I’m sharing a GitLab review agent I’ve been working on. The idea was to keep code reviews on hardware I run. It’s built on LangGraph.js and uses Qwen3.6-35B-A3B (QuantTrio AWQ, 4-bit) on sglang. The box is two 4090s over PCIe, no NVLink, TP=2. Decode is about 180 tok/s single stream at ~50k context and about 160 at ~110k. Prefill is 9-10k tok/s. At 8 concurrent requests I get 985 tok/s total. A review run averages around 26 LLM calls and 2.3M tokens. As of now it has done 1,300+ reviews and about 70% of its comments get resolved by the author. It sometimes catches mistakes in code written with Claude Sonnet/Opus, in the harness’s own repo and in internal projects. I believe it’s good to have different model families in the workflow, you get different perspectives from each. Repetition is the common problem I face with local models (maybe it's the quants). Guardrails catch repeated tool calls and trailing questions, but the model looping on itself still ends about 7% of runs (56 of 847 on my dashboard). Launch flags and the benchmark: https://vrajpal-jhala.github.io/langgraph-harness/sglang-deployment The agent is MIT and also works with Ollama, OpenRouter, Gemini and Groq: https://github.com/vrajpal-jhala/langgraph-harness   submitted by   /u/Professional-Pear351 [link]   [comments]