
Evaluation ReportActive
The LoCoMo Benchmark Report
Our core evaluation matrix checking long-context recall, entity traversal, and temporal consistency across 1,540 simulated developer query runs.
ACCURACY: 73.5% | HIT@10: 88.8% Read Report
Memwyre Research Lab publishes studies, evaluations, and datasets exploring how entity graphs, pruning algorithms, and context compression solve statelessness in AI agent networks.

Our core evaluation matrix checking long-context recall, entity traversal, and temporal consistency across 1,540 simulated developer query runs.
Access our open source benchmark repositories, integrate Model Context Protocol memory servers, and build stateful agents.