orangu
orangu is a terminal-based, locally-hosted AI coding environment - and a complete, self-contained AI coding stack, not just a client. It ships the coding environment, the model coordinator, and a native GGUF inference server, written end to end in Rust , so every line of your code stays on your own machine.
With zero telemetry and no cloud uploads, orangu is built for developers in regulated or air-gapped environments - and eliminates the per-token costs of cloud-based assistants by running your own self-hosted models. After orangu-server and your models are downloaded, no Internet connection is required.
It is named after the Orangutan - the smartest ape.
A complete stack from one vendor
Most local AI setups are a patchwork: one tool for the editor, a separate engine for inference, and glue to manage which model is loaded. orangu is the whole stack in one project - three cooperating programs, one language, one license, one installer:
orangu- the workspace-aware coding environment you drive: the terminal interface, local and Git/forge tools,/reviewand/auto_review, the knowledge graph, semantic/search, and the context-compression engineorangu-coordinator- an optional companion HTTP proxy that starts and stops the server on demand and swaps to whichever model each request needs, so a single-GPU machine can use a different model per role without ever running more than one serverorangu-server- the inference engine itself: GGUF loading, tokenization, the transformer forward pass, sampling, and request scheduling implemented directly in Rust, with no llama.cpp, no ggml, and no Python
Every layer speaks the OpenAI-compatible API, so the pieces stay cleanly separated - yet they build, ship, install, and run as one.
Key Features
- Complete privacy - Your code stays on your machine; zero telemetry, no cloud uploads
- Built-in code review - Interactive (
/review) and automated LLM-driven (/auto_review) review with category-based findings and confidence scoring to filter out false positives - Native inference server -
orangu-serverruns GGUF models on CPU or GPU (Vulkan, CUDA, ROCm, OpenCL) with continuous batching, prefix caching, and an OpenAI-compatible API, and doubles as the machine’s model inventory - Git-centric workflow - Full Git plus GitHub and GitLab support, driven straight from the prompt, with natural-language aliases for nearly every command
- Workspace-aware tools - Read, edit, list, search, and fetch files and run shell commands, with project and git context
- Builds your project -
/builddetects the toolchain (Cargo, CMake, Autotools, Meson, Maven, Python, Go, plainmake) and runs format, lint, build, and test as one reported pipeline - Knowledge graph & visualization - Tree-sitter powered dependency graph of every function, class, and call, with an interactive HTML visualization (
/graph) - Semantic code search - Find code by meaning, not just text, using offline embeddings (
/search) - Code duplication report - Detect duplicate code across 20+ languages using AST similarity scoring (
/duplicates) - Agent skills & memory - Discovers reusable skills and merges persistent
AGENTS.mdinstructions and memory across sessions - Multi-workspace support - Open multiple projects as tabs in a single instance
- Scriptable and schedulable - Run a single prompt or command and exit (
-p), and let the built-in cron-style scheduler run jobs unattended - Single native binary - Written in Rust for fast startup with no runtime dependencies
- Offline documentation - Complete embedded manual with full-text search
- Advanced context compression - AST-aware file downsampling, intelligent Git diffs, and secret redaction to protect the LLM’s context window
Code review
Code review is orangu’s standout feature, and it runs entirely on your own hardware. Both reviewers work on the changes on your current branch - committed work plus what is still in the working tree - measured against the merge base with the default branch.
/review- a full-screen, two-pane view with the file checklist on the right and the selected file’s diff on the left. Approve or reject each file, comment on any diff line under a chosen category (Overall, Code, Security, Memory, Performance, Test Suite, Documentation), ask the model about the selected file on demand, and open any file in your$EDITORwithout leaving the view/auto_review- the model reviews the whole change and each file on its own, sorts findings into the same seven categories, and ends with anorangu approves/rejects this patchverdict. Per-file depth cycles Normal → Deep → Ignore, and a rigorous rubric combined with confidence scoring (0-100) filters out false positives and pedantic nitpicks, so only high-confidence findings are flagged- Share the result -
/export reviewwrites a PDF to the workspace root, and/comment 42 with auto reviewposts the report onto a GitHub or GitLab issue or pull request. Noghorglabneeded
Use Cases
- Privacy-first development - Your code never leaves your workspace
- Air-gapped and regulated environments - A tightly integrated review workflow with no code sent to the cloud
- Cost control - No per-token cloud fees; run the models you already host
- One supplier for the whole stack - Environment, coordinator, and inference engine from a single project, with no integration to own yourself
- Repository-aware coding - Tools understand your project structure and git history
- Codebase exploration - Navigate unfamiliar code with the knowledge graph and semantic search
- Offline development - No internet connection required after initial setup
Installation
The one-liner installer puts the whole stack - orangu, orangu-coordinator, and orangu-server - on the machine in a single step, on Linux, macOS, and Windows:
curl -fsSL https://mnemosyne-systems.github.io/orangu/install.sh | sh