smgrep

Semantic code search, GPU-accelerated.

Natural-language search that works like grep. Fast, local, GPU-accelerated, and built for coding agents.

• Semantic: Finds concepts ("where do transactions get created?"), not just strings.
• GPU-Accelerated: CUDA on NVIDIA GPUs, Metal on Apple Silicon.
• Local & Private: 100% local embeddings. No API keys required.
• Auto-Isolated: Each repository gets its own index automatically.
• On-Demand Grammars: Tree-sitter WASM grammars download automatically as needed.
• Agent-Ready: Native MCP server and Claude Code integration.

Quick Start

1. Install

   cargo install smgrep

   Or build from source:

   git clone https://github.com/can1357/smgrep
   cd smgrep
   cargo build --release

   For CPU-only builds (no CUDA):

   cargo build --release --no-default-features

2. Setup (Recommended)

   smgrep setup

   Downloads embedding models (~500MB) and tree-sitter grammars upfront. If you skip this, models download automatically on first use.

3. Search

   cd my-repo
   smgrep "where do we handle authentication?"

   Your first search will automatically index the repository. Each repository is automatically isolated with its own index. Switching between repos "just works".

Coding Agent Integration

Claude Code

1. Run smgrep claude-install
2. Open Claude Code (claude) and ask questions about your codebase.
3. The plugin auto-starts the smgrep serve daemon and provides semantic search.

MCP Server

smgrep includes a built-in MCP (Model Context Protocol) server:

smgrep mcp

This exposes a sem_search tool that agents can use for semantic code search. The server auto-starts the background daemon if needed.

Commands

smgrep [query]

The default command. Searches the current directory using semantic meaning.

smgrep "how is the database connection pooled?"

Options:

Examples:

# General concept search
smgrep "API rate limiting logic"

# Deep dive (more matches per file)
smgrep "error handling" --per-file 5

# Just the file paths
smgrep "user validation" --compact

# JSON for scripting
smgrep "config parsing" --json

smgrep index

Manually indexes the repository.

smgrep index              # Index current dir
smgrep index --dry-run    # See what would be indexed
smgrep index --reset      # Delete and re-index from scratch

smgrep serve

Runs a background daemon with file watching for instant searches.

• Keeps LanceDB and embedding models resident for fast responses
• Watches the repo and incrementally re-indexes on change
• Communicates via Unix socket (or TCP on Windows)

smgrep serve              # Start daemon for current repo
smgrep serve --path /repo # Start for specific path

smgrep stop / smgrep stop-all

Stop running daemons.

smgrep stop               # Stop daemon for current repo
smgrep stop-all           # Stop all smgrep daemons

smgrep clean

Remove index data and metadata for a store.

smgrep clean              # Clean current directory's store
smgrep clean my-store     # Clean specific store by ID
smgrep clean --all        # Clean all stores

smgrep status

Show status of running daemons.

smgrep list

Lists all indexed repositories and their metadata.

smgrep doctor

Checks installation health, model availability, and grammar status.

smgrep doctor

GPU Acceleration

smgrep uses candle for ML inference with optional CUDA support.

With CUDA (default):

Requires CUDA toolkit installed with environment configured:

export CUDA_ROOT=/usr/local/cuda  # or your CUDA installation path
export PATH="$CUDA_ROOT/bin:$PATH"

cargo build --release

Embedding speed is significantly faster on NVIDIA GPUs.

CPU-only:

cargo build --release --no-default-features

Environment variables:

• SMGREP_DISABLE_GPU=1 - Force CPU even when CUDA is available
• SMGREP_BATCH_SIZE=N - Override batch size (auto-adapts on OOM)

Architecture

smgrep combines several techniques for high-quality semantic search:

1. Smart Chunking: Tree-sitter parses code by function/class boundaries, ensuring embeddings capture complete logical blocks. Grammars download on-demand as WASM modules.

