# How we solved 502 Read Timeouts on slow reasoning LLM APIs using SSE Stream Aggregation in Python

> Source: <https://dev.to/alessandro_pioli_5fcab5ea/how-we-solved-502-read-timeouts-on-slow-reasoning-llm-apis-using-sse-stream-aggregation-in-python-1g36>
> Published: 2026-07-24 13:44:05+00:00

Hi everyone!

Managing multiple LLM providers, rate limits, and API downtime while keeping latency and costs low became a hassle, so I built llmproxy.

What it does:

⚡ Response Caching: Reduces duplicate requests and cuts API costs.

🔄 Automatic Failover: Seamlessly fallbacks to backup providers or local models if your primary API is rate-limited or down.

🏡 Local + Cloud Routing: Route light prompts to local models (Ollama/vLLM) and heavy ones to cloud APIs (OpenAI/Anthropic).

🔌 OpenAI Compatible: Drop-in replacement—just point your base_url to llmproxy.

It’s completely open-source and easy to spin up with Docker.

📂 GitHub: [https://github.com/lordraw77/llmproxy](https://github.com/lordraw77/llmproxy)

I’d love to get your feedback, feature requests, or suggestions on how to improve it!