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Agno Workflows are designed to automate complex processes by defining a series of steps that are executed in sequence. Each step can be executed by an agent, a team, or a custom function. Workflows 2.0

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Why should you use Workflows?

Workflows are a great way to automate complex processes by defining a series of steps that are executed in a flow that you control. This is particularly useful when you need to:
  • Automate a series of tasks
  • Orchestrate multiple agents or teams of agents
  • Have complex logic where you require agents to run in loops, or in parallel, or route different flows based on the output of previous steps
When building multi-agent systems, there is often a need to control the flow of execution, and this is where Workflows come in. Workflows are best suited for deterministic agent automation, in comparison to Teams which are designed for agentic coordination between agents.

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Step-Wise Controlled Execution

Agno Workflows are fundamentally an execution of a series of steps. These steps are executed in sequence, and the output of each step is passed to the next step. Each step can be executed by an agent, a team, or a custom Python function. These are individual components that can work independently but gain enhanced capabilities when orchestrated together:
  • 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
The beauty of this approach is that you have access to the full power of Agno Agents and Teams, with the flexibility of a sophisticated orchestration system. Your agents and teams retain their individual characteristics, memory, and behavior patterns, but now operate within a structured workflow that provides:
  • Sequential step execution with output chaining
  • Session management and state persistence
  • Error handling and retry mechanisms
  • Streaming capabilities for real-time feedback

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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 Structured Inputs 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

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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.v2 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.message}") # <-- 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)
See the full example for more details. To learn more about how to start building workflow systems, check out the development guide page.