Missionaries And Cannibals Puzzle

Explore the missionaries and cannibals puzzle with a separate DeepSeek reasoning stage.

Demonstrates DeepSeek-backed reasoning for logic puzzle solving.

DeepSeek retired the deepseek-reasoner alias after July 24, 2026. Before running this archived example, replace DeepSeek(id="deepseek-reasoner") with DeepSeek(id="deepseek-v4-flash") in the saved program. The main OpenAI model remains a separate call, so both provider keys are required. See the DeepSeek V4 migration notice.

logical_puzzle.py
"""
Missionaries And Cannibals Puzzle
=================================

Demonstrates DeepSeek-backed reasoning for logic puzzle solving.
"""

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

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
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"
)

agent = Agent(
    model=OpenAIResponses(id="gpt-5.6"),
    reasoning_model=DeepSeek(id="deepseek-reasoner"),
    markdown=True,
)

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    agent.print_response(task, stream=True, show_full_reasoning=True)

An explicit reasoning_model runs as a separate, tool-free reasoning stage before the main model response. show_full_reasoning=True displays the reasoning data Agno receives; it cannot reveal a provider's private internal trace. Some adapters use the reasoning stage's answer text when separate reasoning content is unavailable. A failed reasoning stage can still be followed by a main-model answer, so a completed run alone does not prove the reasoning stage succeeded.

Run the Example

Set up your virtual environment

uv venv --python 3.12
source .venv/bin/activate

Install dependencies

uv pip install -U agno openai

Export your API keys

export DEEPSEEK_API_KEY="your_deepseek_api_key_here"
export OPENAI_API_KEY="your_openai_api_key_here"

Run the example

Save the code above as logical_puzzle.py, then run:

python logical_puzzle.py

Full source: cookbook/10_reasoning/agents/logical_puzzle.py