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Building AI Agents with FastAPI & LangChain

Building AI Agents with FastAPI & LangChain

2025-09-18
FastAPILangChainOpenAIPython

Artificial Intelligence agents are becoming essential for automating workflows and building smarter applications. In this tutorial, we’ll show you how to build an AI Agent with FastAPI and LangChain, expose it as an API, and scale it for real-world use cases.

✨ ⚡ Why FastAPI + LangChain?

FastAPI is a modern Python framework for building high-performance APIs, while LangChain provides the building blocks for creating AI agents that connect LLMs to external tools and data sources. Combined, they let you create production-ready AI agents that can plug directly into apps, dashboards, and workflows.

✨ 🛠️ Prerequisites

  • 👉 Python 3.9+
  • 👉 FastAPI, Uvicorn, LangChain, and OpenAI SDK installed
  • 👉 Basic knowledge of APIs and Python
pip install fastapi uvicorn langchain openai

✨ 🚀 Step 1: Set Up FastAPI

from fastapi import FastAPI

app = FastAPI()

@app.get("/")
def read_root():
    return {"message": "AI Agent API is running!"}

Run the server:

uvicorn main:app --reload

✨ 🤖 Step 2: Create a LangChain Agent

from langchain_openai import ChatOpenAI
from langchain.agents import initialize_agent, Tool

def calculator_tool(expression: str) -> str:
    try:
        return str(eval(expression))
    except Exception as e:
        return f"Error: {str(e)}"

tools = [Tool(name="Calculator", func=calculator_tool, description="Useful for math")]

llm = ChatOpenAI(model="gpt-4o-mini")
agent = initialize_agent(tools, llm, agent="chat-zero-shot-react-description", verbose=True)

✨ 🌐 Step 3: Expose the Agent via FastAPI

from pydantic import BaseModel

class Query(BaseModel):
    question: str

@app.post("/ask")
async def ask_agent(query: Query):
    response = agent.run(query.question)
    return {"answer": response}

Send a POST request:

curl -X POST "http://127.0.0.1:8000/ask"     -H "Content-Type: application/json"     -d '{"question": "What is 15 * 12?"}'

✨ 📊 Architecture Overview

Here’s a simple flow diagram of how requests move through the system:

    [Client Request] → [FastAPI Endpoint] → [LangChain Agent] → [LLM / Tools] → [Response]

✨ 🔒 Step 4: Secure & Scale Your Agent

  • 👉 Rate limiting: Prevent abuse with request throttling.
  • 👉 Authentication: Use API keys or OAuth for protection.
  • 👉 Monitoring: Log inputs/outputs for debugging.
  • 👉 Streaming: Enable async streaming for real-time results.
  • 👉 Deployment: Use Docker/Kubernetes for scaling in production.

✨ 🚀 Step 5: Extend Your Agent

  • 👉 Integrate vector databases (Pinecone, Weaviate, MongoDB Atlas) for RAG.
  • 👉 Add domain-specific tools (APIs, file readers, custom logic).
  • 👉 Deploy as SaaS or integrate into Slack/Discord for real-time usage.

✨ ✅ Conclusion

By combining FastAPI and LangChain, developers can create scalable, secure, and intelligent AI agents. Whether it’s for customer support, document automation, or data analysis, this stack gives you the flexibility to go from prototype to production quickly.

If you’re looking to build or scale AI-powered products, our team at Void Core Technologies can help you design, develop, and deploy AI agents tailored to your business needs.

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