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

# Missionaries And Cannibals Puzzle

> Solve the missionaries-and-cannibals river crossing with an ASCII diagram, contrasting gpt-4o built-in chain-of-thought with a deepseek-reasoner reasoning model.

Demonstrates built-in and DeepSeek-backed reasoning for logic puzzle solving.

```python logical_puzzle.py theme={null}
"""
Missionaries And Cannibals Puzzle
=================================

Demonstrates built-in and DeepSeek-backed reasoning for logic puzzle solving.
"""

from agno.agent import Agent
from agno.models.deepseek import DeepSeek
from agno.models.openai import OpenAIChat

# ---------------------------------------------------------------------------
# Create Agents
# ---------------------------------------------------------------------------
task = (
    "Three missionaries and three cannibals need to cross a river. "
    "They have a boat that can carry up to two people at a time. "
    "If, at any time, the cannibals outnumber the missionaries on either side of the river, the cannibals will eat the missionaries. "
    "How can all six people get across the river safely? Provide a step-by-step solution and show the solutions as an ascii diagram"
)

cot_agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    reasoning=True,
    markdown=True,
)

deepseek_agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    reasoning_model=DeepSeek(id="deepseek-reasoner"),
    markdown=True,
)

# ---------------------------------------------------------------------------
# Run Agents
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    print("=== Built-in Chain Of Thought ===")
    cot_agent.print_response(task, stream=True, show_full_reasoning=True)

    print("\n=== DeepSeek Reasoning Model ===")
    deepseek_agent.print_response(task, stream=True, show_full_reasoning=True)
```

## Run the Example

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

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

  <Step title="Export your API keys">
    <CodeGroup>
      ```bash Mac/Linux theme={null}
      export DEEPSEEK_API_KEY="your_deepseek_api_key_here"
      export OPENAI_API_KEY="your_openai_api_key_here"
      ```

      ```bash Windows theme={null}
      $Env:DEEPSEEK_API_KEY="your_deepseek_api_key_here"
      $Env:OPENAI_API_KEY="your_openai_api_key_here"
      ```
    </CodeGroup>
  </Step>

  <Step title="Run the example">
    Save the code above as `logical_puzzle.py`, then run:

    ```bash theme={null}
    python logical_puzzle.py
    ```
  </Step>
</Steps>

Full source: [cookbook/10\_reasoning/agents/logical\_puzzle.py](https://github.com/agno-agi/agno/blob/main/cookbook/10_reasoning/agents/logical_puzzle.py)