{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-kyaukyuai--gpt-all-star","slug":"kyaukyuai--gpt-all-star","name":"gpt-all-star","type":"agent","url":"https://kyaukyuai.github.io/gpt-all-star/","page_url":"https://unfragile.ai/kyaukyuai--gpt-all-star","categories":["app-builders"],"tags":["ai","autonomous-agent","codebase-generation","codegen","coding-assistant","developer-tools","gpt-4","gpt-4o","langchain","langgraph","research-project"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-kyaukyuai--gpt-all-star__cap_0","uri":"capability://planning.reasoning.multi.agent.team.orchestration.for.web.application.development","name":"multi-agent team orchestration for web application development","description":"Coordinates a specialized team of 7 autonomous AI agents (Product Owner, Engineer, Architect, Designer, QA Engineer, Project Manager, Copilot) through a centralized Project class that manages execution flow, agent initialization, and inter-agent communication. Each agent has a defined role, system prompt, and expertise profile. The system uses LangGraph/LangChain for agent state management and chains agent outputs sequentially through development phases, with the Copilot agent serving as the user-facing interface that gathers requirements and provides updates throughout the process.","intents":["I want to generate a complete web application by having AI agents collaborate on different aspects (architecture, implementation, design, testing)","I need agents to work sequentially through requirements gathering, design, implementation, and QA phases without manual handoffs","I want to see how different AI agents with specialized roles can coordinate to build production-ready code"],"best_for":["teams prototyping full-stack web applications with minimal manual coding","developers exploring multi-agent AI workflows and agent coordination patterns","researchers studying autonomous software development and agent collaboration"],"limitations":["Agent coordination is sequential, not parallel — each phase waits for previous agent completion, adding latency","No built-in conflict resolution between agent outputs — relies on downstream agents to handle inconsistencies","Limited to web application development; not generalized for other software domains","Requires careful prompt engineering for each agent role to maintain quality across handoffs"],"requires":["Python 3.9+","API key for OpenAI (GPT-4/GPT-4o) or compatible LLM provider","LangChain and LangGraph libraries","Node.js 18+ (for generated web application runtime)"],"input_types":["natural language project description","user requirements and feedback","project configuration (tech stack, design preferences)"],"output_types":["complete web application codebase","architecture specifications","UI/UX designs","test suites","documentation"],"categories":["planning-reasoning","automation-workflow","multi-agent-coordination"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kyaukyuai--gpt-all-star__cap_1","uri":"capability://automation.workflow.structured.development.workflow.execution.with.step.based.phases","name":"structured development workflow execution with step-based phases","description":"Executes application development through a predefined sequence of steps organized into phases: Specification (requirements gathering, architecture design), Development (backend/frontend implementation, UI design), and Execution/Healing (testing, bug fixing, deployment). Each step is a discrete unit of work with inputs, outputs, and success criteria. The system tracks step completion state, manages dependencies between steps, and allows agents to execute healing steps when initial implementation fails quality checks or tests.","intents":["I want to break down full-stack development into discrete, manageable phases that execute in order","I need the system to automatically retry or heal failed implementation steps rather than stopping","I want to see a clear progression from requirements → design → implementation → testing with defined outputs at each stage"],"best_for":["teams wanting a structured, waterfall-like development process with AI agents","projects where clear phase separation (spec → design → dev → test) is preferred","developers building tools that need deterministic, step-based code generation workflows"],"limitations":["Strictly sequential execution prevents parallel development of independent components","No built-in rollback mechanism if a later phase invalidates earlier decisions","Healing steps are reactive (triggered by failures) rather than proactive","Step interdependencies are implicit in agent prompts, not explicitly modeled"],"requires":["Python 3.9+","LangChain/LangGraph for step execution and state management","Storage system (local filesystem or cloud) for step outputs and artifacts"],"input_types":["project requirements and specifications","user feedback on generated outputs","test results and error logs"],"output_types":["specification documents","architecture designs","implementation code","UI/UX designs","test reports","deployment artifacts"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kyaukyuai--gpt-all-star__cap_10","uri":"capability://planning.reasoning.project.management.and.task.coordination.across.agent.team","name":"project management and task coordination across agent team","description":"The Project Manager agent coordinates tasks across the agent team, manages dependencies between development phases, tracks progress, identifies blockers, and ensures smooth handoffs between agents. Maintains project state, schedules agent execution, and coordinates communication between specialized agents. Ensures that outputs from one agent are properly formatted and available for