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Semble

Code search for agents that uses 98% fewer tokens than grep

Details

External ID
48169874
Source
HN
Company
—
Product
Semble
Website domain
github.com
Launched
May 17, 2026
Cohort
—
Upvotes
445
Upvotes percentile
0.9903069466882067
Tags
—
Fetched at
Sept. 7, 2026, 9:26 p.m.
Updated at
Sept. 7, 2026, 9:26 p.m.

Description

Hey HN! We (Stephan and Thomas) recently open-sourced Semble. We kept running into the same problem while using Claude Code on large codebases: when the agent can't find something directly, it falls back to grep, reading full files or launching subagents. This uses a lot of tokens, and often still misses the relevant code. There are existing tools for this, but they were either too slow to index on demand, needed API keys, or had poor retrieval quality.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 servicesInstall in Claude Code with: claude mcp add semble -s user -- uvx --from "semble[mcp]" sembleOr check our README for other installation instructions, benchmarks, and methodology:Semble: https://github.com/MinishLab/sembleBenchmarks: https://github.com/MinishLab/semble/tree/main/benchmarksModel: https://huggingface.co/minishlab/potion-code-16MLet us know if you have any feedback or questions!

Enrichment

Theme
Claude integrations and coding agents
Vertical
Horizontal
Function
Search & retrieval
Audience
Developer
AI stance
AI feature
Project type
Commercial product
Normalized one-liner
code search optimized for ai agents
Manually corrected
False

Could you build this?

Yes It is a developer CLI/tool that creates a lightweight AST or symbol index (using tree-sitter or ctags) over local code so LLM agents can query definitions and references with minimal token usage.

Discussion

20 comments analyzed.

Competitors mentioned: context-mode, morph-mcp / WarpGrep, pgr (Entire.io), codemogger, cs

Concerns raised: Difficulty measuring end-to-end agent performance improvements objectively, Claims of extreme optimization metrics (99x tokens, 88x better results) lack transparent methodology and margin of error, Unclear magnitude of token savings across different repository sizes, Output code quality improvements hard to quantify, Risk that tool makes agents worse rather than better despite claims

Feature requests: Show plots of average/median/max token savings per query as function of repo size, Publish comprehensive methodology and measurement details for performance claims, Improve relevance ranking for non-code documentation search

Competitors

Other products that read as similar to this one — 262 launches clear the similarity bar, closest 8 shown.

Attention rank: #4 of 263 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 192 days after the earliest competitor.

Other launches for this product