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 app
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:
To solve this, we engineered and open-sourced InfinityDB —a high-performance, local-first neurosymbolic memory engine built on SQLite WAL mode.
Instead of treating memory as an undifferentiated vector store, InfinityDB structures agent memory into a tiered hierarchy:
Evaluated across 300 test scenarios on identical hardware (Apple M2, 16GB) using PersonaMem, LongMemEval, and LoCoMo benchmarks:
In our verified 2,000-turn stress test (10,020 facts ingested and recalled), InfinityDB completed all operations with a 100% success rate and zero errors.