
Persistent memory from agent trace, not just conversation
Memori launched its new agent-native memory infrastructure, enabling agents to create structured, long-term memory directly from agent trace — including execution paths, tool results, workflow steps, outcomes, and decision-making logic. This allows memory to also be generated from what an agent actually does. Benchmark results: 81.95% accuracy on LoCoMo using only 1,294 tokens per query, roughly 5% of full-context cost, saving users 95%+ on inference spend. 15K GitHub stars, 200000+ downloads
Memori offers an agent-native memory infrastructure that generates structured, long-term memory from agent trace, enhancing AI decision-making and workflow efficiency. It boasts 81.95% accuracy on LoCoMo with significantly reduced inference costs and has gained 15K GitHub stars and over 200,000 downloads.