2. Hybrid Search: Dense embeddings (sentence-transformers) for broad recall, ColBERT reranking for precision.

3. Quantized Storage: ColBERT embeddings are quantized to int8 for efficient storage in LanceDB.

4. Automatic Repository Isolation: Stores are named by git remote URL or directory hash.

5. Incremental Indexing: File watcher detects changes and updates only affected chunks.

Supported languages (37): TypeScript, TSX, JavaScript, Python, Go, Rust, C, C++, C#, Java, Kotlin, Scala, Ruby, PHP, Elixir, Haskell, OCaml, Julia, Zig, Lua, Odin, Objective-C, Verilog, HTML, CSS, XML, Markdown, JSON, YAML, TOML, Bash, Make, Starlark, HCL, Terraform, Diff, Regex

Configuration

smgrep uses a TOML config file at ~/.smgrep/config.toml. All options can also be set via environment variables with the SMGREP_ prefix.

Config File

# ~/.smgrep/config.toml

# ============================================================================
# Models
# ============================================================================

# Dense embedding model (HuggingFace model ID)
# Used for initial semantic similarity search
dense_model = "ibm-granite/granite-embedding-small-english-r2"

# ColBERT reranking model (HuggingFace model ID)
# Used for precise reranking of search results
colbert_model = "answerdotai/answerai-colbert-small-v1"

# Model dimensions (must match the models above)
dense_dim = 384
colbert_dim = 96

# Query prefix (some models require a prefix like "query: ")
query_prefix = ""

# Maximum sequence lengths for tokenization
dense_max_length = 256
colbert_max_length = 256

# ============================================================================
# Performance
# ============================================================================

# Batch size for embedding computation
# Higher = faster but more memory. Auto-reduces on OOM.
default_batch_size = 48
max_batch_size = 96

# Maximum threads for parallel processing
max_threads = 32

# Force CPU inference even when CUDA is available
disable_gpu = false

# Low-impact mode: reduces resource usage for background indexing
low_impact = false

# Fast mode: skip ColBERT reranking for quicker (but less precise) results
fast_mode = false

# ============================================================================
# Server
# ============================================================================

# TCP port for daemon communication
port = 4444

# Idle timeout: shutdown daemon after this many seconds of inactivity
idle_timeout_secs = 1800  # 30 minutes

# How often to check for idle timeout
idle_check_interval_secs = 60

# Timeout for embedding worker operations (milliseconds)
worker_timeout_ms = 60000

# ============================================================================
# Debug
# ============================================================================

# Enable model loading debug output
debug_models = false

# Enable embedding debug output
debug_embed = false

# Enable profiling
profile_enabled = false

# Skip saving metadata (for testing)
skip_meta_save = false

Environment Variables

Any config option can be set via environment variable with the SMGREP_ prefix:

# Examples
export SMGREP_DISABLE_GPU=true
export SMGREP_DEFAULT_BATCH_SIZE=24
export SMGREP_IDLE_TIMEOUT_SECS=3600

Ignoring Files

smgrep respects .gitignore and .smignore files.

Create .smignore in your repository root:

# Ignore generated files
dist/
*.min.js

# Ignore test fixtures
test/fixtures/

Manual Store Management

• View all stores: smgrep list
• Override auto-detection: smgrep --store custom-name "query"
• Data location: ~/.smgrep/

Troubleshooting

• Index feels stale? Run smgrep index to refresh.
• Weird results? Run smgrep doctor to verify models and grammars.
• Need a fresh start? smgrep index --reset or delete ~/.smgrep/.
• GPU OOM? Batch size auto-reduces, or set SMGREP_DISABLE_GPU=1.

Building from Source

git clone https://github.com/can1357/smgrep
cd smgrep
cargo build --release

# Run tests
cargo test

Acknowledgments

smgrep is inspired by osgrep and mgrep by MixedBread.

License

Licensed under the Apache License, Version 2.0.
See LICENSE for details.

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