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Agentmemory
Persistent memory for Claude Code, Codex & coding agents
562 followers
Persistent memory for Claude Code, Codex & coding agents
562 followers
You can now give Hermes, Claude Code, and Codex infinite memory. Agentmemory is trending on GitHub with 5,000+ Stars. CLAUDE md dumps 22,000+ tokens into context at 240 observations agentmemory: 1,900 tokens. same observations. 92% less. At 1,000 observations, 80% of your built-in memories become invisible. agentmemory keeps 100% searchable. benchmarked on 240 real coding sessions → Up to 95% fewer tokens per session → 200x more tool calls before hitting context limits → 100% open source
The context compression angle is genuinely interesting — 22k tokens down to 1.9k is a meaningful difference. Curious how it handles prioritisation when observations span very different task types (e.g. a debugging session vs. greenfield architecture work). Does it keep those namespaced, or blend into one pool?
Does the hybrid search (BM25 + embeddings) add noticeable latency on each tool call? Would love to know the typical overhead in a Claude Code session.
Persistent memory is the missing piece for coding agents every new session starts cold and you end up re-explaining context constantly. Does it work across different projects or is memory scoped per repo?
Instead of adding infrastructure on top of Claude or Codex, can't you write this as a skill and do a periodic memory compression and refresh?
Hi team, looks amazing and well done on the explanation docs. I have a quick question though, I work across multiple machines (home, work, laptop etc.), can the memory move with me? Currently I use dropbox to keep everything in sync (hands off), I'm not sure dropbox with a live KV cache is a good idea though (would produce plenty of ("conficted copy") files) and wonder if you have a suggestion.
The good news is that I only use one at a time, and they share a VPN network, with that in mind do you guys have a hands-off way to keep these in-sync?
I really like the hybrid (BM25 + Vector Search) approach to this problem. Pure semantic search for exact function names and file paths in large codebases does not work very well. What do you do about memory staleness. ? If a given architecture is refactored, what is preventing the agent from being able to retrieve the outdated pattern in the next session?
Persistent useful memory seems to be key for multiple AI applications not just codding. 2 genuine questions :
How does it scale with intense usage over the course of a year?
How does it handle successive contradictions ?