the next agent in the workflow.","intents":["I want the system to automatically coordinate work across multiple agents without manual handoffs","I need visibility into project progress and agent task execution","I want the system to identify and resolve blockers between development phases"],"best_for":["complex projects with many interdependent development tasks","teams wanting automated project coordination without manual management","developers exploring agent-based project management"],"limitations":["Project Manager decisions are based on LLM reasoning; may miss real blockers or dependencies","No integration with external project management tools (Jira, Asana, etc.)","Progress tracking is implicit in agent execution; no explicit metrics or dashboards","Cannot handle unexpected issues or require human judgment for complex decisions","Coordination overhead may slow down simple projects"],"requires":["Python 3.9+","LLM provider for Project Manager agent","Clear project structure and phase definitions"],"input_types":["project requirements and phases","agent task definitions","progress updates from agents","blocker and dependency information"],"output_types":["task schedules and execution plans","progress reports and status updates","blocker identification and resolution","handoff coordination between agents"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kyaukyuai--gpt-all-star__cap_11","uri":"capability://planning.reasoning.requirement.specification.and.product.definition.from.user.input","name":"requirement specification and product definition from user input","description":"The Product Owner agent gathers requirements, defines product specifications, creates user stories, and documents acceptance criteria. Translates user intent into structured requirements that guide architecture and implementation. Conducts requirement elicitation through questions, clarifies ambiguities, and produces specification documents that serve as the source of truth for the development team.","intents":["I want the system to gather and structure requirements from natural language descriptions","I need clear product specifications and acceptance criteria before development begins","I want the system to ask clarifying questions when requirements are ambiguous"],"best_for":["teams building applications from vague or informal requirements","startups wanting rapid requirement definition without business analysts","developers exploring AI-driven product management"],"limitations":["Requirement quality depends on user input clarity — garbage in, garbage out","No validation that requirements are feasible or complete","Requirements may be over-specified or under-specified based on LLM interpretation","No stakeholder alignment or sign-off process — requirements are AI-generated","Missing non-functional requirements (performance, security, compliance) unless explicitly mentioned"],"requires":["Python 3.9+","LLM provider for Product Owner agent","User input and project description"],"input_types":["natural language project description","user goals and use cases","constraints and preferences","clarification responses to Product Owner questions"],"output_types":["structured requirements documents","user stories and acceptance criteria","feature specifications","product scope and constraints"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kyaukyuai--gpt-all-star__cap_2","uri":"capability://tool.use.integration.llm.provider.abstraction.with.multi.provider.support.and.token.tracking","name":"llm provider abstraction with multi-provider support and token tracking","description":"Abstracts LLM interactions through a unified interface (gpt_all_star/core/llm.py) that supports multiple providers (OpenAI, Anthropic, Ollama, etc.) with configurable model selection via environment variables. Tracks token usage across all LLM calls for cost monitoring and billing. Implements provider-specific configuration (API keys, model names, temperature, max_tokens) and handles provider-specific response formats, enabling easy switching between GPT-4, GPT-4o, Claude, or local models without code changes.","intents":["I want to switch between different LLM providers (OpenAI, Anthropic, local) without rewriting agent code","I need to track token consumption across all agents to monitor costs and optimize prompt efficiency","I want to use different models for different agents (e.g., GPT-4 for Architect, GPT-4o for Engineer)"],"best_for":["teams experimenting with different LLM providers and models","projects with strict cost budgets requiring token tracking and optimization","developers building multi-provider LLM applications with pluggable model selection"],"limitations":["Provider abstraction adds ~50-100ms latency per LLM call due to wrapper overhead","Token tracking is approximate for streaming responses; exact counts require post-call reconciliation","Not all providers support identical feature sets (e.g., function calling, vision) — fallback behavior not documented","Configuration via environment variables can become unwieldy with many agents and models"],"requires":["Python 3.9+","API keys for selected providers (OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.)",".env file or environment variables for provider configuration","Provider-specific SDK (openai, anthropic, ollama, etc.)"],"input_types":["prompts and messages","model configuration (temperature, max_tokens, top_p)","provider selection (via env var or config)"],"output_types":["LLM completions/responses","token usage metrics (prompt_tokens, completion_tokens, total_tokens)","provider-specific metadata"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kyaukyuai--gpt-all-star__cap_3","uri":"capability://automation.workflow.project.file.storage.and.artifact.management.with.organized.directory.structure","name":"project