Original Reddit post

I’ve been working on Context Swarm Memory (CSM), an open-source R&D memory layer for long-running LLM agents. The problem I’m exploring is simple: AI memory often gets worse as it grows. More history means more irrelevant retrieval, more context pressure, and more chances for the agent to silently mix old facts with current ones. CSM takes a different approach. Memory is split into bounded read-only shards. A query is routed to candidate shards, probed for useful evidence, recalled only from relevant snapshots, then synthesized into a compact cited memory packet. Durable memory changes only through an explicit Committer-gated write path, so query-time reads do not mutate long-term memory. Current measured result: in a full local BEAM 100K comparison, CSM scored 342/400 correct rows vs Hindsight at 326/400, while using 38.2% fewer answer-visible context tokens. The tradeoff is latency: CSM is slower right now. I’m not claiming official SOTA or leaderboard status. This needs independent replication and criticism. Repo/docs: https://github.com/muhamadjawdatsalemalakoum/context-swarm-memory https://muhamadjawdatsalemalakoum.github.io/context-swarm-memory/ I’d especially value feedback on the benchmark methodology, shard architecture, and where this approach might fail at larger scale. submitted by /u/keonakoum

Originally posted by u/keonakoum on r/ArtificialInteligence