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Mnemo
Auditable, citation-backed memory for production AI agents
Memory infrastructure for AI agents — for when "store the latest value" isn't good enough. Event-sourced, append-only records — nothing gets overwritten. Citation-backed retrieval: every result traces to its source. Hybrid semantic + lexical search at ~150ms. Model-agnostic, US-hosted. 85.2% on LongMemEval-S (reproducible). Try it now: drop-in MCP server, npx -y getmnemo-mcp (Claude Desktop, Cursor, Windsurf, VS Code). Hosted API and self-hosting coming soon.
I built Mnemo because I kept running into the same problem — agent memory tools that couldn't handle change.
A user updates their job. Moves cities. Says "actually I'm not vegetarian anymore." And the agent just... keeps answering with the old fact. Not because it forgot. Because it never knew the old fact was old.
Most tools store the latest value and call it done. That's not memory. That's a variable.
Mnemo tracks how facts change over time bi-temporal versioning on an event-sourced core. So when someone says "she left that job last month," it handles that correctly. That means agents can answer "what's true now?" and "what was true six months ago?" from the same memory store.
I didn't want to just claim it works. So I benchmarked it. 85.2% on LongMemEval-S and also made the full methodology public: https://github.com/shhahhussain/...
If you've built an agent that has to remember someone across sessions — I'd genuinely like to know where it breaks for you. What's the memory failure that's been hardest to fix?
About Mnemo on Product Hunt
“Auditable, citation-backed memory for production AI agents”
Mnemo was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #137 on the daily leaderboard. Memory infrastructure for AI agents — for when "store the latest value" isn't good enough. Event-sourced, append-only records — nothing gets overwritten. Citation-backed retrieval: every result traces to its source. Hybrid semantic + lexical search at ~150ms. Model-agnostic, US-hosted. 85.2% on LongMemEval-S (reproducible). Try it now: drop-in MCP server, npx -y getmnemo-mcp (Claude Desktop, Cursor, Windsurf, VS Code). Hosted API and self-hosting coming soon.
On the analytics side, Mnemo competes within API, Developer Tools and Artificial Intelligence — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how Mnemo performed against the three products that launched closest to it on the same day.
Who hunted Mnemo?
Mnemo was hunted by Shah Hussain. 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.
For a complete overview of Mnemo including community comment highlights and product details, visit the product overview.
Shah Hussain