.svg)
Refact.ai is an open-source, autonomous AI agent built for software development. It operates directly inside a developer's IDE, handling tasks end-to-end from planning and writing to debugging and deployment. The agent analyzes the full project repository, integrates with developer tools, and delivers accurate, context-aware code generation tailored to each codebase and stack.
The platform combines an autonomous AI agent, an in-IDE chat interface, and real-time AI autocompletion into a single environment. Code completions are powered by the Qwen2.5-Coder model with Retrieval-Augmented Generation, allowing the system to retrieve project-specific context for every suggestion. The in-IDE chat supports natural language queries, code edits, debugging, and generation with context windows up to 64k tokens on Pro plans.
Refact.ai connects to external tools including GitHub, GitLab, Docker, PostgreSQL, MySQL, and CI/CD pipelines, enabling the agent to interact with the development environment autonomously. It supports MCP server integration, giving the agent access to over 1,900 tools and data sources. Developers can also bring their own API keys for OpenAI, Anthropic, Gemini, Grok, DeepSeek, Mistral, and other providers.
The platform supports on-premise and self-hosted deployment for teams that require complete code privacy. Enterprise features include LLM fine-tuning on company codebases, shared team memory, and dedicated onboarding support. Refact.ai is available as a plugin for VS Code, JetBrains, Visual Studio, Neovim, Sublime Text, PyCharm, WebStorm, GoLand, IntelliJ, and CLion.
- Autonomously completing end-to-end development tasks from planning to deployment inside the IDE
- Generating accurate, context-aware code completions using full repository knowledge and RAG
- Debugging and refactoring existing code through in-IDE natural language chat interactions
- Integrating AI agent with GitHub, Docker, PostgreSQL, and CI/CD pipelines for automated workflows
- Converting design screenshots or images into functional HTML and CSS code
- Reviewing code automatically across multiple programming languages with AI-suggested fixes
- Fine-tuning LLMs on a company codebase to match specific coding style and technology stack
- Deploying a self-hosted AI coding agent on-premise to maintain complete code privacy and data ownership
- Using bring-your-own-key setup to connect external LLM providers without consuming platform credits
- Connecting AI agent to MCP servers for access to Jira, Figma, Supabase, and thousands of external tools
- Generating Python, Java, or multi-language code through dedicated online tools without IDE setup
- Building shared team knowledge bases that improve AI accuracy and collaboration across engineering teams
