qmd

classification with multi-graph traversal (semantic, temporal, and causal beam search), [A-MEM](https://arxiv.org/abs/2510.02178) for self-evolving memory notes, and [Engram](https://github.com/Gentleman-Programming/engram) for deduplication patterns and temporal navigation. None of these were designed to work together. Making them coherent was most of the work. On the inference side, QMD's original stack uses a 300MB embedding model, a 1.1GB query expansion LLM, and a 600MB reranker. These run via llama-server on a GPU or in-process through node-llama-cpp (Metal, Vulkan, or CPU). But the more interesting path is the SOTA upgrade: ZeroEntropy's distillation-paired zembed-1 + zerank-2. These are currently the top-ranked embedding and reranking models on MTEB, and they're designed to work together. The reranker was distilled from the same teacher as the embedder, so they share a semantic space. You need ~12GB VRAM to run both, but retrieval quality is noticeably better than the default stack. There's also a cloud embedding option if you're tight on vram or prefer to offload embedding to a cloud model. For Claude Code specifically, it hooks into lifecycle events. Context-surfacing fires on every prompt to inject relevant memory, decision-extractor and handoff-generator capture session state, and a feedback loop reinforces notes that actually get referenced. That handles about 90% of retrieval automatically. The other 10% is 28 MCP tools for explicit queries. For OpenClaw, it registers as a ContextEngine plugin with the same hook-to-lifecycle mapping, plus 5 REST API tools for the agent to call directly. It runs on Bun with a single SQLite vault (WAL mode, FTS5 + vec0). Everything is on-device; no cloud dependency unless you opt into cloud embedding. The whole system is self-contained. This is a polished WIP, not a finished product. I'm a solo dev. The codebase is around 19K lines and the main store module is a 4K-line god object that probably needs splitting. And of course, the system is only as good as what you index. A vault with three memory files gives deservedly thin results. One with your project docs, research notes, and decision records gives something actually useful. Two questions I'd genuinely like input on: (1) Has anyone else tried running SOTA embedding + reranking models locally for agent memory, and is the quality difference worth the VRAM? (2) For those running multiple agent interfaces (CLI + voice/chat), how are you handling shared memory today?

ng on, what decisions were made, what already failed. It also keeps a mistake ledger. Things that went wrong get tracked and surfaced as warnings so you stop walking into the same walls. Sessions are stored as markdown in .ai-sessions/ (gitignored). Summaries get attached to commits as git notes so context travels with the code. Everything runs locally, nothing leaves your machine. Built with Bun. Hooks run under 100ms. It’s early and rough but anecdotally it feels like it actually works.