Building Workflows

Define steps, loops, conditions, and parallel execution in workflows.

Workflows are a powerful way to orchestrate your agents and teams. They are a series of steps that are executed in a flow that you control.

Building Blocks

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: an Agent, a Team, a custom Python function, or a nested Workflow. 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:

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)