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Memrust is an open-source memory engine for AI agents built in Rust. Instead of treating memory as just vector search, it combines semantic search, BM25, entity graphs and recency-aware ranking to retrieve information the way agents actually need it. With agent-native APIs like remember(), recall() and forget(), built-in memory lifecycle management, multi-agent support, MCP integration, and Python, TypeScript and Rust SDKs, memrust gives AI agents a true memory layer-not just a vector database.
Hey Product Hunt! 👋 I'm Sonu, creator of memrust.
After building AI agents for production, I kept running into the same problem: vector databases are great at semantic search, but agents don't just search-they need to remember. They need to recall exact identifiers, relationships, recent decisions, and knowledge accumulated over time.
That's why I built memrust.
Instead of relying on vectors alone, memrust combines semantic search, BM25, entity graphs, and recency-aware ranking into a single memory engine. It also introduces agent-native concepts like `remember()`, `recall()`, and `forget()`, along with memory lifecycle management, MCP support, and SDKs for Python, TypeScript, and Rust.
The entire project is open source (Apache-2.0), and every benchmark on the website is reproducible from scripts in the repository. If you think the approach can be improved-or you've hit memory problems in your own agents-I’d genuinely love to hear your thoughts.
You can also connect with me on [LinkedIn](https://www.linkedin.com/in/sonu...).
Happy to answer any questions about the architecture, benchmarks, design decisions, or the roadmap. Thanks for checking out memrust! 🚀
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About Memrust on Product Hunt
“Memory infrastructure for AI agents”
Memrust was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #49 on the daily leaderboard. Memrust is an open-source memory engine for AI agents built in Rust. Instead of treating memory as just vector search, it combines semantic search, BM25, entity graphs and recency-aware ranking to retrieve information the way agents actually need it. With agent-native APIs like remember(), recall() and forget(), built-in memory lifecycle management, multi-agent support, MCP integration, and Python, TypeScript and Rust SDKs, memrust gives AI agents a true memory layer-not just a vector database.
Memrust was featured in Open Source (68.7k followers), Developer Tools (517.5k followers), Artificial Intelligence (475.9k followers) and GitHub (41.4k followers) on Product Hunt. Together, these topics include over 233.9k products, making this a competitive space to launch in.
Who hunted Memrust?
Memrust was hunted by AI Anytime. 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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