Refact AI

Refact AI is an open-source, local-first AI coding assistant and autonomous software engineering agent designed to help developers write, understand, debug, refactor, review, and maintain software. It works directly inside development environments and can connect to code repositories, databases, browsers, Docker, command-line tools, and other development infrastructure.

Refact AI is developed by Small Magellanic Cloud AI Ltd. and is positioned around three principles: personalization, open-source development, and self-hosting. Developers can use Refact with different large language models, connect their own API keys, or run supported models locally. For organizations with strict security or data-residency requirements, Refact also supports on-premise deployment.

A central feature is the Refact AI Agent, which is designed to handle software-engineering tasks from start to finish. Rather than simply generating a code snippet, the agent can understand a request, inspect a repository, plan a solution, make changes, run relevant commands, test the result, and iterate. Refact describes the agent as being able to work with the developer’s existing codebase and development stack while allowing the developer to preview and control its actions.

Autonomous coding agent

Refact AI Agent is intended for multi-step engineering work. It can search and analyze repositories, reason through coding tasks, make changes, and interact with connected development tools. Its integrations include GitHub and GitLab for version control, PostgreSQL and MySQL for databases, Docker for container management, browser tooling, shell commands, and MCP servers.

This makes Refact more than a conventional autocomplete tool. Developers can delegate larger tasks such as debugging, implementing changes across multiple files, investigating an unfamiliar codebase, or modifying an existing application.

In-IDE AI chat

Refact includes an integrated AI chat designed specifically for programming. Developers can ask questions about their current code, generate new code, explain unfamiliar sections, refactor existing implementations, and receive development assistance without leaving their IDE.

The chat includes different operating modes, including Quick, Explore, and Agent modes. Refact can use the context of the current project and retrieve information from the workspace when appropriate.

AI code completion

Refact provides real-time code completion as developers type. Its current website highlights Qwen2.5-Coder-powered completion combined with retrieval-augmented generation (RAG) to provide suggestions based on project-specific context.

The platform supports more than 25 programming languages, with examples including Java, Python, JavaScript, Rust, PHP, C++, TypeScript, HTML, React, Ruby, SQL, C, YAML, and CSS3. Its documentation also identifies additional languages supported by its models.

Code generation and refactoring

Refact can generate, optimize, explain, and refactor code. Its AI coding tools include a general AI Code Generator as well as dedicated Python and Java code-generation tools.

The platform can also help developers identify and fix bugs, improve code readability, understand complex implementations, and modify existing code according to natural-language instructions.

AI code review

Refact provides an AI Code Review capability that analyzes source code and identifies potential improvements and fixes. This can provide an additional automated review layer before developers merge or deploy changes.

Human review remains important, particularly for production software, because AI-generated changes can still contain logical, security, compatibility, or architectural problems.

Codebase intelligence and RAG

Refact uses code-aware retrieval to understand project context. Its code-aware vector database can index project information so that the AI has access to relevant code and context rather than relying exclusively on the contents of a single open file.

This approach is useful for larger projects where developers need assistance that takes relationships between files and components into account.

Image to Code

Refact also provides an Image to Code tool that converts screenshots or other images of interfaces into HTML and CSS code. Developers can upload an image, provide requirements, select a model, and generate code based on the visual reference.

Multiple AI models and BYOK

Refact is model-flexible rather than tied to a single proprietary model. Its website currently lists models and providers including Claude, GPT-4o, Gemini, Grok, OpenAI, DeepSeek, and others.

The platform also supports BYOK (Bring Your Own Key), allowing developers to connect their own model-provider credentials. This can be particularly useful for developers and organizations that already have access to preferred AI models or want more control over model selection and costs.

MCP support

Refact supports the Model Context Protocol (MCP), allowing its agent to connect to local or remote MCP servers. This expands the range of external tools and information that the agent can access.

MCP connections can be configured with environment variables and command settings, while confirmation and denial rules can be used to control potentially sensitive operations.

Developer tool integrations

Refact is designed to operate within an existing development workflow. Its documented agent integrations include GitHub, GitLab, Docker, PostgreSQL, MySQL, Chrome, command-line tools, shell commands, and Python debugging tools.

The GitHub integration can interact with repositories and perform operations such as creating issues and managing pull requests through GitHub CLI. PostgreSQL integration allows the agent to query and inspect databases and, where permitted, make database changes. Docker integration allows it to inspect and manage container environments.

IDE support

Refact can be used inside popular development environments. Its current website lists VS Code, JetBrains IDEs, Visual Studio, Neovim, and Sublime Text, with JetBrains covering environments such as PyCharm, WebStorm, GoLand, IntelliJ IDEA, and CLion.

Self-hosting and privacy

Privacy and deployment control are major parts of Refact’s positioning. Developers can run Refact locally or deploy it on their own infrastructure. Enterprise deployments can be installed on-premise or through supported cloud infrastructure such as AWS.

Refact states that on-premise deployment keeps source code under the organization’s control. Its documentation also describes self-hosting, model customization, and enterprise deployment options.

Refact’s privacy documentation states that it does not collect data on the server side and describes encryption, access controls, role-based access controls, and multifactor authentication as part of its security practices.

Fine-tuning

Enterprise and self-hosted deployments can be customized for an organization’s development environment. Refact documentation states that fine-tuning on a company’s codebase is supported in its self-hosted and Enterprise offerings.

This can allow organizations to adapt AI coding assistance to their internal coding standards, architecture, and development practices.

Current product and pricing transition

Refact’s pricing situation has changed significantly. In April 2026, Refact announced that Refact Cloud was being retired and that the project would continue as an open-source, local-first, BYOK, community-maintained product.

The announcement states that hosted accounts, managed inference, cloud credits, subscriptions, cloud billing, team/workspace cloud features, and Refact Cloud storage are being retired. It also states that the local Refact engine, IDE plugins, BYOK providers, local model runtimes, and integrations such as GitHub/GitLab, databases, Docker, MCP, browser, shell, and command-line tools continue.

Because of this transition, the older $10/month Pro cloud subscription should not be presented as the current pricing model. The Refact website still contains older Free and Pro pricing references, but the company’s 2026 announcement supersedes that cloud subscription model.

Review

Refact AI stands out for developers who want an AI coding agent without being locked into a conventional hosted AI coding service. Its combination of open-source software, local execution, BYOK model support, self-hosting, MCP connectivity, and autonomous coding capabilities gives developers substantial control over how AI is incorporated into their development workflow.

The autonomous agent is the platform’s strongest feature. Refact can move beyond autocomplete and basic chat to investigate repositories, make multi-file changes, interact with development tools, and work through engineering tasks. Its GitHub, GitLab, database, Docker, browser, and MCP integrations make it capable of operating within a broader software-development environment rather than only generating isolated snippets.

Its open-source and self-hosted architecture is another major advantage, particularly for developers and organizations that prioritize privacy and control. The ability to use external providers through BYOK or local models also gives users more flexibility than a tool tied exclusively to one model provider.

The main drawback is that Refact requires more technical involvement than fully managed AI coding platforms. Self-hosting can require infrastructure and model-management knowledge, and users who rely on their own API keys are responsible for the associated model-provider costs. The retirement of Refact Cloud also means that users should not treat older cloud subscription information as current.

Refact AI is therefore best suited to developers, engineering teams, open-source contributors, DevOps professionals, and organizations that want customizable and privacy-conscious AI-assisted software development.