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what is semble_rs fr?

johunsang/semble_rs — explained in plain English

Analysis updated 2026-05-18

86RustAudience · developerComplexity · 2/5Setup · easy

tl;dr

A Rust CLI tool that finds relevant code and compresses output so AI coding agents use far fewer tokens.

vibe map

mindmap
  root((semble_rs))
    What it does
      Searches code with BM25 and embeddings
      Compresses directory trees
      Shrinks build and test output
    Tech stack
      Rust
      BM25
      Model2Vec
    Use cases
      Reduce tokens sent to an AI agent
      Find relevant code chunks fast
      Compress CI logs and test output
    Audience
      AI coding agent developers
      Developers on large codebases

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filefunction / class

what do people make with this?

VIBE 1

Search a large codebase for the chunks most relevant to a plain-English question

VIBE 2

Replace a directory listing with a collapsed tree view to save tokens

VIBE 3

Compress build and test output while preserving error messages and stack traces

VIBE 4

See what other code would be affected if a given file changed

what's the stack?

RustBM25Model2Vec

how it stacks up fr

johunsang/semble_rskonippi/servo-fetchl0ng-ai/papr
Stars868788
LanguageRustRustRust
Setup difficultyeasyeasyeasy
Complexity2/53/52/5
Audiencedeveloperdevelopergeneral

Figures from each repo's GitHub metadata at analysis time.

how do i run it?

Difficulty · easy time til it works · 5min

Single binary with no external services, API keys, or GPU required.

The README does not state a license.

in plain english

semble_rs is a command-line code search tool written in Rust, designed specifically to help AI coding agents like Claude Code, Codex, and Cursor work more efficiently inside large codebases. The core problem it solves is that when an AI agent explores a codebase using standard tools like grep, cat, or ls, it sends enormous amounts of text back to the language model, consuming a huge number of tokens (the units that determine AI processing cost and context window limits) without necessarily finding the most relevant code. The tool addresses this with three main capabilities. The search command uses a hybrid approach combining BM25 (a keyword relevance algorithm) and static semantic embeddings from a model called Model2Vec to find the code chunks most relevant to a plain-English question, returning only the useful portions rather than entire files. The tree command replaces the standard directory listing, collapsing irrelevant folders like build outputs and node_modules to reduce what might be 400,000 characters down to 500, a reduction of up to 747 times. The digest command compresses build and test output from tools like cargo, pytest, and GitHub Actions by collapsing progress lines while always preserving error messages and stack traces, achieving up to 99 percent size reduction on real CI logs. It also includes a dependency graph feature showing what a file imports and what other code would be affected if it changed. The tool runs as a single binary with no external services, no API keys, and no GPU required. An AI coding agent or developer trying to reduce token usage when working with large repos would use this.

prompts (copy fr)

prompt 1
Use semble_rs to search this codebase for code relevant to a specific question
prompt 2
Show me how the tree command in this repo collapses irrelevant folders like node_modules
prompt 3
Help me pipe my CI logs through the digest command in this repo to shrink them
prompt 4
Explain how the dependency graph feature in semble_rs works for a given file

Frequently asked questions

what is semble_rs fr?

A Rust CLI tool that finds relevant code and compresses output so AI coding agents use far fewer tokens.

What language is semble_rs written in?

Mainly Rust. The stack also includes Rust, BM25, Model2Vec.

What license does semble_rs use?

The README does not state a license.

How hard is semble_rs to set up?

Setup difficulty is rated easy, with roughly 5min to a first successful run.

Who is semble_rs for?

Mainly developer.

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