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In this article, we explore how to deploy AI agents as real-time APIs using WebSockets and FastAPI, enabling seamless, low-latency communication—ideal for applications like video games, live chatbots, and dynamic AI simulations.
🔗 Full Video Tutorial: Real-Time Agent Applications with WebSockets & FastAPI
Why WebSockets Over HTTP?
HTTP follows a request-response model, requiring the client to initiate every interaction. This becomes inefficient for real-time systems where continuous data exchange is needed.
WebSockets provide:
✔ Persistent connections (no repeated handshakes)
✔ Bi-directional communication (server can push updates)
✔ Lower latency (ideal for gaming, live feeds, and AI agents)
You Should Know: Implementing WebSockets in FastAPI
1. Install Required Libraries
pip install fastapi uvicorn websockets python-socketio
2. FastAPI WebSocket Endpoint
from fastapi import FastAPI, WebSocket
from fastapi.responses import HTMLResponse
app = FastAPI()
WebSocket route
@app.websocket("/ws")
async def websocket_endpoint(websocket: WebSocket):
await websocket.accept()
while True:
data = await websocket.receive_text()
Process AI agent response
response = f"AI Agent processed: {data}"
await websocket.send_text(response)
3. Run the FastAPI Server
uvicorn main:app --reload
4. Testing WebSocket with `websocat` (Linux CLI Tool)
websocat ws://localhost:8000/ws
Type a message, and the AI agent will respond in real time.
5. Handling Multiple Clients
from fastapi import WebSocketDisconnect
@app.websocket("/ws")
async def websocket_endpoint(websocket: WebSocket):
await websocket.accept()
try:
while True:
data = await websocket.receive_text()
response = agent_process(data) Replace with AI logic
await websocket.send_text(response)
except WebSocketDisconnect:
print("Client disconnected")
6. Scaling with `Socket.IO` (For Production)
from socketio import AsyncServer
sio = AsyncServer(async_mode='asgi')
app = FastAPI()
app.mount("/socket.io", socketio.ASGIApp(sio))
@sio.on('message')
async def handle_message(sid, data):
await sio.emit('response', f"AI Agent: {data}")
What Undercode Say
WebSockets unlock real-time AI agent deployment, making them perfect for:
– Game AI (NPC interactions)
– Live chatbots (instant replies)
– Stock trading bots (real-time decision-making)
Linux & Windows Commands for Debugging WebSockets:
Check open WebSocket connections (Linux) ss -tulnp | grep ws Monitor WebSocket traffic (tcpdump) sudo tcpdump -i lo0 'port 8000' -A Windows equivalent (PowerShell) Test-NetConnection -ComputerName localhost -Port 8000
Expected Output:
A fully functional FastAPI WebSocket server that processes AI agent responses in real time, reducing latency compared to traditional HTTP polling.
🔗 Further Learning:
Deploy your AI agents faster, smarter, and real-time with WebSockets! 🚀
References:
Reported By: Migueloteropedrido %F0%9D%90%83%F0%9D%90%9E%F0%9D%90%A9%F0%9D%90%A5%F0%9D%90%A8%F0%9D%90%B2%F0%9D%90%A2%F0%9D%90%A7%F0%9D%90%A0 – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
