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Minta AI Memory Engine
Fix degraded AI memory automatically
Unlike most AI memory solutions that only handle storage and retrieval, leaving the burden of resolving stale, conflicting, or redundant data entirely to the LLM, Minta builds memory quality governance directly into its core. We automatically detect and resolve staleness, conflicts, redundancy, and fragmentation before any memory reaches your model, with fully transparent, reproducible logic. This ensures only verified, high-quality data powers your AI, eliminating the risk of degraded memories.
Hey everyone, Xin here. I built Minta because my AI coding assistant kept suggesting tech I'd already migrated away from. I realized no memory system checks if memories are still valid — they just store more and hope the LLM figures it out.
This is my first open-source project. The core engine is MIT. Happy to answer any questions. Your AI assistant remembers your name, your tech stack, your preferences. But it doesn't remember that you switched from NextAuth to Clerk last month — so it confidently suggests the old config.
This isn't a bug. It's memory decay. Every AI that remembers you is slowly filling up with stale facts, contradictory preferences, and redundant copies. Nobody is checking. Minta is the first memory quality layer for AI. Before your AI sees any memory, Minta scans for four problems:
🔴 Staleness — "You said this 200 days ago. Still true?"
🟡 Conflict — "These two facts contradict. Which is right?"
🟠 Redundancy — "You said the same thing 3 times. Merge?"
🔵 Fragmentation — "Team info scattered across 4 entries. Group?"
Key facts:
- 19 MCP tools — works with Claude Code, Cursor, Codex, VS Code
- 4 memory quality metrics no competitor measures (Conflict F1=0.81, Staleness UFA=0.86)
- 97.1% retrieval recall on LoCoMo benchmark
- Fully local — no cloud, no uploads, 3-second install
- MIT license
- pip install minta → minta launch → done
About Minta AI Memory Engine on Product Hunt
“Fix degraded AI memory automatically”
Minta AI Memory Engine was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #139 on the daily leaderboard. Unlike most AI memory solutions that only handle storage and retrieval, leaving the burden of resolving stale, conflicting, or redundant data entirely to the LLM, Minta builds memory quality governance directly into its core. We automatically detect and resolve staleness, conflicts, redundancy, and fragmentation before any memory reaches your model, with fully transparent, reproducible logic. This ensures only verified, high-quality data powers your AI, eliminating the risk of degraded memories.
On the analytics side, Minta AI Memory Engine competes within Developer Tools, Artificial Intelligence, GitHub and Computers — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how Minta AI Memory Engine performed against the three products that launched closest to it on the same day.
Who hunted Minta AI Memory Engine?
Minta AI Memory Engine was hunted by xin chen. 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.
Reviews
Minta AI Memory Engine has received 1 review on Product Hunt with an average rating of 5.00/5. Read all reviews on Product Hunt.
For a complete overview of Minta AI Memory Engine including community comment highlights and product details, visit the product overview.
xin chen