- [Async Tool Use](https://docs.agno.com/models/providers/gateways/neosantara/usage/async-tool-use.md)
- [Basic Agent](https://docs.agno.com/models/providers/gateways/neosantara/usage/basic.md)
- [Basic Stream](https://docs.agno.com/models/providers/gateways/neosantara/usage/basic-stream.md)
- [Structured Output](https://docs.agno.com/models/providers/gateways/neosantara/usage/structured-output.md)
- [Tool Use](https://docs.agno.com/models/providers/gateways/neosantara/usage/tool-use.md)
- [Nexus](https://docs.agno.com/models/providers/gateways/nexus/overview.md): Use Nexus router models with Agno agents.
- [Async Basic Agent](https://docs.agno.com/models/providers/gateways/nexus/usage/async-basic.md)
- [Async Streaming Agent](https://docs.agno.com/models/providers/gateways/nexus/usage/async-basic-stream.md)
- [Async Agent with Tools](https://docs.agno.com/models/providers/gateways/nexus/usage/async-tool-use.md)
- [Basic Agent](https://docs.agno.com/models/providers/gateways/nexus/usage/basic.md)
- [Basic Streaming Agent](https://docs.agno.com/models/providers/gateways/nexus/usage/basic-stream.md)
- [Agent with Tools](https://docs.agno.com/models/providers/gateways/nexus/usage/tool-use.md)
- [Nvidia](https://docs.agno.com/models/providers/gateways/nvidia/overview.md): Use NVIDIA NeMo models with Agno agents.
- [Async Basic Agent](https://docs.agno.com/models/providers/gateways/nvidia/usage/async-basic.md)
- [Async Streaming Agent](https://docs.agno.com/models/providers/gateways/nvidia/usage/async-basic-stream.md)
- [Async Agent with Tools](https://docs.agno.com/models/providers/gateways/nvidia/usage/async-tool-use.md)
- [Basic Agent](https://docs.agno.com/models/providers/gateways/nvidia/usage/basic.md)
- [Basic Streaming Agent](https://docs.agno.com/models/providers/gateways/nvidia/usage/basic-stream.md)
- [Agent with Tools](https://docs.agno.com/models/providers/gateways/nvidia/usage/tool-use.md)
- [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)
- [Basic Agent with Streaming](https://docs.agno.com/models/providers/gateways/portkey/usage/basic-stream.md)
- [Structured Output Agent](https://docs.agno.com/models/providers/gateways/portkey/usage/structured-output.md)
- [Agent with Tools](https://docs.agno.com/models/providers/gateways/portkey/usage/tool-use.md)
- [Agent with Tools and Streaming](https://docs.agno.com/models/providers/gateways/portkey/usage/tool-use-stream.md)
- [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)
- [Streaming Agent](https://docs.agno.com/models/providers/gateways/requesty/usage/basic-stream.md)
- [Agent with Structured Output](https://docs.agno.com/models/providers/gateways/requesty/usage/structured-output.md)
- [Agent with Tools](https://docs.agno.com/models/providers/gateways/requesty/usage/tool-use.md)
- [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 Basic Agent](https://docs.agno.com/models/providers/gateways/siliconflow/usage/async-basic.md)
- [Async Streaming Agent](https://docs.agno.com/models/providers/gateways/siliconflow/usage/async-basic-stream.md)
- [Async Agent with Tools](https://docs.agno.com/models/providers/gateways/siliconflow/usage/async-tool-use.md)
- [Basic Agent](https://docs.agno.com/models/providers/gateways/siliconflow/usage/basic.md)
- [Basic Streaming Agent](https://docs.agno.com/models/providers/gateways/siliconflow/usage/basic-stream.md)
- [Agent with Tools](https://docs.agno.com/models/providers/gateways/siliconflow/usage/tool-use.md)
- [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)
- [Streaming Agent](https://docs.agno.com/models/providers/gateways/together/usage/basic-stream.md)
- [Image Agent](https://docs.agno.com/models/providers/gateways/together/usage/image-agent.md)
- [Image Input Bytes Content](https://docs.agno.com/models/providers/gateways/together/usage/image-agent-bytes.md)
- [Image Agent with Memory](https://docs.agno.com/models/providers/gateways/together/usage/image-agent-memory.md)
- [Agent with Structured Outputs](https://docs.agno.com/models/providers/gateways/together/usage/structured-output.md)
- [Agent with Tools](https://docs.agno.com/models/providers/gateways/together/usage/tool-use.md)
- [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)
- [Basic Stream](https://docs.agno.com/models/providers/local/llama-cpp/usage/basic-stream.md)
- [Structured Output](https://docs.agno.com/models/providers/local/llama-cpp/usage/structured-output.md)
- [Agent with Tools](https://docs.agno.com/models/providers/local/llama-cpp/usage/tool-use.md)
- [Agent with Tools Stream](https://docs.agno.com/models/providers/local/llama-cpp/usage/tool-use-stream.md)
- [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)
- [Streaming Agent](https://docs.agno.com/models/providers/local/lmstudio/usage/basic-stream.md)
- [Image Agent](https://docs.agno.com/models/providers/local/lmstudio/usage/image-agent.md)
- [Agent with Knowledge](https://docs.agno.com/models/providers/local/lmstudio/usage/knowledge.md)
- [Agent with Storage](https://docs.agno.com/models/providers/local/lmstudio/usage/storage.md)
- [Agent with Structured Outputs](https://docs.agno.com/models/providers/local/lmstudio/usage/structured-output.md)
- [Agent with Tools](https://docs.agno.com/models/providers/local/lmstudio/usage/tool-use.md)
- [Ollama](https://docs.agno.com/models/providers/local/ollama/overview.md): Run local models with Ollama in Agno agents.
- [Async Basic](https://docs.agno.com/models/providers/local/ollama/usage/async-basic.md)
- [Async Basic Stream](https://docs.agno.com/models/providers/local/ollama/usage/async-basic-stream.md)
- [Basic](https://docs.agno.com/models/providers/local/ollama/usage/basic.md)
- [Basic Stream](https://docs.agno.com/models/providers/local/ollama/usage/basic-stream.md)
- [Ollama Cloud](https://docs.agno.com/models/providers/local/ollama/usage/cloud.md)
- [Demo Deepseek R1](https://docs.agno.com/models/providers/local/ollama/usage/demo-deepseek-r1.md)
- [Demo Gemma](https://docs.agno.com/models/providers/local/ollama/usage/demo-gemma.md)
- [Demo Phi4](https://docs.agno.com/models/providers/local/ollama/usage/demo-phi4.md)
- [Demo Qwen](https://docs.agno.com/models/providers/local/ollama/usage/demo-qwen.md)
- [Image Agent](https://docs.agno.com/models/providers/local/ollama/usage/image-agent.md)
- [Knowledge](https://docs.agno.com/models/providers/local/ollama/usage/knowledge.md)
- [Memory](https://docs.agno.com/models/providers/local/ollama/usage/memory.md)
- [Multimodal Agent](https://docs.agno.com/models/providers/local/ollama/usage/multimodal.md)
- [Set Client](https://docs.agno.com/models/providers/local/ollama/usage/set-client.md)
- [Set Temperature](https://docs.agno.com/models/providers/local/ollama/usage/set-temperature.md)
- [Agent with Storage](https://docs.agno.com/models/providers/local/ollama/usage/storage.md)
- [Structured Output](https://docs.agno.com/models/providers/local/ollama/usage/structured-output.md)
- [Tool Use](https://docs.agno.com/models/providers/local/ollama/usage/tool-use.md)
- [Tool Use Stream](https://docs.agno.com/models/providers/local/ollama/usage/tool-use-stream.md)
- [vLLM](https://docs.agno.com/models/providers/local/vllm/overview.md)
- [Async Agent](https://docs.agno.com/models/providers/local/vllm/usage/async-basic.md)
- [Async Agent with Streaming](https://docs.agno.com/models/providers/local/vllm/usage/async-basic-stream.md)
- [Async Agent with Tools](https://docs.agno.com/models/providers/local/vllm/usage/async-tool-use.md)
- [Basic Agent](https://docs.agno.com/models/providers/local/vllm/usage/basic.md)
- [Agent with Streaming](https://docs.agno.com/models/providers/local/vllm/usage/basic-stream.md)
- [Code Generation](https://docs.agno.com/models/providers/local/vllm/usage/code-generation.md)
- [Agent with Memory](https://docs.agno.com/models/providers/local/vllm/usage/memory.md)
- [Agent with Storage](https://docs.agno.com/models/providers/local/vllm/usage/storage.md)
- [Structured Output](https://docs.agno.com/models/providers/local/vllm/usage/structured-output.md)
- [Agent with Tools](https://docs.agno.com/models/providers/local/vllm/usage/tool-use.md)
- [Model Index](https://docs.agno.com/models/providers/model-index.md): Index of all models supported by 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)
- [Streaming Agent](https://docs.agno.com/models/providers/native/anthropic/usage/basic-stream.md)
- [Beta Features](https://docs.agno.com/models/providers/native/anthropic/usage/betas.md): Enable experimental Anthropic beta features in Agno agents.
- [Response Caching](https://docs.agno.com/models/providers/native/anthropic/usage/cache-response.md): Cache model responses to reduce API calls and costs.
- [Code Execution Tool](https://docs.agno.com/models/providers/native/anthropic/usage/code-execution.md): Execute Python code in a sandboxed environment with Anthropic's code execution tool.
- [Context Editing](https://docs.agno.com/models/providers/native/anthropic/usage/context-management.md): Automatically manage context size with Anthropic's context editing.
- [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 Content](https://docs.agno.com/models/providers/native/anthropic/usage/image-input-bytes.md)
- [Image Input URL](https://docs.agno.com/models/providers/native/anthropic/usage/image-input-url.md)
- [Agent with Knowledge](https://docs.agno.com/models/providers/native/anthropic/usage/knowledge.md)
- [PDF Input Bytes Agent](https://docs.agno.com/models/providers/native/anthropic/usage/pdf-input-bytes.md)
- [PDF Input Local Agent](https://docs.agno.com/models/providers/native/anthropic/usage/pdf-input-local.md)
- [PDF Input URL Agent](https://docs.agno.com/models/providers/native/anthropic/usage/pdf-input-url.md)
- [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.
- [Claude Agent Skills](https://docs.agno.com/models/providers/native/anthropic/usage/skills.md): Create PowerPoint presentations, Excel spreadsheets, Word documents, and analyze PDFs with Claude Agent Skills
- [Agent with Structured Outputs](https://docs.agno.com/models/providers/native/anthropic/usage/structured-output.md)
- [Agent with Structured Outputs Streaming](https://docs.agno.com/models/providers/native/anthropic/usage/structured-output-stream.md)
- [Agent with Structured Outputs and Strict Tools](https://docs.agno.com/models/providers/native/anthropic/usage/structured-output-strict-tools.md)
- [Agent with Tools](https://docs.agno.com/models/providers/native/anthropic/usage/tool-use.md)
- [Web Fetch](https://docs.agno.com/models/providers/native/anthropic/usage/web-fetch.md)
- [Cohere](https://docs.agno.com/models/providers/native/cohere/overview.md): Use Cohere command models with Agno agents.
