Refact AI is an open-source autonomous AI coding agent that runs entirely within your IDE, using your own LLM API keys or local model runtimes to handle code completion, multi-step agentic task execution, and codebase-aware chat. It targets individual developers, engineering teams, and enterprises that want AI coding assistance without routing source code through a managed third-party service.
How It Works
Refact AI operates on a local-first architecture. After installing the plugin for VS Code, JetBrains, Visual Studio, Neovim, or Sublime Text, you connect it to a BYOK provider such as OpenAI, Anthropic, or Google Gemini, or to a local runtime like Ollama, LM Studio, or vLLM. The agent builds project-wide context by combining AST-based code analysis with a vector database for retrieval-augmented generation, giving it accurate repository understanding rather than relying solely on a generic prompt window. All processing flows through your chosen provider; no code is handled by Refact’s own servers.
Key Features
- Autonomous agentic execution: Describe a task in natural language and the agent plans and executes multi-step development work—writing code, running tests, managing git operations, and iterating on results with transparent step-by-step reasoning and code diffs.
- Real-time code completion: Context-aware inline suggestions powered by Qwen2.5-Coder with RAG-backed retrieval across 25+ programming languages, including specialized generators for Python, Java, and image-to-code workflows.
- Codebase-aware chat: In-IDE conversation that understands repository structure, open files, connected documentation, and database schemas rather than relying solely on the model’s training data.
- BYOK model flexibility: Compatible with frontier models including Claude 4, GPT-4o, and Gemini 2.5 Pro, as well as open-weight models through local runtimes, so teams can choose models based on cost, capability, or data-privacy requirements.
- Tool and service integrations: Native connections to GitHub and GitLab for PR management, PostgreSQL and MySQL for schema-aware database access, Docker, CI/CD pipelines, Chrome for browser-driven testing, and support for Model Context Protocol (MCP) tooling.
Deployment
Refact AI is fully open source and self-hosted. The local engine runs on your machine alongside the IDE plugin and communicates with your configured LLM provider. For teams, the engine can be deployed on-premise across shared infrastructure with support for fine-tuning custom models on internal codebases and setting team-specific system prompts, making it viable for organizations with strict data-residency requirements. Note: Refact’s hosted cloud service is being discontinued; the project has transitioned to a fully local-first architecture.
Use Cases
- End-to-end feature implementation from a written description, including writing and running tests and committing changes via git
- Codebase-wide refactoring and code review with full repository and documentation context
- Generating schema-aware code and queries using live PostgreSQL or MySQL database connections
- Multi-step automated workflows that include browser-based testing via Chrome
- Privacy-sensitive teams requiring AI coding assistance with no source code leaving internal infrastructure
