Preserved facts
Structured operational memory for AI infrastructure and agent workflows.
BrainCore preserves operational knowledge as evidence-backed context so AI agents can retrieve facts, incidents, and decisions instead of rediscovering them in every session. The public production corpus reports 26,966 extracted facts, 9,074 tracked entities, and P50 retrieval at 71.6 ms.
BrainCore is a public SynapseGrid Labs project for memory-first AI infrastructure. It archives incidents and sessions, extracts evidence-backed facts, and exposes temporal knowledge for automation systems that need continuity.
Operational facts are tied to source evidence so agents can inspect where memory came from.
Production-corpus retrieval is measured at P50 71.6 ms and P95 85.2 ms.
Public claims and metrics are tied to a claims binder and production benchmark evidence.
Structured operational memory for AI infrastructure and agent workflows.
Entity-aware context helps agents connect systems, projects, incidents, and decisions.
Condensed memory artifacts turn operational history into reusable context.
Fast retrieval targets keep memory usable inside agent work loops.
Tail latency stays visible because memory is infrastructure, not decorative context.
Evidence segments support traceable retrieval and source-backed public claims.
BrainCore is positioned for AI infrastructure and agent systems: deterministic parsers, trust classes, temporal facts, PostgreSQL/pgvector retrieval, provenance, and memory MCP tools. It should not be described as hosted SaaS or universal chat memory.
release v1.1.6
stack TypeScript + Python + PostgreSQL + pgvector
retrieval P50 71.6 ms / P95 85.2 ms
proof claims binder + benchmark evidence