Semble is our solution for this. It combines static Model2Vec embeddings (using our latest static model: potion-code-16M) with BM25, fused via RRF and reranked with code-aware signals. Everything runs on CPU since there's no transformers involved. On our benchmark of ~1250 query/document pairs across 63 repos and 19 languages, it uses 98% fewer tokens than grep+read and reaches 99% of the retrieval quality of a 137M-parameter code-trained transformer, while being ~200x faster.
Main features:
- Token-efficient: 98% fewer tokens than grep+read
- Fast: ~250ms to index a typical repo on our benchmark, ~1.5ms per query on CPU (very large repos may take longer)
- Accurate: 0.854 NDCG@10, 99% of the best transformer setup we tested
- MCP server: drop-in for Claude Code, Cursor, Codex, OpenCode
- Zero config: no API keys, no GPU, no external services
Install in Claude Code with: claude mcp add semble -s user -- uvx --from "semble[mcp]" semble
Or check our README for other installation instructions, benchmarks, and methodology:
Semble: https://github.com/MinishLab/semble
Benchmarks: https://github.com/MinishLab/semble/tree/main/benchmarks
Model: https://huggingface.co/minishlab/potion-code-16M
Let us know if you have any feedback or questions!