- [Basic Agent](https://docs.agno.com/models/providers/native/cohere/usage/basic.md)
- [Streaming Agent](https://docs.agno.com/models/providers/native/cohere/usage/basic-stream.md)
- [Image Agent](https://docs.agno.com/models/providers/native/cohere/usage/image-agent.md)
- [Agent with Knowledge](https://docs.agno.com/models/providers/native/cohere/usage/knowledge.md)
- [Agent with Storage](https://docs.agno.com/models/providers/native/cohere/usage/storage.md)
- [Agent with Structured Outputs](https://docs.agno.com/models/providers/native/cohere/usage/structured-output.md)
- [Agent with Tools](https://docs.agno.com/models/providers/native/cohere/usage/tool-use.md)
- [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)
- [Basic Agent with Streaming](https://docs.agno.com/models/providers/native/dashscope/usage/basic-stream.md)
- [Image Agent](https://docs.agno.com/models/providers/native/dashscope/usage/image-agent.md)
- [Image Agent with Bytes](https://docs.agno.com/models/providers/native/dashscope/usage/image-agent-bytes.md)
- [Structured Output Agent](https://docs.agno.com/models/providers/native/dashscope/usage/structured-output.md)
- [Thinking Agent](https://docs.agno.com/models/providers/native/dashscope/usage/thinking-agent.md)
- [Agent with Tools](https://docs.agno.com/models/providers/native/dashscope/usage/tool-use.md)
- [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)
- [Streaming Agent](https://docs.agno.com/models/providers/native/deepseek/usage/basic-stream.md)
- [Agent with Structured Outputs](https://docs.agno.com/models/providers/native/deepseek/usage/structured-output.md)
- [Agent with Tools](https://docs.agno.com/models/providers/native/deepseek/usage/tool-use.md)
- [Gemini](https://docs.agno.com/models/providers/native/google/overview.md): Use Google Gemini models with Agno agents.
- [Audio Input (Bytes Content)](https://docs.agno.com/models/providers/native/google/usage/audio-input-bytes-content.md)
- [Audio Input (Upload the file)](https://docs.agno.com/models/providers/native/google/usage/audio-input-file-upload.md)
- [Audio Input (Local file)](https://docs.agno.com/models/providers/native/google/usage/audio-input-local-file-upload.md)
- [Basic Agent](https://docs.agno.com/models/providers/native/google/usage/basic.md)
- [Streaming Agent](https://docs.agno.com/models/providers/native/google/usage/basic-stream.md)
- [Agent with External URL Input](https://docs.agno.com/models/providers/native/google/usage/external-url-input.md)
- [Flash Thinking Agent](https://docs.agno.com/models/providers/native/google/usage/flash-thinking.md)
- [Agent with GCS File Input](https://docs.agno.com/models/providers/native/google/usage/gcs-file-input.md)
- [Agent with Grounding](https://docs.agno.com/models/providers/native/google/usage/grounding.md)
- [Image Editing Agent](https://docs.agno.com/models/providers/native/google/usage/image-editing.md)
- [Image Generation Agent](https://docs.agno.com/models/providers/native/google/usage/image-generation.md)
- [Image Generation Agent (Streaming)](https://docs.agno.com/models/providers/native/google/usage/image-generation-stream.md)
- [Image Agent](https://docs.agno.com/models/providers/native/google/usage/image-input.md)
- [Image Agent with File Upload](https://docs.agno.com/models/providers/native/google/usage/image-input-file-upload.md)
- [Imagen Tool with OpenAI](https://docs.agno.com/models/providers/native/google/usage/imagen-tool.md)
- [Advanced Imagen Tool with Vertex AI](https://docs.agno.com/models/providers/native/google/usage/imagen-tool-advanced.md)
- [Agent with Knowledge](https://docs.agno.com/models/providers/native/google/usage/knowledge.md)
- [Agent with PDF Input (Local file)](https://docs.agno.com/models/providers/native/google/usage/pdf-input-local.md)
- [Agent with PDF Input (URL)](https://docs.agno.com/models/providers/native/google/usage/pdf-input-url.md)
- [Agent with S3 Pre-signed URL Input](https://docs.agno.com/models/providers/native/google/usage/s3-presigned-url-input.md)
- [Agent with Storage](https://docs.agno.com/models/providers/native/google/usage/storage.md)
- [Agent with Structured Outputs](https://docs.agno.com/models/providers/native/google/usage/structured-output.md)
- [Agent with Tools](https://docs.agno.com/models/providers/native/google/usage/tool-use.md)
- [Agent with URL Context](https://docs.agno.com/models/providers/native/google/usage/url-context.md)
- [Agent with URL Context and Search](https://docs.agno.com/models/providers/native/google/usage/url-context-with-search.md)
- [Agent with Vertex AI](https://docs.agno.com/models/providers/native/google/usage/vertexai.md)
- [Video Input (Bytes Content)](https://docs.agno.com/models/providers/native/google/usage/video-input-bytes-content.md)
- [Video Input (File Upload)](https://docs.agno.com/models/providers/native/google/usage/video-input-file-upload.md)
- [Video Input (Local File Upload)](https://docs.agno.com/models/providers/native/google/usage/video-input-local-file-upload.md)
- [Meta](https://docs.agno.com/models/providers/native/meta/overview.md): Use Meta Llama models with Agno agents.
- [Asynchronous Agent](https://docs.agno.com/models/providers/native/meta/usage/async-basic.md)
- [Asynchronous Streaming Agent](https://docs.agno.com/models/providers/native/meta/usage/async-stream.md)
- [Agent with Async Tool Usage](https://docs.agno.com/models/providers/native/meta/usage/async-tool-use.md)
- [Basic Agent](https://docs.agno.com/models/providers/native/meta/usage/basic.md)
- [Streaming Agent](https://docs.agno.com/models/providers/native/meta/usage/basic-stream.md)
- [Agent with Image Input](https://docs.agno.com/models/providers/native/meta/usage/image-input-bytes.md)
- [Agent With Knowledge](https://docs.agno.com/models/providers/native/meta/usage/knowledge.md)
- [Agent with Memory](https://docs.agno.com/models/providers/native/meta/usage/memory.md)
- [Agent with Structured Outputs](https://docs.agno.com/models/providers/native/meta/usage/structured-output.md)
- [Agent with Tools](https://docs.agno.com/models/providers/native/meta/usage/tool-use.md)
- [Mistral](https://docs.agno.com/models/providers/native/mistral/overview.md): Use Mistral models with Agno agents.
- [Async Basic Agent](https://docs.agno.com/models/providers/native/mistral/usage/async-basic.md)
- [Async Basic Streaming Agent](https://docs.agno.com/models/providers/native/mistral/usage/async-basic-stream.md)
- [Async Structured Output Agent](https://docs.agno.com/models/providers/native/mistral/usage/async-structured-output.md)
- [Async Agent with Tools](https://docs.agno.com/models/providers/native/mistral/usage/async-tool-use.md)
- [Basic Agent](https://docs.agno.com/models/providers/native/mistral/usage/basic.md)
- [Basic Streaming Agent](https://docs.agno.com/models/providers/native/mistral/usage/basic-stream.md)
- [Image Bytes Input Agent](https://docs.agno.com/models/providers/native/mistral/usage/image-bytes-input-agent.md)
- [Image Compare Agent](https://docs.agno.com/models/providers/native/mistral/usage/image-compare-agent.md)
- [Image File Input Agent](https://docs.agno.com/models/providers/native/mistral/usage/image-file-input-agent.md)
- [Image Ocr With Structured Output](https://docs.agno.com/models/providers/native/mistral/usage/image-ocr-with-structured-output.md)
- [Image Transcribe Document Agent](https://docs.agno.com/models/providers/native/mistral/usage/image-transcribe-document-agent.md)
- [Agent with Memory](https://docs.agno.com/models/providers/native/mistral/usage/memory.md)
- [Mistral Small](https://docs.agno.com/models/providers/native/mistral/usage/mistral-small.md)
- [Structured Output](https://docs.agno.com/models/providers/native/mistral/usage/structured-output.md)
- [Structured Output With Tool Use](https://docs.agno.com/models/providers/native/mistral/usage/structured-output-with-tool-use.md)
- [Agent with Tools](https://docs.agno.com/models/providers/native/mistral/usage/tool-use.md)
- [OpenAI](https://docs.agno.com/models/providers/native/openai/completion/overview.md): Use OpenAI GPT and o1 models with Agno agents.
- [Audio Input Agent](https://docs.agno.com/models/providers/native/openai/completion/usage/audio-input-agent.md)
- [Audio Output Agent](https://docs.agno.com/models/providers/native/openai/completion/usage/audio-output-agent.md)
- [Basic Agent](https://docs.agno.com/models/providers/native/openai/completion/usage/basic.md)
- [Streaming Agent](https://docs.agno.com/models/providers/native/openai/completion/usage/basic-stream.md)
- [Response Caching](https://docs.agno.com/models/providers/native/openai/completion/usage/cache-response.md): Cache model responses to reduce API calls and costs.
- [Generate Images](https://docs.agno.com/models/providers/native/openai/completion/usage/generate-images.md)
- [Image Agent](https://docs.agno.com/models/providers/native/openai/completion/usage/image-agent.md)
- [Agent with Knowledge](https://docs.agno.com/models/providers/native/openai/completion/usage/knowledge.md)
- [Agent with Reasoning Effort](https://docs.agno.com/models/providers/native/openai/completion/usage/reasoning-effort.md)
- [Agent with Storage](https://docs.agno.com/models/providers/native/openai/completion/usage/storage.md)
- [Agent with Structured Outputs](https://docs.agno.com/models/providers/native/openai/completion/usage/structured-output.md)
- [Agent with Tools](https://docs.agno.com/models/providers/native/openai/completion/usage/tool-use.md)
- [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)
- [Async Basic](https://docs.agno.com/models/providers/native/openai/responses/usage/async-basic.md)
- [Async Basic Stream](https://docs.agno.com/models/providers/native/openai/responses/usage/async-basic-stream.md)
- [Async Tool Use](https://docs.agno.com/models/providers/native/openai/responses/usage/async-tool-use.md)
- [Basic](https://docs.agno.com/models/providers/native/openai/responses/usage/basic.md)
- [Basic Stream](https://docs.agno.com/models/providers/native/openai/responses/usage/basic-stream.md)
- [Db](https://docs.agno.com/models/providers/native/openai/responses/usage/db.md)
- [Deep Research Agent](https://docs.agno.com/models/providers/native/openai/responses/usage/deep-research-agent.md)
- [Image Agent](https://docs.agno.com/models/providers/native/openai/responses/usage/image-agent.md)
- [Image Agent Bytes](https://docs.agno.com/models/providers/native/openai/responses/usage/image-agent-bytes.md)
- [Image Agent With Memory](https://docs.agno.com/models/providers/native/openai/responses/usage/image-agent-with-memory.md)
- [Image Generation Agent](https://docs.agno.com/models/providers/native/openai/responses/usage/image-generation-agent.md)
- [Knowledge](https://docs.agno.com/models/providers/native/openai/responses/usage/knowledge.md)
- [Memory](https://docs.agno.com/models/providers/native/openai/responses/usage/memory.md)
- [Pdf Input Local](https://docs.agno.com/models/providers/native/openai/responses/usage/pdf-input-local.md)
- [Pdf Input Url](https://docs.agno.com/models/providers/native/openai/responses/usage/pdf-input-url.md)
- [Reasoning O3 Mini](https://docs.agno.com/models/providers/native/openai/responses/usage/reasoning-o3-mini.md)
- [Structured Output](https://docs.agno.com/models/providers/native/openai/responses/usage/structured-output.md)
- [Tool Use](https://docs.agno.com/models/providers/native/openai/responses/usage/tool-use.md)
- [Tool Use Gpt 5](https://docs.agno.com/models/providers/native/openai/responses/usage/tool-use-gpt-5.md)
- [Tool Use O3](https://docs.agno.com/models/providers/native/openai/responses/usage/tool-use-o3.md)
- [Tool Use Stream](https://docs.agno.com/models/providers/native/openai/responses/usage/tool-use-stream.md)
- [Verbosity Control](https://docs.agno.com/models/providers/native/openai/responses/usage/verbosity-control.md)
- [Websearch Builtin Tool](https://docs.agno.com/models/providers/native/openai/responses/usage/websearch-builtin-tool.md)
- [ZDR Reasoning Agent](https://docs.agno.com/models/providers/native/openai/responses/usage/zdr-reasoning-agent.md)
- [Perplexity](https://docs.agno.com/models/providers/native/perplexity/overview.md): Use Perplexity models with built-in web search in Agno agents.
