Tools: How to Connect CopilotKit to a Python Backend Using Direct-to-LLM (FastAPI Guide)

What is CopilotKit? ## Why Use CopilotKit with Direct-to-LLM + Remote Python Backend? ## Setting Up a FastAPI Remote Endpoint for CopilotKit AI copilots are rapidly becoming the primary interface for modern applications. Frameworks like CopilotKit make it easier to build production-grade, AI-powered assistants without manually handling raw LLM interactions or complex prompt pipelines. In this guide, you’ll learn how to connect CopilotKit to a remote Python backend using Direct-to-LLM with FastAPI, and why this approach is often better than heavy orchestration tools like LangGraph. CopilotKit is the Agentic Application Platform — an open-source framework with cloud and self-hosted services for building AI-powered, user-facing agentic applications. It connects your application’s logic, state, UI, and context to agentic backends, enabling interactive experiences across embedded UIs and headless interfaces. Teams use CopilotKit to build, deploy, and operate agentic features that feel deeply integrated into their products. By decoupling your application from specific models, frameworks, or agent protocols, CopilotKit allows you to evolve your AI stack without redesigning your product’s UX. ✅ Lightweight architecture (no heavy orchestration) Many AI systems rely on orchestration frameworks like LangGraph or middleware pipelines, which introduce: With CopilotKit Direct-to-LLM, you keep things simple: **CopilotKit → UI + LLM + intent handling Python (FastAPI) → data + business logic + integrations** ✅ Best for streaming AI responses Direct-to-LLM is ideal when you need: This works especially well for: ✅ Reuse your existing Python backend CopilotKit’s Remote Backend Endpoint lets you integrate all of this without rewriting your logic in Node.js. *How CopilotKit’s Remote Backend Endpoint Works * Here’s the flow: 1️⃣ Install dependencies 2️⃣ Create FastAPI server 3️⃣ Define a CopilotKit backend action poetry run python server.py Your endpoint will be available at: http://localhost:8000/copilotkit_remote *Connecting to Copilot Cloud * CopilotKit will now call your Python backend automatically. Advanced: Thread Pool Configuration Useful for high-traffic applications. Dynamic Agents with CopilotKit Real-World Use Case (In-Body Example) In a recent booking-related AI copilot project, I used CopilotKit Direct-to-LLM with a FastAPI backend to deliver real-time, streaming AI responses without complex orchestration like LangGraph. This kept the system simple, fast, and maintainable. When Should You Use This Architecture? Use this pattern when: Using CopilotKit Direct-to-LLM with a Remote Python Backend gives you: ✔ FastAPI integration ✔ Real-time streaming AI ✔ Minimal orchestration ✔ Clean system design ✔ Production-ready architecture If you’re building AI copilots today, this pattern is worth adopting. Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse CODE_BLOCK: poetry new My-CopilotKit-Remote-Endpoint cd My-CopilotKit-Remote-Endpoint poetry add copilotkit fastapi uvicorn Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: poetry new My-CopilotKit-Remote-Endpoint cd My-CopilotKit-Remote-Endpoint poetry add copilotkit fastapi uvicorn CODE_BLOCK: poetry new My-CopilotKit-Remote-Endpoint cd My-CopilotKit-Remote-Endpoint poetry add copilotkit fastapi uvicorn CODE_BLOCK: Create server.py: from fastapi import FastAPI app = FastAPI() Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: Create server.py: from fastapi import FastAPI app = FastAPI() CODE_BLOCK: Create server.py: from fastapi import FastAPI app = FastAPI() CODE_BLOCK: from fastapi import FastAPI from copilotkit.integrations.fastapi import add_fastapi_endpoint from copilotkit import CopilotKitRemoteEndpoint, Action as CopilotAction app = FastAPI() async def fetch_name_for_user_id(userId: str): return {"name": "User_" + userId} action = CopilotAction( name="fetchNameForUserId", description="Fetches user name from the database for a given ID.", parameters=[ { "name": "userId", "type": "string", "description": "The ID of the user to fetch data for.", "required": True, } ], handler=fetch_name_for_user_id ) sdk = CopilotKitRemoteEndpoint(actions=[action]) add_fastapi_endpoint(app, sdk, "/copilotkit_remote") def main(): import uvicorn