How we solved 502 Read Timeouts on slow reasoning LLM APIs using SSE Stream Aggregation in Python
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: β‘ Response Caching: Reduces duplicate requests and cuts
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:
β‘ Response Caching: Reduces duplicate requests and cuts API costs.
π Automatic Failover: Seamlessly fallbacks to backup providers or local models if your primary API is rate-limited or down.
π‘ Local + Cloud Routing: Route light prompts to local models (Ollama/vLLM) and heavy ones to cloud APIs (OpenAI/Anthropic).
π OpenAI Compatible: Drop-in replacementβjust point your base_url to llmproxy.
Itβs completely open-source and easy to spin up with Docker.
π GitHub: https://github.com/lordraw77/llmproxy
Iβd love to get your feedback, feature requests, or suggestions on how to improve it!
Originally published by Dev.to AI. Aggregated on AIWithGhost for educational purposes β full credit and traffic to the original publisher.