Developer builds offline semantic memory engine for AI agents in Rust

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A developer has released Uteke, a local-first semantic memory engine designed to give AI agents persistent memory across sessions without relying on cloud services or API keys. The tool is written in Rust and ships as a single binary, storing all data locally on the user's machine. Uteke combines vector search using a local 188MB embedding model with SQLite full-text search, merging results via Reciprocal Rank Fusion to handle both semantic and keyword queries. The full retrieval pipeline completes in approximately 45 milliseconds, with vector recall alone taking around 5 milliseconds at 10,000 entries. Released under the Apache 2.0 license, the project has reached version 0.10.1 with over 40 releases and 148 GitHub stars in roughly 58 days since its first commit.

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