> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2-docs-scavio-google-v2.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# What are Workflows?
> Workflows orchestrate agents, teams, and functions through defined steps for repeatable tasks.
A Workflow orchestrates agents, teams, and functions through a defined control flow. Steps can run sequentially, in parallel, in loops, or conditionally based on results. Function steps can access all prior outputs through `StepInput`. Agent, team, and nested-workflow steps receive the most recent output as their input.
## Your First Workflow
This workflow takes a topic, collects relevant HackerNews stories, and writes an article:
```python theme={null}
from agno.agent import Agent
from agno.workflow import Workflow
from agno.tools.hackernews import HackerNewsTools
researcher = Agent(
name="Researcher",
instructions="Find relevant information about the topic",
tools=[HackerNewsTools()]
)
writer = Agent(
name="Writer",
instructions="Write a clear, engaging article based on the research"
)
content_workflow = Workflow(
name="Content Creation",
steps=[researcher, writer]
)
content_workflow.print_response("Write an article about AI trends", stream=True)
```
The agents use Agno's default OpenAI model. Install the dependencies and export your API key before running the example:
```bash theme={null}
uv pip install -U agno openai
```
```bash Mac/Linux theme={null}
export OPENAI_API_KEY="your_openai_api_key_here"
```
```powershell Windows theme={null}
$Env:OPENAI_API_KEY="your_openai_api_key_here"
```
## When to Use Workflows
| Requirement | Use |
| --------------------------------------------------- | ----------------------- |
| Fixed ordering, branches, loops, or parallel groups | Workflow |
| Dynamic delegation among model-driven members | [Team](/teams/overview) |
Workflow control flow is repeatable. Agent and team outputs can still vary between runs. Configure a database when you need persisted run records.
## Step Executors
A `Step` wraps exactly one executor:
| Executor | Description |
| ------------ | ----------------------------------------------------------- |
| **Agent** | Individual AI executor with specific tools and instructions |
| **Team** | Coordinated group of agents for complex sub-tasks |
| **Function** | Custom Python function for specialized logic |
| **Workflow** | Nested workflow for composable, reusable sub-pipelines |
The workflow's `steps` list also accepts `Steps`, `Parallel`, `Loop`, `Condition`, and `Router` containers. Agents, teams, functions, and nested workflows are auto-wrapped when passed directly.
## Controlling Workflows
Workflows support conditional logic, parallel execution, loops, and conversational interactions. See [Workflow patterns](/workflows/workflow-patterns/overview) and the guides below.
## Guides
Define steps, inputs, and outputs.
Execute workflows and handle responses.
Enable chat interactions on your workflows.
## Developer Resources
* [Workflow patterns](/workflows/workflow-patterns/overview)
* [Workflow examples](/examples/workflows/overview)
* [Workflow reference](/reference/workflows/workflow)