> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Capture Reasoning Content Reasoning Tools

> Inspect reasoning_content on a non-streaming RunOutput and the final streaming event.

```python capture_reasoning_content_reasoning_tools.py theme={null}
"""
Capture Reasoning Content Reasoning Tools
=========================================

Demonstrates this reasoning cookbook example.
"""

from textwrap import dedent

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.reasoning import ReasoningTools


# ---------------------------------------------------------------------------
# Create Example
# ---------------------------------------------------------------------------
def run_example() -> None:
    """Test function to verify reasoning_content is populated in RunOutput."""
    print("\n=== Testing reasoning_content generation ===\n")

    # Create an agent with ReasoningTools
    agent = Agent(
        model=OpenAIChat(id="gpt-5.6-luna"),
        tools=[ReasoningTools(add_instructions=True)],
        instructions=dedent("""\
            You are an expert problem-solving assistant with strong analytical skills!         Use step-by-step reasoning to solve the problem.
            \
        """),
    )

    # Test 1: Non-streaming mode
    print("Running with stream=False...")
    response = agent.run(
        "What is the sum of the first 10 natural numbers?", stream=False
    )

    # Check reasoning_content
    if hasattr(response, "reasoning_content") and response.reasoning_content:
        print("[OK] reasoning_content FOUND in non-streaming response")
        print(f"   Length: {len(response.reasoning_content)} characters")
        print("\n=== reasoning_content preview (non-streaming) ===")
        preview = response.reasoning_content[:1000]
        if len(response.reasoning_content) > 1000:
            preview += "..."
        print(preview)
    else:
        print("[NOT FOUND] reasoning_content NOT FOUND in non-streaming response")

    # Process streaming responses to find the final one
    print("\n\n=== Test 2: Processing stream to find final response ===\n")

    # Create another fresh agent
    streaming_agent_alt = Agent(
        model=OpenAIChat(id="gpt-5.6-luna"),
        tools=[ReasoningTools(add_instructions=True)],
        instructions=dedent("""\
            You are an expert problem-solving assistant with strong analytical skills!         Use step-by-step reasoning to solve the problem.
            \
        """),
    )

    # Process streaming responses and look for the final RunOutput
    final_response = None
    for event in streaming_agent_alt.run(
        "What is the value of 3! (factorial)?",
        stream=True,
        stream_events=True,
    ):
        # The final event in the stream should be a RunOutput object
        if hasattr(event, "reasoning_content"):
            final_response = event

    print("--- Checking reasoning_content from final stream event ---")
    if (
        final_response
        and hasattr(final_response, "reasoning_content")
        and final_response.reasoning_content
    ):
        print("[OK] reasoning_content FOUND in final stream event")
        print(f"   Length: {len(final_response.reasoning_content)} characters")
        print("\n=== reasoning_content preview (final stream event) ===")
        preview = final_response.reasoning_content[:1000]
        if len(final_response.reasoning_content) > 1000:
            preview += "..."
        print(preview)
    else:
        print("[NOT FOUND] reasoning_content NOT FOUND in final stream event")


# ---------------------------------------------------------------------------
# Run Example
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    run_example()
```

## Run the Example

<Snippet file="create-venv-step.mdx" />

**1. Install dependencies**

```bash theme={null}
uv pip install -U agno openai
```

**2. Export your OpenAI API key**

```bash Mac/Linux theme={null}
export OPENAI_API_KEY="your_openai_api_key_here"
```

```bash Windows theme={null}
$Env:OPENAI_API_KEY="your_openai_api_key_here"
```

**3. Run the example**

Save the code above as `capture_reasoning_content_reasoning_tools.py`, then run:

```bash theme={null}
python capture_reasoning_content_reasoning_tools.py
```

Full source: [cookbook/10\_reasoning/tools/capture\_reasoning\_content\_reasoning\_tools.py](https://github.com/agno-agi/agno/blob/v3.0.4/cookbook/10_reasoning/tools/capture_reasoning_content_reasoning_tools.py)


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