scops/engrama

Engrama

<!-- mcp-name: io.github.scops/engrama -->

Graph-based long-term memory framework for AI agents.

Engrama gives any AI agent persistent, structured memory backed by a knowledge graph. Instead of flat key-value stores or opaque vector databases, Engrama stores entities, observations, and relationships — and lets agents traverse that graph to reason about their accumulated knowledge.

Two backends are first-class:

• SQLite + sqlite-vec (default since 0.9) — single file, zero external services, pip install engrama and you're running.
• Neo4j 5.26 LTS (opt-in) — for multi-process production setups, large-scale vector search, or teams that already use Cypher.

The data model is identical on both. See docs/backends.md for a full decision guide; the rest of this README assumes the SQLite default.

Since 0.13.0, every node and relation is owned by an (org_id, user_id) identity and reads are fail-closed: a missing or partial scope matches nothing rather than falling back to "see all". A single-process install runs as one stable standalone identity and needs no configuration; a multi-tenant deployment supplies the identity per request from an authenticating gateway. Each identity can permanently erase its own memory through the engrama_gdpr_forget tool (GDPR right-to-erasure). See docs/security.md.

Inspired by Karpathy's second-brain concept, but built for agents instead of humans — and with graphs inste