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

A developer built llmproxy, an open-source Python proxy that solves 502 read timeouts on slow reasoning LLM APIs using SSE stream aggregation. The tool provides response caching, automatic failover to backup providers, local and cloud routing, and an OpenAI-compatible interface.

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