semble

Quickstart • CLI • MCP Server • Installation • Benchmarks

Semble is a code search library built for agents. It returns the exact code snippets they need instantly, using ~98% fewer tokens than grep+read. Indexing and searching a full codebase end-to-end takes under a second, with ~200x faster indexing and ~10x faster queries than a code-specialized transformer, at 99% of its retrieval quality (see benchmarks). Everything runs on CPU with no API keys, GPU, or external services. Use it as an MCP server, a CLI tool via AGENTS.md, or a dedicated sub-agent, and any coding agent (Claude Code, Cursor, Codex, OpenCode, etc.) gets instant access to any repo.

Quickstart

Your agent queries Semble in natural language (e.g. "How is authentication handled?") and gets back only the relevant code snippets, without grepping or reading full files.

The fastest way to get started is the interactive installer. Install uv, then run:

uv tool install semble
semble install

semble install detects installed coding agents such as Claude Code, Codex, and OpenCode, and then lets you choose which integrations to enable:

• MCP server: lets the agent call Semble directly as a tool.
• Instructions: adds CLI usage guidance to AGENTS.md / CLAUDE.md.
• Sub-agent: installs a dedicated semble-search sub-agent.

To undo the setup, run semble uninstall.

For manual setup instructions (MCP config per agent, AGENTS.md snippet, sub-agent files), see the installation docs.

uv tool upgrade semble   # upgrade
uv cache clean semble    # for MCP users (restart your MCP client after)

Main Features

• Fast: indexes an average repo in ~250 ms and answers queries in ~1.5 ms, all on CPU.
• Accurate: NDCG@10 of 0.854 on our benchmarks, on par with code-specialized transformer models, at a fraction of the size and cost.
• Token-efficient: returns only the relevant chunks, using ~98% fewer tokens than grep+read.
• Zero setup: runs on CPU with no API keys, GPU, or external services required.
• MCP server: works with Claude Code, Cursor, Codex, OpenCode, VS Code, and any other MCP-compatible agent.
• Local and remote: pass a local path or a git URL.

CLI

Semble also ships as a standalone CLI. This is useful in scripts or anywhere you want search results without an MCP session. Indexes are built and cached on first run, and invalidated automatically when files change.

# Search a local repo (index is built and cached automatically)
semble search "authentication flow" ./my-project

# Search a remote repo (cloned on demand)
semble search "save model to disk" https://github.com/MinishLab/model2vec

# Limit results
semble search "save model to disk" ./my-project --top-k 10

# Search docs/config/everything instead of just code
semble search "deployment guide" ./my-project --content docs   # or: config, all

# Find code similar to a known location
semble find-related src/auth.py 42 ./my-project

--content accepts code (default), docs, config, or all. path defaults to the current directory when omitted; git URLs are accepted. If semble is not on $PATH, use uvx --from "semble[mcp]" semble in its place.

Semble reads .gitignore and .sembleignore files to determine which files to index. Both files use standard gitignore syntax and their patterns are merged. .sembleignore lets you add semble-specific rules without touching .gitignore. Rules are applied recursively, so a .sembleignore in a subdirectory applies to that subtree.

Excluding files: add patterns the same way you would in .gitignore:

# .sembleignore
generated/     # exclude generated dir
*.pb.go.       # exclude Go protobuf files

Including non-default extensions: prefix the extension pattern with ! to force-include files that semble wouldn't index by default:

# .sembleignore
!*.proto       # include Protobuf files
!*.cob         # include COBOL files

Semble also always skips a set of well-known non-source directories regardless of ignore files (e.g. node_modules/, .venv/, dist/, build/, __pycache__/, and similar).

semble savings shows how many tokens semble has saved across all your searches:

semble savings

  Semble Token Savings
  ════════════════════════════════════════════════════════════════════════

  Total saved:  ~714.2M tokens  (94%)
  Total calls:  14.3k
  Efficiency:  ███████████████████████░  94%

  By Period
  ────────────────────────────────────────────────────────────────────────
  Period             Calls           Saved  Ratio
  ────────────────────────────────────────────────────────────────────────
  Today                198    ~1.4M tokens  ███████████████████████░  95%
  Last 7 days        13.1k  ~707.2M tokens  ███████████████████████░  94%
  All time           14.3k  ~714.2M tokens  ███████████████████████░  94%

  By Call Type
  ────────────────────────────────────────────────────────────────────────
  #     Call type            Calls  Share
  ────────────────────────────────────────────────────────────────────────
  1.    search               14.1k  ████████████████    99%
  2.    find_related           205  █░░░░░░░░░░░░░░░     1%
  ════════════════════════════════════════════════════════════════════════

Savings are calculated as follows: for each call, semble records the total character count of the unique files containing returned chunks and the character count of the snippets returned. Estimated tokens saved is (file chars − snippet chars) / 4 (4 chars per token). This is a conservative estimate: the baseline is reading matched files in full, which is how coding agents often explore unfamiliar code.

By default, your Semble savings statistics and any saved indexes are stored in the OS cache folder (~/Library/Caches/semble/ on macOS, ~/.cache/semble/ on Linux, %LOCALAPPDATA%\semble\Cache\ on Windows). To override this location you can supply an environment variable SEMBLE_CACHE_LOCATION which should be the full path to the target cache location e.g. ~/my-folder/my-caches/semble.

Semble can also be used as a Python library for programmatic access, useful when building custom tooling or integrating search directly into your own code.

from semble import ContentType, SembleIndex

# Index a local directory (code only, the default)
index = SembleIndex.from_path("./my-project")

# Index docs and prose (markdown, rst, etc.)
index = SembleIndex.from_path("./my-project", content=ContentType.DOCS)

# Index everything (code, docs, and config)
index = SembleIndex.from_path("./my-project", content=[ContentType.CODE, ContentType.DOCS, ContentType.CONFIG])

# Index code and docs together
index = SembleIndex.from_path("./my-project", content=[ContentType.CODE, ContentType.DOCS])

# Index a remote git repository
index = SembleIndex.from_git("https://github.com/MinishLab/model2vec")

# Search the index with a natural-language or code query
results = index.search("save model to disk", top_k=3)

# Find code similar to a specific result
related = index.find_related(results[0], top_k=3)

# Each result exposes the matched chunk
result = results[0]
result.chunk.file_path   # "model2vec/model.py"
result.chunk.start_line  # 127
result.chunk.end_line    # 150
result.chunk.content     # "def save_pretrained(self, path: PathLike, ..."

MCP Server

Semble runs as an MCP server so agents can search any codebase directly as a native tool call. Repos are indexed on demand and cached; local paths are re-indexed automatically on file changes.

For per-agent setup instructions, see the installation docs.

Benchmarks

We benchmark quality and speed across ~1,250 queries over 63 repositories in 19 languages (left), and token efficiency against grep+read at equivalent recall levels (right).

The quality benchmark (left) scores retrieval quality (NDCG@10) against total latency; semble achieves 99% of the quality of the 137M-parameter [CodeRankEmbed](https://huggingface.co/nomic-ai/CodeRankE