Fast and Accurate Code Search for Agents
_{Uses ~99% fewer tokens than grep+read}

[Quickstart](#quickstart) • [CLI](#cli) • [MCP Server](#mcp-server) • [Installation](docs/installation.md) • [Benchmarks](#benchmarks)

Semble is a code search library built for agents. It returns the exact code snippets they need instantly, using ~99% fewer tokens than grep+read. Indexing and searching a full codebase end-to-end takes under a second for most repos, matching the retrieval quality of a code-specialized transformer while indexing ~480x faster and querying ~23x faster (see [benchmarks](#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.

semble install detecting Claude Code, Cursor, and Codex, then semble search returning ranked code snippets from pydantic

## 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](https://docs.astral.sh/uv/getting-started/installation/), then run: ```bash 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. - **Skill**: installs a `semble` skill plus a one-line pointer to it in AGENTS.md / CLAUDE.md, instead of the full instructions. 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](docs/installation.md).

Updating Semble

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

Unattended install

For sandboxed or scripted environments, skip the prompts with `--agent` and, optionally, `--type`: ```bash semble install --agent claude --type mcp subagent --yes ``` `--agent` accepts one or more agent ids (e.g. `claude`, `codex`, `pi`); `--type` accepts `mcp`, `instructions`, `subagent`, `skill`, or `all` (default: all but `skill`); `--yes` skips the confirmation prompt (requires `--agent` for a fully non-interactive run).

## Main Features - **Fast**: indexes an average repo in ~500 ms and answers queries in ~1 ms, all on CPU. - **Accurate**: NDCG@10 of 0.855 on our [benchmarks](#benchmarks), on par with code-specialized transformer models, at a fraction of the size and cost. - **Token-efficient**: returns only the relevant chunks, using [~99% fewer tokens than grep+read](#benchmarks). - **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, or several of them to search related repos together. ## 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. ```bash # 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 # Search several repos at once (results are prefixed with the repo name) semble search "invoice endpoint" ./service-a ./service-b # 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 # Show only the first N lines of each result's snippet (0 = path/line range only) semble search "authentication flow" ./my-project --max-snippet-lines 10 ``` `--content` accepts `code` (default), `docs`, `config`, or `all`. `--format` accepts `json` (default) or `text`. `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 --version` (or `-V`) prints the installed version. Passing several paths or URLs searches them as one corpus, so a query from repo A can find an endpoint defined in repo B. Each index is cached per repo and merged at query time. Result paths are prefixed with the repo name (`service-b/api/invoices.py`) and the output includes a `repos` map from prefix to absolute path or URL. Pass the prefixed path to `find-related` to search across all repos from a known location.

Controlling which files are indexed

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 ``` Run `semble file-types` to see which extensions are indexed per content type. Extensions not listed (including data files such as `.json` and `.csv`) are not indexed by default and can be force-included with a `!` pattern as above. 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).

Savings

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

Storage

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`. Files larger than 1 MB are skipped during indexing to keep index builds lean. Skipped files are reported as a warning at index time. If you work with large generated or ingested documents, you can raise (or lower) this limit with the `SEMBLE_MAX_FILE_BYTES` environment variable (in bytes). On first use, Semble also downloads the embedding model from Hugging Face and caches it in the standard Hugging Face cache (`~/.cache/huggingface/` by default, or `$HF_HOME` if set); this only happens once and requires network access. Use `semble clear` to remove cached data: `semble clear index` (saved indexes), `semble clear savings` (usage stats), `semble clear orphans` (indexes for repos no longer present on disk), or `semble clear all` (everything).

Library usage

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. ```python 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") # Merge indexes from several repos into one (chunk paths are prefixed with the repo name) index = SembleIndex.merge([("./service-a", SembleIndex.from_path("./service-a")), ("./service-b", SembleIndex.from_path("./service-b"))]) # 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, ..." ``` When many files need indexing, the CLI and MCP server chunk them in 4 worker processes (`SEMBLE_INDEX_WORKERS` sets the number, `0` turns them off). In the Python API they are off by default. To turn them on, set `SEMBLE_INDEX_WORKERS` and call Semble from inside an `if __name__ == "__main__":` block, since each worker re-imports your script.

