{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"refact-ai","slug":"refact-ai","name":"Refact AI","type":"agent","url":"https://refact.ai","page_url":"https://unfragile.ai/refact-ai","categories":["code-editors"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"refact-ai__cap_0","uri":"capability://code.generation.editing.real.time.codebase.aware.code.completion.with.multi.level.scope","name":"real-time codebase-aware code completion with multi-level scope","description":"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.","intents":["Get intelligent code suggestions that understand my project's patterns and conventions","Complete multi-line functions without leaving the editor","Reduce boilerplate typing while maintaining code consistency across the codebase"],"best_for":["Solo developers and small teams prioritizing code privacy and local execution","Organizations with strict data residency requirements (healthcare, finance, government)","Teams building domain-specific codebases where fine-tuning on proprietary code is critical"],"limitations":["Completion quality degrades for unfamiliar language constructs or novel architectural patterns not represented in training data","RAG context window size not documented — may miss relevant symbols in very large monorepos (>1M LOC)","No explicit handling of circular dependencies or forward references in completion ranking","Fine-tuning mechanism not documented — unclear if incremental learning or full retraining required"],"requires":["Self-hosted Refact deployment with Qwen2.5-Coder model loaded","IDE plugin compatible with VS Code, JetBrains, or Neovim","Minimum 8GB RAM for model inference (estimated, not documented)","Repository indexed and accessible to Refact agent for symbol analysis"],"input_types":["partial code (line prefix)","function signature","class definition stub","comment describing intent"],"output_types":["code completion (single line to full function body)","multiple ranked suggestions","confidence score (inferred)"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"refact-ai__cap_1","uri":"capability://planning.reasoning.autonomous.multi.step.task.execution.with.iterative.human.in.the.loop.control","name":"autonomous multi-step task execution with iterative human-in-the-loop control","description":"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.","intents":["Delegate complex multi-day coding tasks and get them completed in minutes with my oversight","Iteratively refine task execution by providing feedback after each step","Maintain control over agent actions while benefiting from autonomous planning and execution"],"best_for":["Development teams building features with well-defined requirements and clear success criteria","Rapid prototyping scenarios where speed matters more than perfect autonomy","Organizations wanting to automate repetitive multi-step workflows (schema migration, API integration, test generation)"],"limitations":["Planning algorithm not documented — unclear if using ReAct, tree search, or simpler chain-of-thought; no specification of branching factor or depth limits","No documented failure recovery — unclear how agent handles dead-end states, contradictory feedback, or tool failures","Task decomposition quality depends on initial prompt clarity; vague requirements may result in incorrect step sequencing","No explicit rollback mechanism documented — if agent modifies database or deploys code, reverting changes requires manual intervention","Context window limitations not specified — very large tasks may exceed model's ability to maintain coherent plan across steps"],"requires":["Self-hosted Refact deployment with agentic capabilities enabled","Access to target systems (GitHub repos, databases, CI/CD pipelines, APIs) from Refact infrastructure","Clear task description with success criteria","User availability for iterative feedback (not fully autonomous)"],"input_types":["natural language task description","code snippets or examples","repository links","database schema or API specifications"],"output_types":["generated code (functions, modules, tests)","database modifications (schema changes, migrations)","API calls and responses","deployment artifacts","step-by-step execution log with reasoning"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"refact-ai__cap_10","uri":"capability://code.generation.editing.bug.detection.and.automated.fix.generation.with.severity.assessment","name":"bug detection and automated fix generation with severity assessment","description":"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.","intents":["Identify bugs and security vulnerabilities in code without manual code review","Get automated fix suggestions with severity assessment and confidence scores","Understand root causes of bugs through AI-generated explanations"],"best_for":["Development teams seeking to automate code quality and security analysis","Organizations with security-sensitive code requiring thorough bug detection","Developers debugging complex issues and needing AI-assisted analysis"],"limitations":["Bug detection accuracy not documented — no specification of false positive rates or detection coverage","Analysis may miss context-dependent bugs (race conditions, timing-dependent issues, distributed system bugs)","Fix generation may not handle complex scenarios requiring architectural changes","Severity assessment may not account for business context or risk tolerance","No documented mechanism for handling false positives or suppressing known issues"],"requires":["Self-hosted Refact deployment with bug detection enabled","Code written in supported language (exact list not documented)","Sufficient context for analysis (full function or class scope recommended)"],"input_types":["source code files","code snippets","error messages or stack traces","test cases demonstrating bugs"],"output_types":["bug reports with severity and confidence scores","root cause analysis","fix suggestions with code diffs","impact