Refact AI

Provides real-time code completion by analyzing every symbol typed in the editor and using retrieval-augmented generation (RAG) to retrieve project-specific context from the codebase. Powered by Qwen2.5-Coder model running locally or on-premise, it generates line-level, function-level, and class-level completions that respect the existing codebase architecture and naming conventions without sending code to external servers.

Executes complex coding tasks end-to-end through iterative planning and execution loops, where the agent decomposes user requests into sub-tasks, executes them step-by-step with tool calls (GitHub, databases, CI/CD, web automation), and presents results for human review before proceeding. Uses chain-of-thought reasoning to analyze the codebase, determine execution strategy, and adapt based on intermediate results, while maintaining user control through explicit approval checkpoints.

Offers a free tier for individual developers and small teams to start using Refact AI in their favorite IDE, with optional enterprise deployment for organizations requiring on-premise infrastructure, advanced support, and custom integrations. Pricing model details are not specified, but free tier is emphasized as the entry point.

Refact AI is open-source, allowing developers to inspect the codebase, contribute improvements, and customize the agent for their specific needs. Community contributions enable feature development, bug fixes, and integrations without waiting for vendor releases.

Searches and analyzes the entire codebase using RAG to retrieve relevant files, functions, and symbols based on semantic meaning rather than keyword matching. The agent builds an understanding of repository architecture, dependencies, and patterns to inform code generation and refactoring decisions, enabling it to make changes that respect the existing system design.

Orchestrates calls to external tools and APIs including GitHub (for code push/pull/review), database connections (MySQL example provided), CI/CD pipelines, and browser automation (Chrome for WordPress admin tasks). The agent selects appropriate tools based on task requirements, chains tool calls together in sequences, and handles tool responses to inform subsequent actions, all while maintaining execution context across multiple tool invocations.

Provides a chat interface embedded directly in the IDE where users can ask questions, request code edits, debug issues, and generate code without leaving the editor. The chat maintains context of the current file and project, allows users to select code snippets for targeted operations, and displays agent responses with inline code suggestions and diffs that can be accepted or rejected.

Deploys the entire agent and inference stack on-premise or in a self-hosted environment, keeping all code, model weights, and inference computations within the user's infrastructure. Uses Qwen2.5-Coder as the primary completion model and allows selection of alternative LLMs for different tasks, eliminating cloud-based telemetry and data transmission while giving users full control over model versions, resource allocation, and data retention.

Analyzes reported bugs or failing code by examining error messages, stack traces, and codebase context to identify root causes, then generates targeted fixes that address the underlying issue rather than symptoms. The agent searches the codebase for related code, understands the execution flow, and proposes code changes with explanations of why the fix works, demonstrated by examples like fixing WordPress plugins in 30 minutes.

Builds new features end-to-end from natural language descriptions by analyzing the codebase to understand architecture and patterns, designing the feature implementation, generating code that integrates with existing systems, and handling related tasks like UI creation, database schema changes, and API endpoints. Demonstrated by building GUIs from GitHub repository specifications in 14 minutes.

Performs large-scale refactoring tasks (e.g., 80-hour rewrites completed in 30 minutes) by analyzing the codebase to understand patterns and dependencies, planning refactoring strategy, and applying changes consistently across multiple files while preserving functionality and architectural integrity. Uses RAG-based codebase analysis to ensure refactored code maintains consistency with existing patterns.

Generates code that respects project-specific patterns, naming conventions, and architectural styles by analyzing the codebase to extract patterns and applying them to new code generation. Uses RAG to retrieve similar code examples from the project and ensures generated code is stylistically and architecturally consistent with existing code.