Building a Sub-Millisecond Neurosymbolic Memory Engine for AI Agents
Autonomous AI agents are increasingly bottlenecked by long-term memory. Most existing memory frameworks compress interaction history into opaque vector embeddings stored in a vector database. While vector search is effective for semantic text retrieval, it exhibits severe operational flaws when applied to multi-turn agent systems: High Token Costs: Relying on auxiliary LLM calls to extract and…
Autonomous AI agents face challenges with long-term memory, leading to high token costs, latency issues, and auditability concerns. InfinityDB is a high-performance, local-first neurosymbolic memory engine built on SQLite WAL mode, addressing these problems. It structures agent memory into a tiered hierarchy, including L0 (Raw Interaction Log), L1 (Deterministic Fact Extraction), L2 (Episodic Causal Graph), L3 (Dynamic Persona & Conflict Auto-Resolution), L4 (Enterprise Policy Engine), L5 (Ebbinghaus Decay), and L6 (Cryptographic Proof Chain).
InfinityDB has been benchmarked against other memory engines like PersonaMem, LongMemEval, and TencentDB, showing superior recall performance, lower token costs, and zero latency. The architecture eliminates high token costs, reduces memory usage, and provides cryptographic proof chains for fact verification. Quick start guides are available for Python and Node.js/TypeScript, with integrations for LangChain, CrewAI, and Model Context Protocol.
The open-source project is hosted on GitHub, with a live demo available on Hugging Face and a Smithery Skill Registry listing.
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