{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-metagpt","slug":"metagpt","name":"MetaGPT","type":"framework","url":"https://github.com/FoundationAgents/MetaGPT","page_url":"https://unfragile.ai/metagpt","categories":["automation"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-metagpt__cap_0","uri":"capability://text.generation.language.requirement.to.prd.generation.with.role.based.decomposition","name":"requirement-to-prd generation with role-based decomposition","description":"Transforms a single-line requirement into a comprehensive Product Requirements Document by orchestrating multiple specialized LLM agents (Product Manager, Architect, Engineer) that collaborate through a message-passing system. Each agent operates within a defined role context and uses chain-of-thought reasoning to progressively refine requirements into structured PRD sections (features, user stories, acceptance criteria). The framework manages agent state, conversation history, and output validation across the multi-turn interaction pipeline.","intents":["I want to convert a vague product idea into a detailed PRD without manual writing","I need to generate structured requirements that multiple teams can act on immediately","I want to see how different perspectives (PM, architect, engineer) interpret the same requirement"],"best_for":["startup founders and product managers prototyping MVPs quickly","teams adopting AI-driven development workflows","organizations standardizing requirement documentation format"],"limitations":["PRD quality depends on LLM model capability; weaker models produce generic, non-specific documents","No domain-specific knowledge injection — generates generic PRDs without industry context","Requires multiple LLM API calls (3-5 per requirement), increasing latency and cost","No human-in-the-loop refinement loop built-in; outputs require manual review"],"requires":["Python 3.9+","API key for OpenAI GPT-4 or compatible LLM provider","MetaGPT framework installed with dependencies"],"input_types":["text (single-line requirement or short description)"],"output_types":["structured text (PRD document)","JSON (parsed PRD sections)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-metagpt__cap_1","uri":"capability://text.generation.language.design.document.generation.from.requirements","name":"design document generation from requirements","description":"Accepts PRD output and generates technical design documents (architecture diagrams, component specifications, API contracts) through a specialized Architect agent that reasons about system design patterns, scalability constraints, and technology choices. The agent uses structured prompting to produce design artifacts in multiple formats (text descriptions, pseudo-code, data models) and maintains consistency with the PRD constraints.","intents":["I need a technical design document that aligns with the PRD without manual architecture work","I want to generate API contracts and data models from requirements","I need to document system architecture decisions before implementation starts"],"best_for":["technical leads designing systems before engineering begins","teams needing design documentation for code review and onboarding","organizations automating the requirements-to-design pipeline"],"limitations":["Design quality depends on LLM's understanding of architectural patterns; may suggest suboptimal designs for complex systems","No validation against actual performance constraints or infrastructure capabilities","Cannot generate executable architecture diagrams; outputs are text-based descriptions","Limited to common architectural patterns; novel or domain-specific designs may be generic"],"requires":["Python 3.9+","Completed PRD from requirement-to-PRD capability","API key for LLM provider"],"input_types":["structured text (PRD document)"],"output_types":["text (design document)","pseudo-code (component interfaces)","structured data (data models, API schemas)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-metagpt__cap_10","uri":"capability://code.generation.editing.multi.language.code.generation.with.language.specific.idioms","name":"multi-language code generation with language-specific idioms","description":"Generates code in multiple programming languages (Python, JavaScript, Go, Java, Rust) with language-specific idioms, conventions, and best practices. The Code Generator agent uses language-specific prompting and post-processing to ensure generated code follows community standards (PEP 8 for Python, ESLint for JavaScript, etc.). Outputs include language-specific build files and dependency specifications.","intents":["I want to generate code in multiple languages from a single design specification","I need code that follows language-specific conventions and best practices","I want to generate polyglot projects with multiple language components"],"best_for":["teams building polyglot systems with multiple language components","organizations standardizing code generation across different tech stacks","developers who want language-idiomatic code rather than generic templates"],"limitations":["Code quality varies by language; better support for mainstream languages (Python, JavaScript) than niche languages","Generated code may not follow