- [Async Basic Agent](https://docs.agno.com/models/providers/native/perplexity/usage/async-basic.md)
- [Async Basic Streaming Agent](https://docs.agno.com/models/providers/native/perplexity/usage/async-basic-stream.md)
- [Basic Agent](https://docs.agno.com/models/providers/native/perplexity/usage/basic.md)
- [Basic Streaming Agent](https://docs.agno.com/models/providers/native/perplexity/usage/basic-stream.md)
- [Agent with Knowledge](https://docs.agno.com/models/providers/native/perplexity/usage/knowledge.md)
- [Agent with Memory](https://docs.agno.com/models/providers/native/perplexity/usage/memory.md)
- [Agent with Structured Output](https://docs.agno.com/models/providers/native/perplexity/usage/structured-output.md)
- [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)
- [Streaming Agent](https://docs.agno.com/models/providers/native/vercel/usage/basic-stream.md)
- [Image Agent](https://docs.agno.com/models/providers/native/vercel/usage/image-agent.md)
- [Agent with Knowledge](https://docs.agno.com/models/providers/native/vercel/usage/knowledge.md)
- [Agent with Tools](https://docs.agno.com/models/providers/native/vercel/usage/tool-use.md)
- [xAI](https://docs.agno.com/models/providers/native/xai/overview.md): Use xAI Grok models with Agno agents.
- [Async Tool Use](https://docs.agno.com/models/providers/native/xai/usage/async-tool-use.md)
- [Basic](https://docs.agno.com/models/providers/native/xai/usage/basic.md)
- [Async Basic Agent](https://docs.agno.com/models/providers/native/xai/usage/basic-async.md)
- [Async Streaming Agent](https://docs.agno.com/models/providers/native/xai/usage/basic-async-stream.md)
- [Basic Stream](https://docs.agno.com/models/providers/native/xai/usage/basic-stream.md)
- [Image Agent](https://docs.agno.com/models/providers/native/xai/usage/image-agent.md)
- [Image Agent Bytes](https://docs.agno.com/models/providers/native/xai/usage/image-agent-bytes.md)
- [Live Search Agent](https://docs.agno.com/models/providers/native/xai/usage/live-search-agent.md)
- [Live Search Agent Stream](https://docs.agno.com/models/providers/native/xai/usage/live-search-agent-stream.md)
- [Reasoning Agent](https://docs.agno.com/models/providers/native/xai/usage/reasoning-agent.md)
- [Structured Output](https://docs.agno.com/models/providers/native/xai/usage/structured-output.md)
- [Tool Use](https://docs.agno.com/models/providers/native/xai/usage/tool-use.md)
- [Tool Use Stream](https://docs.agno.com/models/providers/native/xai/usage/tool-use-stream.md)
- [OpenAI-compatible models](https://docs.agno.com/models/providers/openai-like.md): Use any OpenAI-compatible endpoint with Agno agents.
- [Multimodal Agents](https://docs.agno.com/multimodal/agent/overview.md): Build agents that process and generate images, audio, video, and files.
- [Agent Using Multimodal Tool Response in Runs](https://docs.agno.com/multimodal/agent/usage/agent-using-multimodal-tool-response-in-runs.md)
- [Audio Input Output](https://docs.agno.com/multimodal/agent/usage/audio-input-output.md)
- [Audio Multi Turn](https://docs.agno.com/multimodal/agent/usage/audio-multi-turn.md)
- [Audio Sentiment Analysis](https://docs.agno.com/multimodal/agent/usage/audio-sentiment-analysis.md)
- [Audio Streaming](https://docs.agno.com/multimodal/agent/usage/audio-streaming.md)
- [Audio to Text Transcription](https://docs.agno.com/multimodal/agent/usage/audio-to-text.md)
- [Audio Generation Tools](https://docs.agno.com/multimodal/agent/usage/audio_generation.md): Generate audio with text-to-speech tools in Agno agents.
- [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.
- [File Input for Tools](https://docs.agno.com/multimodal/agent/usage/file-input-for-tool.md)
- [Generate output image using DALL-E](https://docs.agno.com/multimodal/agent/usage/generate-image.md)
- [Generate output image using DALL-E with intermediate steps](https://docs.agno.com/multimodal/agent/usage/generate-image-with-intermediate-steps.md)
- [Generate Music using Models Lab](https://docs.agno.com/multimodal/agent/usage/generate-music-agent.md)
- [Generate Video (ModelsLabTools)](https://docs.agno.com/multimodal/agent/usage/generate-video-using-models-lab.md)
- [Generate Video (ReplicateTools)](https://docs.agno.com/multimodal/agent/usage/generate-video-using-replicate.md)
- [Image Generation Tools](https://docs.agno.com/multimodal/agent/usage/image-generation.md): Generate images using OpenAI tools.
- [Image As Input](https://docs.agno.com/multimodal/agent/usage/image-input.md): Pass images to agents for analysis and description.
- [Image Input for Tools](https://docs.agno.com/multimodal/agent/usage/image-input-for-tool.md)
- [High Fidelity Image Input](https://docs.agno.com/multimodal/agent/usage/image-input-high-fidelity.md)
- [Image Model Output](https://docs.agno.com/multimodal/agent/usage/image-output.md): Return generated images from model responses.
- [Image to Audio Story Generation](https://docs.agno.com/multimodal/agent/usage/image-to-audio.md)
- [Image to Image Generation Agent](https://docs.agno.com/multimodal/agent/usage/image-to-image-agent.md)
- [Image to Structured Output](https://docs.agno.com/multimodal/agent/usage/image-to-structured-output.md)
- [Image to Text Analysis](https://docs.agno.com/multimodal/agent/usage/image-to-text.md)
- [Video Caption Agent](https://docs.agno.com/multimodal/agent/usage/video-caption.md)
- [Shorts from Video](https://docs.agno.com/multimodal/agent/usage/video-to-shorts.md)
- [Video Output](https://docs.agno.com/multimodal/agent/usage/video_generation.md): Generate videos with AI tools in Agno agents.
- [Video Input](https://docs.agno.com/multimodal/agent/usage/video_input.md): Process video as input with Agno agents.
- [Overview](https://docs.agno.com/multimodal/overview.md): Process and generate images, audio, video, and files with agents and teams.
- [Multimodal Teams](https://docs.agno.com/multimodal/team/overview.md): Create teams that process text, images, audio, video, and files.
- [Audio Sentiment Analysis Team](https://docs.agno.com/multimodal/team/usage/audio-sentiment-analysis.md)
- [Audio Transcription Team](https://docs.agno.com/multimodal/team/usage/audio-to-text.md)
- [Image Generation Team](https://docs.agno.com/multimodal/team/usage/generate-image-with-team.md)
- [Image Transformation Team](https://docs.agno.com/multimodal/team/usage/image-to-image-transformation.md)
- [Image to Structured Movie Script Team](https://docs.agno.com/multimodal/team/usage/image-to-structured-output.md)
- [Image to Text Team](https://docs.agno.com/multimodal/team/usage/image-to-text.md)
- [Video Captioning Team](https://docs.agno.com/multimodal/team/usage/video-caption-generation.md)
- [AgentOps](https://docs.agno.com/observability/agentops.md): Integrate Agno with AgentOps to send traces and logs to a centralized observability platform.
- [Arize](https://docs.agno.com/observability/arize.md): Integrate Agno with Arize Phoenix to send traces and gain insights into your agent's performance.
- [Atla](https://docs.agno.com/observability/atla.md): Integrate `Atla` with Agno for real-time monitoring, automated evaluation, and performance analytics of your AI agents.
- [LangDB](https://docs.agno.com/observability/langdb.md): Integrate Agno with LangDB to trace agent execution, tool calls, and gain comprehensive observability into your agent's performance.
- [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): Integrate Agno with LangSmith to send traces and gain insights into your agent's performance.
- [Langtrace](https://docs.agno.com/observability/langtrace.md): Integrate Agno with Langtrace to send traces and gain insights into your agent's performance.
- [LangWatch](https://docs.agno.com/observability/langwatch.md): Integrate Agno with LangWatch 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.
- [OpenTelemetry](https://docs.agno.com/observability/overview.md): Agno supports observability through OpenTelemetry, integrating seamlessly with popular tracing and monitoring platforms.
- [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.
- [AgentUI](https://docs.agno.com/other/agent-ui.md): An Open Source AgentUI for your AgentOS
- [Contributing to Agno](https://docs.agno.com/other/contribute.md): Contribute to Agno through the fork and pull request workflow.
- [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
- [Database Migrations](https://docs.agno.com/other/database-migrations.md): Migrate Agno database tables between versions.
- [Install & Setup](https://docs.agno.com/other/install.md)
- [Agno v2.0 Changelog](https://docs.agno.com/other/v2-changelog.md)
- [Migrating to Agno v2.0](https://docs.agno.com/other/v2-migration.md): Guide to migrate your Agno applications from v1 to v2.
- [Migrating to Workflows 2.0](https://docs.agno.com/other/workflows-migration.md): Migrate your Workflows from 1.0 to 2.0.
- [What is Reasoning?](https://docs.agno.com/reasoning/overview.md): Reasoning gives Agents the ability to "think" before responding and "analyze" the results of their actions (i.e. tool calls), greatly improving the Agents' ability to solve problems that require sequential tool calls.
- [Reasoning Agents](https://docs.agno.com/reasoning/reasoning-agents.md): Transform any model into a reasoning system through structured chain-of-thought processing, perfect for complex problems that require multiple steps, tool use, and self-validation.
- [Reasoning Models](https://docs.agno.com/reasoning/reasoning-models.md): Reasoning models are a class of large language models pre-trained to think before they answer. They produce a long internal chain of thought before responding.
- [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.
- [Basic Reasoning Agent](https://docs.agno.com/reasoning/usage/agents/basic-cot.md): Equip agents with chain-of-thought reasoning capabilities.