assessment"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"refact-ai__cap_11","uri":"capability://code.generation.editing.multi.language.code.generation.with.language.specific.idioms.and.best.practices","name":"multi-language code generation with language-specific idioms and best practices","description":"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.","intents":["Generate code in multiple languages with proper idioms and conventions","Ensure generated code follows language-specific best practices and style guides","Integrate generated code with language-specific ecosystems and frameworks"],"best_for":["Polyglot development teams working across multiple languages","Organizations building multi-language systems (backend in Java, frontend in TypeScript, scripts in Python, etc.)","Developers seeking to learn language idioms through AI-generated examples"],"limitations":["Language support limited to documented set — no support for niche or domain-specific languages","Code generation quality varies by language — some languages may have better training data than others","Language-specific idioms may not be perfectly captured — generated code may not follow all community conventions","No explicit handling of language-specific performance characteristics or optimization patterns"],"requires":["Self-hosted Refact deployment with multi-language support enabled","Language-specific tooling installed (compilers, interpreters, linters for validation)","Language-specific codebase for fine-tuning (optional but recommended)"],"input_types":["natural language code requirements","code snippets in target language","language-specific type definitions or interfaces"],"output_types":["generated code in target language","code with language-specific idioms and conventions","integration examples with language-specific frameworks"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"refact-ai__cap_12","uri":"capability://automation.workflow.self.hosted.deployment.with.full.data.privacy.and.infrastructure.control","name":"self-hosted deployment with full data privacy and infrastructure control","description":"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.","intents":["Deploy AI coding assistant without exposing proprietary code to external services","Maintain full control over infrastructure, data, and model versions","Comply with data residency and privacy requirements (HIPAA, GDPR, etc.)"],"best_for":["Organizations with strict data privacy and security requirements","Teams handling proprietary or regulated code (healthcare, finance, government)","Companies wanting to avoid vendor lock-in and maintain infrastructure control","Enterprises with existing on-premise infrastructure and deployment pipelines"],"limitations":["Deployment and maintenance responsibility falls on user — no managed service or support from Refact","Resource requirements not documented — unclear what compute, memory, and storage are needed","Scaling and high-availability setup not documented — unclear how to deploy for multiple users or teams","Model updates and security patches require manual deployment — no automatic update mechanism documented","Troubleshooting and debugging requires infrastructure expertise — limited documentation for deployment issues"],"requires":["Infrastructure to host Refact (bare metal, Docker, Kubernetes, cloud VMs)","Sufficient compute resources (GPU recommended for inference, exact specs not documented)","Network connectivity between development machines and Refact deployment","System administration expertise for deployment and maintenance","Open-source Refact codebase (claimed but no repository link provided)"],"input_types":["deployment configuration (Docker Compose, Kubernetes manifests, etc.)","infrastructure specifications (compute, memory, storage)","model and fine-tuning data"],"output_types":["running Refact instance","IDE plugin configuration for local deployment","deployment logs and monitoring data"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"refact-ai__cap_13","uri":"capability://tool.use.integration.open.source.codebase.with.community.contributions.and.transparency","name":"open-source codebase with community contributions and transparency","description":"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.","intents":["Understand how Refact AI works by reviewing source code","Contribute improvements and features to the project","Customize Refact for specific use cases without licensing restrictions"],"best_for":["Organizations valuing transparency and wanting to audit AI system internals","Developers wanting to contribute to open-source AI projects","Teams needing to customize Refact for specific requirements or integrations"],"limitations":["Open-source status claimed but no repository link provided — unclear where source code is hosted or what license is used","Community support may be limited compared to commercial products — no guaranteed response times or SLAs","Contribution process not documented — unclear how to submit PRs or get changes merged","No documented security vulnerability disclosure process — unclear how to report security issues responsibly"],"requires":["Access to Refact source code repository (location not documented)","Development environment for building and testing Refact","Understanding of Refact architecture and codebase"],"input_types":["source code contributions","bug reports and feature requests","security vulnerability reports"],"output_types":["merged contributions","bug fixes and feature implementations","community discussions and feedback"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"refact-ai__cap_2","uri":"capability://text.generation.language.in.ide.chat.interface.with.code.aware.context.and.inline.editing","name":"in-ide chat interface with code-aware context and inline editing","description":"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.","intents":["Ask questions about code without leaving the editor or switching context","Get code explanations, bug analysis, and refactoring suggestions