Fashion AI Agents - Open Source Vision
This document shares 24+ AI agent ideas for the fashion industry. We're inviting the open-source community to build these agents together and create a comprehensive Fashion AI Agents ecosystem. Whether you're building agents already or want to start, we welcome your contributions!
Our Vision
We envision an open-source ecosystem of Fashion AI Agents that works seamlessly with OpenTryOn (our open-source library) and integrates with TryOn AI (our cloud-hosted platform for fashion brands, designers, and e-commerce marketplaces). This ecosystem will:
- Democratize Fashion AI: Make advanced fashion technology accessible to everyone
- Foster Innovation: Enable developers, researchers, and fashion enthusiasts to contribute
- Build Together: Create a collaborative community around fashion AI agents
- Share Knowledge: Document best practices, patterns, and implementations
- Integrate Seamlessly: Design agents that work with OpenTryOn library and TryOn AI platform
Are you building fashion AI agents? Have ideas for new agents? Want to contribute to open-source? We'd love to have you! See our Call to Action section below.
Overview
We've identified 24+ AI agent ideas organized into five categories that can enhance virtual try-on capabilities and provide comprehensive fashion technology solutions:
- Data Collection: Extract and process product information
- Analysis: Evaluate images, fits, and styles
- Recommendation: Provide personalized suggestions
- Utility: Support functions for other agents
- Orchestration: Coordinate multiple agents
These agents can work individually or together to create powerful fashion technology applications.
OpenTryOn includes powerful image generation APIs that agents can leverage:
- Nano Banana (Gemini 2.5 Flash): Fast, efficient image generation (1024px)
- Nano Banana Pro (Gemini 3 Pro): Advanced generation with 4K support
- FLUX.2 [PRO]: High-quality image generation with standard controls
- FLUX.2 [FLEX]: Flexible generation with advanced controls
These APIs support text-to-image, image editing, multi-image composition, and style transfer, enabling agents to generate, edit, mix, and compose fashion images programmatically.
OpenTryOn includes virtual try-on APIs that agents can leverage to create realistic try-on experiences:
- Amazon Nova Canvas: AWS Bedrock-based virtual try-on with automatic garment detection, multiple garment classes (Upper body, Lower body, Full body, Footwear), and custom mask support (max 4.1M pixels)
- Kling AI: Asynchronous virtual try-on processing with automatic polling, multiple model versions, webhook support, and high-resolution support (max 16M pixels)
These APIs enable agents to combine person images with garment images to generate realistic virtual try-on results, perfect for e-commerce, styling, and fit analysis applications.
Agent Ideas Catalog
Below are 24+ agent ideas we're sharing with the community. Each agent includes:
- Purpose and Capabilities
- Use Cases and Integration Points
- Priority for implementation
Many agents can leverage OpenTryOn's APIs:
- Image Generation: Nano Banana, Nano Banana Pro, FLUX.2 PRO, FLUX.2 FLEX for creating, editing, and composing fashion images
- Virtual Try-On: Amazon Nova Canvas, Kling AI for generating realistic try-on results
Agents that use image generation or virtual try-on capabilities are clearly marked in their descriptions.
1. Look Analyzer Agent
Category: Analysis
Priority: High
Analyzes outfits on people and provides comprehensive styling feedback.
Capabilities:
- Analyzes color harmony between garment and person's complexion
- Evaluates garment fit based on body shape, size, height, and weight
- Assesses hairstyle compatibility with outfit
- Analyzes face shape and suggests complementary styles
- Evaluates accessories coordination
- Provides feedback in simple, actionable language
- Can use virtual try-on APIs to generate try-on images for analysis
Use Cases:
- Personal styling feedback
- E-commerce product recommendations
- Fashion consultation services
Integration: Works with Personal Stylist, Outfit Compatibility, and Color Coordination agents. Can leverage Amazon Nova Canvas or Kling AI for generating try-on images.
