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omengrep (og)

Local semantic code search using embeddings and BM25.

cargo install --path .
og build ./src
og "authentication flow" ./src

What it does

omengrep extracts functions, classes, and methods from source files using tree-sitter, then indexes each block with both embeddings and BM25 keywords. Queries match against both indexes, so searching "error handling" finds errorHandler() and AppError — not just comments containing those words.

$ og build ./src
Found 69 files (0.0s)
Indexed 801 blocks from 69 files (10.8s)

$ og "error handling" ./src
src/cli/search.rs:42 function handle_search
  pub fn handle_search(args: &SearchArgs) -> Result<()> {

src/types.rs:15 enum SearchError
  pub enum SearchError {
      IndexNotFound,

2 results (0.27s)

Query	grep finds	omengrep finds
"error handling"	Comments mentioning it	`errorHandler()`, `AppError`
"authentication"	Strings containing "auth"	`login()`, `verify_token()`
"database"	Config files, comments	`Connection`, `query()`, `Db`

Use grep/ripgrep for exact strings. Use omengrep when you want implementations, not mentions.

Install

Requires Rust nightly toolchain.

git clone https://github.com/nijaru/omengrep && cd omengrep
cargo install --path .

The embedding model (~17MB) downloads automatically on first use.

Usage

og build [path]                # Build index (required first)
og "query" [path]              # Search
og file.rs#func_name           # Find code similar to a named block
og file.rs:42                  # Find code similar to a specific line
og status [path]               # Show index info
og list [path]                 # List all indexes under path
og clean [path]                # Delete index
og mcp                         # Start MCP server (JSON-RPC over stdio)

# Options
og -n 5 "error handling" .     # Limit to 5 results
og --json "auth" .             # JSON output
og -l "config" .               # List matching files only
og -t py,js "api" .            # Filter by file type
og --exclude "tests/*" "fn" .  # Exclude patterns
og --code-only "handler" .     # Skip docs (md, txt, rst)

Set OG_AUTO_BUILD=1 to build the index automatically on first search.

How it works

omengrep uses tree-sitter to parse source files into AST blocks (functions, classes, methods), then builds two indexes per block:

Embedding index — per-token embeddings from a ColBERT-style model (LateOn-Code-edge, 17M params, INT8 ONNX). Stored as MuVERA compressed multi-vectors, searched with MaxSim reranking.
BM25 index — keyword search with identifier-aware tokenization.

At search time, both indexes run in parallel and results merge by ID, keeping the higher score.

Runs locally on CPU. Search latency is 270-440ms.

Built on omendb.

Supported languages

Code (25 languages): Bash, C, C++, C#, CSS, Elixir, Go, HCL, HTML, Java, JavaScript, JSON, Kotlin, Lua, PHP, Python, Ruby, Rust, Swift, TOML, TypeScript, YAML, Zig

Text: Markdown, plain text (chunked by headers)

License

MIT