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Your AI coding agent forgets your project every session. Most memory tools store your context on their servers. PMB keeps it on yours. It captures decisions, lessons and facts as you work and feeds the relevant ones back automatically via MCP hooks, so your agent shows up already knowing the project. Different: 100% local (one SQLite file, no cloud, no API keys), automatic (no "remember" step), cross-agent (Claude Code, Cursor, Codex), hybrid offline retrieval.
Hey Product Hunt 👋
I build with AI coding agents every day, and the same thing kept getting me: every new session the agent forgets everything. I'd re-explain the same decisions, conventions and dead-ends I'd already gone over that week. The hosted "memory" tools solved it by putting my private project context on someone else's servers, which I didn't want.
So I built PMB: a memory layer that's local-first and actually mine. It captures decisions, facts and lessons as I work and feeds the relevant ones back to the agent automatically over MCP, so it shows up already knowing the project. Everything lives in one SQLite file on my machine, no cloud, no API keys.
The hard part turned out to be not storing memory, but surfacing the right memory at the right moment without slowing the agent down. That's where most of the work went: hybrid retrieval, automatic capture via hooks, and a per-project lexicon that sharpens over time.
Open source (Apache-2.0), just hit 1.0, works across Claude Code, Cursor and Codex. Would love your honest feedback, especially if you've tried other memory setups. What's worked for you?
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About GitHub on Product Hunt
“Local-first memory for your AI coding agent”
GitHub was submitted on Product Hunt and earned 10 upvotes and 1 comments, placing #33 on the daily leaderboard. Your AI coding agent forgets your project every session. Most memory tools store your context on their servers. PMB keeps it on yours. It captures decisions, lessons and facts as you work and feeds the relevant ones back automatically via MCP hooks, so your agent shows up already knowing the project. Different: 100% local (one SQLite file, no cloud, no API keys), automatic (no "remember" step), cross-agent (Claude Code, Cursor, Codex), hybrid offline retrieval.
GitHub was featured in Open Source (68.6k followers), Developer Tools (514.8k followers), Artificial Intelligence (472.2k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 213.8k products, making this a competitive space to launch in.
Who hunted GitHub?
GitHub was hunted by Oleksii Bondar. 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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Oleksii Bondar