Engram – open-source persistent memory for AI agents (Bun and SQLite)
SQLite-backed agent memory with graph viz when Mem0 and Zep already dominate.
Auditable memory for agent teams. Self-hosted, deterministic retrieval, no LLM in the critical path. Python + MCP + Docker.
No LLM in the critical path — deterministic retrieval beats vector search latency.
Developers building multi-agent AI systems
LangChain Memory · LlamaIndex · Zep
SQLite-backed agent memory with graph viz when Mem0 and Zep already dominate.
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.
Scope-hierarchical memory for agents across projects—Mem0 and Zep flatten context too much.
Knowledge graph memory beats pure vector search, but Mem0 and LangChain already own this space.
Autonomous memory healing every 5 min, but Mem0 and LangGraph already solve this.
Removes LLM from memory CRUD path—sub-10ms reads beat Mem0/Zep's 200-500ms by design.