A developer rebuilt their AI agent architecture from OpenClaw to Norax, achieving a cost reduction from $8-12/day to $1.80/day and implementing 11,000+ canonical memories with entity graph retrieval. …
Norax builds an entity graph from its memory store to improve retrieval for AI agents. The graph uses entity extraction, co-occurrence edges, community detection, and weighted edges to capture relatio…
Norax developed a multi-signal memory architecture for AI agents that combines exact term matching, dense vector similarity, temporal decay, and entity graph reranking. The system outperforms single-s…
A developer built Norax, a 7th-generation autonomous AI agent with persistent memory, autonomous decision-making, and cost optimization. The agent uses a scratchpad for hot state, semantic/procedural/…