- [Capture Reasoning Content](https://docs.agno.com/reasoning/usage/agents/capture-reasoning-content-cot.md)
- [Non-Reasoning Model Agent](https://docs.agno.com/reasoning/usage/agents/non-reasoning-model-cot.md)
- [Team with Chain of Thought](https://docs.agno.com/reasoning/usage/agents/team-cot.md)
- [Azure AI Foundry](https://docs.agno.com/reasoning/usage/models/azure-ai-foundry/azure-ai-foundry.md)
- [Azure OpenAI o1](https://docs.agno.com/reasoning/usage/models/azure-openai/o1.md)
- [Azure OpenAI o3](https://docs.agno.com/reasoning/usage/models/azure-openai/o3.md)
- [Azure OpenAI GPT 4.1](https://docs.agno.com/reasoning/usage/models/azure-openai/reasoning-model-gpt4-1.md)
- [DeepSeek Reasoner](https://docs.agno.com/reasoning/usage/models/deepseek/deepseek-reasoner.md)
- [Groq DeepSeek R1](https://docs.agno.com/reasoning/usage/models/groq/groq.md)
- [Groq Claude + DeepSeek R1](https://docs.agno.com/reasoning/usage/models/groq/groq-plus-claude.md)
- [Ollama DeepSeek R1](https://docs.agno.com/reasoning/usage/models/ollama/ollama.md)
- [OpenAI GPT-5-mini](https://docs.agno.com/reasoning/usage/models/openai/gpt5-mini.md)
- [OpenAI gpt-5-mini with Tools](https://docs.agno.com/reasoning/usage/models/openai/gpt5-mini-tools.md)
- [OpenAI o1 pro](https://docs.agno.com/reasoning/usage/models/openai/o1-pro.md)
- [OpenAI o4-mini](https://docs.agno.com/reasoning/usage/models/openai/o4-mini.md)
- [OpenAI gpt-5-mini with reasoning effort](https://docs.agno.com/reasoning/usage/models/openai/reasoning-effort.md)
- [OpenAI GPT-4.1](https://docs.agno.com/reasoning/usage/models/openai/reasoning-model-gpt4-1.md)
- [OpenAI o4-mini with reasoning summary](https://docs.agno.com/reasoning/usage/models/openai/reasoning-summary.md)
- [xAI Grok 3 Mini](https://docs.agno.com/reasoning/usage/models/xai/reasoning-effort.md)
- [Azure OpenAI with Reasoning Tools](https://docs.agno.com/reasoning/usage/tools/azure-openai-reasoning-tools.md)
- [Capture Reasoning Content with Knowledge Tools](https://docs.agno.com/reasoning/usage/tools/capture-reasoning-content-knowledge-tools.md)
- [Capture Reasoning Content with Reasoning Tools](https://docs.agno.com/reasoning/usage/tools/capture-reasoning-content-reasoning-tools.md)
- [Cerebras Llama with Reasoning Tools](https://docs.agno.com/reasoning/usage/tools/cerebras-llama-reasoning-tools.md)
- [Claude with Reasoning Tools](https://docs.agno.com/reasoning/usage/tools/claude-reasoning-tools.md)
- [Gemini with Reasoning Tools](https://docs.agno.com/reasoning/usage/tools/gemini-reasoning-tools.md)
- [Groq with Reasoning Tools](https://docs.agno.com/reasoning/usage/tools/groq-reasoning-tools.md)
- [Team with Knowledge Tools](https://docs.agno.com/reasoning/usage/tools/knowledge-tool-team.md)
- [Reasoning Agent with Knowledge Tools](https://docs.agno.com/reasoning/usage/tools/knowledge-tools.md)
- [Ollama with Reasoning Tools](https://docs.agno.com/reasoning/usage/tools/ollama-reasoning-tools.md)
- [OpenAI with Reasoning Tools](https://docs.agno.com/reasoning/usage/tools/openai-reasoning-tools.md)
- [Team with Reasoning Tools](https://docs.agno.com/reasoning/usage/tools/reasoning-tool-team.md)
- [Reasoning Tools](https://docs.agno.com/reasoning/usage/tools/reasoning-tools.md)
- [Vercel with Reasoning Tools](https://docs.agno.com/reasoning/usage/tools/vercel-reasoning-tools.md)
- [AgentOS API Overview](https://docs.agno.com/reference-api/overview.md): Complete API reference for interacting with AgentOS programmatically
- [Cancel Agent Task](https://docs.agno.com/reference-api/schema/a2a/cancel-agent-task.md): Cancel a running agent task.
- [Cancel Team Task](https://docs.agno.com/reference-api/schema/a2a/cancel-team-task.md): Cancel a running team task.
- [Get Agent Card](https://docs.agno.com/reference-api/schema/a2a/get-agent-card.md)
- [Get Agent Task](https://docs.agno.com/reference-api/schema/a2a/get-agent-task.md): Get the status and result of an agent task by ID.
- [Get Team Card](https://docs.agno.com/reference-api/schema/a2a/get-team-card.md)
- [Get Team Task](https://docs.agno.com/reference-api/schema/a2a/get-team-task.md): Get the status and result of a team task by ID.
- [Get Workflow Card](https://docs.agno.com/reference-api/schema/a2a/get-workflow-card.md)
- [Run Message Agent](https://docs.agno.com/reference-api/schema/a2a/run-message-agent.md): Send a message to an Agno Agent (non-streaming). The Agent is identified via the path parameter '{id}'. Optional: Pass user ID via X-User-ID header (recommended) or 'userId' in params.message.metadata.
- [Run Message Team](https://docs.agno.com/reference-api/schema/a2a/run-message-team.md): Send a message to an Agno Team (non-streaming). The Team is identified via the path parameter '{id}'. Optional: Pass user ID via X-User-ID header (recommended) or 'userId' in params.message.metadata.
- [Run Message Workflow](https://docs.agno.com/reference-api/schema/a2a/run-message-workflow.md): Send a message to an Agno Workflow (non-streaming). The Workflow is identified via the path parameter '{id}'. Optional: Pass user ID via X-User-ID header (recommended) or 'userId' in params.message.metadata.
- [Send Message](https://docs.agno.com/reference-api/schema/a2a/send-message.md): [DEPRECATED] Send a message to an Agno Agent, Team, or Workflow. The Agent, Team or Workflow is identified via the 'agentId' field in params.message or X-Agent-ID header. Optional: Pass user ID via X-User-ID header (recommended) or 'userId' in params.message.metadata.
- [Stream Message](https://docs.agno.com/reference-api/schema/a2a/stream-message.md): [DEPRECATED] Stream a message to an Agno Agent, Team, or Workflow. The Agent, Team or Workflow is identified via the 'agentId' field in params.message or X-Agent-ID header. Optional: Pass user ID via X-User-ID header (recommended) or 'userId' in params.message.metadata. Returns real-time updates as…
- [Stream Message Agent](https://docs.agno.com/reference-api/schema/a2a/stream-message-agent.md): Stream a message to an Agno Agent (streaming). The Agent is identified via the path parameter '{id}'. Optional: Pass user ID via X-User-ID header (recommended) or 'userId' in params.message.metadata. Returns real-time updates as newline-delimited JSON (NDJSON).
- [Stream Message Team](https://docs.agno.com/reference-api/schema/a2a/stream-message-team.md): Stream a message to an Agno Team (streaming). The Team is identified via the path parameter '{id}'. Optional: Pass user ID via X-User-ID header (recommended) or 'userId' in params.message.metadata. Returns real-time updates as newline-delimited JSON (NDJSON).
- [Stream Message Workflow](https://docs.agno.com/reference-api/schema/a2a/stream-message-workflow.md): Stream a message to an Agno Workflow (streaming). The Workflow is identified via the path parameter '{id}'. Optional: Pass user ID via X-User-ID header (recommended) or 'userId' in params.message.metadata. Returns real-time updates as newline-delimited JSON (NDJSON).
- [Cancel Agent Run](https://docs.agno.com/reference-api/schema/agents/cancel-agent-run.md): Cancel a currently executing agent run. This will attempt to stop the agent's execution gracefully.
- [Continue Agent Run](https://docs.agno.com/reference-api/schema/agents/continue-agent-run.md): Continue a paused or incomplete agent run with updated tool results.
- [Create Agent Run](https://docs.agno.com/reference-api/schema/agents/create-agent-run.md): Execute an agent with a message and optional media files. Supports both streaming and non-streaming responses.
- [Get Agent Details](https://docs.agno.com/reference-api/schema/agents/get-agent-details.md): Retrieve detailed configuration and capabilities of a specific agent.
- [Get Agent Run](https://docs.agno.com/reference-api/schema/agents/get-agent-run.md): Retrieve the status and output of an agent run. Use this to poll for background run completion.
- [List Agent Runs](https://docs.agno.com/reference-api/schema/agents/list-agent-runs.md): List runs for an agent within a session, optionally filtered by status.
- [List All Agents](https://docs.agno.com/reference-api/schema/agents/list-all-agents.md): Retrieve a comprehensive list of all agents configured in this OS instance.
- [Get Status](https://docs.agno.com/reference-api/schema/agui/get-status.md)
- [Run Agent](https://docs.agno.com/reference-api/schema/agui/run-agent.md)
- [Delete Approval](https://docs.agno.com/reference-api/schema/approvals/delete-approval.md)
- [Get Approval](https://docs.agno.com/reference-api/schema/approvals/get-approval.md)
- [Get Approval Count](https://docs.agno.com/reference-api/schema/approvals/get-approval-count.md)
- [Get Approval Status](https://docs.agno.com/reference-api/schema/approvals/get-approval-status.md)
- [List Approvals](https://docs.agno.com/reference-api/schema/approvals/list-approvals.md)
- [Resolve Approval](https://docs.agno.com/reference-api/schema/approvals/resolve-approval.md)
- [Create Component](https://docs.agno.com/reference-api/schema/components/create-component.md): Create a new component (agent, team, or workflow) with initial config.
- [Create Config Version](https://docs.agno.com/reference-api/schema/components/create-config-version.md): Create a new config version for a component.
- [Delete Component](https://docs.agno.com/reference-api/schema/components/delete-component.md): Delete a component by ID.
- [Delete Config Version](https://docs.agno.com/reference-api/schema/components/delete-config-version.md): Delete a specific draft config version. Cannot delete published or current configs.
- [Get Component](https://docs.agno.com/reference-api/schema/components/get-component.md): Retrieve a component by ID.
- [Get Config Version](https://docs.agno.com/reference-api/schema/components/get-config-version.md): Get a specific config version by number.
- [Get Current Config](https://docs.agno.com/reference-api/schema/components/get-current-config.md): Get the current config version for a component.
- [List Components](https://docs.agno.com/reference-api/schema/components/list-components.md): Retrieve a paginated list of components with optional filtering by type.
- [List Configs](https://docs.agno.com/reference-api/schema/components/list-configs.md): List all configs for a component.
- [Set Current Config Version](https://docs.agno.com/reference-api/schema/components/set-current-config-version.md): Set a published config version as current (for rollback).
- [Update Component](https://docs.agno.com/reference-api/schema/components/update-component.md): Partially update a component by ID.
- [Update Draft Config](https://docs.agno.com/reference-api/schema/components/update-draft-config.md): Update an existing draft config. Cannot update published configs.
- [Get Available Models](https://docs.agno.com/reference-api/schema/core/get-available-models.md): Retrieve a list of all unique models currently used by agents and teams in this OS instance. This includes the model ID and provider information for each model.
- [Get OS Configuration](https://docs.agno.com/reference-api/schema/core/get-os-configuration.md): Retrieve the complete configuration of the AgentOS instance, including:
- [Migrate All Databases](https://docs.agno.com/reference-api/schema/database/migrate-all-databases.md): Migrate all database schemas to the given target version. If a target version is not provided, all databases will be migrated to the latest version.
- [Migrate Database](https://docs.agno.com/reference-api/schema/database/migrate-database.md): Migrate the given database schema to the given target version. If a target version is not provided, the database will be migrated to the latest version.
- [Delete Evaluation Runs](https://docs.agno.com/reference-api/schema/evals/delete-evaluation-runs.md): Delete multiple evaluation runs by their IDs. This action cannot be undone.
- [Execute Evaluation](https://docs.agno.com/reference-api/schema/evals/execute-evaluation.md): Run evaluation tests on agents or teams. Supports accuracy, agent-as-judge, performance, and reliability evaluations. Requires either agent_id or team_id, but not both.
- [Get Evaluation Run](https://docs.agno.com/reference-api/schema/evals/get-evaluation-run.md): Retrieve detailed results and metrics for a specific evaluation run.
- [List Evaluation Runs](https://docs.agno.com/reference-api/schema/evals/list-evaluation-runs.md): Retrieve paginated evaluation runs with filtering and sorting options. Filter by agent, team, workflow, model, or evaluation type.
- [Update Evaluation Run](https://docs.agno.com/reference-api/schema/evals/update-evaluation-run.md): Update the name or other properties of an existing evaluation run.
- [Health Check](https://docs.agno.com/reference-api/schema/health/health-check.md): Check the health status of the AgentOS API. Returns a simple status indicator.
- [API Information](https://docs.agno.com/reference-api/schema/home/api-information.md): Get basic information about this AgentOS API instance, including:
- [Delete All Content](https://docs.agno.com/reference-api/schema/knowledge/delete-all-content.md): Permanently remove all content from the knowledge base. This is a destructive operation that cannot be undone. Use with extreme caution.
- [Delete Content by ID](https://docs.agno.com/reference-api/schema/knowledge/delete-content-by-id.md): Permanently remove a specific content item from the knowledge base. This action cannot be undone.
