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Update structured output docs to make tool use example more obvious #191

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96d58b7
Update strucutred output docs to make tool use exmaple more obvious
Unshure Jul 30, 2025
ca43fef
Update structured-output.md
Unshure Jul 30, 2025
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50 changes: 28 additions & 22 deletions docs/user-guide/concepts/agents/structured-output.md
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Expand Up @@ -78,6 +78,34 @@ print(f"Age: {result.age}") # 30
print(f"Job: {result.occupation}") # "software engineer"
```

### Using Tools and Conversation History

Structured output can work with tools and conversation history, but the agent must be primed with that information before calling the `structured_output` function.

```python
from strands import Agent
from pydantic import BaseModel
from strands_tools import http_request

agent = Agent(tools=[http_request])

# Build up conversation context
agent("What do you know about Paris, France?")
agent("Can you check the weather there now?")

# Extract structured information with a prompt
class CityInfo(BaseModel):
city: str
country: str
weather: str

# Uses existing conversation context with a prompt
result = agent.structured_output(CityInfo, "Extract structured information about Paris")
print(result.city)
print(result.country)
print(result.weather)
```

### Multi-Modal Input

Extract structured information from prompts containing images, documents, and other content types:
Expand Down Expand Up @@ -112,28 +140,6 @@ result = agent.structured_output(
For a complete list of supported content types, please refer to the [API Reference](../../../api-reference/types.md#strands.types.content.ContentBlock).


### Using Conversation History

Structured output can work with existing conversation context:

```python
agent = Agent()

# Build up conversation context
agent("What do you know about Paris, France?")
agent("Tell me about the weather there in spring.")

# Extract structured information with a prompt
class CityInfo(BaseModel):
city: str
country: str
population: Optional[int] = None
climate: str

# Uses existing conversation context with a prompt
result = agent.structured_output(CityInfo, "Extract structured information about Paris")
```

### Complex Nested Models

Handle sophisticated data structures:
Expand Down