Refact – Open-Source AI Agent, Code Generator & Chat for JavaScript, Python, TypeScript, Java, PHP, Go, and more.

Provides real-time code suggestions within the VS Code editor using a locally-deployed Qwen2.5-Coder-1.5B model combined with Retrieval-Augmented Generation (RAG) to fetch project-specific code snippets. The system analyzes the current file context, retrieves semantically similar patterns from the codebase, and generates completions that align with existing code style and architecture, reducing latency by performing local inference rather than cloud round-trips.

Provides an integrated chat sidebar within VS Code that allows developers to ask questions and request code changes without leaving the editor. The system supports @-command syntax (@file, @web, @definition, @references, @tree) to explicitly attach context sources, enabling precise control over what information the AI model receives. This architecture avoids context pollution by letting users selectively include relevant code snippets, definitions, or external information rather than sending entire projects.

Provides @definition and @references commands that enable developers to attach symbol definitions and all usage locations to chat messages. The @definition command retrieves the definition of a symbol (function, class, variable) at the cursor position, while @references finds all locations where that symbol is used. This allows developers to provide the AI with complete context about how a symbol is defined and used across the codebase without manually copying code snippets.

Provides a @web command that allows developers to attach web pages, documentation, or external resources to chat messages by URL. The system fetches and parses the web content, extracting relevant information and including it in the AI's context. This enables developers to reference external APIs, documentation, design specifications, or standards without manually copying content, and allows the AI to generate code that conforms to external specifications.

Offers a freemium pricing model with unlimited access to basic features (inline code completion, chat interface, context attachment) at no cost, while optional paid features or higher usage tiers may require subscription. The free tier includes the local Qwen2.5-Coder-1.5B model for completions and basic chat access, with paid tiers likely offering access to more powerful cloud models (Claude, GPT-4, Gemini) and higher rate limits. This enables developers to use Refact without financial commitment while providing monetization for advanced features.

Claims to rank #1 on the SWE-bench verified leaderboard for free open-source AI agents, a standardized benchmark measuring autonomous software engineering task completion. The leaderboard evaluates agents on their ability to autonomously resolve GitHub issues, implement features, and fix bugs in real-world repositories. This ranking serves as a third-party validation of the agent's capabilities, though the specific evaluation methodology, test set, and performance metrics are not detailed in available documentation.

Abstracts multiple LLM providers (Claude 3.7/4 Sonnet, GPT-4.1/4o, o3-mini, Gemini 2.5 Pro) behind a unified interface, allowing users to select different models for different tasks based on complexity and cost. The system routes requests to the appropriate provider based on user configuration, supporting both cloud-hosted models and on-premise deployments. Users can bring their own API keys (BYOK) for any supported provider, maintaining control over billing and data routing.

Enables the AI agent to autonomously execute multi-step software engineering tasks by integrating with external tools including GitHub/GitLab (version control), PostgreSQL/MySQL (databases), Docker (containerization), Python debugger (pdb), shell commands, and MCP (Model Context Protocol). The system decomposes high-level user requests into executable subtasks, invokes appropriate tools, interprets results, and iteratively refines execution until task completion. This architecture allows the agent to modify code, run tests, commit changes, and deploy without manual intervention.

Indexes the entire project codebase into a vector database, enabling semantic search and retrieval of relevant code snippets based on natural language queries or code context. When a user asks a question or requests a change, the system retrieves the most semantically similar code patterns, definitions, and implementations from the index, providing the LLM with precise, project-specific context. This approach scales to large codebases by avoiding full-context transmission and instead fetching only the most relevant snippets.

Generates and refactors code across 25+ programming languages (Python, JavaScript, TypeScript, Java, Rust, Go, PHP, C++, C#, etc.) while automatically adapting to the project's coding style, naming conventions, and architectural patterns. The system analyzes existing code to infer style preferences (indentation, naming, error handling patterns) and applies them to generated code, ensuring consistency without explicit configuration. Supports both single-file refactorings and cross-file changes that maintain referential integrity.

Accepts images (screenshots, diagrams, architecture drawings) as context input in the chat interface, enabling developers to reference visual documentation, UI mockups, or system diagrams when requesting code changes. The system analyzes images using vision capabilities to extract relevant information and incorporates it into code generation or explanation tasks. This enables developers to describe requirements visually rather than in text, reducing ambiguity in complex architectural or UI-related tasks.

Supports self-hosted deployment of Refact.ai infrastructure, allowing organizations to run the entire system (indexing, inference, chat backend) within their own infrastructure without transmitting code to external servers. This enables compliance with data sovereignty requirements, intellectual property protection, and regulatory constraints (HIPAA, GDPR, etc.). Users can configure which LLM providers to use (local models or private API endpoints) and maintain complete control over data retention and processing.

Allows users to define custom system prompts that shape the AI's behavior, tone, and reasoning approach without modifying the underlying model. Users can specify preferences for code style, documentation requirements, error handling philosophy, or domain-specific conventions. The custom prompt is prepended to all requests, influencing how the AI interprets tasks and generates responses. This enables teams to enforce organizational standards and coding philosophies at the AI level.

Provides a @tree command that displays the project's directory and file structure within the chat interface, enabling developers to explore and understand codebase organization without manually navigating the file system. The system generates a hierarchical view of the project, helping developers understand module organization, identify relevant files for tasks, and provide context to the AI about project structure. This is particularly useful for onboarding to unfamiliar projects or understanding large codebases.