- [Get Config](https://docs.agno.com/reference-api/schema/knowledge/get-config.md): Retrieve available readers, chunkers, and configuration options for content processing. This endpoint provides metadata about supported file types, processing strategies, and filters.
- [Get Content by ID](https://docs.agno.com/reference-api/schema/knowledge/get-content-by-id.md): Retrieve detailed information about a specific content item including processing status and metadata.
- [Get Content Status](https://docs.agno.com/reference-api/schema/knowledge/get-content-status.md): Retrieve the current processing status of a content item. Useful for monitoring asynchronous content processing progress and identifying any processing errors.
- [List Content](https://docs.agno.com/reference-api/schema/knowledge/list-content.md): Retrieve paginated list of all content in the knowledge base with filtering and sorting options. Filter by status, content type, or metadata properties.
- [Search Knowledge](https://docs.agno.com/reference-api/schema/knowledge/search-knowledge.md): Search the knowledge base for relevant documents using query, filters and search type.
- [Update Content](https://docs.agno.com/reference-api/schema/knowledge/update-content.md): Update content properties such as name, description, metadata, or processing configuration. Allows modification of existing content without re-uploading.
- [Upload Content](https://docs.agno.com/reference-api/schema/knowledge/upload-content.md): Upload content to the knowledge base. Supports file uploads, text content, or URLs. Content is processed asynchronously in the background. Supports custom readers and chunking strategies.
- [Upload Remote Content](https://docs.agno.com/reference-api/schema/knowledge/upload-remote-content.md): Upload content from a remote source (S3, GCS, SharePoint, GitHub) to the knowledge base. Content is processed asynchronously in the background.
- [Create Memory](https://docs.agno.com/reference-api/schema/memory/create-memory.md): Create a new user memory with content and associated topics. Memories are used to store contextual information for users across conversations.
- [Delete Memory](https://docs.agno.com/reference-api/schema/memory/delete-memory.md): Permanently delete a specific user memory. This action cannot be undone.
- [Delete Multiple Memories](https://docs.agno.com/reference-api/schema/memory/delete-multiple-memories.md): Delete multiple user memories by their IDs in a single operation. This action cannot be undone and all specified memories will be permanently removed.
- [Get Memory by ID](https://docs.agno.com/reference-api/schema/memory/get-memory-by-id.md): Retrieve detailed information about a specific user memory by its ID.
- [Get Memory Topics](https://docs.agno.com/reference-api/schema/memory/get-memory-topics.md): Retrieve all unique topics associated with memories in the system. Useful for filtering and categorizing memories by topic.
- [Get User Memory Statistics](https://docs.agno.com/reference-api/schema/memory/get-user-memory-statistics.md): Retrieve paginated statistics about memory usage by user. Provides insights into user engagement and memory distribution across users.
- [List Memories](https://docs.agno.com/reference-api/schema/memory/list-memories.md): Retrieve paginated list of user memories with filtering and search capabilities. Filter by user, agent, team, topics, or search within memory content.
- [Optimize User Memories](https://docs.agno.com/reference-api/schema/memory/optimize-user-memories.md): Optimize all memories for a given user using the default summarize strategy. This operation combines all memories into a single comprehensive summary, achieving maximum token reduction while preserving all key information. To use a custom model, specify the model parameter in 'provider:model_id' for…
- [Update Memory](https://docs.agno.com/reference-api/schema/memory/update-memory.md): Update an existing user memory's content and topics. Replaces the entire memory content and topic list with the provided values.
- [Get AgentOS Metrics](https://docs.agno.com/reference-api/schema/metrics/get-agentos-metrics.md): Retrieve AgentOS metrics and analytics data for a specified date range. If no date range is specified, returns all available metrics.
- [Refresh Metrics](https://docs.agno.com/reference-api/schema/metrics/refresh-metrics.md): Manually trigger recalculation of system metrics from raw data. This operation analyzes system activity logs and regenerates aggregated metrics. Useful for ensuring metrics are up-to-date or after system maintenance.
- [List Registry](https://docs.agno.com/reference-api/schema/registry/list-registry.md): List all resources in the registry with optional filtering.
- [Create Schedule](https://docs.agno.com/reference-api/schema/schedules/create-schedule.md)
- [Delete Schedule](https://docs.agno.com/reference-api/schema/schedules/delete-schedule.md)
- [Disable Schedule](https://docs.agno.com/reference-api/schema/schedules/disable-schedule.md)
- [Enable Schedule](https://docs.agno.com/reference-api/schema/schedules/enable-schedule.md)
- [Get Schedule](https://docs.agno.com/reference-api/schema/schedules/get-schedule.md)
- [Get Schedule Run](https://docs.agno.com/reference-api/schema/schedules/get-schedule-run.md)
- [List Schedule Runs](https://docs.agno.com/reference-api/schema/schedules/list-schedule-runs.md)
- [List Schedules](https://docs.agno.com/reference-api/schema/schedules/list-schedules.md)
- [Trigger Schedule](https://docs.agno.com/reference-api/schema/schedules/trigger-schedule.md)
- [Update Schedule](https://docs.agno.com/reference-api/schema/schedules/update-schedule.md)
- [Create New Session](https://docs.agno.com/reference-api/schema/sessions/create-new-session.md): Create a new empty session with optional configuration. Useful for pre-creating sessions with specific session_state, metadata, or other properties before running any agent/team/workflow interactions. The session can later be used by providing its session_id in run requests.
- [Delete Multiple Sessions](https://docs.agno.com/reference-api/schema/sessions/delete-multiple-sessions.md): Delete multiple sessions by their IDs in a single operation. This action cannot be undone and will permanently remove all specified sessions and their runs.
- [Delete Session](https://docs.agno.com/reference-api/schema/sessions/delete-session.md): Permanently delete a specific session and all its associated runs. This action cannot be undone and will remove all conversation history.
- [Get Run by ID](https://docs.agno.com/reference-api/schema/sessions/get-run-by-id.md): Retrieve a specific run by its ID from a session. Response schema varies based on the run type (agent run, team run, or workflow run).
- [Get Session by ID](https://docs.agno.com/reference-api/schema/sessions/get-session-by-id.md): Retrieve detailed information about a specific session including metadata, configuration, and run history. Response schema varies based on session type (agent, team, or workflow).
- [Get Session Runs](https://docs.agno.com/reference-api/schema/sessions/get-session-runs.md): Retrieve all runs (executions) for a specific session with optional timestamp filtering. Runs represent individual interactions or executions within a session. Response schema varies based on session type.
- [List Sessions](https://docs.agno.com/reference-api/schema/sessions/list-sessions.md): Retrieve paginated list of sessions with filtering and sorting options. Supports filtering by session type (agent, team, workflow), component, user, and name. Sessions represent conversation histories and execution contexts.
- [Rename Session](https://docs.agno.com/reference-api/schema/sessions/rename-session.md): Update the name of an existing session. Useful for organizing and categorizing sessions with meaningful names for better identification and management.
- [Update Session](https://docs.agno.com/reference-api/schema/sessions/update-session.md): Update session properties such as session_name, session_state, metadata, or summary. Use this endpoint to modify the session name, update state, add metadata, or update the session summary.
- [Slack Events](https://docs.agno.com/reference-api/schema/slack/slack-events.md): Process incoming Slack events
- [Cancel Team Run](https://docs.agno.com/reference-api/schema/teams/cancel-team-run.md): Cancel a currently executing team run. This will attempt to stop the team's execution gracefully.
- [Create Team Run](https://docs.agno.com/reference-api/schema/teams/create-team-run.md): Execute a team collaboration with multiple agents working together on a task.
- [Get Team Details](https://docs.agno.com/reference-api/schema/teams/get-team-details.md): Retrieve detailed configuration and member information for a specific team.
- [Get Team Run](https://docs.agno.com/reference-api/schema/teams/get-team-run.md): Retrieve the status and output of a team run. Use this to poll for background run completion.
- [List All Teams](https://docs.agno.com/reference-api/schema/teams/list-all-teams.md): Retrieve a comprehensive list of all teams configured in this OS instance.
- [List Team Runs](https://docs.agno.com/reference-api/schema/teams/list-team-runs.md): List runs for a team within a session, optionally filtered by status.
- [Get Trace or Span Detail](https://docs.agno.com/reference-api/schema/traces/get-trace-or-span-detail.md): Retrieve detailed trace information with hierarchical span tree, or a specific span within the trace.
- [Get Trace Statistics by Session](https://docs.agno.com/reference-api/schema/traces/get-trace-statistics-by-session.md): Retrieve aggregated trace statistics grouped by session ID with pagination.
- [List Traces](https://docs.agno.com/reference-api/schema/traces/list-traces.md): Retrieve a paginated list of execution traces with optional filtering.
- [Status](https://docs.agno.com/reference-api/schema/whatsapp/status.md)
- [Verify Webhook](https://docs.agno.com/reference-api/schema/whatsapp/verify-webhook.md): Handle WhatsApp webhook verification
- [Webhook](https://docs.agno.com/reference-api/schema/whatsapp/webhook.md): Handle incoming WhatsApp messages
- [Cancel Workflow Run](https://docs.agno.com/reference-api/schema/workflows/cancel-workflow-run.md): Cancel a currently executing workflow run, stopping all active steps and cleanup. **Note:** Complex workflows with multiple parallel steps may take time to fully cancel.
- [Execute Workflow](https://docs.agno.com/reference-api/schema/workflows/execute-workflow.md): Execute a workflow with the provided input data. Workflows can run in streaming or batch mode.
- [Get Workflow Details](https://docs.agno.com/reference-api/schema/workflows/get-workflow-details.md): Retrieve detailed configuration and step information for a specific workflow.
- [Get Workflow Run](https://docs.agno.com/reference-api/schema/workflows/get-workflow-run.md): Retrieve the status and output of a workflow run. Use this to poll for run completion.
- [List All Workflows](https://docs.agno.com/reference-api/schema/workflows/list-all-workflows.md): Retrieve a comprehensive list of all workflows configured in this OS instance.
