Cymatix Context runs retrieval on CPU with plain math (IDF-weighted, deterministic), with no model in the query path. Collaborators have already reproduced it on ARM64 hardware, which got me thinking about places where cloud RAG just isn't allowed or isn't practical: air-gapped plants, shop floors, field laptops, regulated offices, a Pi in a closet.
If you've got a box or an environment like that, what would you want to search there? And what would make or break it for you: RAM, index size, ingest speed, recall latency or something else?
Local-first context engine for LLM agents: an OpenAI-compatible proxy and MCP server over a SQLite knowledge store, with no model inference on the default retrieval path.
If you're running agents in edge deployments, cymatix is for you and your agents!