uvicorn.run("server:app", host="0.0.0.0", port=8000, reload=True) if __name__ == "__main__": main() Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: from fastapi import FastAPI from copilotkit.integrations.fastapi import add_fastapi_endpoint from copilotkit import CopilotKitRemoteEndpoint, Action as CopilotAction app = FastAPI() async def fetch_name_for_user_id(userId: str): return {"name": "User_" + userId} action = CopilotAction( name="fetchNameForUserId", description="Fetches user name from the database for a given ID.", parameters=[ { "name": "userId", "type": "string", "description": "The ID of the user to fetch data for.", "required": True, } ], handler=fetch_name_for_user_id ) sdk = CopilotKitRemoteEndpoint(actions=[action]) add_fastapi_endpoint(app, sdk, "/copilotkit_remote") def main(): import uvicorn uvicorn.run("server:app", host="0.0.0.0", port=8000, reload=True) if __name__ == "__main__": main() CODE_BLOCK: from fastapi import FastAPI from copilotkit.integrations.fastapi import add_fastapi_endpoint from copilotkit import CopilotKitRemoteEndpoint, Action as CopilotAction app = FastAPI() async def fetch_name_for_user_id(userId: str): return {"name": "User_" + userId} action = CopilotAction( name="fetchNameForUserId", description="Fetches user name from the database for a given ID.", parameters=[ { "name": "userId", "type": "string", "description": "The ID of the user to fetch data for.", "required": True, } ], handler=fetch_name_for_user_id ) sdk = CopilotKitRemoteEndpoint(actions=[action]) add_fastapi_endpoint(app, sdk, "/copilotkit_remote") def main(): import uvicorn uvicorn.run("server:app", host="0.0.0.0", port=8000, reload=True) if __name__ == "__main__": main() CODE_BLOCK: add_fastapi_endpoint(app, sdk, "/copilotkit_remote", max_workers=10) Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: add_fastapi_endpoint(app, sdk, "/copilotkit_remote", max_workers=10) CODE_BLOCK: add_fastapi_endpoint(app, sdk, "/copilotkit_remote", max_workers=10) CODE_BLOCK: <CopilotKit properties={{ someProperty: "xyz" }}> <YourApp /> </CopilotKit> Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: <CopilotKit properties={{ someProperty: "xyz" }}> <YourApp /> </CopilotKit> CODE_BLOCK: <CopilotKit properties={{ someProperty: "xyz" }}> <YourApp /> </CopilotKit> CODE_BLOCK: def build_agents(context): return [ LangGraphAgent( name="some_agent", description="This agent does something", graph=graph, langgraph_config={ "some_property": context["properties"]["someProperty"] } ) ] app = FastAPI() sdk = CopilotKitRemoteEndpoint(agents=build_agents) Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: def build_agents(context): return [ LangGraphAgent( name="some_agent", description="This agent does something", graph=graph, langgraph_config={ "some_property": context["properties"]["someProperty"] } ) ] app = FastAPI() sdk = CopilotKitRemoteEndpoint(agents=build_agents) CODE_BLOCK: def build_agents(context): return [ LangGraphAgent( name="some_agent", description="This agent does something", graph=graph, langgraph_config={ "some_property": context["properties"]["someProperty"] } ) ] app = FastAPI() sdk = CopilotKitRemoteEndpoint(agents=build_agents) - Direct integration with any agentic backend - Connectivity via AG-UI, MCP, and A2A protocols - Native integrations with popular agent frameworks through AG-UI - More infrastructure - Higher latency - More maintenance complexity - Real-time AI streaming responses - Low-latency conversational AI - Smooth user experience - Customer support copilots - Booking / planning assistants - SaaS dashboard copilots - Data analytics copilots - Most teams already use: - FastAPI / Django / Flask - PostgreSQL / MySQL / MongoDB - Python-based ML models - User → CopilotKit - CopilotKit → Python FastAPI backend - Backend returns structured JSON - CopilotKit → Direct-to-LLM - LLM streams response back to user - Go to Copilot Cloud dashboard - Register your FastAPI endpoint as a Remote Endpoint - Use either: - Local tunnel, or - Hosted backend URL - User asks a question - CopilotKit calls FastAPI → fetches structured data - CopilotKit sends data directly to LLM - LLM streams response in real time - You already have a Python backend - You need real-time streaming responses - You want to avoid complex orchestration - You need production-ready scalability