- [AgentOS](https://docs.agno.com/reference/agent-os/agent-os.md)
- [AuthorizationConfig](https://docs.agno.com/reference/agent-os/authorization-config.md)
- [AgentOSConfig](https://docs.agno.com/reference/agent-os/configuration.md)
- [JWTMiddleware](https://docs.agno.com/reference/agent-os/jwt-middleware.md)
- [Agent](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)
- [AgentSession](https://docs.agno.com/reference/agents/session.md)
- [ag infra config](https://docs.agno.com/reference/agno-infra/cli/ws/config.md)
- [ag infra create](https://docs.agno.com/reference/agno-infra/cli/ws/create.md)
- [ag infra delete](https://docs.agno.com/reference/agno-infra/cli/ws/delete.md)
- [ag infra down](https://docs.agno.com/reference/agno-infra/cli/ws/down.md)
- [ag infra patch](https://docs.agno.com/reference/agno-infra/cli/ws/patch.md)
- [ag infra restart](https://docs.agno.com/reference/agno-infra/cli/ws/restart.md)
- [ag infra up](https://docs.agno.com/reference/agno-infra/cli/ws/up.md)
- [A2AClient](https://docs.agno.com/reference/clients/a2a-client.md): Python client for communicating with A2A-compatible agent servers
- [AgentOSClient](https://docs.agno.com/reference/clients/agentos-client.md): Python client for interacting with AgentOS API endpoints
- [CompressionManager](https://docs.agno.com/reference/compression/compression-manager.md)
- [BaseGuardrail](https://docs.agno.com/reference/hooks/base-guardrail.md)
- [OpenAIModerationGuardrail](https://docs.agno.com/reference/hooks/openai-moderation-guardrail.md)
- [PIIDetectionGuardrail](https://docs.agno.com/reference/hooks/pii-guardrail.md)
- [Post-hooks](https://docs.agno.com/reference/hooks/post-hooks.md)
- [Pre-hooks](https://docs.agno.com/reference/hooks/pre-hooks.md)
- [PromptInjectionGuardrail](https://docs.agno.com/reference/hooks/prompt-injection-guardrail.md)
- [Agentic Chunking](https://docs.agno.com/reference/knowledge/chunking/agentic.md)
- [Code Chunking](https://docs.agno.com/reference/knowledge/chunking/code.md)
- [CSV Row Chunking](https://docs.agno.com/reference/knowledge/chunking/csv-row.md)
- [Document Chunking](https://docs.agno.com/reference/knowledge/chunking/document.md)
- [Fixed Size Chunking](https://docs.agno.com/reference/knowledge/chunking/fixed-size.md)
- [Markdown Chunking](https://docs.agno.com/reference/knowledge/chunking/markdown.md)
- [Recursive Chunking](https://docs.agno.com/reference/knowledge/chunking/recursive.md)
- [Semantic Chunking](https://docs.agno.com/reference/knowledge/chunking/semantic.md)
- [Azure OpenAI](https://docs.agno.com/reference/knowledge/embedder/azure-openai.md)
- [Cohere](https://docs.agno.com/reference/knowledge/embedder/cohere.md)
- [FastEmbed](https://docs.agno.com/reference/knowledge/embedder/fastembed.md)
- [Fireworks](https://docs.agno.com/reference/knowledge/embedder/fireworks.md)
- [Gemini](https://docs.agno.com/reference/knowledge/embedder/gemini.md)
- [Hugging Face](https://docs.agno.com/reference/knowledge/embedder/huggingface.md)
- [Mistral](https://docs.agno.com/reference/knowledge/embedder/mistral.md)
- [Nebius](https://docs.agno.com/reference/knowledge/embedder/nebius.md)
- [Ollama](https://docs.agno.com/reference/knowledge/embedder/ollama.md)
- [OpenAI](https://docs.agno.com/reference/knowledge/embedder/openai.md)
- [Sentence Transformer](https://docs.agno.com/reference/knowledge/embedder/sentence-transformer.md)
- [Together](https://docs.agno.com/reference/knowledge/embedder/together.md)
- [vLLM](https://docs.agno.com/reference/knowledge/embedder/vllm.md)
- [VoyageAI](https://docs.agno.com/reference/knowledge/embedder/voyageai.md)
- [Knowledge](https://docs.agno.com/reference/knowledge/knowledge.md)
- [Arxiv Reader](https://docs.agno.com/reference/knowledge/reader/arxiv.md)
- [Reader](https://docs.agno.com/reference/knowledge/reader/base.md)
- [CSV Reader](https://docs.agno.com/reference/knowledge/reader/csv.md)
- [Docx Reader](https://docs.agno.com/reference/knowledge/reader/docx.md)
- [Field Labeled CSV Reader](https://docs.agno.com/reference/knowledge/reader/field-labeled-csv.md)
- [FireCrawl Reader](https://docs.agno.com/reference/knowledge/reader/firecrawl.md)
- [JSON Reader](https://docs.agno.com/reference/knowledge/reader/json.md)
- [PDF Reader](https://docs.agno.com/reference/knowledge/reader/pdf.md)
- [PPTX Reader](https://docs.agno.com/reference/knowledge/reader/pptx.md)
- [Text Reader](https://docs.agno.com/reference/knowledge/reader/text.md)
- [Web Search Reader](https://docs.agno.com/reference/knowledge/reader/web-search.md)
- [Website Reader](https://docs.agno.com/reference/knowledge/reader/website.md)
- [Wikipedia Reader](https://docs.agno.com/reference/knowledge/reader/wikipedia.md)
- [YouTube Reader](https://docs.agno.com/reference/knowledge/reader/youtube.md)
- [GCS Content](https://docs.agno.com/reference/knowledge/remote-content/gcs-content.md)
- [S3 Content](https://docs.agno.com/reference/knowledge/remote-content/s3-content.md)
- [Cohere Reranker](https://docs.agno.com/reference/knowledge/reranker/cohere.md)
- [Memory Manager](https://docs.agno.com/reference/memory/memory.md)
- [AI/ML API](https://docs.agno.com/reference/models/aimlapi.md)
- [Claude](https://docs.agno.com/reference/models/anthropic.md)
- [Azure AI Foundry](https://docs.agno.com/reference/models/azure.md)
- [Azure OpenAI](https://docs.agno.com/reference/models/azure-open-ai.md)
- [AWS Bedrock](https://docs.agno.com/reference/models/bedrock.md): AWS Bedrock model parameters and configuration.
- [AWS Bedrock Claude](https://docs.agno.com/reference/models/bedrock-claude.md)
- [Cohere](https://docs.agno.com/reference/models/cohere.md)
- [DeepInfra](https://docs.agno.com/reference/models/deepinfra.md)
- [DeepSeek](https://docs.agno.com/reference/models/deepseek.md)
- [Fireworks](https://docs.agno.com/reference/models/fireworks.md)
- [Gemini](https://docs.agno.com/reference/models/gemini.md)
- [Groq](https://docs.agno.com/reference/models/groq.md)
- [HuggingFace](https://docs.agno.com/reference/models/huggingface.md)
- [IBM WatsonX](https://docs.agno.com/reference/models/ibm-watsonx.md)
- [InternLM](https://docs.agno.com/reference/models/internlm.md)
- [Meta](https://docs.agno.com/reference/models/meta.md)
- [Mistral](https://docs.agno.com/reference/models/mistral.md)
- [Model](https://docs.agno.com/reference/models/model.md)
- [N1N](https://docs.agno.com/reference/models/n1n.md)
- [Nebius](https://docs.agno.com/reference/models/nebius.md)
- [Nvidia](https://docs.agno.com/reference/models/nvidia.md)
- [Ollama](https://docs.agno.com/reference/models/ollama.md)
- [Ollama Responses](https://docs.agno.com/reference/models/ollama-responses.md)
- [Open Responses](https://docs.agno.com/reference/models/open-responses.md)
- [OpenAI](https://docs.agno.com/reference/models/openai.md)
- [OpenAI Like](https://docs.agno.com/reference/models/openai-like.md)
- [OpenRouter](https://docs.agno.com/reference/models/openrouter.md)
- [OpenRouter Responses](https://docs.agno.com/reference/models/openrouter-responses.md)
- [Perplexity](https://docs.agno.com/reference/models/perplexity.md)
- [Requesty](https://docs.agno.com/reference/models/requesty.md)
- [Sambanova](https://docs.agno.com/reference/models/sambanova.md)
- [Together](https://docs.agno.com/reference/models/together.md)
- [Vercel v0](https://docs.agno.com/reference/models/vercel.md)
- [xAI](https://docs.agno.com/reference/models/xai.md)
- [RunContext](https://docs.agno.com/reference/run/run-context.md)
- [SessionSummaryManager](https://docs.agno.com/reference/session/summary_manager.md)
- [DynamoDB](https://docs.agno.com/reference/storage/dynamodb.md)
- [FirestoreDb](https://docs.agno.com/reference/storage/firestore.md)
- [GcsJsonDb](https://docs.agno.com/reference/storage/gcs.md)
- [InMemoryDb](https://docs.agno.com/reference/storage/in_memory.md)
- [JsonDb](https://docs.agno.com/reference/storage/json.md)
- [MigrationManager](https://docs.agno.com/reference/storage/migrations.md): API reference for the MigrationManager class used to handle database migrations.
- [MongoDB](https://docs.agno.com/reference/storage/mongodb.md)
- [MySQLDb](https://docs.agno.com/reference/storage/mysql.md)
- [PostgresDb](https://docs.agno.com/reference/storage/postgres.md)
- [RedisDb](https://docs.agno.com/reference/storage/redis.md)
- [SingleStoreDb](https://docs.agno.com/reference/storage/singlestore.md)
- [SqliteDb](https://docs.agno.com/reference/storage/sqlite.md)
- [SurrealDb](https://docs.agno.com/reference/storage/surrealdb.md)
- [RemoteTeam](https://docs.agno.com/reference/teams/remote-team.md): Execute teams hosted on a remote AgentOS instance
- [Team Session](https://docs.agno.com/reference/teams/session.md)
- [Team](https://docs.agno.com/reference/teams/team.md)
- [TeamRunOutput](https://docs.agno.com/reference/teams/team-response.md)
- [Tool Decorator](https://docs.agno.com/reference/tools/decorator.md): Reference for the @tool decorator.
- [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.
- [Toolkit](https://docs.agno.com/reference/tools/toolkit.md): Reference for the Toolkit class.
- [Span](https://docs.agno.com/reference/tracing/span.md)
- [Trace](https://docs.agno.com/reference/tracing/trace.md)
- [Conditional Steps](https://docs.agno.com/reference/workflows/conditional-steps.md)
- [Loop Steps](https://docs.agno.com/reference/workflows/loop-steps.md)
- [Parallel Steps](https://docs.agno.com/reference/workflows/parallel-steps.md)
- [RemoteWorkflow](https://docs.agno.com/reference/workflows/remote-workflow.md): Execute workflows hosted on a remote AgentOS instance
- [Router Steps](https://docs.agno.com/reference/workflows/router-steps.md)
- [WorkflowRunOutput](https://docs.agno.com/reference/workflows/run-output.md)
- [WorkflowSession](https://docs.agno.com/reference/workflows/session.md)
- [Step](https://docs.agno.com/reference/workflows/step.md)
- [StepInput](https://docs.agno.com/reference/workflows/step_input.md)
- [StepOutput](https://docs.agno.com/reference/workflows/step_output.md)
- [Steps](https://docs.agno.com/reference/workflows/steps-step.md)
- [Workflow](https://docs.agno.com/reference/workflows/workflow.md)
- [Agent Run Cancellation](https://docs.agno.com/run-cancellation/agent-cancel-run.md): Cancel a running agent execution from another thread.
- [Cancelling a Run](https://docs.agno.com/run-cancellation/overview.md): Cancel running agent, team, or workflow executions.
- [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.
- [Scheduler](https://docs.agno.com/scheduler/overview.md): Create and manage cron schedules with the Scheduler SDK and AgentOS.
- [History Management](https://docs.agno.com/sessions/history-management.md): Control how conversation history is accessed and used
- [Agent Metrics](https://docs.agno.com/sessions/metrics/agent.md): Agent run and session metrics for token usage and performance.
- [Metrics](https://docs.agno.com/sessions/metrics/overview.md): Run and session metrics for token usage, duration, and performance.
- [Team Metrics](https://docs.agno.com/sessions/metrics/team.md): Team run and session metrics for token usage and performance.
- [Agent Extra Metrics](https://docs.agno.com/sessions/metrics/usage/agent-extra-metrics.md)
- [Agent Metrics and Performance Monitoring](https://docs.agno.com/sessions/metrics/usage/agent-metrics.md)
- [Team Metrics Analysis](https://docs.agno.com/sessions/metrics/usage/team-metrics.md)
- [Workflow Metrics](https://docs.agno.com/sessions/metrics/workflow.md): Workflow run and session metrics for token usage and performance.
- [Sessions](https://docs.agno.com/sessions/overview.md): Multi-turn conversation threads with persistent 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
- [Session Management](https://docs.agno.com/sessions/session-management.md): Manage session identifiers, names, and performance optimization
- [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.
- [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.
- [Introduction to Skills](https://docs.agno.com/skills/overview.md): Skills provide agents with structured domain expertise through instructions, scripts, and reference documentation.
