Multi-Provider LLM Router, or How I Got Tired of Forgetting Which API Format I Had To Use

#python #fastapi #ai

If you've ever built an application that integrates with multiple LLM providers (Anthropic, Google, OpenAI, DeepSeek), you already know the pain:

Each provider has its own distinct Python SDK.
Streaming responses using Server-Sent Events (SSE) requires divergent parser logic.
Thinking / Reasoning blocks are formatted completely differently.

I recently extracted the core streaming router from my platform into an open-source FastAPI template. Here is how it works.

Objective

A single asynchronous endpoint:
POST /v1/chat/stream

It accepts a unified request payload and returns a standardized SSE stream emitting four clean events:

event: thinking — Internal model reasoning tokens (streamed in real-time).
event: content — User-facing response text.
event: tool_call — Function calling requests.
event: done — Stream completion ([DONE]).

Architecture

Instead of pulling heavy wrapper frameworks, use direct asynchronous HTTP via httpx.AsyncClient and the official Google GenAI SDK:

fastapi-multi-llm-starter/
├── app/
│   ├── config.py       # Pydantic Settings loading environment variables
│   ├── main.py         # FastAPI app with CORS, health check & test playground
│   ├── models.json     # Dynamic model catalog (Claude, Gemini, GPT)
│   ├── router.py       # Unified multi-provider async stream dispatcher
│   └── schemas.py      # Strict Pydantic v2 validation models
├── tests/              # Automated unit tests (pytest)
├── requirements.txt
└── README.md

Dynamic Model Catalog

I disliked the idea of hardcoded models, so I decoupled them into a models.json file:

{
  "models": [
    {
      "id": "claude-sonnet-5",
      "name": "Claude Sonnet 5",
      "provider": "Anthropic",
      "thinking": true
    },
    {
      "id": "gemini-3.8-flash",
      "name": "Gemini 3.8 Flash",
      "provider": "Google",
      "thinking": true
    },
    {
      "id": "gpt-5.6-terra",
      "name": "GPT 5.6 Terra",
      "provider": "OpenAI",
      "thinking": true
    }
  ]
}

Now, if you want to add another model, you just edit the JSON. The backend and the embedded UI dynamically populate available models via GET /v1/models.

Testing Playground

The repository includes a testing playground running directly at http://localhost:8000/. You can immediately test prompts, check streaming latency, and verify reasoning blocks without setting up a frontend framework.

Of course, you'll need your own API keys.

Open Source Code

The full core code is open-source under the MIT License on GitHub:

👉 github.com/wolfnomknight/fastapi-multi-llm-starter

Includes full pytest test coverage, .env.example, and clean Pydantic v2 schemas.

Feel free to fork it, use it in your side projects or micro-SaaS, and let me know if you run into any issues or have ideas for additional providers!

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