Long-term memory for AI agents over MCP — episodic + semantic memory, a temporal knowledge graph, and a dialectic user model, exposed as 32 tools (recall, remember, context, graph, dreaming, peers). Zero dependencies, runs fully offline; leads the LoCoMo benchmark at ~35x fewer LLM calls.
Persistent shared memory for AI coding agents. Stores facts as entity/key/value triples with hybrid semantic search, task checkpoints, and conflict resolution — shared across Claude Code, Codex CLI, and GitHub Copilot.
Associative key-graph memory for LLM agents — recall facts by association (recall → read_key → read_memory) instead of vector similarity alone, with persistent cross-session memory and cross-lingual keys.
Provides persistent long-term memory for AI agents through semantic search and automated knowledge graph extraction. It enables agents to store, recall, and reason over facts, preferences, and relationships across multiple conversations and sessions.
Persistent shared memory for AI agents. Hybrid search (pgvector + tsvector), knowledge graph, cognitive scoring, and 16-language temporal extraction.
97.2% Recall@10 on LongMemEval with one PostgreSQL query. Works across Claude Code, Cursor, Codex, OpenClaw, and any MCP client.