- [Agentic State](https://docs.agno.com/state/agent/agentic-session-state.md)
- [Change State on Run](https://docs.agno.com/state/agent/change-state-on-run.md)
- [Dynamic State](https://docs.agno.com/state/agent/dynamic-session-state.md)
- [Last N Messages](https://docs.agno.com/state/agent/last-n-session-messages.md)
- [Agent Session State](https://docs.agno.com/state/agent/overview.md): Manage persistent state in agents across multiple runs within a session
- [Advanced State](https://docs.agno.com/state/agent/session-state-advanced.md)
- [Basic State](https://docs.agno.com/state/agent/session-state-basic.md)
- [State in Context](https://docs.agno.com/state/agent/session-state-in-context.md)
- [State in Instructions](https://docs.agno.com/state/agent/session-state-in-instructions.md)
- [Multiple Users](https://docs.agno.com/state/agent/session-state-multiple-users.md)
- [State Management](https://docs.agno.com/state/overview.md): Persist and share data across agent runs, team coordination, and workflow execution
- [Agentic State](https://docs.agno.com/state/team/agentic-session-state.md)
- [Change State on Run](https://docs.agno.com/state/team/change-state-on-run.md)
- [Team Session State](https://docs.agno.com/state/team/overview.md): Share and coordinate state across multiple agents in a team
- [State in Instructions](https://docs.agno.com/state/team/session-state-in-instructions.md)
- [Share Member Interactions](https://docs.agno.com/state/team/share-member-interactions.md)
- [State in Condition](https://docs.agno.com/state/workflows/access-session-state-in-condition-evaluator-function.md): This example demonstrates how to access the run context in the evaluator function of a condition step
- [State in Custom Function](https://docs.agno.com/state/workflows/access-session-state-in-custom-python-function-step.md): This example demonstrates how to access the run context in a custom python function step
- [State in Router](https://docs.agno.com/state/workflows/access-session-state-in-router-selector-function.md): This example demonstrates how to access the run context in the selector function of a router step
- [Workflow Session State](https://docs.agno.com/state/workflows/overview.md): Coordinate state across workflow steps, agents, teams, and custom functions
- [Building Teams](https://docs.agno.com/teams/building-teams.md): Define team members, roles, and structure for multi-agent coordination.
- [Debugging Teams](https://docs.agno.com/teams/debugging-teams.md): Troubleshoot and inspect team behavior with debug mode, tracing, and common failure patterns.
- [Delegation](https://docs.agno.com/teams/delegation.md): Control how the team leader delegates tasks to members.
- [What are Teams?](https://docs.agno.com/teams/overview.md): Groups of agents that collaborate to solve complex tasks.
- [Running Teams](https://docs.agno.com/teams/running-teams.md): Execute teams with Team.run() and process their output.
- [Basic Team](https://docs.agno.com/teams/usage/basic-team.md): A team of AI agents working together to research topics.
- [Direct Response Mode](https://docs.agno.com/teams/usage/respond-directly.md): Route requests to specialized agents who respond directly.
- [Team Streaming](https://docs.agno.com/teams/usage/streaming.md): Stream responses from a team in real-time.
- [Team with Followup Suggestions](https://docs.agno.com/teams/usage/team-with-followup-suggestions.md): Generate actionable followup prompts after every team response.
- [Agno Telemetry](https://docs.agno.com/telemetry.md): Control what usage data Agno collects
- [Agent Tools](https://docs.agno.com/tools/agent.md): Equip agents with functions and toolkits for external actions.
- [Updating an Agent's Tools](https://docs.agno.com/tools/attaching-tools.md): Add or update tools on Agents and Teams after initialization.
- [Tool Result Caching](https://docs.agno.com/tools/caching.md): Cache tool results to reduce repeated API calls and improve performance.
- [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.
- [Exceptions & Retries](https://docs.agno.com/tools/exceptions.md): Handle tool errors with exceptions and automatic retries.
- [Tool Hooks](https://docs.agno.com/tools/hooks.md): Use pre and post hooks to modify tool behavior.
- [Dynamic Headers](https://docs.agno.com/tools/mcp/dynamic-headers.md): Setting dynamic headers with Agno MCP tools
- [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): Understanding how to connect to multiple MCP servers with Agno
- [Model Context Protocol (MCP)](https://docs.agno.com/tools/mcp/overview.md): Connect agents to external systems through the standardized MCP interface.
- [Understanding Server Parameters](https://docs.agno.com/tools/mcp/server-params.md): Understanding how to configure the server parameters for the MCPTools and MultiMCPTools classes
- [SSE Transport](https://docs.agno.com/tools/mcp/transports/sse.md)
- [Stdio Transport](https://docs.agno.com/tools/mcp/transports/stdio.md)
- [Streamable HTTP Transport](https://docs.agno.com/tools/mcp/transports/streamable_http.md)
- [Airbnb MCP agent](https://docs.agno.com/tools/mcp/usage/airbnb.md)
- [GitHub MCP agent](https://docs.agno.com/tools/mcp/usage/github.md)
- [Notion MCP agent](https://docs.agno.com/tools/mcp/usage/notion.md)
- [Parallel MCP agent](https://docs.agno.com/tools/mcp/usage/parallel.md)
- [Pipedream Auth](https://docs.agno.com/tools/mcp/usage/pipedream-auth.md): This example shows how to add authorization when integrating Pipedream MCP servers with Agno Agents.
- [Pipedream Google Calendar](https://docs.agno.com/tools/mcp/usage/pipedream-google-calendar.md): This example shows how to use the Google Calendar Pipedream MCP server with Agno Agents.
- [Pipedream LinkedIn](https://docs.agno.com/tools/mcp/usage/pipedream-linkedin.md): This example shows how to use the LinkedIn Pipedream MCP server with Agno Agents.
- [Pipedream Slack](https://docs.agno.com/tools/mcp/usage/pipedream-slack.md): This example shows how to use the Slack Pipedream MCP server with Agno Agents.
- [Stagehand MCP agent](https://docs.agno.com/tools/mcp/usage/stagehand.md): A web scraping agent that uses the Stagehand MCP server to automate browser interactions and create a structured content digest from Hacker News.
- [Stripe MCP agent](https://docs.agno.com/tools/mcp/usage/stripe.md)
- [Supabase MCP agent](https://docs.agno.com/tools/mcp/usage/supabase.md)
- [What are Tools?](https://docs.agno.com/tools/overview.md): Tools are functions Agents call to interact with external systems.
- [Knowledge Tools](https://docs.agno.com/tools/reasoning_tools/knowledge-tools.md)
- [Memory Tools](https://docs.agno.com/tools/reasoning_tools/memory-tools.md)
- [Reasoning Tools](https://docs.agno.com/tools/reasoning_tools/reasoning-tools.md)
- [Workflow Tools](https://docs.agno.com/tools/reasoning_tools/workflow-tools.md)
- [Including and excluding tools](https://docs.agno.com/tools/selecting-tools.md): Include and exclude specific tools from a Toolkit.
- [Tool Call Limit](https://docs.agno.com/tools/tool-call-limit.md): Limit the number of tool calls an agent can make.
- [CSV](https://docs.agno.com/tools/toolkits/database/csv.md): The CsvTools toolkit enables an Agent to read and write 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.
- [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.
- [Calculator](https://docs.agno.com/tools/toolkits/local/calculator.md)
- [Docker](https://docs.agno.com/tools/toolkits/local/docker.md)
- [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 write files to the local file system with automatic directory management.
- [Python](https://docs.agno.com/tools/toolkits/local/python.md)
- [Shell](https://docs.agno.com/tools/toolkits/local/shell.md)
- [Sleep](https://docs.agno.com/tools/toolkits/local/sleep.md)
- [Azure OpenAI](https://docs.agno.com/tools/toolkits/models/azure-openai.md): AzureOpenAITools provides access to Azure OpenAI services including DALL-E image generation.
- [Gemini](https://docs.agno.com/tools/toolkits/models/gemini.md)
- [Groq](https://docs.agno.com/tools/toolkits/models/groq.md)
- [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): NebiusTools provides access to Nebius Token Factory's text-to-image generation capabilities with advanced AI models.
- [OpenAI](https://docs.agno.com/tools/toolkits/models/openai.md)
- [Airflow](https://docs.agno.com/tools/toolkits/others/airflow.md)
- [Apify](https://docs.agno.com/tools/toolkits/others/apify.md)
- [AWS Lambda](https://docs.agno.com/tools/toolkits/others/aws-lambda.md)
- [AWS SES](https://docs.agno.com/tools/toolkits/others/aws-ses.md)
- [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)
- [Cartesia](https://docs.agno.com/tools/toolkits/others/cartesia.md): Tools for interacting with Cartesia Voice AI services including text-to-speech and voice localization
- [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)
- [Confluence](https://docs.agno.com/tools/toolkits/others/confluence.md)
- [Custom API](https://docs.agno.com/tools/toolkits/others/custom-api.md)
- [Dalle](https://docs.agno.com/tools/toolkits/others/dalle.md)
- [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): DesiVocalTools provides text-to-speech capabilities using Indian voices through the Desi Vocal API.
- [E2B](https://docs.agno.com/tools/toolkits/others/e2b.md): Enable your Agents to run code in a remote, secure sandbox.
- [Eleven Labs](https://docs.agno.com/tools/toolkits/others/eleven-labs.md)
- [EVM (Ethereum Virtual Machine)](https://docs.agno.com/tools/toolkits/others/evm.md): EvmTools enables agents to interact with Ethereum and EVM-compatible blockchains for transactions and smart contract operations.
- [Fal](https://docs.agno.com/tools/toolkits/others/fal.md)
- [Financial Datasets API](https://docs.agno.com/tools/toolkits/others/financial-datasets.md)
- [Giphy](https://docs.agno.com/tools/toolkits/others/giphy.md)
- [Github](https://docs.agno.com/tools/toolkits/others/github.md)
- [Gitlab](https://docs.agno.com/tools/toolkits/others/gitlab.md): GitlabTools provides read-focused access to GitLab projects, merge requests, and issues.
- [Google Maps](https://docs.agno.com/tools/toolkits/others/google-maps.md): Tools for interacting with Google Maps services including place search, directions, geocoding, and more
- [Google Sheets](https://docs.agno.com/tools/toolkits/others/google-sheets.md)
- [Google Slides](https://docs.agno.com/tools/toolkits/others/google-slides.md)
- [Google Calendar](https://docs.agno.com/tools/toolkits/others/googlecalendar.md)
- [Jira](https://docs.agno.com/tools/toolkits/others/jira.md)
- [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)
- [Lumalabs](https://docs.agno.com/tools/toolkits/others/lumalabs.md)
- [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)
- [ModelsLabs](https://docs.agno.com/tools/toolkits/others/models-labs.md)
- [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.
- [Nano Banana](https://docs.agno.com/tools/toolkits/others/nano-banana.md)
- [Notion Tools](https://docs.agno.com/tools/toolkits/others/notion.md): The NotionTools toolkit enables Agents to interact with your Notion pages.
- [OpenBB](https://docs.agno.com/tools/toolkits/others/openbb.md)
- [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.
- [OpenWeather](https://docs.agno.com/tools/toolkits/others/openweather.md)
- [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)
- [Resend](https://docs.agno.com/tools/toolkits/others/resend.md)
- [Shopify](https://docs.agno.com/tools/toolkits/others/shopify.md): Tools to interact with Shopify.
- [Spotify](https://docs.agno.com/tools/toolkits/others/spotify.md)
- [Todoist](https://docs.agno.com/tools/toolkits/others/todoist.md)
- [Trello](https://docs.agno.com/tools/toolkits/others/trello.md): Agno TrelloTools helps to integrate Trello functionalities into your agents, enabling management of boards, lists, and cards.
- [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.
- [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): WebTools provides utilities for working with web URLs including URL expansion and web-related operations.
- [Yfinance](https://docs.agno.com/tools/toolkits/others/yfinance.md)
- [Youtube](https://docs.agno.com/tools/toolkits/others/youtube.md)
- [Zendesk](https://docs.agno.com/tools/toolkits/others/zendesk.md)
- [Toolkit Index](https://docs.agno.com/tools/toolkits/overview.md): Index of all toolkits supported by Agno.
