{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-recraft","slug":"recraft","name":"Recraft","type":"product","url":"https://www.recraft.ai/","page_url":"https://unfragile.ai/recraft","categories":["image-generation","rag-knowledge"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-recraft__cap_0","uri":"capability://image.visual.text.to.image.generation.with.style.control","name":"text-to-image generation with style control","description":"Generates original images from natural language prompts using a diffusion-based generative model with fine-grained style parameters. The system accepts descriptive text input and applies learned style embeddings to produce images matching specified artistic directions (e.g., photorealistic, illustration, 3D render). Architecture likely uses a CLIP-based text encoder to convert prompts into latent space representations, then conditions a diffusion model to iteratively denoise toward the target image.","intents":["I need to generate multiple variations of a product image with different artistic styles without hiring a designer","I want to create marketing assets that match a specific visual brand aesthetic from text descriptions","I need to rapidly prototype visual concepts for a campaign before committing to production photography"],"best_for":["solo creators and small design teams without in-house illustration resources","marketing teams needing rapid asset iteration across multiple styles","product designers prototyping visual concepts quickly"],"limitations":["Generated images may contain artifacts or anatomical inconsistencies at extreme aspect ratios","Style consistency across multiple generations requires careful prompt engineering and parameter tuning","Computational latency for high-resolution outputs (1024x1024+) typically 30-60 seconds per image"],"requires":["Web browser with modern JavaScript support","Internet connection for cloud-based inference","Recraft account with active subscription or credits"],"input_types":["natural language text prompts","style parameter selections (dropdown/enum)","aspect ratio specifications"],"output_types":["PNG/JPEG images at configurable resolution (up to 2048x2048)","image metadata including generation parameters and seed"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-recraft__cap_1","uri":"capability://image.visual.vector.art.generation.and.editing","name":"vector art generation and editing","description":"Generates vector graphics (SVG format) from text prompts or raster images, producing scalable artwork suitable for logos, icons, and illustrations. The system uses a specialized vector generation model that outputs parametric bezier curves and shape primitives rather than pixel data, enabling infinite scaling without quality loss. Architecture involves either a dedicated vector diffusion model or a raster-to-vector conversion pipeline using stroke prediction and curve fitting algorithms.","intents":["I need to create a scalable logo or icon set that works at any resolution without pixelation","I want to generate vector illustrations that I can edit and customize in design tools like Figma or Adobe Illustrator","I need to produce print-ready vector assets for packaging or large-format applications"],"best_for":["brand designers and agencies requiring production-ready vector assets","icon and logo designers seeking rapid iteration","teams using design systems that require SVG-native assets"],"limitations":["Complex photorealistic scenes are difficult to vectorize cleanly; works best for graphic, illustrative, or geometric content","Generated vectors may require manual refinement in design tools for production use","Curve complexity and stroke count can impact file size and rendering performance in some applications"],"requires":["Web browser with SVG rendering support","Design tool compatible with SVG import (Figma, Adobe Illustrator, Inkscape, etc.) for post-generation editing","Recraft account with vector generation credits"],"input_types":["natural language text prompts","raster image files (PNG, JPEG) for conversion","style and complexity parameters"],"output_types":["SVG files with editable paths and shapes","PNG preview for quick review","vector metadata including layer structure"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-recraft__cap_10","uri":"capability://memory.knowledge.asset.library.and.organization.system","name":"asset library and organization system","description":"Provides a searchable, taggable library for organizing and managing generated assets with metadata, collections, and smart search. The system stores generation history with full parameters, enables tagging and categorization, and provides full-text and semantic search across assets. Architecture likely uses a vector database (Pinecone, Weaviate) for semantic search on asset descriptions/tags, plus traditional SQL indexing for metadata queries.","intents":["I want to find previously generated assets without scrolling through hundreds of generations","I need to organize assets by project, client, or campaign","I want to search for assets by visual similarity or semantic meaning"],"best_for":["designers managing large asset libraries","agencies organizing assets across multiple projects","teams reusing and repurposing generated assets"],"limitations":["Semantic search quality depends on asset metadata quality; poorly tagged assets may not be discoverable","Storage limits may apply; very large libraries may require pagination or archival","Search performance may degrade with very large libraries (10k+ assets)"],"requires":["Recraft account with asset library access","Generated assets stored in Recraft system","Time to tag and organize assets for discoverability"],"input_types":["search query (text or image)","filter parameters (date, style, project)","tag assignments"],"output_types":["search results with thumbnails","asset metadata (generation parameters, creation date, tags)","collections and folders"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-recraft__cap_11","uri":"capability://text.generation.language.prompt.optimization.and.suggestion.system","name":"prompt optimization and suggestion system","description":"Analyzes user prompts and suggests improvements or variations to enhance