FastAPI is incredibly fast, easy to learn, and auto-generates documentation (Swagger UI). Here is the absolute minimum boilerplate you need to expose your LLM agent.

The Code

`from fastapi import FastAPI, HTTPException from pydantic import BaseModel

Assume we have an agent object from LangGraph or LangChain

from my_agent_logic import agent_graph

1. Initialize FastAPI

app = FastAPI(title=“My AI Agent API”)

2. Define the Request format

class ChatRequest(BaseModel): user_message: str session_id: str

3. Define the Response format

class ChatResponse(BaseModel): ai_response: str tokens_used: int

4. Create the Endpoint

@app.post(“/api/chat”, response_model=ChatResponse) async def chat_endpoint(request: ChatRequest): try: # Pass the input to your agent # (This syntax depends on your specific agent framework) result = await agent_graph.ainvoke( {“messages”: [(“user”, request.user_message)]}, config={“configurable”: {“thread_id”: request.session_id}} )

    # Extract the final message content
    final_text = result["messages"][-1].content

    return ChatResponse(
        ai_response=final_text,
        tokens_used=0 # You can extract token metadata if needed
    )
except Exception as e:
    raise HTTPException(status_code=500, detail=str(e))`

How to Run It

      Save the code above as `main.py`. Then run the Uvicorn server:

uvicorn main:app --reload

      Navigate to `http://localhost:8000/docs` in your browser. You will see an interactive Swagger UI where you can immediately test your endpoint!