> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/firebase/genkit/llms.txt
> Use this file to discover all available pages before exploring further.

# Flows API

> Flows API reference (Python)

Flows orchestrate multi-step AI tasks with automatic tracing.

## @ai.flow() Decorator

Defines a flow.

```python theme={null}
@ai.flow()
async def summarize_article(url: str) -> str:
    """Summarizes an article from a URL."""
    # Flow implementation
    content = await fetch_article(url)
    
    response = await ai.generate(
        prompt=f"Summarize: {content}",
    )
    
    return response.text

# Call the flow
result = await summarize_article("https://example.com")
```

### Parameters

<ParamField path="name" type="str | None">
  Flow name (defaults to function name)
</ParamField>

<ParamField path="input_schema" type="type | dict | None">
  Input schema for validation
</ParamField>

<ParamField path="output_schema" type="type | dict | None">
  Output schema for validation
</ParamField>

## Flow Execution

```python theme={null}
# Direct call
result = await my_flow(input_data)

# Access flow metadata
my_flow.name          # Flow name
my_flow.action        # Underlying Action object
```

## Nested Flows

```python theme={null}
@ai.flow()
async def process_data(data: str) -> str:
    """Processes data."""
    return data.upper()

@ai.flow()
async def main_workflow(input: str) -> str:
    """Main workflow calling sub-flow."""
    # Call another flow
    processed = await process_data(input)
    
    response = await ai.generate(
        prompt=f"Analyze: {processed}",
    )
    
    return response.text
```

## FlowWrapper

```python theme={null}
from genkit import FlowWrapper

flow: FlowWrapper = ai.flow()(my_function)

# Execute
result = await flow(input_data)
```