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How to Build a Multi-Agent Support System with LangGraph and FastAPI

AI AgentsLangGraphFastAPIPython

Building AI agents that reliably perform complex, multi-step reasoning tasks is one of the hardest challenges in modern LLM engineering. While single-prompt applications are easy to build, real-world workflows require agents that can loop, self-correct, and collaborate.

In this tutorial, we will explore how to build a Multi-Agent Customer Support System using LangGraph to orchestrate state, and FastAPI to serve it efficiently in production.

Why LangGraph over standard LangChain?

Standard chains (like LLMChain or SequentialChain) are executed sequentially as Directed Acyclic Graphs (DAGs). This means they flow in one direction and stop.

However, AI agents inherently require cyclic graphs. An agent needs to:

  1. Observe an input
  2. Take an action (call a tool)
  3. Receive the observation
  4. Loop back to step 1 to evaluate if the task is complete

LangGraph solves this by treating agent workflows as state machines.

The Architecture

Our support system will consist of three nodes:

  • Triage Agent: Categorizes the ticket and routes it.
  • Technical Support Agent: Has access to a RAG pipeline with documentation to solve technical issues.
  • Billing Agent: Has access to order databases to process refunds.
from langgraph.graph import StateGraph, END
from typing import TypedDict, Annotated
import operator

# 1. Define the state
class AgentState(TypedDict):
    messages: Annotated[list, operator.add]
    ticket_category: str
    resolved: bool

# 2. Initialize Graph
workflow = StateGraph(AgentState)

# 3. Add Nodes
workflow.add_node("triage", triage_agent)
workflow.add_node("technical", technical_agent)
workflow.add_node("billing", billing_agent)

Serving with FastAPI

LangGraph runs synchronously or asynchronously in Python. To serve this to a Next.js frontend, we need a high-performance API. FastAPI is the perfect fit because it handles asynchronous I/O out of the box, which is critical when waiting for OpenAI API responses.

Here is a simplified endpoint to trigger our graph:

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel

app = FastAPI()

class TicketRequest(BaseModel):
    user_message: str

@app.post("/api/support")
async def handle_support_ticket(request: TicketRequest):
    try:
        # Initialize state
        initial_state = {
            "messages": [("user", request.user_message)],
            "resolved": False
        }
        
        # Stream the graph execution
        final_state = await app_workflow.ainvoke(initial_state)
        
        return {"response": final_state["messages"][-1].content}
        
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

Conclusion

Combining LangGraph and FastAPI provides a highly robust foundation for agentic AI systems. By explicitly managing state in a graph structure, you eliminate the unpredictable infinite loops of traditional agents (like AutoGPT), while FastAPI ensures your backend can handle hundreds of concurrent agent executions.

Looking to implement a custom AI Agent for your business? Get in touch to discuss your project.