Lightweight persistent memory for AI agents using a single SQLite file with hybrid search (keywords + semantics). Zero to 12MB install, no cloud or server required.
Persistent semantic memory for AI agents. SQLite-backed, local-first, zero config. Semantic search via Ollama embeddings with keyword fallback. Tools: remember, recall, history, forget, stats.
Semantic memory for AI agents — local-first MCP server with hybrid search, knowledge graph, contradiction detection, and plan-then-commit consolidation.
Durable, local-first memory for AI coding agents over MCP — zero-dependency (pure Python + SQLite/FTS5), curated and semantically de-duped. Works with Claude Code, Codex and any MCP host, and you own the data as plain rows.
🧠 High-performance persistent memory system for Model Context Protocol (MCP) powered by libSQL. Features vector search, semantic knowledge storage, and efficient relationship management - perfect for AI agents and knowledge graph applications.