2. Garment Scraper Agent
Category: Data Collection
Priority: High
Extracts product information from e-commerce Product Detail Pages (PDPs).
Capabilities:
- Scrapes product images (main, alternate angles, zoom images)
- Extracts product name, description, and properties
- Captures measurements, sizing information, and specifications
- Handles multiple e-commerce platforms (Amazon, Shopify, custom sites)
- Uses multiple scraping tools (Selenium, BeautifulSoup, API calls)
- Handles dynamic content and JavaScript-rendered pages
Use Cases:
- Product catalog building
- Competitive analysis
- Price monitoring
- Inventory management
Integration: Provides data for Product Comparison, PDP Analyzer, and other agents.
3. PDP Analyzer Agent
Category: Data Collection
Priority: Medium
Evaluates Product Detail Page quality and suggests improvements.
Capabilities:
- Screenshots and analyzes PDP layout
- Evaluates title quality and SEO optimization
- Analyzes product description completeness
- Assesses image quality and quantity
- Reviews product properties and specifications
- Provides actionable improvement suggestions
Use Cases:
- E-commerce optimization
- Content quality assurance
- Conversion rate optimization
Integration: Uses Garment Scraper Agent for data collection.
4. Size Recommendation Agent
Category: Recommendation
Priority: High
Recommends garment sizes based on body measurements and fit preferences.
Capabilities:
- Takes body measurements (chest, waist, hips, height, weight)
- Analyzes garment size charts and measurements
- Considers fit preferences (slim, regular, relaxed)
- Accounts for brand-specific sizing variations
- Provides size recommendations with confidence scores
- Suggests alternative sizes if primary recommendation unavailable
- Can generate virtual try-on images to visualize fit before purchase
Use Cases:
- E-commerce size selection
- Reducing return rates
- Personalized shopping experiences
- Size chart interpretation
Integration: Works with Look Analyzer Agent to analyze fit on person images. Can use Amazon Nova Canvas or Kling AI to generate try-on visualizations for different sizes.
5. Outfit Compatibility Agent
Category: Recommendation
Priority: High
Evaluates how well multiple garments work together as an outfit.
Capabilities:
- Analyzes color coordination between garments
- Evaluates style compatibility (formal, casual, sporty, etc.)
- Checks pattern mixing rules
- Assesses texture combinations
- Suggests complementary pieces
- Provides outfit scoring and alternatives
- Generates outfit visualizations using image generation APIs
Use Cases:
- Outfit building tools
- Wardrobe planning
- Styling recommendations
- Complete look generation
Integration: Works seamlessly with OpenTryOn's outfit generation features. Can leverage image generation APIs (Nano Banana, FLUX.2) to create outfit visualizations.
6. Personal Stylist Agent
Category: Recommendation
Priority: Medium
Provides personalized styling advice based on user preferences and body type.
Capabilities:
- Builds user style profile (preferences, body type, skin tone, lifestyle)
- Suggests outfits for different occasions
- Provides seasonal styling recommendations
- Tracks wardrobe and suggests additions
- Offers trend-aware suggestions
- Maintains conversation history for context
Use Cases:
- Personal shopping assistants
- Style consultation services
- Wardrobe management apps
- Subscription box curation
Integration: Leverages Look Analyzer and Outfit Compatibility agents.
7. Fit Prediction Agent
Category: Analysis
Priority: Medium
Predicts how well a garment will fit a person before virtual try-on.
Capabilities:
- Analyzes garment measurements and construction
- Compares with person's body measurements
- Predicts fit issues (too tight, too loose, length issues)
- Identifies potential problem areas
- Provides fit visualization
- Suggests alterations if needed
- Can generate try-on images using virtual try-on APIs to validate predictions
Use Cases:
- Pre-try-on filtering
- Fit quality assurance
- Alteration recommendations
- Return prediction
Integration: Complements virtual try-on by pre-filtering garments. Can use Amazon Nova Canvas or Kling AI to generate try-on results for validation.