inline","Apply suggested changes with visual diff preview before committing"],"best_for":["Individual developers seeking rapid feedback on code quality and design","Teams onboarding new members who need quick explanations of existing codebases","Developers debugging complex code paths and needing AI-assisted analysis"],"limitations":["Chat context limited to current file + repository symbols — no cross-repository context or external documentation integration","Refactoring suggestions are generated but actual code modification requires manual application or explicit approval","No conversation history persistence documented — unclear if multi-session context is maintained","Chat latency depends on local inference speed; very large context windows may cause noticeable delays (>2 seconds)"],"requires":["IDE plugin installed (VS Code, JetBrains, or Neovim)","Self-hosted Refact deployment running and accessible from IDE","Code file open in editor with syntax highlighting support"],"input_types":["natural language questions","selected code snippets","file paths and line numbers","refactoring intent descriptions"],"output_types":["natural language explanations","code suggestions with inline diff","bug reports with severity assessment","refactoring recommendations with before/after code"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"refact-ai__cap_3","uri":"capability://code.generation.editing.automated.code.refactoring.with.ast.aware.transformations","name":"automated code refactoring with ast-aware transformations","description":"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.","intents":["Identify and fix code quality issues across large codebases without manual review","Apply consistent naming conventions and architectural patterns project-wide","Modernize legacy code to use current language idioms and best practices"],"best_for":["Teams maintaining large legacy codebases with inconsistent patterns","Organizations enforcing coding standards and architectural guidelines","Developers preparing code for production deployment and seeking quality improvements"],"limitations":["AST parsing support limited to languages with documented grammar — no support for domain-specific languages or custom syntax extensions","Refactoring suggestions may miss context-dependent correctness requirements (e.g., thread-safety, performance characteristics) not visible in AST","No explicit handling of refactoring side effects — changes to public APIs or breaking changes require manual verification","Batch refactoring across large codebases may require multiple iterations due to interdependencies"],"requires":["Self-hosted Refact deployment with refactoring engine enabled","Code written in supported language (Python, JavaScript, TypeScript, Java, C++, Go, Rust — exact list not documented)","IDE plugin for diff preview and selective application","Git repository for tracking changes and rollback"],"input_types":["source code files","refactoring rules or patterns","code quality metrics or linting results","architectural guidelines"],"output_types":["refactoring suggestions with reasoning","code diffs showing proposed changes","impact analysis (files affected, breaking changes)","refactored code with preserved semantics"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"refact-ai__cap_4","uri":"capability://memory.knowledge.repository.wide.symbol.indexing.and.retrieval.augmented.generation.rag","name":"repository-wide symbol indexing and retrieval-augmented generation (rag)","description":"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.","intents":["Ensure code generation respects existing project structure and conventions","Enable agent to answer questions about codebase without loading entire repository into context","Provide accurate code suggestions based on how similar functionality is implemented elsewhere in the project"],"best_for":["Large monorepos (>100K LOC) where full codebase context is infeasible","Teams with strong architectural patterns and naming conventions that should be preserved","Projects with complex interdependencies where understanding symbol relationships is critical"],"limitations":["Indexing latency not documented — unclear how long initial index creation takes for large repositories","Index update strategy not specified — unclear if incremental updates are supported or full reindexing required on code changes","Semantic search quality depends on embedding model quality — no specification of embedding model or dimensionality","RAG retrieval may miss relevant context if query doesn't semantically match indexed symbols (e.g., searching for 'authentication' may miss 'login' function)","No documented mechanism for handling symbol name collisions or namespace conflicts"],"requires":["Self-hosted Refact deployment with indexing service enabled","Repository accessible to Refact for AST parsing and symbol extraction","Vector database or embedding storage (implementation details not documented)","Minimum disk space for index (size not documented)"],"input_types":["source code files","natural language queries","code snippets for similarity search"],"output_types":["ranked list of relevant symbols with context","code snippets showing symbol usage","semantic similarity scores"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"refact-ai__cap_5","uri":"capability://tool.use.integration.github.repository.integration.with.automated.code.analysis.and.pr.generation","name":"github repository integration with automated code analysis and pr generation","description":"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.","intents":["Analyze GitHub repositories and generate PRs with bug fixes or improvements","Automate code review tasks by identifying issues and suggesting fixes","Integrate Refact agent into GitHub-based development workflows"],"best_for":["Open-source project maintainers seeking automated code quality improvements","Development teams using GitHub for version control and wanting to automate PR generation","Organizations