Agentmemory

The token reduction angle is useful, but the part I’d want to stress-test is retrieval quality over time. For coding agents, stale decisions and half-remembered debug notes can be worse than no memory unless there is a clear way to expire or scope observations per repo.

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?

Hey! Love it. How well would it help with handling pivots and knowing how my seed-stage startup's narrative/pitch deck and product spec changes over time? I've got canonical documents set up in Cursor, but it still takes a LOT of tidying work and any new scratch brainstorming files ruin the source of truth...

My claude code desktop fails to connect after installing this. :-(

Is this similar to Claude mem?

Cool project, how are you handling caching to ensure that it doesn't reprocess tokens unnecessarily in longer conversations?

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?

Congrats on the launch. 2 questions: Will this impact more usage on tokens? since the agent need looking around and search on newer chats?Will the memory be persistent only in CLI agents or also on their desktop application as Codex, Claude, Cursor

Congrats on the launch. 2 questions: Will this impact more usage on tokens? since the agent need looking around and search on newer chats?Will the memory be persistent only in CLI agents or also on their desktop application as Codex, Claude, Cursor

Persistent memory across sessions is one of those things that sounds like a dev tool problem but actually changes how useful AI agents are in practice. Right now every session with Claude Code starts from scratch — re-explaining context, re-loading preferences. Curious how Agentmemory handles conflicts when the same context gets updated across sessions. Does it merge, overwrite, or flag it for review?

Well done team! How do you detect when a stored memory contradicts current code state or is pruning still manual?

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?

Wonderful project. Already used it locally with Claude Code and it provides an amazing developer experience. Absolutely love the underlying architecture powered by iii = very scalable. very efficient and hands down the best memory solution otu there

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 sourceFree to run locallyNo external database requiredWorks via MCP, REST, and simple hooksBuilt for real coding workflows, not toy “chat history” memoryOn 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:Run: npx @agentmemory/agentmemoryOpen: http://localhost:3113Or try the demo: npx @agentmemory/agentmemory demoIf 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