** orangu**는 로컬, workspace-aware, 터미널의 도구 중심 코딩 환경입니다.

100% 개인 및 오프라인: 다운로드 후 orangu-server 및 모델, 인터넷 연결이 필요하지 않습니다.

A 완료, 자체 포함 AI 코딩 스택: 그것은 모든 3 층을 발송, Rust에서 끝을 쓴:

1. orangu - 터미널 코딩 환경, 검토 워크스테이션, 도구 조정기.
2. orangu-coordinator - 주문형 모델 관리자 및 서버 프록시.
3. orangu-server - 네이티브, 순수-Rust GGUF inference 엔진(no llama.cpp, ggml, Python).

각 층은 OpenAI 호환 API를 통해 통신합니다.

열쇠 힘

-Pure-Rust End-to-End Stack - CLI에서 변압기 전달 패스로 단일 툴체인. 빠른 시작, 작은 발자국 및 Zero Python/C++ 실행 시간 두통.

• **100% 로컬 & 개인 ** - Zero telemetry. 코드, 디프 및 프롬프트는 하드웨어를 결코 떠나지 않습니다.
• Dual-Mode Code Review - 내장 대화 형 2 팬 디프 리뷰 (/review]) 및 자동 LLM 구동 루퍼 리뷰 신뢰 득점 ([/auto_review]), /create_patch] 작업 코드로 찾기.

orangu

orangu is a local, workspace-aware, tool-driven coding environment for your terminal.

100% private and offline: after downloading orangu-server and your models, no internet connection is required.

A complete, self-contained AI coding stack: It ships all three layers, written end-to-end in Rust:

1. orangu - Terminal coding environment, review workstation, and tool coordinator.
2. orangu-coordinator - On-demand model manager and server proxy.
3. orangu-server - Native, pure-Rust GGUF inference engine (no llama.cpp, no ggml, no Python).

Every layer communicates over OpenAI-compatible APIs.

Key Strengths

• Pure-Rust End-to-End Stack - Single toolchain from CLI to transformer forward pass. Fast startup, small footprint, and zero Python/C++ runtime headaches.
• 100% Local & Private - Zero telemetry. Your code, diffs, and prompts never leave your hardware.
• Dual-Mode Code Review - Built-in interactive two-pane diff review (/review) and automated LLM-driven rubric review with confidence scoring (/auto_review), turning findings into working code with /create_patch.
• Model Context Protocol (MCP) - Seamless integration with Streamable HTTP MCP servers for extensible tooling and system integration.
• Text, Image, Video & Speech Generation - Native serving of chat, completion, embedding, text-to-image models (Qwen-Image via /v1/images/generations), text-to-video with sound (LTX-2.5 via /v1/videos), and text-to-speech (Qwen3-TTS via /v1/audio/speech).
• Offline Knowledge Graph & Search - Incremental Tree-sitter symbol graphs (/graph) and semantic code retrieval (/search) powered by local embeddings.
• Built-in Build & Git Automation - Full Git loop directly inside the prompt, plus /build detecting Cargo, CMake, Make, Go, Maven, and Python.

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Install

Linux / macOS (via curl or wget):

curl -fsSL https://mnemosyne-systems.github.io/orangu/install.sh | sh

Windows (PowerShell):

curl -fsSL https://mnemosyne-systems.github.io/orangu/install.cmd -o install.cmd && install.cmd

This installs the full stack (orangu, orangu-coordinator, orangu-server, orangu-bench, and orangu-gguf) into ~/.local/bin (Linux/macOS) or %USERPROFILE%\.local\bin (Windows), and warns if the directory is not in your PATH.

Custom install directory: set INSTALL_DIR before running the script:

# Linux / macOS
curl -fsSL https://mnemosyne-systems.github.io/orangu/install.sh | INSTALL_DIR=/usr/local/bin sh

:: Windows
set "INSTALL_DIR=C:\Tools" && install.cmd

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Shell Completions

Run orangu -s to print completion scripts for bash, zsh, fish, or PowerShell (supported by all binaries: orangu, orangu-server, orangu-coordinator, orangu-bench, and orangu-gguf):

# bash
orangu -s >> ~/.bashrc && source ~/.bashrc

# zsh