This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Agent Memory System gives any repository a durable memory layer that AI agents can read before they work. It scans the codebase, generates structured Markdown memory, creates a machine-readable topic index, tracks structural changes, and records handoffs so context survives when work moves between Antigravity, Codex, Claude, Cursor, or another assistant.
I built this because AI coding agents are powerful, but they still lose project context too easily. A task can start in one tool, continue in another, and suddenly the next agent has to rediscover the repo from scratch.
Agent Memory System gives every repository a durable memory layer: project structure, architecture notes, API and security context, worklogs, graph intelligence, and handoff summaries that agents can read before they start working.
It is open source, works with tools like Codex, Claude, Cursor, and Antigravity, and includes CI checks so memory stays fresh as the codebase changes.
I’d love feedback from developers using AI agents in real projects:
What context do your agents keep re-reading?
Where do handoffs break down?
What should memory tooling capture next?
Thanks for checking it out. Excited to hear what you think.
This is very helpful. I was looking for this kind of project. Are you planning to keep this as open source? I am going to fork after trying this.
About Agent Memory System on Product Hunt
“Open Source Context Infrastructure for AI Agents”
Agent Memory System was submitted on Product Hunt and earned 10 upvotes and 4 comments, placing #18 on the daily leaderboard. Agent Memory System gives any repository a durable memory layer that AI agents can read before they work. It scans the codebase, generates structured Markdown memory, creates a machine-readable topic index, tracks structural changes, and records handoffs so context survives when work moves between Antigravity, Codex, Claude, Cursor, or another assistant.
Agent Memory System was featured in Developer Tools (512.2k followers), Artificial Intelligence (468k followers) and GitHub (41.2k followers) on Product Hunt. Together, these topics include over 180.8k products, making this a competitive space to launch in.
Who hunted Agent Memory System?
Agent Memory System was hunted by Gaurav Singh. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Gaurav Singh
Hey Product Hunt, I’m Gaurav, founder of Agent Memory System.
I built this because AI coding agents are powerful, but they still lose project context too easily. A task can start in one tool, continue in another, and suddenly the next agent has to rediscover the repo from scratch.
Agent Memory System gives every repository a durable memory layer: project structure, architecture notes, API and security context, worklogs, graph intelligence, and handoff summaries that agents can read before they start working.
It is open source, works with tools like Codex, Claude, Cursor, and Antigravity, and includes CI checks so memory stays fresh as the codebase changes.
I’d love feedback from developers using AI agents in real projects:
What context do your agents keep re-reading?
Where do handoffs break down?
What should memory tooling capture next?
Thanks for checking it out. Excited to hear what you think.