Show HN: LLM Proxy - Python with SSE stream aggregation and timeout prevention Developer lordraw77 released llmproxy, a lightweight Flask server that emulates Ollama, OpenAI, and llama.cpp APIs and forwards every request to NVIDIA's OpenAI-compatible API, enabling any existing tool to use NVIDIA-hosted models without client-side changes. The proxy supports streaming, multi-model discovery, automatic retries, and a live /stats dashboard, and is available under the MIT License on Docker Hub as lordraw/llmproxy. A lightweight, high-performance LLM proxy for caching, automatic failover, cost tracking, and seamless integration between local and cloud AI providers. llmproxy is a lightweight Flask server that emulates the HTTP APIs of several popular local LLM runtimes Ollama https://ollama.com , the OpenAI https://platform.openai.com /v1 API, and llama.cpp https://github.com/ggerganov/llama.cpp 's llama-server and transparently forwards every request to NVIDIA's OpenAI-compatible API https://integrate.api.nvidia.com/v1 . This lets any tool that already speaks Ollama, OpenAI, or llama.cpp talk to a NVIDIA-hosted model without any client-side changes — you simply point the client at llmproxy instead of at a real local runtime. It covers chat, completions, and embeddings , supports streaming, multi-model discovery, optional inbound authentication, automatic retries on transient upstream errors, and a live /stats metrics & process dashboard. The proxy starts, exposes the models, and answers both an OpenAI-compatible /v1/chat/completions call and a native Ollama streaming /api/chat call — every request forwarded to NVIDIA. The recording is scripted in source cast: scripts/demo.sh . assets/demo.cast flowchart LR client "Your client