For agent memory, building structure that lets you trace back to evidence may beat just adding more retrieval 🧠 Building Evidence-Grounded Agent Memory with OpenWiki and Graphs This LangChain post combines a compiled wiki (OpenWiki) with an evidence graph built from its citations to improve accuracy on questions that span multiple documents. Three highlights: 📚 A wiki tied to sources by hash Each wiki statement is linked to supporting source lines and a hash of them. When the source changes, affected claims are flagged without a model call, and only those are re-checked by a model, so the memory can self-correct. 🔗 Typed edges from existing citations Edges such as cite, supersede, and refers_to are built from existing citations and in-document references with no extra model calls. The agent just uses locate to find a starting point and expand to follow edges. The graph built in under a second. 📊 About 5x accuracy on connected questions On a synthetic corpus of 111 insurance documents, accuracy on connected questions rose from 0.10 with search to 0.52 with the wiki. Adding the evidence graph improved accuracy by 27% and cut cost by 58%. For simple lookups, search did just as well. Memory pays off when agents repeatedly reason across multiple sources. #AIAgents# #AgentMemory#