Tabby is an open-source, self-hosted AI coding assistant designed as a privacy-focused alternative to cloud-based tools like GitHub Copilot. It enables developers to leverage large language models for code completion, chat-based assistance, and contextual coding help entirely on their own infrastructure. Built in Rust and optimized for consumer-grade GPUs, Tabby eliminates the need for external API calls or data exposure to third-party clouds. This makes it ideal for enterprises with strict compliance requirements, teams working in air-gapped environments, or developers who prioritize data sovereignty. With integrations for VSCode, Vim, IntelliJ, and more, Tabby brings AI-powered development capabilities directly into your preferred IDE while maintaining full control over models and data.
What You Get
- Self-hosted AI code completion - Provides real-time code suggestions based on local LLMs like StarCoder, CodeLlama, CodeGemma, and CodeQwen without sending code to external servers.
- Chat-based assistant with context awareness - Offers a side-panel chat interface that understands your codebase, recent edits, and repository context to answer questions and suggest changes.
- Multi-model support - Supports multiple LLMs for both code completion (e.g., StarCoder-1B) and chat (e.g., Qwen2-1.5B-Instruct), with configurable model switching via CLI or UI.
- Integrated IDE extensions - Native plugins for VSCode, Vim, and JetBrains IDEs that enable inline completions, chat panels, and commit message generation directly in your editor.
- Repository context integration - Uses RAG (Retrieval-Augmented Generation) to pull relevant code snippets, declarations, and recent modifications from your local repositories for more accurate suggestions.
- Admin UI and team management - Includes a full-featured admin dashboard for user management, usage analytics, storage monitoring, and activity tracking across teams.
- OpenAPI interface - Exposes a REST API to integrate Tabby with Cloud IDEs, custom tooling, or CI/CD pipelines for automated code assistance.
- GitLab and GitHub integration - Supports SSO, repository context extraction from GitLab merge requests, and seamless authentication with self-hosted code platforms.
- Docker-based deployment - One-command containerized setup with volume mounting for persistent model and configuration storage.
- Apple M1/M2 Metal support - Optimized inference on Apple Silicon GPUs for local development without requiring NVIDIA hardware.
Common Use Cases
- Building a secure enterprise codebase - Engineering teams in finance or healthcare use Tabby to provide AI-assisted coding without violating data residency laws by keeping all model inference on-premises.
- Creating a developer experience for air-gapped environments - Organizations with no internet access deploy Tabby locally to give developers AI-powered code suggestions while maintaining network isolation.
- Problem: Inconsistent AI assistance across teams → Solution: Tabby - Companies using multiple cloud-based tools face varying quality and compliance risks; Tabby standardizes AI assistance with a single, auditable, self-hosted backend.
- Team workflow: DevOps managing microservices with internal documentation - Teams use Tabby’s Answer Engine to index internal docs, code comments, and repo history so developers can ask natural language questions about legacy systems directly in their IDE.
Under The Hood
Tabby is an open-source AI coding assistant designed to enhance developer productivity by providing intelligent code completions and chat-based assistance within various development environments. It leverages a modular architecture that supports integration across multiple platforms and tools, emphasizing extensibility and performance.
Architecture
Tabby adopts a layered and modular architecture that clearly separates backend services from frontend UIs, enabling scalable and maintainable development.
- The system is structured with distinct modules for AI inference, context analysis, and user interface components
- Strong emphasis on separation of concerns with clear boundaries between core logic and external integrations
- Utilizes a component-based design that supports cross-platform extensions such as Eclipse plugins
Tech Stack
The project is built using a diverse tech stack that balances performance, flexibility, and developer familiarity.
- Rust powers the core backend services for high performance and memory safety
- React and TypeScript are used for building responsive web-based UIs with type safety
- Python is integrated for machine learning tasks and model utilities, including Hugging Face and torch dependencies
- The system integrates with Docker, CI/CD pipelines, and Makefiles for streamlined development and deployment
Code Quality
Tabby maintains a strong focus on code quality through consistent practices and comprehensive testing.
- The codebase includes extensive test coverage that ensures reliability across various components and use cases
- Error handling is implemented with standard try/catch patterns, particularly in client-side integrations
- Consistent naming conventions and modular structure support long-term maintainability
- Linting and type safety practices are in place to enforce code standards and reduce bugs
What Makes It Unique
Tabby distinguishes itself through its flexible architecture and deep integration capabilities across development tools.
- A modular design enables consistent AI interaction patterns across different IDEs and platforms
- Custom inline completion rendering engine adapts to various editor environments and display requirements
- Deep integration with Git providers allows context-aware code suggestions based on repository state
- Extensive use of language-specific parsers and tree-sitter for accurate code understanding and generation