- [Arxiv](https://docs.agno.com/tools/toolkits/search/arxiv.md)
- [BaiduSearch](https://docs.agno.com/tools/toolkits/search/baidusearch.md)
- [Brave Search](https://docs.agno.com/tools/toolkits/search/bravesearch.md)
- [DuckDuckGo](https://docs.agno.com/tools/toolkits/search/duckduckgo.md)
- [Exa](https://docs.agno.com/tools/toolkits/search/exa.md)
- [Hacker News](https://docs.agno.com/tools/toolkits/search/hackernews.md)
- [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)
- [Searxng](https://docs.agno.com/tools/toolkits/search/searxng.md)
- [Seltz](https://docs.agno.com/tools/toolkits/search/seltz.md)
- [Serpapi](https://docs.agno.com/tools/toolkits/search/serpapi.md)
- [SerperApi](https://docs.agno.com/tools/toolkits/search/serper.md)
- [Tavily](https://docs.agno.com/tools/toolkits/search/tavily.md)
- [Valyu](https://docs.agno.com/tools/toolkits/search/valyu.md): ValyuTools provides academic and web search capabilities with advanced filtering and relevance scoring.
- [Web Search](https://docs.agno.com/tools/toolkits/search/websearch.md)
- [Wikipedia](https://docs.agno.com/tools/toolkits/search/wikipedia.md)
- [Discord](https://docs.agno.com/tools/toolkits/social/discord.md)
- [Email](https://docs.agno.com/tools/toolkits/social/email.md)
- [Gmail](https://docs.agno.com/tools/toolkits/social/gmail.md)
- [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)
- [Telegram](https://docs.agno.com/tools/toolkits/social/telegram.md)
- [Twilio](https://docs.agno.com/tools/toolkits/social/twilio.md)
- [Webex](https://docs.agno.com/tools/toolkits/social/webex.md)
- [WhatsApp](https://docs.agno.com/tools/toolkits/social/whatsapp.md)
- [X (Twitter)](https://docs.agno.com/tools/toolkits/social/x.md)
- [Zoom](https://docs.agno.com/tools/toolkits/social/zoom.md)
- [AgentQL](https://docs.agno.com/tools/toolkits/web-scrape/agentql.md)
- [BrightData](https://docs.agno.com/tools/toolkits/web-scrape/brightdata.md)
- [Browserbase](https://docs.agno.com/tools/toolkits/web-scrape/browserbase.md)
- [Crawl4AI](https://docs.agno.com/tools/toolkits/web-scrape/crawl4ai.md)
- [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)
- [Newspaper](https://docs.agno.com/tools/toolkits/web-scrape/newspaper.md)
- [Newspaper4k](https://docs.agno.com/tools/toolkits/web-scrape/newspaper4k.md)
- [Oxylabs](https://docs.agno.com/tools/toolkits/web-scrape/oxylabs.md)
- [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.
- [Spider](https://docs.agno.com/tools/toolkits/web-scrape/spider.md)
- [Trafilatura](https://docs.agno.com/tools/toolkits/web-scrape/trafilatura.md): TrafilaturaTools provides advanced web scraping and text extraction capabilities with support for crawling and content analysis.
- [Website Tools](https://docs.agno.com/tools/toolkits/web-scrape/website.md)
- [Async Team with Tools](https://docs.agno.com/tools/usage/async-team-with-tools.md)
- [Team with Custom Tools](https://docs.agno.com/tools/usage/team-with-custom-tools.md)
- [Team with Tool Hooks](https://docs.agno.com/tools/usage/team-with-tool-hooks.md)
- [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
- [Tracing](https://docs.agno.com/tracing/overview.md): Gain deep visibility into your Agno agents with OpenTelemetry-based observability
- [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.
- [Accessing Multiple Previous Steps](https://docs.agno.com/workflows/access-previous-steps.md): How to access multiple previous steps
- [Additional Data and Metadata](https://docs.agno.com/workflows/additional-data.md): How to pass additional data to workflows
- [Background Workflow Execution](https://docs.agno.com/workflows/background-execution.md): How to execute workflows as non-blocking background tasks
- [Building Workflows](https://docs.agno.com/workflows/building-workflows.md): Define steps, loops, conditions, and parallel execution in workflows.
- [Conversational Workflows](https://docs.agno.com/workflows/conversational-workflows.md): Build multi-turn conversational workflows in Agno.
- [Early Stopping](https://docs.agno.com/workflows/early-stop.md): How to early stop workflows
- [Condition HITL](https://docs.agno.com/workflows/hitl/condition.md): Let users decide which branch to execute in conditional workflows.
- [Error Handling HITL](https://docs.agno.com/workflows/hitl/error-handling.md): Pause on step failures to let users retry or skip.
- [Loop HITL](https://docs.agno.com/workflows/hitl/loop.md): Confirm before starting iterative execution in workflows.
- [Human-in-the-Loop in Workflows](https://docs.agno.com/workflows/hitl/overview.md): Pause workflow execution for user confirmation, input, or decisions at the step level.
- [Router HITL](https://docs.agno.com/workflows/hitl/router.md): Let users select routes or confirm automated routing decisions.
- [Step HITL](https://docs.agno.com/workflows/hitl/step.md): Pause individual steps for confirmation or user input before execution.
- [Steps HITL](https://docs.agno.com/workflows/hitl/steps.md): Confirm before executing a pipeline of grouped steps.
- [What are Workflows?](https://docs.agno.com/workflows/overview.md): Workflows orchestrate agents, teams, and functions through defined steps for repeatable tasks.
- [Running Workflows](https://docs.agno.com/workflows/running-workflows.md): Execute workflows with Workflow.run() and process their output.
- [Access Multiple Previous Steps Output](https://docs.agno.com/workflows/usage/access-multiple-previous-steps-output.md): This example demonstrates **Workflows 2.0** advanced data flow capabilities
- [Async Events Streaming](https://docs.agno.com/workflows/usage/async-events-streaming.md): This example demonstrates how to stream events from a workflow.
- [Basic Conversational Workflow](https://docs.agno.com/workflows/usage/basic-workflow-agent.md): This example demonstrates a basic conversational workflow with a WorkflowAgent.
- [Class-based Executor](https://docs.agno.com/workflows/usage/class-based-executor.md): This example demonstrates how to use a class-based executor in a workflow.
- [Condition and Parallel Steps Workflow](https://docs.agno.com/workflows/usage/condition-and-parallel-steps-stream.md): This example demonstrates **Workflows 2.0** advanced pattern combining conditional execution with parallel processing.
- [Conditional Workflow](https://docs.agno.com/workflows/usage/condition-steps-workflow-stream.md): Execute steps based on content analysis or business logic.
- [Condition with list of steps](https://docs.agno.com/workflows/usage/condition-with-list-of-steps.md): This example demonstrates how to use conditional step to execute multiple steps in parallel.
- [Early Stop a Workflow](https://docs.agno.com/workflows/usage/early-stop-workflow.md): This example demonstrates **Workflows 2.0** early termination of a running workflow.
- [Function instead of steps](https://docs.agno.com/workflows/usage/function-instead-of-steps.md): This example demonstrates how to use just a single function instead of steps in a workflow.
- [Loop Iterative Accumulation](https://docs.agno.com/workflows/usage/loop-iterative-accumulation.md): Demonstrates loop iterations carrying forward output from previous iterations.
- [Loop Steps Workflow](https://docs.agno.com/workflows/usage/loop-steps-workflow.md): This example demonstrates **Workflows 2.0** loop execution for quality-driven iterative processes.
- [Loop with Parallel Steps Workflow](https://docs.agno.com/workflows/usage/loop-with-parallel-steps-stream.md): This example demonstrates **Workflows 2.0** most sophisticated pattern combining loop execution with parallel processing and real-time streaming.
- [Parallel Workflow](https://docs.agno.com/workflows/usage/parallel-steps-workflow.md): Execute independent tasks simultaneously to reduce total execution time.
- [Conditional Branching Workflow](https://docs.agno.com/workflows/usage/router-steps-workflow.md): This example demonstrates **Workflows 2.0** router pattern for intelligent, content-based workflow routing.
- [Router with Loop Steps](https://docs.agno.com/workflows/usage/router-with-loop-steps.md): This example demonstrates **Workflows 2.0** advanced pattern combining Router-based intelligent path selection with Loop execution for iterative quality improvement.
- [Router with Step Choices](https://docs.agno.com/workflows/usage/router-with-step-choices.md): Router step_choices parameter and string, Step, and List[Step] returns.
- [Sequential Workflow](https://docs.agno.com/workflows/usage/sequence-of-steps.md): Use named steps for sequential execution with clear tracking.
- [Step with function](https://docs.agno.com/workflows/usage/step-with-function.md): This example demonstrates how to use named steps with custom function executors.
- [Step with Function using Additional Data](https://docs.agno.com/workflows/usage/step-with-function-additional-data.md): This example demonstrates **Workflows 2.0** support for passing metadata and contextual information to steps via `additional_data`.
- [Step with custom function streaming on AgentOS](https://docs.agno.com/workflows/usage/step-with-function-streaming-agentos.md): This example demonstrates how to use named steps with custom function executors and streaming on AgentOS.
- [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): This example demonstrates **Workflows 2.0** event storage capabilities
- [Structured I/O](https://docs.agno.com/workflows/usage/structured-io-at-each-step-level.md): Type-safe data flow between agents, teams, and custom functions using Pydantic models.
- [Workflow Cancellation](https://docs.agno.com/workflows/usage/workflow-cancellation.md): This example demonstrates **Workflows 2.0** support for cancelling running workflow executions, including thread-based cancellation and handling cancelled responses.
- [Workflow with Input Schema Validation](https://docs.agno.com/workflows/usage/workflow-with-input-schema.md): This example demonstrates **Workflows** support for input schema validation using Pydantic models to ensure type safety and data integrity at the workflow entry point.
- [Advanced Workflow Patterns](https://docs.agno.com/workflows/workflow-patterns/advanced-workflow-patterns.md): Combine multiple workflow patterns to build sophisticated, production-ready automation systems
- [Branching Workflow](https://docs.agno.com/workflows/workflow-patterns/branching-workflow.md): Complex decision trees requiring dynamic path selection based on content analysis
- [Conditional Workflow](https://docs.agno.com/workflows/workflow-patterns/conditional-workflow.md): Deterministic branching based on input analysis or business rules
- [Custom Functions in Workflows](https://docs.agno.com/workflows/workflow-patterns/custom-function-step-workflow.md): How to use custom functions in workflows
- [Fully Python Workflow](https://docs.agno.com/workflows/workflow-patterns/fully-python-workflow.md): Keep it Simple with Pure Python, in v1 workflows style
- [Grouped Steps Workflow](https://docs.agno.com/workflows/workflow-patterns/grouped-steps-workflow.md): Organize multiple steps into reusable, logical sequences for complex workflows with clean separation of concerns
- [Iterative Workflow](https://docs.agno.com/workflows/workflow-patterns/iterative-workflow.md): Quality-driven processes requiring repetition until specific conditions are met
- [Workflow Patterns](https://docs.agno.com/workflows/workflow-patterns/overview.md): Master deterministic workflow patterns including sequential, parallel, conditional, and looping execution for reliable multi-agent automation.
- [Parallel Workflow](https://docs.agno.com/workflows/workflow-patterns/parallel-workflow.md): Independent, concurrent tasks that can execute simultaneously for improved efficiency
- [Sequential Workflows](https://docs.agno.com/workflows/workflow-patterns/sequential.md): Linear, deterministic processes where each step depends on the output of the previous step.
- [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
- [Workflow Tools](https://docs.agno.com/workflows/workflow-tools.md): How to execute a workflow inside an Agent or Team

## OpenAPI Specs

- [openapi](https://docs.agno.com/reference-api/openapi.yaml)


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