Agno Workflows enable you to build deterministic, controlled agentic flows by orchestrating agents, teams, and functions through a series of defined steps. Unlike free-form agent interactions, workflows provide structured automation with predictable execution patterns, making them ideal for production systems that require reliable, repeatable processes. Workflows flow diagram

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Why should you use Workflows?

Workflows provide deterministic control over your agentic systems, enabling you to build reliable automation that executes consistently every time. They’re essential when you need: Deterministic Execution
  • Predictable step-by-step processing with defined inputs and outputs
  • Consistent results across multiple runs
  • Clear audit trails for production systems
Complex Orchestration
  • Multi-agent coordination with controlled handoffs
  • Parallel processing and conditional branching
  • Loop structures for iterative tasks
Workflows excel at deterministic agent automation, while Teams are designed for dynamic agentic coordination. Use workflows when you need predictable, repeatable processes; use teams when you need flexible, collaborative problem-solving.

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Deterministic Step Execution

Workflows execute as a controlled sequence of steps, where each step produces deterministic outputs that feed into the next step. This creates predictable data flows and consistent results, unlike free-form agent conversations. Step Types
  • Agents: Individual AI executors with specific capabilities and instructions
  • Teams: Coordinated groups of agents working together on complex problems
  • Functions: Custom Python functions for specialized processing logic
Deterministic Benefits Your agents and teams retain their individual characteristics and capabilities, but now operate within a structured framework that ensures:
  • Predictable execution: Steps run in defined order with controlled inputs/outputs
  • Repeatable results: Same inputs produce consistent outputs across runs
  • Clear data flow: Output from each step explicitly becomes input for the next
  • Controlled state: Session management and state persistence between steps
  • Reliable error handling: Built-in retry mechanisms and error recovery

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Workflow Input

Workflows support multiple input types for maximum flexibility:
Input TypeExampleUse Case
String"Analyze AI trends"Simple text prompts
Pydantic ModelResearchRequest(topic="AI", depth=5)Type-safe structured input
List["AI", "ML", "LLMs"]Multiple items to process
Dictionary{"query": "AI", "sources": ["web", "academic"]}Key-value pairs
When this input is passed to an Agent or Team, it will be serialized to a string before being passed to the agent or team.
See more on Pydantic as input in the Advanced Workflows documentation.

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Architectural components

  1. The Workflow class is the top-level orchestrator that manages the entire execution process.
  2. Step is the fundamental unit of work in the workflow system. Each step encapsulates exactly one executor - either an Agent, a Team, or a custom Python function. This design ensures clarity and maintainability while preserving the individual characteristics of each executor.
  3. Loop is a construct that allows you to execute one or more steps multiple times. This is useful when you need to repeat a set of steps until a certain condition is met.
  4. Parallel is a construct that allows you to execute one or more steps in parallel. This is useful when you need to execute a set of steps concurrently with the outputs joined together.
  5. Condition makes a step conditional based on criteria you specify.
  6. Router allows you to specify which step(s) to execute next, effectively creating branching logic in your workflow.
When using a custom Python function as an executor for a step, StepInput and StepOutput provides standardized interfaces for data flow between steps:
Workflows step IO flow diagram

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How to make your first workflow?

There are different types of patterns you can use to build your workflows. For example you can combine agents, teams, and functions to build a workflow. 
from agno.workflow import Step, Workflow, StepOutput

def data_preprocessor(step_input):
    # Custom preprocessing logic

    # Or you can also run any agent/team over here itself
    # response = some_agent.run(...)
    return StepOutput(content=f"Processed: {step_input.input}") # <-- Now pass the agent/team response in content here

workflow = Workflow(
    name="Mixed Execution Pipeline",
    steps=[
        research_team,      # Team
        data_preprocessor,  # Function
        content_agent,      # Agent
    ]
)

workflow.print_response("Analyze the competitive landscape for fintech startups", markdown=True)

View Complete Example

See the full example with agents, teams, and functions working together

Use in AgentOS

Run your workflows through the AgentOS chat interface