LightOn Tools

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ColGREP

Semantic code search for your terminal and your coding agents. Searches combine regex filtering with semantic ranking. All local, your code never leaves your machine.

Quick start

Install:

# Homebrew (macOS / Linux)
brew install lightonai/tap/colgrep

# Shell installer
curl --proto '=https' --tlsv1.2 -LsSf https://github.com/lightonai/next-plaid/releases/latest/download/colgrep-installer.sh | sh

Build the index:

colgrep init /path/to/project  # specific project
colgrep init                   # current directory

Search:

colgrep "database connection pooling"

That's it. No server, no API, no dependencies. ColGREP is a single Rust binary with everything baked in. colgrep init builds the index for the first time. After that, every search detects file changes and updates the index automatically before returning results.

Regex meets semantics:

colgrep -e "async.*await" "error handling"

Agent integrations

Tool	Install
Claude Code	colgrep --install-claude-code
OpenCode	colgrep --install-opencode
Codex	colgrep --install-codex

Restart your agent after installing. Claude Code has full hooks support. OpenCode and Codex integrations are basic for now, PRs welcome.

How it works

flowchart TD
    A["Your codebase"] --> B["Tree-sitter"]
    B --> C["Structured representation"]
    C --> D["LateOn-Code-edge · 17M"]
    D --> E["NextPlaid"]
    E --> F["Search"]

    B -.- B1["Parse functions, methods, classes"]
    C -.- C1["Signature, params, calls, docstring, code"]
    D -.- D1["Multi-vector embedding per code unit · runs on CPU"]
    E -.- E1["Rust index binary · quantized · memory-mapped · incremental"]
    F -.- F1["grep-compatible flags · SQLite filtering · semantic ranking
100% local, your code never leaves your machine"]

    style A fill:#4a90d9,stroke:#357abd,color:#fff
    style B fill:#50b86c,stroke:#3d9956,color:#fff
    style C fill:#50b86c,stroke:#3d9956,color:#fff
    style D fill:#e8913a,stroke:#d07a2e,color:#fff
    style E fill:#e8913a,stroke:#d07a2e,color:#fff
    style F fill:#9b59b6,stroke:#8445a0,color:#fff
    style B1 fill:none,stroke:#888,stroke-dasharray:5 5,color:#888
    style C1 fill:none,stroke:#888,stroke-dasharray:5 5,color:#888
    style D1 fill:none,stroke:#888,stroke-dasharray:5 5,color:#888
    style E1 fill:none,stroke:#888,stroke-dasharray:5 5,color:#888
    style F1 fill:none,stroke:#888,stroke-dasharray:5 5,color:#888

What the model sees. Each code unit is converted to structured text before embedding:

# Function: fetch_with_retry
# Signature: def fetch_with_retry(url: str, max_retries: int = 3) -> Response
# Description: Fetches data from a URL with retry logic.
# Parameters: url, max_retries
# Returns: Response
# Calls: range, client.get
# Variables: i, e
# Uses: client, RequestError
# File: src/utils/http_client.py

def fetch_with_retry(url: str, max_retries: int = 3) -> Response:
    """Fetches data from a URL with retry logic."""
    for i in range(max_retries):
        try:
            return client.get(url)
        except RequestError as e:
            if i == max_retries - 1:
                raise e

This structured input gives the model richer signal than raw code alone.

More: install variants, performance tuning, all flags and options → colgrep/README.md

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Why multi-vector?

Standard vector search collapses an entire document into one embedding. That's a lossy summary. Fine for short text, bad for code where a single function has a name, parameters, a docstring, control flow, and dependencies.

Multi-vector keeps ~300 embeddings of dimension 128 per document instead of one. At query time, each query token finds its best match across all document tokens (MaxSim). More storage upfront. That's what NextPlaid solves with quantization and memory-mapped indexing.

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NextPlaid

A local-first multi-vector database with a REST API. It's what powers ColGREP under the hood, but it's a general-purpose engine you can use for any retrieval workload.