chore: import upstream snapshot with attribution

examples/integration-pydantic-ai/agent.py

@@ -0,0 +1,84 @@
+"""
+Simple weather assistant agent using PydanticAI.
+
+This agent demonstrates structured outputs by returning weather information
+in a consistent format using Pydantic models.
+"""
+
+from pydantic import BaseModel
+from pydantic_ai import Agent, RunContext
+
+# Use the OpenAI Responses API with the smallest current GPT-5 reasoning model.
+# The `openai-responses:` prefix selects the Responses API explicitly (the bare
+# `openai:` prefix also resolves there in PydanticAI v2.0+).
+DEFAULT_MODEL = "openai-responses:gpt-5.4-nano"
+
+
+class WeatherResponse(BaseModel):
+    """Structured weather response"""
+
+    location: str
+    temperature: str
+    description: str
+
+
+def get_weather(ctx: RunContext, location: str) -> dict:
+    """Get weather data for a location (mock implementation for demo)"""
+    # Simple mock weather data for demonstration
+    mock_weather = {
+        "london": {"temp": "18°C", "desc": "Cloudy"},
+        "new york": {"temp": "22°C", "desc": "Sunny"},
+        "tokyo": {"temp": "16°C", "desc": "Rainy"},
+    }
+
+    location_lower = location.lower()
+    for city, weather in mock_weather.items():
+        if city in location_lower:
+            return {
+                "location": location,
+                "temperature": weather["temp"],
+                "description": weather["desc"],
+            }
+
+    # Default response for unknown locations
+    return {"location": location, "temperature": "21°C", "description": "Clear"}
+
+
+def get_weather_agent(model: str = DEFAULT_MODEL) -> Agent:
+    """Create a weather agent with structured output"""
+    agent = Agent(
+        model,
+        output_type=WeatherResponse,
+        system_prompt=(
+            "You are a helpful weather assistant. "
+            "Use the get_weather tool to fetch weather data for locations. "
+            "Always return responses in the required structured format."
+        ),
+    )
+
+    agent.tool(get_weather)
+    return agent
+
+
+async def run_weather_agent(query: str, model: str = DEFAULT_MODEL) -> WeatherResponse:
+    """Run the weather agent with a query"""
+    try:
+        agent = get_weather_agent(model)
+        result = await agent.run(query)
+        return result.output
+    except Exception as e:
+        return WeatherResponse(
+            location="Unknown", temperature="N/A", description=f"Error: {str(e)}"
+        )
+
+
+if __name__ == "__main__":
+    import asyncio
+
+    async def test_agent():
+        queries = ["What's the weather like in London?"]
+        for query in queries:
+            result = await run_weather_agent(query)
+            print(f"{query} -> {result.model_dump_json()}")
+
+    asyncio.run(test_agent())