file storage and artifact management with organized directory structure","description":"Manages project files and generated artifacts through a hierarchical storage system with dedicated directories for different artifact types: Root Storage (main project), Docs Storage (specifications and documentation), App Storage (generated application code), and component-specific folders. Implements file I/O operations for reading/writing code, specifications, designs, and test files. Provides a unified interface for agents to access and modify project artifacts without direct filesystem manipulation, enabling version tracking and artifact organization.","intents":["I want generated code, specs, and designs organized in a predictable directory structure","I need agents to read and write files without managing filesystem paths directly","I want to track which artifacts were generated at each development phase"],"best_for":["projects requiring organized artifact management and clear separation of concerns","teams wanting to version control generated code and specifications","developers building code generation tools that need structured output organization"],"limitations":["No built-in version control or artifact history — overwrites files without backup","No concurrent file access protection — multiple agents writing simultaneously can cause conflicts","Storage is local filesystem only; no cloud storage or distributed artifact management","No automatic cleanup of intermediate/temporary artifacts"],"requires":["Python 3.9+","Write permissions to project root directory","Sufficient disk space for generated code and artifacts"],"input_types":["file paths and content to write","artifact type (code, spec, design, test)","read requests for existing artifacts"],"output_types":["organized directory structure","written files (code, specs, designs, tests)","file metadata (paths, timestamps)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kyaukyuai--gpt-all-star__cap_4","uri":"capability://text.generation.language.copilot.agent.interface.for.user.interaction.and.feedback.gathering","name":"copilot agent interface for user interaction and feedback gathering","description":"Implements a dedicated Copilot agent that serves as the primary user-facing interface, asking clarifying questions about requirements, providing progress updates, gathering user feedback on generated outputs, and iterating based on user input. The Copilot uses natural language interaction to understand user intent, translates user feedback into actionable requirements for other agents, and maintains conversational context throughout the development process. Acts as a bridge between non-technical users and the specialized technical agents.","intents":["I want to interact with the AI development system in natural language without understanding agent architecture","I need the system to ask clarifying questions when requirements are ambiguous","I want to provide feedback on generated code/designs and have the system iterate based on my input"],"best_for":["non-technical founders and product managers directing AI-driven development","teams wanting natural language interaction with code generation systems","projects requiring iterative refinement based on user feedback"],"limitations":["Copilot understanding is limited by LLM capabilities — complex or ambiguous requirements may be misinterpreted","Feedback loop is sequential; each iteration requires full re-execution of downstream agents","No memory of previous conversations or projects — context resets between sessions","Copilot decisions are not explainable — users cannot see reasoning behind clarifying questions"],"requires":["Python 3.9+","LLM provider (OpenAI, Anthropic, etc.) for Copilot agent","User interaction interface (CLI, web UI, or API)"],"input_types":["natural language project descriptions","user feedback and preferences","clarification responses to agent questions"],"output_types":["structured requirements for technical agents","progress updates and status messages","clarifying questions and prompts"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kyaukyuai--gpt-all-star__cap_5","uri":"capability://planning.reasoning.agent.role.based.specialization.with.customizable.profiles.and.expertise","name":"agent role-based specialization with customizable profiles and expertise","description":"Defines specialized agent roles (Product Owner, Engineer, Architect, Designer, QA Engineer, Project Manager) with distinct system prompts, expertise areas, and default names/personas. Each agent has a profile that includes its color code, default model selection, and specialized capabilities. Agents can be customized with different prompts, models, or expertise areas via configuration. The system uses role-based routing to direct tasks to appropriate agents based on the type of work (e.g., architecture decisions to Architect, implementation to Engineer).","intents":["I want different agents to have specialized expertise and perspectives on development tasks","I need to customize agent behavior and expertise without modifying core system code","I want agents to make decisions within their domain of expertise rather than having a monolithic AI"],"best_for":["teams exploring role-based agent specialization and expertise division","projects where different development concerns (architecture, implementation, testing) need specialized handling","developers building customizable multi-agent systems with pluggable roles"],"limitations":["Role boundaries are defined by prompts, not enforced by system — agents can exceed their scope","No explicit conflict resolution