8. Color Coordination Agent
Category: Utility
Priority: Medium
Suggests color combinations and palettes for outfits.
Capabilities:
- Analyzes color theory (complementary, analogous, triadic schemes)
- Considers skin tone compatibility
- Suggests color palettes for different occasions
- Provides color harmony scoring
- Suggests accent colors
- Handles pattern and print colors
Use Cases:
- Color matching tools
- Outfit color suggestions
- Wardrobe color planning
- Seasonal color recommendations
Integration: Works with Look Analyzer and Outfit Compatibility agents.
9. Occasion-Based Outfit Agent
Category: Recommendation
Priority: Medium
Suggests appropriate outfits for specific occasions or events.
Capabilities:
- Understands dress codes (business casual, formal, cocktail, etc.)
- Considers event type, time, and location
- Suggests weather-appropriate options
- Provides multiple outfit options per occasion
- Considers cultural and regional preferences
- Suggests accessories and footwear
Use Cases:
- Event planning tools
- Wardrobe planning apps
- Styling services
- Fashion consultation
Integration: Uses Personal Stylist Agent for user preferences.
10. Wardrobe Analyzer Agent
Category: Analysis
Priority: Low
Analyzes a user's wardrobe and suggests improvements or additions.
Capabilities:
- Catalogs wardrobe items (from images or descriptions)
- Identifies gaps in wardrobe
- Suggests essential pieces to add
- Analyzes color distribution
- Identifies underutilized items
- Suggests outfit combinations from existing wardrobe
- Tracks wear frequency
Use Cases:
- Wardrobe management apps
- Capsule wardrobe creation
- Sustainable fashion (maximize existing items)
- Shopping list generation
Integration: Works with Outfit Compatibility and Personal Stylist agents.
11. Product Comparison Agent
Category: Analysis
Priority: Medium
Compares similar products across different retailers or brands.
Capabilities:
- Identifies similar products using image and text matching
- Compares prices across retailers
- Compares quality indicators (materials, construction, reviews)
- Compares sizing and fit information
- Provides side-by-side comparison
- Suggests best value options
Use Cases:
- Price comparison tools
- Product research
- Shopping assistants
- Competitive analysis
Integration: Uses Garment Scraper Agent for product data.
12. Image Quality Analyzer Agent
Category: Utility
Priority: High
Analyzes garment image quality for optimal virtual try-on results.
Capabilities:
- Evaluates image resolution and clarity
- Checks for proper garment visibility
- Identifies background complexity
- Assesses lighting conditions
- Detects image artifacts or distortions
- Suggests image improvements
- Scores images for try-on suitability
Use Cases:
- Pre-processing quality control
- E-commerce image optimization
- Dataset curation
- API input validation
Integration: Essential for OpenTryOn's virtual try-on pipeline.
13. Pose Detection & Analysis Agent
Category: Utility
Priority: Medium
Detects and analyzes poses in fashion images for better try-on results.
Capabilities:
- Detects human pose keypoints
- Analyzes pose suitability for try-on
- Identifies optimal poses for garment display
- Suggests pose adjustments
- Handles multiple people in images
- Provides pose quality scoring
Use Cases:
- Pre-processing for try-on
- Image quality assessment
- Pose normalization
- Dataset preparation
Integration: Complements OpenTryOn's pose estimation preprocessing.
14. Fabric & Material Analyzer Agent
Category: Utility
Priority: Low
Identifies fabric types and properties from images.
Capabilities:
- Identifies fabric types (cotton, silk, denim, etc.)
- Analyzes texture and weave patterns
- Predicts material properties (drape, stretch, weight)
- Suggests care instructions
- Identifies fabric quality indicators
- Provides material compatibility analysis
Use Cases:
- Product information extraction
- Material-based recommendations
- Care instruction generation
- Quality assessment
Integration: Enhances product descriptions scraped by Garment Scraper Agent.
