loregrep 0.5.0

Fast repository indexing and code analysis tools for AI coding assistants - 6 standardized tools for Claude, GPT, and more

Loregrep

Structural code intelligence for AI coding agents.

Loregrep parses a repository with tree-sitter into a fast in-memory index and exposes it as a small set of tools an agent can call — returning precise, structured JSON (names, signatures, callers, imports, line numbers) instead of raw text matches. It's the context engine an agent calls; it is not an AI itself.

Ask "where is parse_config defined", "what calls it", "what does this file export", or "give me a map of this repo" — and get exact answers, cheaper on tokens than grepping and reading files.

Languages: Rust · Python · TypeScript/TSX  (Go, JavaScript planned)

---

flowchart TB
    A(["🤖  Coding agent"]):::agent

    subgraph IF["Interfaces"]
        direction LR
        S["Claude Code skill"]:::iface
        P["pi extension"]:::iface
        C["exec-tool CLI"]:::iface
    end

    subgraph ENG["loregrep engine"]
        direction LR
        SC["Scanner<br/>gitignore-aware"]:::eng
        AN["Tree-sitter analyzers<br/>Rust · Python · TS / TSX"]:::eng
        IX[("Index · RepoMap<br/>persistent cache")]:::index
        SC --> AN --> IX
    end

    T["6 structural tools"]:::tools
    J(["Structured JSON<br/>names · signatures · lines"]):::out

    A -->|invoke| IF
    IF -->|exec-tool| ENG
    IX --> T
    T --> J
    J -->|precise context| A

    classDef agent fill:#6366f1,stroke:#4338ca,color:#ffffff,font-weight:bold
    classDef iface fill:#8b5cf6,stroke:#6d28d9,color:#ffffff
    classDef eng fill:#0284c7,stroke:#075985,color:#ffffff
    classDef index fill:#f59e0b,stroke:#b45309,color:#1f2937,font-weight:bold
    classDef tools fill:#14b8a6,stroke:#0f766e,color:#ffffff
    classDef out fill:#10b981,stroke:#047857,color:#ffffff,font-weight:bold

Why

A coding agent that greps and reads whole files burns tokens on noise and still misses cross-file structure. Loregrep gives it structured, symbol-level access:

• Precise, not textual — definitions and call sites, with signatures and line numbers, not every string match.
• Token-cheap — compact JSON results instead of file dumps.
• Fast & cached — parse once; a persistent index makes repeated queries instant, and edits auto-invalidate it.
• Agent-native — one command per tool, JSON on stdout; ships as a Claude Code skill and a pi extension.
• Embeddable — a Rust crate and a Python wheel with the same tool API.

Install

cargo install loregrep     # CLI + Rust library
pip install loregrep       # Python bindings

Use it three ways

1. As an agent tool (CLI)

exec-tool runs one analysis tool and prints JSON to stdout (diagnostics go to stderr):

loregrep exec-tool search_functions --params '{"pattern":"auth","limit":20}' --path .

{
  "success": true,
  "data": {
    "count": 1,
    "results": [
      { "name": "authenticate", "file_path": "src/auth.rs",
        "start_line": 42, "is_async": true, "is_public": true,
        "return_type": "Result<Session>", "parameters": [ ... ] }
    ]
  }
}

The index is cached under .loregrep/, so repeated calls are instant; editing a source file invalidates the cache automatically.

2. Inside your coding agent (skill / extension)

• Claude Code — the loregrep skill teaches the agent when and how to call these tools.
• pi — install the loregrep-pi extension: pi install npm:loregrep-pi.

Both wrap the same six tools, so your agent reaches for structural search instead of grep.

3. As a library

Rust:

use loregrep::LoreGrep;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut lg = LoreGrep::builder()
        .with_all_analyzers()   // Rust, Python, TypeScript/TSX
        .build()?;

    lg.scan(".").await?;

    let result = lg.execute_tool(
        "search_functions",
        serde_json::json!({ "pattern": "auth", "limit": 20 }),
    ).await?;

    println!("{}", serde_json::to_string_pretty(&result)?);
    Ok(())
}

Python:

import asyncio, loregrep

async def main():
    lg = loregrep.LoreGrep.polyglot_project(".")   # Rust + Python + TypeScript
    await lg.scan(".")
    result = await lg.execute_tool("search_functions", {"pattern": "auth", "limit": 20})
    print(result)

asyncio.run(main())

Tools

Tool	Purpose	Required params
search_functions	Find functions by name/regex	pattern
search_structs	Find structs/classes/interfaces by name/regex	pattern
find_callers	All call sites of a function	function_name
get_dependencies	A file's imports/exports	file_path
analyze_file	A file's skeleton (functions/structs/imports/calls)	file_path
get_repository_tree	Repository overview / tree	—

Optional: limit, language (rust/python/typescript), include_content, include_file_details, max_depth. Get machine-readable schemas with LoreGrep::get_tool_definitions().

Language support

Language	Functions	Structs/Classes	Imports/Exports	Calls
Rust	✅	✅	✅	✅
Python	✅	✅	✅	✅
TypeScript / TSX	✅	✅	✅	✅
JavaScript, Go	planned

Adding a language is a drop-in contribution: implement one trait in one file — see docs/adding-a-language.md.

How it works

The registry dispatches by language, so analyzers are additive; the scanner is gitignore-aware, and the index persists to disk and re-scans only when files change (see the diagram above). Full design in ARCHITECTURE.md.

Contributing

Contributions — especially new language analyzers — are welcome. See CONTRIBUTING.md and docs/adding-a-language.md. CI runs cargo fmt, cargo test, and the Python binding tests on every PR.

License

Dual-licensed under either MIT or Apache-2.0, at your option.