when agents disagree on decisions within overlapping domains","Customization requires modifying prompts and configuration; no UI for role customization","Role specialization adds complexity; unclear if benefits justify overhead for simple projects"],"requires":["Python 3.9+","Understanding of agent roles and development phases","Ability to write effective system prompts for specialized roles"],"input_types":["agent role definitions (name, expertise, prompt)","task descriptions and context","configuration for role customization"],"output_types":["role-specific outputs (specs from Product Owner, code from Engineer, designs from Designer)","agent profiles and expertise metadata"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kyaukyuai--gpt-all-star__cap_6","uri":"capability://code.generation.editing.web.application.code.generation.with.react.javascript.and.chakra.ui","name":"web application code generation with react, javascript, and chakra ui","description":"Generates complete web applications using React for frontend, JavaScript/Node.js for backend, and Chakra UI for component library and styling. The Engineer agent specializes in implementing code using these specific technologies, generating production-ready components, API endpoints, and styling. Supports full-stack development from database schema to UI components, with built-in knowledge of React patterns, hooks, component composition, and Chakra UI theming.","intents":["I want to generate a complete React web application without writing code manually","I need full-stack code generation (backend API + frontend UI) in a single workflow","I want generated code to use modern React patterns and Chakra UI for consistent styling"],"best_for":["teams building React/JavaScript web applications and wanting AI-assisted development","startups prototyping web applications quickly with minimal manual coding","developers exploring code generation for full-stack JavaScript applications"],"limitations":["Limited to React/JavaScript stack — no support for Vue, Angular, Python, or other frameworks","Generated code may not follow all best practices or performance optimizations","No built-in support for complex state management (Redux, Zustand) — relies on basic React hooks","Chakra UI dependency may not suit all design requirements or branding needs","Generated code requires manual review and testing before production deployment"],"requires":["Node.js 18+","npm or yarn for dependency management","React 18+","Chakra UI and its peer dependencies"],"input_types":["application requirements and features","UI/UX design specifications","API endpoint definitions"],"output_types":["React component files (.jsx/.tsx)","Node.js/Express backend code","CSS/styling with Chakra UI","API route definitions","package.json with dependencies"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kyaukyuai--gpt-all-star__cap_7","uri":"capability://automation.workflow.automated.testing.and.quality.assurance.with.healing.loops","name":"automated testing and quality assurance with healing loops","description":"Implements a QA Engineer agent that generates and executes test suites, validates generated code against requirements, and identifies bugs or quality issues. When tests fail or quality checks detect problems, the system triggers healing steps where the Engineer agent re-implements or fixes the problematic code. The healing loop continues until tests pass or quality thresholds are met. Supports unit tests, integration tests, and specification validation.","intents":["I want generated code to be automatically tested and validated before delivery","I need the system to automatically fix bugs and quality issues rather than stopping at failures","I want confidence that generated code meets requirements and passes test suites"],"best_for":["teams wanting automated quality assurance in code generation workflows","projects where test-driven development and validation are critical","developers building code generation systems that need quality gates"],"limitations":["Test generation quality depends on requirement clarity — vague specs lead to incomplete tests","Healing loops can be expensive (multiple LLM calls per fix) and slow (sequential retries)","No guarantee that healing will succeed — infinite loops possible if root cause is misunderstood","Test coverage may be incomplete; generated tests may miss edge cases or security issues","Healing is reactive (triggered by failures) rather than proactive (preventing issues)"],"requires":["Python 3.9+","Test framework (Jest for JavaScript, pytest for Python, etc.)","Clear requirements and acceptance criteria for validation"],"input_types":["generated code to test","requirements and acceptance criteria","test failure reports and error logs"],"output_types":["test suites (unit, integration, e2e)","test execution reports","quality metrics and coverage","healed/fixed code"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kyaukyuai--gpt-all-star__cap_8","uri":"capability://planning.reasoning.architecture.and.system.design.generation.with.technical.stack.decisions","name":"architecture and system design generation with technical stack decisions","description":"The Architect agent generates system architecture, technology stack recommendations, database schema design, API structure, and deployment architecture. Analyzes requirements to make informed decisions about frameworks, databases, deployment platforms, and scalability considerations. Produces architecture documentation, technology rationale, and technical specifications that guide the Engineer's implementation. Considers factors like performance, scalability, maintainability, and cost in architecture decisions.","intents":["I want AI to