## 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. | Tool | Description | |------|-------------| | `search` | Search a codebase with a natural-language or code query. Pass `repo` as a local path or an https:// git URL (or a list of them to search several repos together) and `content` as `code`, `docs`, `config`, or `all` (default: `code`). | | `find_related` | Given a file path and line number, return chunks semantically similar to the code at that location. | For per-agent setup instructions, see the [installation docs](docs/installation.md#mcp-server). ## 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).

Token efficiency: recall vs. retrieved tokens

The quality benchmark (left) scores retrieval quality (NDCG@10) against total latency; semble matches the quality of the 137M-parameter [CodeRankEmbed](https://huggingface.co/nomic-ai/CodeRankEmbed) while indexing 480x faster. The token efficiency benchmark (right) measures how many tokens each method needs to reach a given recall level; semble uses 99% fewer tokens on average and hits 96% recall at only 2k tokens, while grep+read needs a full 100k context window to reach 85%. See [benchmarks](benchmarks/README.md) for per-language results, ablations, and full methodology. ## How it works Semble splits each file into code-aware chunks using [tree-sitter](https://github.com/tree-sitter/py-tree-sitter), then scores every query against the chunks with two complementary retrievers: static [Model2Vec](https://github.com/MinishLab/model2vec) embeddings using the code-specialized [potion-code-16M-v2](https://huggingface.co/minishlab/potion-code-16M-v2) model for semantic similarity, and BM25 for lexical matches on identifiers and API names. The two score lists are fused with Reciprocal Rank Fusion (RRF). After fusing, results are reranked with a set of code-aware signals:

Ranking signals

- **Adaptive weighting.** Symbol-like queries (`Foo::bar`, `_private`, `getUserById`) get more lexical weight, while natural-language queries stay balanced between semantic and lexical retrievers. - **Definition boosts.** A chunk that defines the queried symbol (a `class`, `def`, `func`, etc.) is ranked above chunks that merely reference it. - **Identifier stems.** Query tokens are stemmed and matched against identifier stems in a chunk, giving an additional weight to chunks that contain them. For example, querying `parse config` boosts chunks containing `parseConfig`, `ConfigParser`, or `config_parser`. - **File coherence.** When multiple chunks from the same file match the query, the file is boosted so the top result reflects broad file-level relevance rather than a single out-of-context chunk. - **Noise penalties.** Test files, `compat/`/`legacy/` shims, example code, and `.d.ts` declaration stubs are down-ranked so canonical implementations surface first.

Because the embedding model is static with no transformer forward pass at query time, all of this runs in milliseconds on CPU. Indexes are cached to disk automatically on the first search. On subsequent runs, Semble walks the file tree and compares modification times; added, removed, or changed files are reindexed incrementally, without rebuilding the rest of the index. A full rebuild only happens if the indexing settings change (e.g., after a semble upgrade that changes the model, chunking, or cache format). In MCP mode, the index is checked and refreshed automatically as files change, so results stay current across the session. ### Using a custom model If you would like to use another model, you can set your `SEMBLE_MODEL_NAME` environment variable to a local path or Hugging Face repository. This path is read verbatim, and should contain a [`Model2Vec`](https://github.com/MinishLab/model2vec) compatible model. This is particularly useful if you can't access Hugging Face at runtime. ## Acknowledgements Thanks to [Greptile](https://greptile.com) for providing free access to their AI code review platform. ## License MIT ## Citing If you use Semble in your research, please cite the following: ```bibtex @software{minishlab2026semble, author = {{van Dongen}, Thomas and Stephan Tulkens}, title = {Semble: Fast and Accurate Code Search for Agents}, year = {2026}, publisher = {Zenodo}, doi = {10.5281/zenodo.19785932}, url = {https://github.com/MinishLab/semble}, license = {MIT} } ```

Fast and Accurate Code Search for Agents Uses ~99% fewer tokens than grep+read

Fast and Accurate Code Search for Agents
_{Uses ~99% fewer tokens than grep+read}