running automated code analysis pipelines with PR-based workflows"],"limitations":["GitHub API authentication mechanism not documented — unclear if using personal access tokens, OAuth, or other methods","PR generation quality depends on code analysis accuracy — no specification of false positive rates or issue detection coverage","No documented mechanism for handling merge conflicts or branch protection rules","PR approval and merge workflows not documented — unclear if agent can merge PRs or requires manual approval","Rate limiting not specified — unclear how many PRs can be generated per hour/day"],"requires":["GitHub repository with read/write access","GitHub API token or OAuth credentials configured in Refact","Self-hosted Refact deployment with GitHub integration enabled","Repository must be cloneable by Refact infrastructure"],"input_types":["GitHub repository URL","branch name or commit hash","issue description or code analysis criteria"],"output_types":["pull request with code changes","commit messages with detailed descriptions","code review comments","issue analysis reports"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"refact-ai__cap_6","uri":"capability://data.processing.analysis.database.schema.analysis.and.automated.migration.generation","name":"database schema analysis and automated migration generation","description":"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.","intents":["Generate database migrations from natural language descriptions","Analyze existing database schemas and suggest improvements","Ensure generated code matches database schema structure and constraints"],"best_for":["Development teams using database-driven applications with frequent schema changes","Organizations automating database deployment pipelines","Developers building features that require schema modifications alongside code changes"],"limitations":["Database connection mechanism not documented — unclear if supporting connection pooling, SSL, or other security features","Migration generation may not handle complex scenarios (data transformation, zero-downtime migrations, rollback strategies)","No documented support for database-specific features (PostgreSQL JSON columns, MySQL generated columns, etc.)","Schema analysis may miss implicit constraints or application-level validation rules","No explicit handling of migration conflicts or concurrent schema changes"],"requires":["Self-hosted Refact deployment with database integration enabled","Database credentials and connection details configured","Supported database system (MySQL, PostgreSQL — exact versions not documented)","Network access from Refact infrastructure to database server"],"input_types":["database connection string","natural language schema requirements","existing schema (introspected from database)","migration scripts or SQL statements"],"output_types":["database schema analysis","migration scripts (SQL or ORM-specific format)","schema diagrams or relationship analysis","constraint and index recommendations"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"refact-ai__cap_7","uri":"capability://tool.use.integration.web.browsing.and.api.interaction.via.chrome.tool.integration","name":"web browsing and api interaction via chrome tool integration","description":"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.","intents":["Automate interactions with web-based systems and SaaS platforms","Integrate code deployment with web-based configuration or content management systems","Enable agent to gather information from web APIs and web interfaces for code generation"],"best_for":["Teams integrating code deployment with web-based platforms (WordPress, Shopify, etc.)","Organizations automating multi-system workflows that span code and web-based tools","Developers building integrations with SaaS platforms that lack robust APIs"],"limitations":["Browser automation may be fragile to UI changes — no documented mechanism for handling dynamic or JavaScript-heavy interfaces","Credential management not documented — unclear how passwords and API keys are stored and protected","No explicit handling of CAPTCHA, multi-factor authentication, or other security challenges","Browser automation latency may be significant (seconds per action) — not suitable for real-time interactions","No documented support for headless browser or performance optimization"],"requires":["Self-hosted Refact deployment with Chrome/Chromium installed","Web credentials configured securely (mechanism not documented)","Network access from Refact infrastructure to target web services","Target websites must be compatible with Chromium automation"],"input_types":["website URL","login credentials","action descriptions (click button, fill form, extract data)","JavaScript code for custom interactions"],"output_types":["extracted web page data","screenshots or DOM snapshots","action execution logs","API responses from web services"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"refact-ai__cap_8","uri":"capability://automation.workflow.ci.cd.pipeline.integration.and.automated.deployment.orchestration","name":"ci/cd pipeline integration and automated deployment orchestration","description":"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.","intents":["Automate code deployment as part of end-to-end task execution","Integrate code generation with existing CI/CD pipelines","Enable agent to monitor deployment status and handle failures"],"best_for":["Development teams with mature CI/CD pipelines seeking to automate deployment orchestration","Organizations wanting to enable agent-driven code generation with automatic deployment","Teams building automated workflows that span code generation, testing, and deployment"],"limitations":["CI/CD integration mechanism not documented — unclear which systems are supported and how authentication is configured","Deployment orchestration may not handle complex scenarios (canary deployments, blue-green deployments, rollback strategies)","No documented mechanism for handling