generation quality and consistency. The system uses NLP and generation history analysis to identify common patterns, suggest keywords, and recommend parameter combinations. Architecture likely uses a language model to analyze prompts, compare against successful historical generations, and suggest improvements based on learned patterns.","intents":["I want help writing better prompts that produce higher-quality results","I need suggestions for style parameters or keywords to achieve a specific aesthetic","I want to learn what makes prompts effective for different generation types"],"best_for":["users new to AI image generation learning prompt engineering","designers optimizing prompts for consistency","teams standardizing prompt templates"],"limitations":["Suggestions are heuristic-based; may not always improve results","Prompt optimization requires historical data; new users have limited suggestions","Over-optimization may reduce creative variation"],"requires":["Recraft account with generation history","Prompt input field with suggestion UI"],"input_types":["user-written prompt","generation history for pattern analysis","target style or aesthetic"],"output_types":["suggested prompt improvements","keyword recommendations","parameter suggestions","example successful prompts"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-recraft__cap_2","uri":"capability://image.visual.3d.model.generation.and.preview","name":"3d model generation and preview","description":"Generates 3D models (likely in glTF or similar formats) from text prompts or 2D images, with real-time preview and basic manipulation capabilities. The system uses a 3D generative model (possibly a diffusion model operating on 3D representations like NeRF or mesh data) to produce volumetric or mesh-based outputs. Architecture likely includes a neural renderer for interactive preview and export pipelines for standard 3D formats compatible with game engines and 3D software.","intents":["I need to generate 3D product mockups or assets for e-commerce without 3D modeling expertise","I want to create 3D illustrations or character concepts for game prototyping or animation","I need to produce 3D assets for AR/VR experiences or web-based 3D visualization"],"best_for":["game developers and indie studios prototyping 3D assets quickly","e-commerce teams creating product visualizations","AR/VR developers needing rapid asset generation","motion designers and animators building 3D concept art"],"limitations":["Generated 3D models may have topology issues, missing details, or non-manifold geometry requiring cleanup in 3D software","Texture quality and material properties are often simplified; complex materials require manual refinement","Model complexity and polygon count may not be optimized for real-time rendering in game engines","Anatomical accuracy for human/animal models is limited compared to hand-crafted assets"],"requires":["Web browser with WebGL support for 3D preview","3D software (Blender, Maya, Unity, Unreal Engine) for post-generation refinement","Recraft account with 3D generation credits"],"input_types":["natural language text prompts","2D reference images","style and complexity parameters"],"output_types":["glTF/glb 3D model files","WebGL preview with interactive rotation and zoom","texture maps (diffuse, normal, roughness) where applicable"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-recraft__cap_3","uri":"capability://image.visual.iterative.image.refinement.and.variation.generation","name":"iterative image refinement and variation generation","description":"Enables users to iteratively refine generated images through targeted edits, parameter adjustments, and variation generation. The system maintains generation context (seed, parameters, prompt embeddings) and applies incremental modifications using inpainting or conditional regeneration techniques. Architecture likely uses a diffusion model with inpainting capabilities to selectively regenerate regions while preserving other elements, or uses latent space interpolation to generate smooth variations.","intents":["I generated an image but want to change the background or specific elements without regenerating from scratch","I need to create multiple variations of a design to A/B test with stakeholders","I want to fine-tune colors, composition, or style details on an existing generation"],"best_for":["designers iterating on concepts with clients or stakeholders","marketing teams A/B testing visual assets","creators exploring design variations without starting over"],"limitations":["Inpainting quality degrades with large masked regions; works best for targeted edits","Maintaining semantic consistency across iterations requires careful prompt and parameter management","Variation generation may drift from original intent if seed/parameters are not properly preserved"],"requires":["Previously generated image from Recraft","Web-based editor interface with masking/selection tools","Sufficient generation credits for multiple iterations"],"input_types":["existing generated image","mask/selection defining edit region","modified text prompt or parameter adjustments","variation strength parameter (0-1 scale)"],"output_types":["refined image with targeted edits applied","multiple variation thumbnails for comparison","generation history with parameter snapshots"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-recraft__cap_4","uri":"capability://image.visual.batch.image.generation.and.export","name":"batch image generation and export","description":"Supports generating multiple images in parallel or sequence with consistent parameters, and exporting results in bulk with metadata. The system queues generation requests, manages concurrent inference across multiple GPU instances, and provides batch export with configurable formats and resolutions. Architecture likely uses a job queue (Redis/RabbitMQ) and distributed inference workers to parallelize generation, with batch export pipelines for format conversion and optimization.","intents":["I need to generate 50 product images in different styles for an e-commerce catalog","I want to create multiple variations of a design and download them all at once","I need to export generated assets in multiple formats (PNG, SVG, WebP) for different use cases"],"best_for":["e-commerce teams generating product asset catalogs","marketing teams producing large campaign asset sets","agencies delivering multiple design variations to clients"],"limitations":["Batch generation may have queue delays during peak usage; no guaranteed SLA for completion time","Export file size can be large for high-resolution batches; may require pagination or streaming downloads","Batch operations consume credits proportionally; no volume discounts mentioned"],"requires":["Recraft account with sufficient credits for batch size","Web interface or API access for batch submission","Storage capacity for downloaded assets"],"input_types":["CSV or JSON file with prompt list and parameters","template parameters (style, aspect ratio, quality level)","export format specifications"],"output_types":["ZIP archive containing all generated images","CSV metadata file with generation parameters and timestamps","images in requested formats (PNG, JPEG, WebP, SVG, glTF)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-recraft__cap_5","uri":"capability://image.visual.style.aware.image.to.image.transformation","name":"style-aware image-to-image transformation","description":"Transforms existing images into different artistic styles (photorealistic, illustration, 3D, vector, etc.) while preserving composition and content. The system uses a style transfer or conditional image-to-image diffusion model that encodes the input image and applies style embeddings to guide generation. Architecture likely uses CLIP-based image encoding combined with style-specific model adapters or LoRA weights to achieve consistent style transformation.","intents":["I have a photograph and want to convert it to an illustration or 3D render style","I need to apply a consistent artistic style to a set of reference images","I want to reimagine existing product photos in different visual styles for marketing"],"best_for":["designers converting reference images to target styles","marketing teams reimagining product photography","artists exploring style variations on existing artwork"],"limitations":["Style transformation may lose fine details or introduce artifacts, especially with extreme style shifts","Composition is generally preserved but may be subtly altered to fit style requirements","Works best with clear, well-lit source images; poor-quality inputs produce poor transformations"],"requires":["Source image file (PNG, JPEG, WebP)","Target style selection from available options","Recraft account with image transformation credits"],"input_types":["image file (PNG, JPEG, WebP, up to 2048x2048)","target style parameter (photorealistic, illustration, 3D, vector, etc.)","optional intensity/strength parameter"],"output_types":["transformed image in target style","preview thumbnail","generation metadata"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-recraft__cap_6","uri":"capability://image.visual.interactive.design.canvas.with.real.time.preview","name":"interactive design canvas with real-time preview","description":"Provides a web-based design canvas where users can compose prompts, adjust parameters, and see real-time previews of generated content. The system uses a responsive UI framework (likely React) with WebGL rendering for image/3D preview, and maintains live synchronization between parameter inputs and preview updates. Architecture includes a client-side parameter state manager, debounced preview requests to avoid excessive API calls, and optimized image rendering for fast feedback.","intents":["I want to experiment with different prompts and parameters and see results instantly","I need a collaborative design interface where I can share work-in-progress with teammates","I want to organize and manage my generated assets in one place"],"best_for":["designers and creators preferring visual, interactive workflows","teams collaborating on design iterations","users exploring creative possibilities through experimentation"],"limitations":["Real-time preview requires debouncing to avoid excessive API calls; preview latency typically 2-5 seconds","Canvas performance may degrade with very large images or 3D models on lower-end devices","Undo/redo history is limited to recent generations; full version control not available"],"requires":["Modern web browser (Chrome, Firefox, Safari, Edge)","JavaScript enabled","Stable internet connection for real-time preview","Recraft account"],"input_types":["text prompts via text input field","parameter adjustments via sliders, dropdowns, color pickers","uploaded reference images","style selections"],"output_types":["live preview image/3D model in canvas","generation history sidebar","downloadable assets"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-recraft__cap_7","uri":"capability://image.visual.brand.consistency.and.template.system","name":"brand consistency and template system","description":"Enables users to define brand guidelines (colors, styles, tone) and apply them consistently across multiple generations through template-based workflows. The system stores brand parameters as reusable templates and applies them as constraints during generation, ensuring outputs align with brand identity. Architecture likely uses a template database with parameter inheritance and conditional generation logic that enforces brand constraints during diffusion.","intents":["I need to generate marketing assets that all match my brand's visual identity","I want to create a template so my team can generate on-brand assets without design expertise","I need to ensure consistency across a large campaign with multiple asset types"],"best_for":["brand teams and agencies managing visual consistency","marketing teams generating on-brand assets at scale","companies with strict brand guidelines"],"limitations":["Brand constraint enforcement may limit creative variation; very strict constraints can reduce output quality","Template setup requires initial investment in defining brand parameters","Brand consistency is best-effort; edge cases may require manual review"],"requires":["Recraft account with brand management features","Brand guidelines documentation (colors, fonts, style references)","Time to set up initial brand template"],"input_types":["brand