> ## 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.

# Trolley Problem Analysis

> Solve the trolley problem with ASCII diagrams using a built-in chain-of-thought agent and a deepseek-reasoner-backed agent.

Demonstrates DeepSeek-backed reasoning for ethical analysis.

```python trolley_problem.py theme={null}
"""
Trolley Problem Analysis
========================

Demonstrates DeepSeek-backed reasoning for ethical analysis.
"""

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

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
task = (
    "You are a philosopher tasked with analyzing the classic 'Trolley Problem'. In this scenario, a runaway trolley "
    "is barreling down the tracks towards five people who are tied up and unable to move. You are standing next to "
    "a large stranger on a footbridge above the tracks. The only way to save the five people is to push this stranger "
    "off the bridge onto the tracks below. This will kill the stranger, but save the five people on the tracks. "
    "Should you push the stranger to save the five people? Provide a well-reasoned answer considering utilitarian, "
    "deontological, and virtue ethics frameworks. "
    "Include a simple ASCII art diagram to illustrate the scenario."
)

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)
```

## Run the Example

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

**1. Install dependencies**

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

**2. Export your API keys**

```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"
```

**3. Run the example**

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

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

Full source: [cookbook/10\_reasoning/agents/trolley\_problem.py](https://github.com/agno-agi/agno/blob/v3.0.4/cookbook/10_reasoning/agents/trolley_problem.py)


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