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Basic Information

Refact is an open-source AI software development agent designed to autonomously handle end-to-end engineering tasks within real codebases. The repository provides the agent, plugins and self-hosting options so developers can run an AI that deeply understands project code, integrates with existing tools and automates complex multi-step workflows. It targets developer workflows by offering integrated in-IDE chat for VS Code and JetBrains, support for on-premise deployment and Docker images, and the ability to connect external LLMs with bring-your-own-key. The agent is positioned to generate, refactor, explain and debug code, produce unit tests and documentation, and interact with version control and databases. The README emphasizes practical setup paths including pip installation, Docker deployment, plugin configuration and documentation for users who want to run or extend the agent in their own environments.

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3190
App URL
https://github.com/smallcloudai/refact
Github Repository
https://github.com/smallcloudai/refact/blob/main/README.md

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App Details

Features
The README highlights several core capabilities and integrations. It provides unlimited context-aware auto-completion powered by a coder model and Retrieval-Augmented Generation. Integrated in-IDE chat connects the agent to developers inside VS Code and JetBrains. Tool integrations include GitHub and GitLab for version control, PostgreSQL and MySQL for databases, Pdb for debugging, Docker and shell command support. Model options include Claude 4, GPT-4o variants and many external LLMs via BYOK. The project supports over 25 programming languages and common developer tasks such as code generation, refactoring, explanation, debugging, unit-test generation, code review, documentation and docstring generation. Distribution options include pip installation, GPU-oriented install flags and a self-hosted Docker image. Plugins require setting a local inference URL for the self-hosted server.
Use Cases
Refact is helpful to development teams and individual engineers by automating repetitive and multi-step software engineering tasks and by providing contextual assistance inside real projects. It can speed up development by generating code from natural language, offering context-aware auto-completions, refactoring existing code, explaining unfamiliar code sections and producing unit tests and documentation. Self-hosting and Docker support let organizations deploy the agent on-premise for data control. BYOK and broad model support allow teams to use preferred LLMs. Integrations with version control, databases, debuggers and shell commands let the agent operate within existing workflows. Documentation, community resources and IDE plugins make adoption and customization practical for engineering teams seeking to reduce manual effort and improve code quality.

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