> ## Documentation Index
> Fetch the complete documentation index at: https://docs-v1.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Reasoning Agent with Reasoning Tools

This example shows how to create an agent that uses the ReasoningTools to solve
complex problems through step-by-step reasoning. The agent breaks down questions,
analyzes intermediate results, and builds structured reasoning paths to arrive at
well-justified conclusions.

## Code

```python cookbook/reasoning/tools/reasoning_tools.py theme={null}


from textwrap import dedent

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

reasoning_agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    tools=[ReasoningTools(add_instructions=True)],
    instructions=dedent("""\
        You are an expert problem-solving assistant with strong analytical skills! 🧠
        
        Your approach to problems:
        1. First, break down complex questions into component parts
        2. Clearly state your assumptions
        3. Develop a structured reasoning path
        4. Consider multiple perspectives
        5. Evaluate evidence and counter-arguments
        6. Draw well-justified conclusions
        
        When solving problems:
        - Use explicit step-by-step reasoning
        - Identify key variables and constraints
        - Explore alternative scenarios
        - Highlight areas of uncertainty
        - Explain your thought process clearly
        - Consider both short and long-term implications
        - Evaluate trade-offs explicitly
        
        For quantitative problems:
        - Show your calculations
        - Explain the significance of numbers
        - Consider confidence intervals when appropriate
        - Identify source data reliability
        
        For qualitative reasoning:
        - Assess how different factors interact
        - Consider psychological and social dynamics
        - Evaluate practical constraints
        - Address value considerations
        \
    """),
    add_datetime_to_instructions=True,
    stream_intermediate_steps=True,
    show_tool_calls=True,
    markdown=True,
)

# Example usage with a complex reasoning problem
reasoning_agent.print_response(
    "Solve this logic puzzle: A man has to take a fox, a chicken, and a sack of grain across a river. "
    "The boat is only big enough for the man and one item. If left unattended together, the fox will "
    "eat the chicken, and the chicken will eat the grain. How can the man get everything across safely?",
    stream=True,
)


```

## Usage

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

  <Step title="Set your API key">
    ```bash theme={null}
    export OPENAI_API_KEY=xxx
    ```
  </Step>

  <Step title="Install libraries">
    ```bash theme={null}
    pip install -U openai agno
    ```
  </Step>

  <Step title="Run Example">
    <CodeGroup>
      ```bash Mac theme={null}
      python cookbook/reasoning/tools/reasoning_tools.py
      ```

      ```bash Windows theme={null}
      python cookbook/reasoning/tools/reasoning_tools.py
      ```
    </CodeGroup>
  </Step>
</Steps>