Refact AI

Generates code completions at line, function, and class scope by leveraging Qwen2.5-Coder model fine-tuned on user's codebase via RAG-based context retrieval. Analyzes repository symbols and project-specific patterns to produce contextually relevant suggestions that respect existing code style and architecture. Executes inference locally on self-hosted infrastructure, eliminating cloud round-trips and enabling sub-100ms latency for interactive IDE integration.

Decomposes high-level coding tasks (e.g., 'build a user authentication module') into executable steps, executes each step using integrated tools (code generation, database queries, API calls), and presents results for user review before proceeding. Uses step-by-step reasoning (chain-of-thought style execution, mechanism unspecified) to plan task decomposition and tool selection. Maintains task state across multiple iterations, allowing users to redirect or refine agent behavior mid-execution without restarting from scratch.

Analyzes code to identify bugs, security vulnerabilities, and code quality issues using static analysis and semantic understanding. Generates fixes with severity assessment (critical, high, medium, low) and confidence scores. Supports multiple bug categories (null pointer dereferences, SQL injection, race conditions, logic errors, performance issues, etc.). Integrates with code generation to propose fixes inline with reasoning about root cause and impact. Executes analysis locally without sending code to external services.

Generates code in multiple programming languages (Python, JavaScript, TypeScript, Java, C++, Go, Rust, etc.) with language-specific idioms, conventions, and best practices. Understands language-specific patterns (async/await in JavaScript, type hints in Python, generics in Java, etc.) and generates code that follows community standards and style guides. Supports generating code that integrates with language-specific ecosystems (npm packages, Python libraries, Java frameworks, etc.). Fine-tuning on language-specific codebases enables model to learn project conventions and generate consistent code.

Enables deployment of Refact AI entirely on user's infrastructure (on-premise or private cloud) with no data transmission to external services. All code, model inference, and data processing execute locally, ensuring proprietary code and sensitive data never leave the organization. Supports deployment on various infrastructure (bare metal, Docker, Kubernetes, cloud VMs) with flexible resource allocation. Provides full control over model versions, fine-tuning, and system configuration without dependency on external services or SaaS providers.

Refact AI is open-source, enabling community contributions, security audits, and transparency into implementation details. Users can review source code, understand how the system works, and contribute improvements. Open-source model (Qwen2.5-Coder) enables fine-tuning and customization without licensing restrictions. Community-driven development allows users to influence product direction and contribute features aligned with their needs.

Provides conversational AI interface within IDE (VS Code, JetBrains, Neovim) that understands selected code, current file context, and repository structure. Supports natural language queries about code ('explain this function', 'find bugs in this module'), generates inline code edits with diff preview, and executes refactoring suggestions with user confirmation. Chat context includes repository-wide symbol information retrieved via RAG, enabling questions that reference project-specific abstractions and patterns.

Analyzes code structure using abstract syntax tree (AST) parsing to identify refactoring opportunities (dead code, naming inconsistencies, architectural violations, performance anti-patterns). Generates refactoring suggestions with precise code transformations that preserve semantics and maintain compilation/runtime correctness. Supports language-specific refactoring rules (e.g., Python idioms, JavaScript async patterns) and integrates with IDE to apply changes with full diff preview and rollback capability.

Indexes all code symbols (functions, classes, variables, types, imports) across repository using AST parsing and semantic analysis. Stores indexed symbols in vector embeddings for fast semantic search. When generating code or answering questions, retrieves relevant symbols and code context via RAG to ground responses in actual codebase structure. Enables agent to understand project architecture, naming conventions, and design patterns without requiring full codebase in model context window.

Connects to GitHub repositories to analyze code, identify issues, and generate pull requests with fixes or improvements. Supports cloning repositories, analyzing code quality, detecting bugs, and creating PRs with detailed commit messages and change descriptions. Integrates with GitHub API for authentication, branch management, and PR workflow. Enables agent to work directly with GitHub-hosted code without manual repository setup.

Analyzes database schemas (MySQL, PostgreSQL, etc.) to understand table structure, relationships, and constraints. Generates database migrations and schema modifications based on natural language requirements (e.g., 'add user authentication table with email and password fields'). Supports connecting to live databases for schema inspection and can generate migration scripts compatible with popular migration tools (Alembic, Flyway, etc.). Integrates with agent to enable database-aware code generation and schema-driven development.

Enables agent to control Chrome browser for web browsing, form filling, and API interaction with web-based systems. Supports logging into web interfaces (WordPress admin, SaaS platforms, etc.) and performing automated actions (clicking buttons, filling forms, extracting data). Integrates with agent planning to enable multi-step workflows that combine code generation with web automation (e.g., 'update WordPress theme and deploy code changes'). Executes browser automation on self-hosted infrastructure, maintaining privacy for credentials and sensitive data.

Integrates with CI/CD systems (GitHub Actions, GitLab CI, Jenkins, etc.) to trigger builds, run tests, and orchestrate deployments. Enables agent to deploy generated code changes directly to production or staging environments as part of multi-step task execution. Supports monitoring deployment status, handling rollbacks, and coordinating with existing deployment pipelines. Integrates with agent planning to enable end-to-end workflows from code generation to production deployment.

Enables fine-tuning of Qwen2.5-Coder model on user's proprietary codebase to improve code generation quality for project-specific patterns, naming conventions, and architectural styles. Fine-tuning process analyzes repository code to extract training examples and adapts model weights to project context. Supports incremental fine-tuning as codebase evolves, allowing model to learn from new code patterns without full retraining. Fine-tuning executes on self-hosted infrastructure, keeping proprietary code private and enabling customization without external services.