Great for connecting your local LLM coding and vision models to Claude Code and Codex.
General improvements
> Vision pipeline - images described by your vision model, transparent to the client
> Dual OCR pipeline - smart routing for PDFs and tool output (text extraction first, vision fallback for scanned docs). Dedicated OCR models like
> PaddleOCR-VL are ~17x faster than general vision models on document pages
> Brave & Tavily search integration - native behavior for Claude Code and Codex when configured on the proxy
> Per-model processor routing - override vision, OCR, and search settings per model
> Context window auto-detection from backends
SSE keepalive improvements during pipeline processing
Full MCP SSE endpoint for web search on OpenCode, Qwen Code, Claw, and other MCP-compatible agents
Docker update for easier deployment (limited testing so far)
Codex-specific
> Full Responses API translation - Chat Completions under the hood, your local backend doesn't need to support /v1/responses
Pretty similar to litellm[proxy], but supports the Responses API and also some re-write. This is pretty much targeted at coding TUIs but I do use it a lot for text embeddings and streaming inference in applications too.
I run a few different models on my compute nodes and was constantly editing json files managing configs for which one was where. Built this to solve the problem of aggregating them into one place behind a public nginx reverse proxy. My goal was hooking it to claude-code or qwen when I run out of tokens so I could use minimax or glm-5, but it works great for that and also sharing those with other people.