I wanted to share a breakdown for anyone running Hermes long enough to have hit the MEMORY.md consolidation lag. As part of the team building Atomic Memory, I’ve been waiting to share this to the Hermes community and we’ve been running it inside Hermes as a memory layer underneath the agent runtime. https://preview.redd.it/bqflh9apcw3h1.jpg?width=2728&format=pjpg&auto=webp&s=725ed19d0316ae9eff591ac982fbc521b5a036bb Take note that this is an upgrade to Hermes, not a replacement. Hermes built-in memory still works fine for slow-changing facts and low-volume chats. The clearest way to see the difference is what happens when you change the same fact multiple times in a single session. Native Hermes memory updates on the next flush cycle, by then, the agent has already processed several turns on the old version. Atomic Memory classifies the change per turn, detects the conflict immediately, and supersedes the old fact before it influences the next response. The full technical breakdown is in our docs, but the short version of what Atomic Memory adds on top of Hermes built-in: Per-turn AUDN decisions Semantic recall (vs whole MEMORY.md injected into every prompt) Conflict detection at write time No 2.2KB cap on memory Cheap to run and inspect. Every memory is queryable directly from Postgres so you can see exactly what your agent believes and why Uses a tiny dedicated 3B model so it doesn’t eat into your main agent’s tokens My team built this because we kept hitting the same wall with MEMORY.md with corrections not sticking and stale facts surfacing weeks later. The 2.2KB cap forcing us to decide what to throw away so Atomic Memory is our answer to that and we wanted to share it with the community that uses the same tool we do. I would love to hear your feedback especially if you’re using Hermes. Sharing the repo and docs below this comment. submitted by /u/Limp_Statistician529
Originally posted by u/Limp_Statistician529 on r/ArtificialInteligence
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