vecgrep 2.0.0

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.

Benchmarks

Measured on the VecGrep codebase itself (5 source files, ~26k tokens raw).

Token usage per query

Mode	Avg tokens returned	vs raw read	Savings
Raw file read (baseline)	26,009	—	—
search_code (top_k=8)	~3,007	11.6%	88%
hybrid_search (top_k=8)	~3,324	12.8%	87%
search_graph (limit=8)	~47	0.2%	>99%

search_graph returns structured node metadata only (name, kind, file, line range) — no source code — so it's ultra-cheap for structural questions ("where is X defined?", "what calls Y?").

Query latency (median, 5 runs)

Mode	Latency
search_graph	~3ms
hybrid_search	~76ms
search_code	~83ms

search_graph is ~30× faster than vector search — pure in-memory graph traversal, no embedding model call.

Result correctness (structural queries)

For name-based structural queries, pure vector search can rank documentation (CHANGELOG, README) above source code. The graph index fixes this:

Query	search_code #1	hybrid_search #1
"VectorStore search method"	[WRONG] CHANGELOG.md	[OK] store.py
"GraphStore build"	[WRONG] CHANGELOG.md	[OK] server.py
"embedding provider factory"	[OK] embedder.py	[OK] embedder.py
"AST chunking tree-sitter"	[OK] chunker.py	[OK] chunker.py

The graph score (graph_score: 1.00) overrides a misleading vector match whenever the query directly names a known symbol.

Rule of thumb: use search_code for semantic/behaviour queries, search_graph for structural/navigation queries, hybrid_search when you need both.

---

How it works

1. Chunk — Parses source files with tree-sitter to extract semantic units (functions, classes, methods)
2. Embed — Encodes each chunk using the configured embedding provider:
   • Local (default) — all-MiniLM-L6-v2-code-search-512 via fastembed ONNX (~100ms startup, no API key) or PyTorch, with auto device detection (Apple Silicon, CUDA, CPU)
   • Cloud (BYOK) — OpenAI, Voyage AI, or Google Gemini via your own API key (higher-quality embeddings, optional)
3. Store — Saves embeddings + metadata in LanceDB under ~/.vecgrep/<project_hash>/; vector dimensions adapt automatically to the chosen provider
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
"Build a knowledge graph of my project"	index_graph
"What calls the VectorStore.search method?"	search_graph + graph_neighbors
"Find code structurally related to authentication"	hybrid_search

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, watch=False, provider=None)

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)"

# Use OpenAI embeddings instead of local
index_codebase("/path/to/myproject", provider="openai")

Provider lock: once a project is indexed with a provider, re-indexing with a different provider requires force=True (this rebuilds the vector table with the new embedding dimensions).

Note: watch=True is only supported with the local provider — live sync with cloud providers would incur unbounded API costs.

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, including the embedding provider used.

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
  Provider:       local
  Model:          isuruwijesiri/all-MiniLM-L6-v2-code-search-512
  Dimensions:     384

index_graph(path, force=False)

Build a structural knowledge graph from the codebase using tree-sitter AST extraction. No LLM required — extracts files, functions, classes, and methods as nodes; contains, calls, imports, and inherits as directed edges. Independent of the vector index.

index_graph("/path/to/myproject")
# → "Graph built: 496 nodes, 1251 edges, 35 files processed."

search_graph(query, path, limit=20)

Keyword search over node labels (function names, class names, file names). Returns structural nodes with source location and connectivity degree. Ultra-cheap: ~47 tokens average, ~3ms latency.

search_graph("VectorStore", "/path/to/myproject")
# → [1] CLASS  VectorStore  (score: 1.00, degree: 39)
#       src/vecgrep/store.py:49-352

graph_neighbors(node_id, path, depth=1)

Return the structural neighbourhood of any node — callers, callees, imports, contained methods, and inheritance edges. Use search_graph first to find the node ID.

graph_neighbors("VectorStore", "/path/to/myproject", depth=1)
# → Callers (18): _get_store, migrate_project, test fixtures...
#   Contains (18): search, add_chunks, replace_file_chunks...

hybrid_search(query, path, top_k=8, alpha=0.6, min_score=0.0)

Vector similarity search re-ranked by graph proximity. Final score = alpha * vector_score + (1 - alpha) * graph_score. Fixes cases where documentation ranks above source code on pure embedding similarity.

hybrid_search("VectorStore search method", "/path/to/myproject", alpha=0.6)
# → [1] src/vecgrep/store.py:292-320 (blended: 0.70, vec: 0.49, graph: 1.00)

Requires both index_codebase and index_graph to have been run. Degrades gracefully to pure vector search if the graph index is absent.

Configuration

VecGrep can be tuned via environment variables:

Local provider

Variable	Default	Description
VECGREP_BACKEND	onnx	Local 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 (local provider only)

Backend comparison:

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

Cloud providers (BYOK — Bring Your Own Key)

VecGrep supports three cloud embedding providers. Each requires an API key environment variable and the corresponding optional dependency.

Provider	Env var	Model	Dims	Install extra
openai	VECGREP_OPENAI_KEY	text-embedding-3-small	1536	vecgrep[openai]
voyage	VECGREP_VOYAGE_KEY	voyage-code-3	1024	vecgrep[voyage]
gemini	VECGREP_GEMINI_KEY	gemini-embedding-exp-03-07	3072	vecgrep[gemini]

Install cloud extras:

# Single provider
uv tool install --python 3.12 'vecgrep[openai]'
pip install 'vecgrep[openai]'

# All cloud providers at once
pip install 'vecgrep[cloud]'

Use a cloud provider:

# Set your API key
export VECGREP_OPENAI_KEY=sk-...

# Index with OpenAI embeddings
index_codebase("/path/to/myproject", provider="openai")

# Or tell Claude to use it:
# "Index my project at /path/to/myproject using openai embeddings"

Switch providers (requires force re-index to rebuild the vector table):

index_codebase("/path/to/myproject", provider="voyage", force=True)

Local backend 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

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