15. Trend Analysis Agent
Category: Utility
Priority: Low
Analyzes fashion trends and suggests trendy items.
Capabilities:
- Tracks current fashion trends
- Identifies trending colors, styles, and patterns
- Analyzes trend longevity
- Suggests trend-appropriate items
- Provides seasonal trend forecasts
- Considers regional trend variations
Use Cases:
- Trend-aware recommendations
- Seasonal collection planning
- Fashion forecasting
- Trend-based styling
Integration: Enhances Personal Stylist and Outfit Compatibility agents.
16. Sustainability Analyzer Agent
Category: Analysis
Priority: Low
Analyzes environmental impact and sustainability of fashion items.
Capabilities:
- Identifies sustainable materials
- Evaluates production methods
- Assesses brand sustainability practices
- Provides carbon footprint estimates
- Suggests sustainable alternatives
- Scores items on sustainability metrics
Use Cases:
- Sustainable fashion platforms
- Eco-conscious shopping
- Brand sustainability assessment
- Consumer education
Integration: Works with Product Comparison Agent for sustainable options.
17. Style Transfer Agent
Category: Utility
Priority: Low
Applies different styles to garments while maintaining fit and structure.
Capabilities:
- Transfers patterns and textures between garments
- Applies color schemes to garments
- Maintains garment structure and fit
- Handles various style categories
- Provides style previews
- Suggests style combinations
- Uses image generation APIs for style transfer and composition
Use Cases:
- Customization tools
- Style exploration
- Design inspiration
- Virtual customization
Integration: Extends OpenTryOn's virtual try-on capabilities. Uses image generation APIs (Nano Banana, FLUX.2) to apply style transformations while maintaining garment structure.
18. Accessory Matching Agent
Category: Recommendation
Priority: Medium
Suggests accessories that complement outfits.
Capabilities:
- Analyzes outfit style and color
- Suggests matching jewelry, bags, shoes, belts
- Considers occasion appropriateness
- Provides multiple accessory options
- Evaluates accessory coordination
- Suggests alternative accessories
Use Cases:
- Complete look generation
- Accessory recommendations
- Outfit completion tools
- Styling services
Integration: Complements Look Analyzer and Outfit Compatibility agents.
19. Price Drop Alert Agent
Category: Utility
Priority: Low
Monitors product prices and alerts users of discounts.
Capabilities:
- Tracks product prices across retailers
- Monitors price history
- Sets price drop alerts
- Compares current prices with historical data
- Identifies best deals
- Suggests optimal purchase timing
Use Cases:
- Price monitoring tools
- Deal alert services
- Shopping optimization
- Budget-conscious shopping
Integration: Works with Garment Scraper and Product Comparison agents.
20. Virtual Fitting Room Agent
Category: Orchestration
Priority: High
Orchestrates multiple agents for comprehensive virtual try-on experience.
Capabilities:
- Coordinates virtual try-on workflow using Amazon Nova Canvas or Kling AI
- Integrates multiple agents (fit prediction, look analysis, etc.)
- Provides comprehensive try-on results
- Suggests alternatives and improvements
- Manages user preferences and history
- Provides personalized recommendations
- Generates virtual try-on images using virtual try-on APIs
- Can leverage image generation for creating alternative looks
Use Cases:
- Complete virtual try-on platform
- E-commerce integration
- Fashion consultation services
- Shopping assistants
Integration: Orchestrates all other agents for unified experience. Uses Amazon Nova Canvas or Kling AI for virtual try-on generation. Can integrate with TryOn AI platform for production deployment.
21. Fashion Image Generator Agent
Category: Utility
Priority: Medium
Generates fashion images using OpenTryOn's image generation APIs.
