Engram – open-source persistent memory for AI agents (Bun and SQLite)
SQLite-backed agent memory with graph viz when Mem0 and Zep already dominate.

Local-first agent memory with SQLite graphs, but requires a license key.
AI agent developers, Privacy-focused engineers
Mem0 · Zep · LangChain Memory
If anyone has deep experience in DB product testing and wants to test out the final release 1.3.6, reach out via the emal/website and mention you read this via HN thread and we will provide a free product test code license. We do need detailed feedback: [email protected]
SQLite-backed agent memory with graph viz when Mem0 and Zep already dominate.
Local-only agent memory when mem0 and supermemory require servers.
Scope-hierarchical memory for agents across projects—Mem0 and Zep flatten context too much.
Single binary with zero dependencies ships vector semantic memory in SQLite.
Putting the memory layer on-disk as a .afs/ tree is a gutsy, practical move — you get searchable JSON, FTS5 for text queries, HNSW vectors for similarity, and msgpack edges for relationships without running a separate DB service. It feels like a thoughtful toolkit for agents that must persist thinking artifacts (observations → reflections → knowledge), though I want to see details on concurrency, index portability, and how this performs at scale before betting production workloads on it.
Single-file mmap storage plus an HNSW vector index and explicit graph edges is an elegant, practical combo — think "SQLite for agent memory" with CRC-32 crash recovery and zero-server convenience. The C++20 core + nanobind gives zero-copy NumPy views and GIL-free searches, and the claimed FAISS-like throughput makes this genuinely interesting for local setups; main caveat is build/toolchain friction and how rich the surrounding ecosystem becomes.