trolley_problem.py
"""
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
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv pip install -U agno openai
3
Export your API keys
export DEEPSEEK_API_KEY="your_deepseek_api_key_here"
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:DEEPSEEK_API_KEY="your_deepseek_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
4
Run the example
Save the code above as
trolley_problem.py, then run:python trolley_problem.py