Your AI's anchor to reality. ⚓
Compresses 28M tokens to 100k queryable chars local-only; duplicates RAG problems at smaller scale.
Power users maintaining long-term AI chat sessions; researchers building local LLM memory systems
LLMMemory · Mem0 · Anyscale context engine
Deterministic graphs instead of vector embeddings sound clever, but long-context windows and RAG tools already solve this problem cheaper.
Identity-based memory vs similarity—clean separation of deterministic truth from probabilistic reasoning.
Graph-based context compression beats lossy summarization when tokens run out.
No LLMs — template matching beats stochastic bash generation for reproducibility.
Non-LLM deterministic semantic decomposition—14ms, no hallucination, MCP-ready.
No LLM in the critical path — deterministic retrieval beats vector search latency.
