A local-first memory daemon for AI agents: SQLite + ONNX, zero API calls

A developer has released Awareness, a local-first memory daemon for AI agents that uses SQLite and ONNX embeddings to keep agent context on the user's machine, with zero API calls. The daemon achieves 95.6% recall@5 on LongMemEval_S on an M1 with 8GB RAM, and can optionally sync to cloud pgvector for team sharing.

Cloud memory tiers have a fundamental problem: your agent context - the most sensitive data you have - leaves your machine. I wanted memory that never does. So Awareness runs a local-first daemon: npx @awareness.market/local start No account. No API key. No cloud. SQLite + ONNX embeddings on your machine, served to Claude Code, Cursor, or any MCP client on localhost. | Storage | SQLite, human-readable, yours forever | | Embeddings | all-MiniLM-L6-v2 as ONNX 23MB - zero API calls | | Retrieval | hybrid BM25 + vector RRF - 1.7s per query on an M1 8GB | | Memory types | knowledge cards, bi-temporal facts, conflict detection | | Upgrade path | optional cloud sync to pgvector when you want team sharing | Three things a cloud memory tier can never give you: 95.6% recall@5 on LongMemEval S, on an M1 with 8GB RAM and zero LLM calls at retrieval. Two competitors edge us by ~1 point on recall, running hosted stacks. Full methodology with the tables we do not win: https://awareness.market/benchmarks https://awareness.market/benchmarks Local-first does not mean local-only. Burst inference, team-shared memories, and cross-device sync are real needs - the daemon upgrades to cloud pgvector with one command when you need it. The point is that local is the default, not the fallback. Repo and SDK: https://github.com/everest-an/Awareness https://github.com/everest-an/Awareness What would make you switch your agent memory to local-first?