Hebbs is a single Rust binary with embedded storage. No Postgres. No Redis. No Pinecone. No infra to manage. The core operations:
- remember — store experiences with automatic importance scoring.
- recall — four retrieval strategies (similarity, temporal, causal, analogical), all sub-10ms p99.
- reflect — consolidate raw memories into distilled insights with full lineage tracking. This is how agents go from "I remember what you said" to "I know what I've learned"
- revise — update beliefs without appending contradictions.
- forget — GDPR-safe targeted erasure, plus active pruning of stale knowledge.
Everything is 100% configurable ( embedding model, half life decay period, ef_search, analogical_alpha ), relevance, importance, recency and reinforcement weights, but ships with smart defaults that worked well for me building what I was focused on - so you don't need to tune anything on day one.
Built for both edge ( robots ) and server. Run it embedded in your agent process for zero-latency local memory, or run the server for multi-agent setups. Reflect syncs only distilled insights to a master server - so your sub-agents stay lightweight while the fleet shares institutional knowledge.
The thesis: agents that learn compound over time. Call 500 is dramatically better than call 1 - not because the agent has more data, but because it has distilled knowledge. No current system delivers that. You need to duct-tape solutions and that path is painful and tweak to make it yours as you live on their default configs.
