Launched this week
Agentmemory
Persistent memory for Claude Code, Codex & coding agents
549 followers
Persistent memory for Claude Code, Codex & coding agents
549 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
Agentmemory
Hey Product Hunt 👋
I built AgentMemory because coding agents still have one painful limitation: they forget between sessions.
You explain your architecture once.
You debug a production issue once.
You decide on a library or pattern once.
Then the next session starts from zero again.
AgentMemory gives AI coding agents persistent memory across sessions, so they can actually build on what they’ve already learned about your codebase. It automatically captures what your agent does, compresses it into structured memories, indexes them with hybrid search, and injects the right context back into future sessions.
It works with Claude Code, Cursor, Codex CLI, Gemini CLI, Windsurf, Kilo Code, OpenCode, Cline, Roo, Goose, Aider, Hermes, OpenClaw, and basically any MCP or REST-capable agent.
From day one, I wanted it to be:
100% open source
Free to run locally
No external database required
Works via MCP, REST, and simple hooks
Built for real coding workflows, not toy “chat history” memory
On benchmarks, AgentMemory gets 95.2% R@5 and 98.6% R@10 on the LongMemEval-S retrieval suite using BM25 + vector search, while cutting context usage by around 92%.
Quick start:
Open: http://localhost:3113
Or try the demo: npx @agentmemory/agentmemory demo
If you live in your coding agents every day, this is for the moment you think: “Wait, I already explained this yesterday.”
Would love feedback from builders, heavy agent users, and open‑source maintainers.
GitHub: https://github.com/rohitg00/agentmemory
@agentmemory @rohit_ghumare How do you think about secrets or sensitive data accidentally entering memory? Is there filtering/redaction built in, or do you recommend teams handle that at the hook/integration layer?
OpenHuman
well done @rohit_ghumare i'd love to know what's the business model you intend to persue? looks like everything is free and opensource. just wondering would u be making this a hobby project or building it seriously or something else?
Agentmemory
OpenHuman
@rohit_ghumare mashallah, i wish you guys more success :D :D we're going to natively integrate this into OH
Agentmemory
Persistent memory for coding agents is a harder problem than it sounds. You're not just storing conversation history, you're storing codebase context, decisions made, patterns established. The benchmark claim is what I'd want to dig into. Memory that's fast to write is useless if retrieval is noisy. How does it handle context that's become stale after a refactor?
Really interesting, can it pick up past sessions or does it start only once i integrate? On another side note is there a way to not use an agentic db and maybe postgres?
Great traction! I will give it a try on my current project and see if it brings down hallucination. I like the graph view so you can easily see whats going on.
Just wondering how long did this take to make? The database side is very interesting and i think has a lot of potential to many other things. Good luck!
Agentmemory
The cross-session forgetting problem is real. The deeper one you'll hit at scale: when an agent makes a wrong call in week 4 because it remembered a misleading decision from week 1, where does ownership of that mistake sit? Two questions worth thinking about: 1. Can memory be exported in an open format so agents move with their user, not their runtime? 2. Is there a way to mark a memory entry as disputed or superseded? Without those, an agent's persistent memory becomes a liability dressed as a feature.
Agentmemory
The 92% context compression is the number that actually matters here.. I've run into the CLAUDE.md token dump problem myself where the context window gets eaten before the agent even starts doing real work. The hybrid BM25+ vector search approach makes sense for this use case too, pure vector search misses exact identifier matches which in a codebase is basically everything important.. function names, variable names, file paths.
One thing I'm curious about.. how are you handling memory staleness? If I refactored a module two weeks ago and the old architecture is still in memory, the agent might confidently suggest patterns that no longer apply. Is there a TTL mechanism or does it rely on the agent to overwrite outdated memories when it encounters contradictions? That's the failure mode I'd be most worried about in a real codebase.
I built something similar for my production agents — each one maintains a JSON-based memory file tracking known issues, competitor changes, and operational state. The hardest part wasn't storing the memory, it was deciding what to forget. Without pruning, the context window fills with stale observations from weeks ago and the agent starts making decisions based on outdated reality.
95% token reduction is a big claim — how does it handle memory conflicts when the same topic gets updated by different sessions? In my system I had to implement a "trust what you observe now, update the stale memory" rule because old memories would override fresh observations.