Show HN: LLM Proxy - Python with SSE stream aggregation and timeout prevention
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. Demo 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.
- ▪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.
- ▪Demo 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.
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Story provenance
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Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/lordraw77/llmproxy |
| Publication time | Fri, 24 Jul 2026 10:38:26 +0000 |
| Retrieval time | 2026-07-24T10:52:38.088Z |
| Last seen | 2026-07-24T10:52:38.088Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | kKeAucBVuw4I |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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, the OpenAI /v1 API, and 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.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.