Building the API Backbone

The API is the "Nervous System" of your agentic application. It must handle the messy, unpredictable nature of web traffic and translate it into structured inputs for LangGraph.

In this lesson, we will build a FastAPI backbone specifically designed for Asynchronous Agents.

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1. Setting Up FastAPI for Agents

We start with a standard FastAPI app but configure it for async performance.

from fastapi import FastAPI, BackgroundTasks, HTTPException
from pydantic import BaseModel

app = FastAPI(title="AgentCore API")

class ChatRequest(BaseModel):
    message: str
    thread_id: str = "default_user"
    user_context: dict = {}

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2. Managing "Threads" (Session Management)

A production API doesn't just "talk" to an agent; it manages Threads. Every user interaction must be mapped to a thread_id.

The Persistence Hook

Before the agent starts, the API must check the database for the history of that thread_id.

from langgraph.checkpoint.postgres import PostgresSaver

# This is called once on startup
checkpointer = PostgresSaver.from_conn_string(DB_URL)

@app.get("/history/{thread_id}")
async def get_history(thread_id: str):
    # Retrieve past messages from the LangGraph checkpointer
    state = await app_graph.get_state({"configurable": {"thread_id": thread_id}})
    return state.values.get("messages", [])

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3. The Execution Endpoint: Sync vs Background

Should the API wait for the agent to finish?

Scenario A: High-Speed Query

• UX: User waits for 2 seconds for a response.
• API: Standard await graph.ainvoke(...).

Scenario B: Long-Running Task

• UX: User gets a "Task Started" message and can navigate away.
• API: Uses FastAPI BackgroundTasks or a Celery worker.

@app.post("/run-task")
async def run_long_agent(req: ChatRequest, bg_tasks: BackgroundTasks):
    # This runs the agent without blocking the HTTP response
    bg_tasks.add_task(graph.ainvoke, {"input": req.message}, {"configurable": {"thread_id": req.thread_id}})
    return {"status": "accepted", "thread_id": req.thread_id}

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4. Handling Streaming Responses

As discussed in Module 9.3, we use EventSourceResponse (SSE). The backbone must bridge the LangGraph generator to the HTTP stream.

from sse_starlette.sse import EventSourceResponse

@app.post("/chat")
async def stream_chat(req: ChatRequest):
    async def event_generator():
        # Iterate over the graph's events
        async for event in graph.astream_events({"messages": [req.message]}, version="v1"):
            # Transform internal LangGraph events into JSON for the UI
            yield {"data": serialize_event(event)}
            
    return EventSourceResponse(event_generator())

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5. Metadata and Authentication Middleware

Your backbone is the only place where you can enforce business rules.

• Auth: Check if the user has a valid JWT.
• Rate Limiting: Check if the user has hits their 100-request-per-hour cap.
• CORS: Ensure your React frontend (on port 3000) can talk to your API (on port 8000).

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6. The "Health Check" of an Agent

Unlike a static API, a "Hung" agent is hard to detect. You should implement a /status endpoint that doesn't just say True, but checks:

• "Is the Vector Database connected?"
• "Is the LLM API responding?"
• "How many active agent threads are currently running?"

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Summary and Mental Model

Think of the API Backbone like a Switchboard Operator.

• It receives calls (HTTP).
• It checks if the caller is allowed (Auth).
• It connects the caller to the right department (LangGraph Thread).
• It stays on the line to make sure the message is delivered (Streaming).

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Exercise: API Design

1. Endpoint Design: How would you design an endpoint that allows a user to "Cancel" a running agent?
   • (Hint: You need to send a SIGINT or a "Cancel Signal" to the thread).
2. Persistence: If the FastAPI server crashes while an agent is mid-step, how can the backbone "Self-Heal" when it restarts?
3. Data Protection: Implement a check in the ChatRequest model to prevent a message that is longer than 10,000 characters. Why is this important for system stability? Ready to build the frontend? Next lesson: Managing Authentication and User Context.