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.
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.
Verified memory for AI agents — agents propose memories that are quarantined until verified against evidence, and recall() returns only trusted, fresh, and in-scope facts, preventing poisoned or hallucinated data from spreading.
Long-term memory for AI agents. Compiles conversations into a structured knowledge base with Claim/Evidence model, source provenance, append-only timeline, and contradiction detection. Multi-path retrieval (Exact + BM25 + Graph + weighted RRF + reranker) — 96.6% R@5 on LongMemEval-S, zero vector dependencies.
Persistent long-term memory for AI agents via MCP, saving 80-90% memory-related token costs by enabling on-demand recall instead of always-injecting context.