Capabilities:
- Text-to-Fashion: Generates fashion images from text descriptions
- Image Editing: Edits existing fashion images (change colors, styles, backgrounds)
- Multi-Image Composition: Combines multiple fashion images into layouts
- Style Transfer: Applies styles to fashion images
- Batch Generation: Creates multiple variations efficiently
- Supports Nano Banana, Nano Banana Pro, FLUX.2 PRO, and FLUX.2 FLEX
Use Cases:
- Fashion catalog generation
- Product visualization
- Marketing material creation
- Design exploration
- E-commerce image generation
Integration: Directly uses OpenTryOn's image generation APIs. Can be deployed on TryOn AI platform.
22. Product Image Enhancer Agent
Category: Utility
Priority: Medium
Enhances product images using image generation and editing capabilities.
Capabilities:
- Background Replacement: Changes product backgrounds
- Lighting Enhancement: Improves lighting conditions
- Color Correction: Adjusts colors and tones
- Resolution Upscaling: Enhances image quality
- Style Consistency: Maintains brand style across images
- Batch Processing: Processes multiple images efficiently
Use Cases:
- E-commerce image optimization
- Product catalog enhancement
- Brand consistency maintenance
- Marketing asset creation
Integration: Uses OpenTryOn's image generation APIs (FLUX.2 for high quality, Nano Banana for speed). Can integrate with Garment Scraper Agent for bulk processing.
23. Virtual Try-On Generator Agent
Category: Utility
Priority: High
Generates virtual try-on images using OpenTryOn's virtual try-on APIs.
Capabilities:
- Person + Garment Try-On: Combines person images with garment images
- Multiple Garment Types: Supports upper body, lower body, full body, and footwear
- Batch Processing: Processes multiple try-on requests efficiently
- API Selection: Chooses between Amazon Nova Canvas and Kling AI based on requirements
- Quality Optimization: Selects optimal API based on image size and quality needs
- Asynchronous Processing: Handles long-running tasks with Kling AI's async support
Use Cases:
- E-commerce product pages
- Shopping cart try-on previews
- Styling service demonstrations
- Fashion catalog generation
- Customer engagement tools
Integration: Directly uses OpenTryOn's virtual try-on APIs (Amazon Nova Canvas, Kling AI). Can be deployed on TryOn AI platform. Works with Garment Scraper Agent to get product images.
24. Multi-Product Try-On Agent
Category: Utility
Priority: Medium
Generates try-on images for multiple products simultaneously.
Capabilities:
- Batch Try-On: Processes multiple garments with a single person image
- Outfit Combinations: Tries on multiple garments together (top + bottom)
- Comparison Views: Generates side-by-side try-on comparisons
- API Optimization: Selects best API for each garment type
- Parallel Processing: Handles multiple API calls concurrently
- Result Aggregation: Combines results into unified output
Use Cases:
- Outfit builder tools
- Product comparison pages
- Styling recommendations
- Complete look generation
Integration: Uses Amazon Nova Canvas and Kling AI APIs. Works with Outfit Compatibility Agent to suggest combinations. Can integrate with Fashion Image Generator Agent for enhanced visuals.