recommend appropriate technology stack and architecture for my application","I need system design and architecture documentation generated from requirements","I want the system to make informed technical decisions about databases, APIs, and deployment"],"best_for":["teams building new applications and needing architecture guidance","startups wanting rapid architecture decisions without hiring architects","developers exploring AI-driven technical decision making"],"limitations":["Architecture recommendations are based on LLM training data; may not reflect latest technologies or trends","No validation that recommended architecture actually works for the specific use case","Architecture decisions are not explained in depth — rationale may be unclear","No consideration of team expertise or existing infrastructure constraints","Generated architecture may be over-engineered for simple projects or under-engineered for complex ones"],"requires":["Python 3.9+","LLM provider with strong technical knowledge (GPT-4 recommended)","Clear project requirements and constraints"],"input_types":["project requirements and features","performance and scalability requirements","budget and infrastructure constraints","team expertise and preferences"],"output_types":["architecture diagrams and descriptions","technology stack recommendations","database schema design","API structure and endpoint definitions","deployment architecture","technical rationale and decision documentation"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kyaukyuai--gpt-all-star__cap_9","uri":"capability://planning.reasoning.ui.ux.design.generation.with.component.specifications","name":"ui/ux design generation with component specifications","description":"The Designer agent generates UI/UX designs, component specifications, layout designs, and visual design guidelines. Produces design artifacts that guide the Engineer's implementation of frontend components. Considers user experience, accessibility, responsive design, and visual consistency. Generates component libraries, design tokens, and styling specifications that ensure visual coherence across the application.","intents":["I want AI to generate UI/UX designs from requirements without hiring a designer","I need design specifications and component definitions to guide frontend implementation","I want consistent visual design and component library across the application"],"best_for":["teams building applications without dedicated designers","startups wanting rapid UI/UX design without design costs","developers exploring AI-driven design generation"],"limitations":["Generated designs may not match brand guidelines or specific design preferences","No interactive design tools or visual feedback — designs are text-based specifications","Design quality depends on requirement clarity and Designer agent capability","No user testing or validation of generated designs","Accessibility considerations may be incomplete or incorrect"],"requires":["Python 3.9+","LLM provider with design knowledge","Clear design requirements and brand guidelines (if applicable)"],"input_types":["application features and user flows","brand guidelines and design preferences","accessibility requirements","target audience and user personas"],"output_types":["UI/UX design specifications","component designs and layouts","design tokens and styling guidelines","responsive design specifications","accessibility considerations"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","API key for OpenAI (GPT-4/GPT-4o) or compatible LLM provider","LangChain and LangGraph libraries","Node.js 18+ (for generated web application runtime)","LangChain/LangGraph for step execution and state management","Storage system (local filesystem or cloud) for step outputs and artifacts","LLM provider for Project Manager agent","Clear project structure and phase definitions","LLM provider for Product Owner agent","User input and project description"],"failure_modes":["Agent coordination is sequential, not parallel — each phase waits for previous agent completion, adding latency","No built-in conflict resolution between agent outputs — relies on downstream agents to handle inconsistencies","Limited to web application development; not generalized for other software domains","Requires careful prompt engineering for each agent role to maintain quality across handoffs","Strictly sequential execution prevents parallel development of independent components","No built-in rollback mechanism if a later phase invalidates earlier decisions","Healing steps are reactive (triggered by failures) rather than proactive","Step interdependencies are implicit in agent prompts, not explicitly modeled","Project Manager decisions are based on LLM reasoning; may miss real blockers or dependencies","No integration with external project management tools (Jira, Asana, etc.)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.2774296563456046,"quality":0.49,"ecosystem":0.6000000000000001,"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":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:57:09.058Z","last_commit":"2026-04-30T15:02:42Z"},"community":{"stars":229,"forks":28,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=kyaukyuai--gpt-all-star","compare_url":"https://unfragile.ai/compare?artifact=kyaukyuai--gpt-all-star"}},"signature":"ceucZ5Qm/u3cJ3s5/CHVgRthWHJb9Qll7XABdtwCZ8G/8cIZUKoGfvar3e+CRu9jjixVo4fXU+05k/LGm7/rAw==","signedAt":"2026-06-21T17:32:44.607Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/kyaukyuai--gpt-all-star","artifact":"https://unfragile.ai/kyaukyuai--gpt-all-star","verify":"https://unfragile.ai/api/v1/verify?slug=kyaukyuai--gpt-all-star","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"}}