engram-rs
Memory engine for AI agents. Two axes: time (three-layer decay & promotion) and space (self-organizing topic tree). Important memories get promoted, noise fades, related knowledge clusters automatically.
Most agent memory is a flat store — dump everything in, keyword search to get it back. No forgetting, no organization, no lifecycle. engram-rs adds the part that makes memory actually useful: the ability to forget what doesn't matter and surface what does.
Single Rust binary, one SQLite file, zero external dependencies. No Python, no Redis, no vector DB — curl | bash and it runs. ~10 MB binary, ~100 MB RSS, single-digit ms search latency.
Quick Start
# Install (interactive — will prompt for embedding provider config)
curl -fsSL https://raw.githubusercontent.com/kael-bit/engram-rs/main/install.sh | bash
# Store a memory
curl -X POST http://localhost:3917/memories \
-d '{"content": "Always run tests before deploying", "tags": ["deploy"]}'
# Recall by meaning
curl -X POST http://localhost:3917/recall \
-d '{"query": "deployment checklist"}'
# Restore full context (session start)
curl http://localhost:3917/resume