Implementation Priorities
Phase 1: Foundation (High Priority)
Focus on agents that provide immediate value and are essential for core functionality:
- Image Quality Analyzer Agent - Essential for try-on quality
- Size Recommendation Agent - High user value, reduces returns, uses virtual try-on
- Outfit Compatibility Agent - Leverages existing outfit generation and image APIs
- Look Analyzer Agent - Core analysis capability with virtual try-on support
- Garment Scraper Agent - Data foundation
- Fashion Image Generator Agent - Direct use of image generation APIs
- Virtual Try-On Generator Agent - Direct use of virtual try-on APIs
Phase 2: Enhancement (Medium Priority)
Build upon Phase 1 with recommendation and analysis capabilities:
- Personal Stylist Agent
- Fit Prediction Agent
- Color Coordination Agent
- PDP Analyzer Agent
- Accessory Matching Agent
- Product Image Enhancer Agent
- Multi-Product Try-On Agent
Phase 3: Advanced Features (Low Priority)
Add specialized agents for advanced use cases:
- Wardrobe Analyzer Agent
- Trend Analysis Agent
- Sustainability Analyzer Agent
- Style Transfer Agent
- Price Drop Alert Agent
Agent Architecture
Category Breakdown
| Category | Agents | Purpose |
|---|---|---|
| Data Collection | Garment Scraper, PDP Analyzer | Extract and process product information |
| Analysis | Look Analyzer, Fit Prediction, Image Quality, Product Comparison, Wardrobe Analyzer, Sustainability | Evaluate images, fits, and styles |
| Recommendation | Personal Stylist, Outfit Compatibility, Size Recommendation, Occasion-Based, Accessory Matching | Provide personalized suggestions |
| Utility | Color Coordination, Fabric Analyzer, Pose Detection, Trend Analysis, Style Transfer, Price Drop Alert, Fashion Image Generator, Product Image Enhancer, Virtual Try-On Generator, Multi-Product Try-On | Support functions, image generation, and virtual try-on capabilities |
| Orchestration | Virtual Fitting Room | Coordinate multiple agents |
Integration Points
OpenTryOn Preprocessing
- Image Quality Analyzer
- Pose Detection & Analysis
- Fabric & Material Analyzer
Virtual Try-On Pipeline
- Virtual Try-On Generator Agent - Core try-on generation using Amazon Nova Canvas or Kling AI
- Fit Prediction Agent - Pre-try-on fit analysis
- Look Analyzer Agent - Post-try-on analysis
- Size Recommendation Agent - Size suggestions with try-on visualization
- Image Quality Analyzer - Input validation
- Multi-Product Try-On Agent - Batch try-on processing
Outfit Generation
- Outfit Compatibility Agent
- Color Coordination Agent
- Accessory Matching Agent
- Personal Stylist Agent
E-commerce Integration
- Garment Scraper Agent
- PDP Analyzer Agent
- Product Comparison Agent
- Price Drop Alert Agent
Call to Action
We're building an open-source ecosystem of Fashion AI Agents, and we need your help!
For Agent Builders
Are you already building fashion AI agents?
- Contribute Your Agent: Share your existing agent with the community
- Open Source It: Make it available under an open-source license
- Document It: Help others understand and use your agent
- Integrate: Connect your agent with OpenTryOn and other platforms
How to Contribute:
- Open an issue or discussion on GitHub describing your agent
- Create a pull request with your agent implementation
- Follow our Contributing Guide
- Join our Discord community to discuss
For Developers & Researchers
Want to build one of these agents?
- Pick an Agent: Choose any agent from the list above
- Start Building: Use your preferred framework (LangChain, AutoGPT, etc.)
- Share Progress: Keep the community updated on your progress
- Get Support: Ask questions in our Discord or GitHub Discussions
Getting Started:
- Review the agent specifications above
- Check our Contributing Guide
- Join our Discord for collaboration
- Share your progress and get feedback
For Fashion Enthusiasts & Domain Experts
Have ideas for new agents?
