agno-workflow

name	agno-workflow
description	Build step-based workflows with Agno for sequential agent pipelines. Trigger this skill when: importing agno.workflow, creating Workflow or Step instances, building pipelines, chaining agents, or asking "how do I build a workflow with Agno?"
license	Apache-2.0
metadata	{"version":"1.0.0","author":"agno-team","tags":["workflow","pipeline","agno","orchestration"]}

Build Agno Workflows

Use agno.workflow.Workflow and agno.workflow.Step to chain agents into sequential pipelines. Install with pip install agno.

Quick Start

from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.db.sqlite import SqliteDb
from agno.workflow import Step, Workflow

db = SqliteDb(db_file="tmp/agents.db")

# Step 1: Gather data
data_agent = Agent(
    name="Data Gatherer",
    model=Claude(id="claude-sonnet-4-5"),
    instructions="Fetch comprehensive data. Don't analyze -- just gather and organize.",
    db=db,
    add_history_to_context=True,
    num_history_runs=5,
)

# Step 2: Analyze
analyst = Agent(
    name="Analyst",
    model=Claude(id="claude-sonnet-4-5"),
    instructions="Interpret the data. Identify strengths, weaknesses, and benchmarks.",
    db=db,
    add_history_to_context=True,
    num_history_runs=5,
)

# Step 3: Write report
writer = Agent(
    name="Report Writer",
    model=Claude(id="claude-sonnet-4-5"),
    instructions="Synthesize into a concise brief. Lead with a one-line summary.",
    db=db,
    add_history_to_context=True,
    num_history_runs=5,
    markdown=True,
)

# Create workflow
workflow = Workflow(
    name="Research Pipeline",
    description="Data -> Analysis -> Report",
    steps=[
        Step(name="Data Gathering", agent=data_agent, description="Fetch data"),
        Step(name="Analysis", agent=analyst, description="Analyze data"),
        Step(name="Report", agent=writer, description="Write report"),
    ],
)

workflow.print_response("Analyze NVIDIA for investment", stream=True)

Step Configuration

Each Step wraps an agent with metadata about its role in the pipeline:

from agno.workflow import Step

step = Step(
    name="Step Name",                    # Display name
    agent=my_agent,                      # Agent that handles this step
    description="What this step does",   # Used for routing and documentation
)

Workflow Parameters

from agno.workflow import Workflow

Workflow(
    name="Workflow Name",
    id="unique-id",                       # Auto-generated if omitted
    description="What this workflow does",
    steps=[step1, step2, step3],          # Ordered list of steps
    db=SqliteDb(db_file="workflow.db"),   # Storage backend
    add_workflow_history_to_steps=True,   # Pass prior step outputs to next step
)

Running Workflows

# Sync
workflow.print_response("input prompt", stream=True)
response = workflow.run("input prompt")

# Async
await workflow.aprint_response("input prompt", stream=True)
response = await workflow.arun("input prompt")

Workflow vs Team

Feature	Workflow	Team
Execution	Sequential steps, predictable order	Dynamic, leader decides
Data flow	Output of step N feeds step N+1	Leader synthesizes
Best for	Pipelines, ETL, structured processes	Collaboration, debate
Control	Explicit, repeatable	Flexible, adaptive

Use Workflow when:

• Steps must happen in a specific order
• Each step has a clear, specialized role
• You want predictable, repeatable execution
• Output from step N feeds into step N+1

Use Team when:

• Agents need to collaborate dynamically
• The leader should decide who to involve
• Tasks benefit from back-and-forth discussion

Anti-Patterns

• Don't use workflows for simple tasks — a single agent is faster
• Don't skip add_workflow_history_to_steps=True if later steps need context from earlier ones
• Don't give every step the same instructions — each step should have a distinct, specialized role
• Don't make pipelines too deep — 3-5 steps is ideal; more creates latency

Further Reading

For advanced workflow features (parallel steps, conditional routing), read references/api-patterns.md.