vecgrep 1.6.0

Cursor-style vector search MCP plugin for Claude Code

VecGrep

Cursor-style semantic code search as an MCP plugin for Claude Code.

Instead of grepping 50 files and sending 30,000 tokens to Claude, VecGrep returns the top 8 semantically relevant code chunks (~1,600 tokens). That's a ~95% token reduction for codebase queries.

How it works

1. Chunk — Parses source files with tree-sitter to extract semantic units (functions, classes, methods)
2. Embed — Encodes each chunk locally using all-MiniLM-L6-v2-code-search-512 (384-dim, ~80MB one-time download) via the fastembed ONNX backend (~100ms startup) or PyTorch, automatically using Metal (Apple Silicon), CUDA (NVIDIA), or CPU
3. Store — Saves embeddings + metadata in LanceDB under ~/.vecgrep/<project_hash>/
4. Search — ANN index (IVF-PQ) for fast approximate search on large codebases

Incremental re-indexing via mtime/size checks skips unchanged files.

Architecture

Installation

Requires Python 3.12 and uv.

Note: Python 3.12 is required — tree-sitter-languages does not yet have wheels for Python 3.13+.

pip install vecgrep                        # standard pip
uv tool install --python 3.12 vecgrep     # uv tool (recommended)

Claude Code integration

Run once — works for every project:

claude mcp add --scope user vecgrep -- vecgrep

This installs VecGrep as a persistent binary and registers it in your user config (~/.claude.json) so it's available globally across all projects. Starts instantly — no download delay on Claude Code launch.

Usage with Claude

You don't trigger VecGrep manually - Claude decides when to call the tools based on what you ask.

What you say to Claude	Tool invoked
"Index my project at /Users/me/myapp"	index_codebase
"How does authentication work in this codebase?"	search_code
"Find where database connections are set up"	search_code
"How many files are indexed?"	get_index_status

Typical first-time flow:

You:    "Search for how payments are handled in /Users/me/myapp"
Claude: [calls index_codebase automatically since no index exists]
Claude: [calls search_code with your query]
Claude: "Here's how payments work — in src/payments.py:42..."

After the first index, subsequent searches skip unchanged files automatically — no re-indexing needed unless your code changes.

Tools

index_codebase(path, force=False)

Index a project directory. Skips unchanged files on subsequent calls.

index_codebase("/path/to/myproject")
# → "Indexed 142 file(s), 1847 chunk(s) added (0 file(s) skipped, unchanged)"

search_code(query, path, top_k=8)

Semantic search. Auto-indexes if no index exists.

search_code("how does user authentication work", "/path/to/myproject")

Returns formatted snippets with file paths, line numbers, and similarity scores:

[1] src/auth.py:45-72 (score: 0.87)
def authenticate_user(token: str) -> User:
    ...

[2] src/middleware.py:12-28 (score: 0.81)
...

get_index_status(path)

Check index statistics.

Index status for: /path/to/myproject
  Files indexed:  142
  Total chunks:   1847
  Last indexed:   2026-02-22T07:20:31+00:00
  Index size:     28.4 MB

Configuration

VecGrep can be tuned via environment variables:

Variable	Default	Description
VECGREP_BACKEND	onnx	Embedding backend: onnx (fastembed, fast startup) or torch (sentence-transformers, any HF model)
VECGREP_MODEL	isuruwijesiri/all-MiniLM-L6-v2-code-search-512	HuggingFace model ID to use for embeddings

Backend comparison:

Backend	Startup	PyTorch required	Custom HF models
onnx (default)	~100ms	No	ONNX-exported models only
torch	~2–3s	Yes	Any HuggingFace model

Examples:

# Use a different model with the torch backend
VECGREP_BACKEND=torch VECGREP_MODEL=sentence-transformers/all-MiniLM-L6-v2 vecgrep

# Use a custom ONNX model
VECGREP_MODEL=my-org/my-onnx-model vecgrep

Supported languages

Python, JavaScript/TypeScript, Rust, Go, Java, C/C++, Ruby, Swift, Kotlin, C#

All other text files fall back to sliding-window line chunks.

Index location

~/.vecgrep/<sha256-of-project-path>/index.db

Each project gets its own isolated index. Delete the directory to wipe the index.

Acknowledgements

The embedding model used by VecGrep is all-MiniLM-L6-v2-code-search-512, a model fine-tuned specifically for semantic code search by @isuruwijesiri.

@misc{all_MiniLM_L6_v2_code_search_512,
  author    = {isuruwijesiri},
  title     = {all-MiniLM-L6-v2-code-search-512},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/isuruwijesiri/all-MiniLM-L6-v2-code-search-512}
}

Community

❓ Questions	Start a Q&A discussion
💡 Ideas	Share an idea
🚀 Show & Tell	Share how you use VecGrep
🐛 Bugs	Open an issue