- Share Ideas: Propose new agent ideas or improvements
- Provide Feedback: Review agent specifications and provide domain expertise
- Test Agents: Help test and validate agent implementations
- Document Use Cases: Share real-world applications
How to Participate:
- Open a GitHub Discussion with your ideas
- Review and comment on agent specifications
- Test agent implementations and provide feedback
- Share use cases and requirements
Technical Considerations
Agent Framework Recommendations
- Orchestration: LangChain, AutoGPT, or similar framework for agent orchestration
- Tool Calling: Implement tool calling for agent capabilities
- Vector Databases: Use for product and style matching
- Caching: Implement caching for frequently accessed data
- API Design: RESTful APIs for each agent with unified orchestration API
Integration with OpenTryOn Library
All agents should be designed to integrate seamlessly with OpenTryOn (the open-source library):
- Use OpenTryOn SDK: Leverage existing virtual try-on and image generation capabilities
- Virtual Try-On: Utilize Amazon Nova Canvas and Kling AI for generating realistic try-on results
- Image Generation: Utilize Nano Banana, Nano Banana Pro, FLUX.2 PRO, and FLUX.2 FLEX for generating, editing, and composing fashion images
- API Selection: Choose appropriate APIs based on requirements (Nova Canvas for AWS integration, Kling AI for async/high-res)
- Follow Standards: Adhere to OpenTryOn's code style and architecture
- Document Integration: Provide clear integration examples
- Test Compatibility: Ensure agents work with OpenTryOn's pipeline
TryOn AI Platform Integration
Agents can also integrate with TryOn AI, our cloud-hosted platform for:
- Fashion Brands: Deploy agents for brand-specific use cases
- Fashion Designers: Use agents for design workflows
- E-Commerce Marketplaces: Integrate agents into shopping experiences
Agents built with OpenTryOn can be deployed on TryOn AI platform for production use.
Data Requirements
- Product Databases: For scraping and comparison
- Style Databases: For recommendations
- User Preference Storage: For personalization
- Historical Data: For trend analysis
Community Resources
Get Involved
- GitHub: github.com/tryonlabs/opentryon
- Discord: Join our community
- Documentation: Full documentation
- Issues: Report bugs or request features
Share Your Work
- Showcase: Share your agent implementations
- Blog Posts: Write about your agent development journey
- Tutorials: Create tutorials for building agents
- Case Studies: Document real-world applications
Next Steps
For the Community
- Explore Ideas: Review all 24+ agent ideas above
- Choose Your Agent: Pick an agent to build or contribute
- Join Discussions: Participate in GitHub Discussions
- Start Building: Begin implementation using OpenTryOn SDK
- Share Progress: Keep the community updated
- Contribute: Submit your agent to the repository
For Maintainers
- Review Contributions: Evaluate agent submissions
- Provide Guidance: Help contributors with implementation
- Document Patterns: Create best practices documentation
- Build Infrastructure: Set up agent registry and discovery
- Foster Community: Encourage collaboration and sharing
Contributing Agents
Submission Process
- Open an Issue: Create an issue describing your agent
- Get Feedback: Discuss your approach with the community
- Implement: Build your agent following best practices
- Test: Ensure your agent works correctly
- Document: Provide comprehensive documentation
- Submit PR: Create a pull request with your agent
Agent Requirements
- Open Source License: Use a compatible open-source license
- Documentation: Include README, API docs, and examples
- Tests: Provide unit tests and integration tests
- Integration: Show how it integrates with OpenTryOn
- Examples: Include usage examples and demos
Recognition
Contributors will be:
- Listed: Added to contributors list
- Credited: Proper attribution in documentation
- Showcased: Featured in community highlights
- Thanked: Public recognition for contributions
Vision Statement
We envision a future where:
- Every fashion technology need has an open-source agent solution
- Developers worldwide collaborate on fashion AI agents using OpenTryOn library
- Fashion brands, designers, and e-commerce marketplaces can deploy agents via TryOn AI platform
- Researchers can build upon existing agent implementations
- The community drives innovation in fashion technology
- Image generation capabilities enable creative fashion applications
Together, we can build the most comprehensive open-source Fashion AI Agents ecosystem.
- OpenTryOn: Open-source Python library for fashion developers (what you're contributing to)
- TryOn AI: Cloud-hosted platform for fashion brands, designers, and e-commerce marketplaces (production deployment)
Share this vision on LinkedIn, Twitter, Discord, Telegram, and other platforms. Help us grow the community!
Questions?
- GitHub Discussions: Ask questions
- Discord: Join the conversation
- Email: Contact us through GitHub
Let's build the future of Fashion AI together!