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

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. What the daemon gives you | 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 | Why local matters more than you think Three things a cloud memory tier can never give you: - No vendor risk. The provider cannot deprecate your workflow, raise your price, or read your context. - No token billing surprises. Retrieval is deterministic compute - the same query costs the same every time, forever. - Cross-tool continuity. One local daemon serves Claude Code, Cursor, Windsurf and anything that speaks MCP - the memory is shared, not siloed per vendor. The honest numbers 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 When cloud is still right 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 What would make you switch your agent memory to local-first? Top comments (0)