deployment failures or rollback triggers","Deployment status monitoring may have latency — unclear how frequently status is checked","No explicit handling of deployment approval workflows or manual gates"],"requires":["Self-hosted Refact deployment with CI/CD integration enabled","CI/CD system credentials and API access configured","Supported CI/CD platform (GitHub Actions, GitLab CI, Jenkins — exact versions not documented)","Network access from Refact infrastructure to CI/CD system"],"input_types":["CI/CD pipeline configuration","deployment environment specification","code changes to deploy","deployment approval criteria"],"output_types":["deployment status and logs","build artifacts","deployment confirmation","rollback notifications"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"refact-ai__cap_9","uri":"capability://code.generation.editing.fine.tuning.on.proprietary.codebase.with.incremental.learning","name":"fine-tuning on proprietary codebase with incremental learning","description":"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.","intents":["Improve code generation accuracy for project-specific patterns and conventions","Adapt model to domain-specific abstractions and architectural styles","Maintain model customization as codebase evolves without external fine-tuning services"],"best_for":["Teams with large, mature codebases with strong architectural patterns and conventions","Organizations with proprietary code that cannot be shared with external fine-tuning services","Projects where code generation accuracy is critical and generic models are insufficient"],"limitations":["Fine-tuning mechanism not documented — unclear if using LoRA, full fine-tuning, or other techniques","Compute requirements for fine-tuning not specified — unclear how long fine-tuning takes or what GPU/CPU resources are needed","Incremental learning strategy not documented — unclear if supporting continuous learning or requiring periodic retraining","No documented mechanism for evaluating fine-tuning quality or detecting overfitting","Fine-tuning data requirements not specified — unclear how much code is needed for effective fine-tuning"],"requires":["Self-hosted Refact deployment with fine-tuning capability enabled","Sufficient compute resources for model fine-tuning (GPU recommended, exact specs not documented)","Repository code accessible for fine-tuning data extraction","Time for fine-tuning process (duration not documented)"],"input_types":["source code repository","fine-tuning configuration (learning rate, epochs, etc. — not documented)","code examples or patterns to emphasize"],"output_types":["fine-tuned model weights","fine-tuning metrics and evaluation results","improved code generation quality"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"refact-ai__headline","uri":"capability://code.generation.editing.self.hosted.ai.coding.assistant","name":"self-hosted ai coding assistant","description":"Refact AI is a self-hosted AI coding assistant that offers code completion, chat, and automated refactoring, ensuring user privacy by keeping all code and model inference on local infrastructure.","intents":["best self-hosted AI coding assistant","AI coding assistant for privacy-focused development","top coding assistant for automated refactoring","self-hosted code completion tool","AI tool for local code analysis"],"best_for":["developers seeking privacy","teams needing local deployment"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":59,"verified":false,"data_access_risk":"high","permissions":["Self-hosted Refact deployment with Qwen2.5-Coder model loaded","IDE plugin compatible with VS Code, JetBrains, or Neovim","Minimum 8GB RAM for model inference (estimated, not documented)","Repository indexed and accessible to Refact agent for symbol analysis","Self-hosted Refact deployment with agentic capabilities enabled","Access to target systems (GitHub repos, databases, CI/CD pipelines, APIs) from Refact infrastructure","Clear task description with success criteria","User availability for iterative feedback (not fully autonomous)","Self-hosted Refact deployment with bug detection enabled","Code written in supported language (exact list not documented)"],"failure_modes":["Completion quality degrades for unfamiliar language constructs or novel architectural patterns not represented in training data","RAG context window size not documented — may miss relevant symbols in very large monorepos (>1M LOC)","No explicit handling of circular dependencies or forward references in completion ranking","Fine-tuning mechanism not documented — unclear if incremental learning or full retraining required","Planning algorithm not documented — unclear if using ReAct, tree search, or simpler chain-of-thought; no specification of branching factor or depth limits","No documented failure recovery — unclear how agent handles dead-end states, contradictory feedback, or tool failures","Task decomposition quality depends on initial prompt clarity; vague requirements may result in incorrect step sequencing","No explicit rollback mechanism documented — if agent modifies database or deploys code, reverting changes requires manual intervention","Context window limitations not specified — very large tasks may exceed model's ability to maintain coherent plan across steps","Bug detection accuracy not documented — no specification of false positive rates or detection coverage","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.3,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.061Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=refact-ai","compare_url":"https://unfragile.ai/compare?artifact=refact-ai"}},"signature":"uWiPUtw0HfgfIuOSYbAGAIMDSCDfM5ydxBeMHyl3z7SQCRqUwsYrhN4rfvnvKFzNbyFAbQnIy3Iuo6QUQrO3Dw==","signedAt":"2026-06-20T13:15:04.505Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/refact-ai","artifact":"https://unfragile.ai/refact-ai","verify":"https://unfragile.ai/api/v1/verify?slug=refact-ai","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}