color palette (hex codes)","style references (images or descriptions)","tone/voice guidelines","template name and description"],"output_types":["saved brand template","generated assets constrained to brand parameters","brand consistency report"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-recraft__cap_8","uri":"capability://image.visual.api.based.programmatic.generation.and.integration","name":"api-based programmatic generation and integration","description":"Exposes generation capabilities via REST or GraphQL API, enabling integration into external applications, workflows, and automation systems. The system provides endpoints for text-to-image, image-to-image, vector generation, and 3D generation with request/response schemas, authentication via API keys, and webhook support for async job completion. Architecture uses standard API patterns with request queuing, rate limiting, and async job tracking.","intents":["I want to integrate Recraft generation into my SaaS product or web application","I need to automate asset generation as part of my CI/CD or content pipeline","I want to build custom tools or scripts that leverage Recraft's generation capabilities"],"best_for":["developers building AI-powered applications","teams automating content generation workflows","SaaS companies offering white-label design features"],"limitations":["API rate limits may restrict high-volume generation; requires careful quota management","Async job model introduces latency; synchronous generation may timeout for complex requests","API documentation and SDK availability unknown; may require reverse-engineering or limited official support"],"requires":["Recraft API key (obtained from account settings)","HTTP client library (curl, requests, axios, etc.)","Understanding of async job patterns and webhook handling","Sufficient API credits for integration usage"],"input_types":["JSON request body with prompt, parameters, style","image file upload for image-to-image operations","API key in Authorization header"],"output_types":["JSON response with job ID and status","image URL or file download link","webhook callback with completed asset"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-recraft__cap_9","uri":"capability://automation.workflow.collaborative.design.workspace.with.sharing.and.feedback","name":"collaborative design workspace with sharing and feedback","description":"Enables multiple users to collaborate on design projects through shared workspaces, commenting, and version history. The system manages user permissions, tracks changes across generations, and provides commenting/annotation tools for feedback. Architecture likely uses a real-time collaboration framework (similar to Figma's multiplayer model) with operational transformation or CRDT for conflict-free concurrent edits, plus a comment/annotation system with threading.","intents":["I want to share work-in-progress designs with my team for feedback without downloading files","I need to track who made what changes and when during a design project","I want to iterate on designs with client feedback in real-time"],"best_for":["design teams collaborating on projects","agencies working with clients on design approval","distributed teams needing asynchronous design feedback"],"limitations":["Real-time collaboration may have latency or sync issues during high-concurrency editing","Comment threads may not support rich formatting or media attachments","Version history may be limited to recent changes; full audit trail not guaranteed"],"requires":["Recraft account with collaboration features enabled","Shared workspace link or invitation","Web browser for real-time collaboration"],"input_types":["design project/workspace","comments and annotations","permission assignments (view, edit, admin)"],"output_types":["shared workspace URL","version history with timestamps","comment threads with user attribution","activity log"],"categories":["automation-workflow","collaboration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":30,"verified":false,"data_access_risk":"high","permissions":["Web browser with modern JavaScript support","Internet connection for cloud-based inference","Recraft account with active subscription or credits","Web browser with SVG rendering support","Design tool compatible with SVG import (Figma, Adobe Illustrator, Inkscape, etc.) for post-generation editing","Recraft account with vector generation credits","Recraft account with asset library access","Generated assets stored in Recraft system","Time to tag and organize assets for discoverability","Recraft account with generation history"],"failure_modes":["Generated images may contain artifacts or anatomical inconsistencies at extreme aspect ratios","Style consistency across multiple generations requires careful prompt engineering and parameter tuning","Computational latency for high-resolution outputs (1024x1024+) typically 30-60 seconds per image","Complex photorealistic scenes are difficult to vectorize cleanly; works best for graphic, illustrative, or geometric content","Generated vectors may require manual refinement in design tools for production use","Curve complexity and stroke count can impact file size and rendering performance in some applications","Semantic search quality depends on asset metadata quality; poorly tagged assets may not be discoverable","Storage limits may apply; very large libraries may require pagination or archival","Search performance may degrade with very large libraries (10k+ assets)","Suggestions are heuristic-based; may not always improve results","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.49,"ecosystem":0.35000000000000003,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.048Z","last_scraped_at":"2026-05-03T14:00:20.516Z","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=recraft","compare_url":"https://unfragile.ai/compare?artifact=recraft"}},"signature":"aUjpKy/5x4e/IeACc536lD1SMBuCSwi5xd7lSL2kliNFhKrlORo3sSfmiq84E6oDzOUm+J6M2iyx/4RUwvcMDw==","signedAt":"2026-06-23T08:22:16.530Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/recraft","artifact":"https://unfragile.ai/recraft","verify":"https://unfragile.ai/api/v1/verify?slug=recraft","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"}}