semble
MinishLab
CPU-only semantic code search for agents: query in natural language, get back only the relevant snippets.
What is semble?
A code-search tool built for AI coding agents: you query in natural language and get back only the relevant snippets instead of reading whole files or grepping. It runs entirely on CPU with tree-sitter parsing, Model2Vec static embeddings and BM25, needs no API keys or GPU, and ships as a Python library, a CLI and an MCP server.
Pros & Cons
Pros
- Zero-setup local operation, no API keys, GPU or cloud, from the Model2Vec/Potion team
- Triple distribution (library, CLI and MCP server) fits both agent and human workflows
- MIT-licensed with releases, tests and docs
Cons
- Pre-1.0 (0.4.x), so interfaces may break
- Headline efficiency numbers (98% fewer tokens, 218x faster indexing) are unverified project benchmarks
- Static-embedding retrieval quality on very large or unusual codebases is not independently validated
License
MIT (OSI-open)
When it is interesting
Coding-agent users who want to cut context-token spend on code retrieval with a local CPU tool.
When it is too early
If you need a stable 1.0 API, or proven retrieval quality on your specific monorepo first.
Commercial alternative & related
- Commercial counterpart: Greptile
This repo featured in the 2026-07 edition of the Open-Source AI Radar.
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