all language-specific best practices; requires linting and review","No validation of language-specific syntax; requires compilation/interpretation before use","Build file generation (requirements.txt, package.json) may be incomplete or incorrect"],"requires":["Python 3.9+","Design document and task breakdown","API key for LLM provider","Target programming languages specified"],"input_types":["structured text (design document, task breakdown)"],"output_types":["code (Python, JavaScript, Go, Java, Rust, etc.)","build files (requirements.txt, package.json, go.mod, pom.xml, Cargo.toml)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-metagpt__cap_11","uri":"capability://planning.reasoning.requirement.validation.and.consistency.checking","name":"requirement validation and consistency checking","description":"Validates requirements for completeness, consistency, and feasibility through automated analysis. A Validator agent checks for missing acceptance criteria, conflicting requirements, unrealistic scope, and ambiguous language. The framework produces a validation report with severity levels (error, warning, info) and suggests corrections or clarifications needed before proceeding to design.","intents":["I want to validate requirements before design to catch issues early","I need to identify missing acceptance criteria or conflicting requirements","I want to ensure requirements are clear and unambiguous before implementation"],"best_for":["product managers validating requirements before handoff to engineering","teams adopting AI-assisted requirement validation","organizations implementing quality gates in the requirement-to-code pipeline"],"limitations":["Validation rules are heuristic-based; may produce false positives or miss subtle issues","Cannot validate against actual business constraints or market requirements","No integration with requirement management tools; requires manual review of validation report","Validation quality depends on requirement clarity; poorly written requirements may pass validation"],"requires":["Python 3.9+","Initial requirement text","API key for LLM provider"],"input_types":["text (requirement description)"],"output_types":["structured data (validation report with issues and suggestions)","text (human-readable validation summary)"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-metagpt__cap_2","uri":"capability://planning.reasoning.task.decomposition.and.sprint.planning","name":"task decomposition and sprint planning","description":"Breaks down design documents into granular, assignable engineering tasks with effort estimates, dependencies, and priority ordering. An Engineer agent analyzes the design specification and generates a task breakdown structure (TBS) that maps to sprint planning, including subtasks, acceptance criteria, and resource requirements. The framework produces output compatible with project management tools (Jira-style task lists with story points).","intents":["I want to convert a design document into a prioritized task list for my engineering team","I need to estimate effort and dependencies for sprint planning without manual breakdown","I want to generate task descriptions with acceptance criteria automatically"],"best_for":["engineering managers planning sprints from design documents","teams adopting AI-assisted project planning","organizations scaling from ad-hoc to structured task management"],"limitations":["Effort estimates are heuristic-based and may not reflect team velocity or skill distribution","Cannot account for team-specific context (existing technical debt, infrastructure constraints)","Dependency detection is syntactic; may miss implicit dependencies or circular dependencies","No integration with actual project management tools; requires manual export/import"],"requires":["Python 3.9+","Completed design document from design-generation capability","API key for LLM provider"],"input_types":["structured text (design document)"],"output_types":["structured text (task list with story points)","JSON (task breakdown structure with dependencies)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-metagpt__cap_3","uri":"capability://code.generation.editing.code.skeleton.generation.with.file.structure","name":"code skeleton generation with file structure","description":"Generates initial code repository structure (directory layout, file stubs, boilerplate) based on the design document and task breakdown. The framework uses a Code Generator agent that produces language-specific project scaffolding (Python packages, Node.js modules, etc.) with proper module organization, import statements, and class/function signatures matching the design specification. Outputs include a complete file tree ready for implementation.","intents":["I want to generate a project skeleton that matches the design without manual setup","I need to create the initial code structure with proper module organization","I want to generate function signatures and class definitions from the design"],"best_for":["teams starting greenfield projects with AI-assisted scaffolding","developers who want to skip boilerplate setup and focus on business logic","organizations standardizing project structure across teams"],"limitations":["Generated code is skeleton-only; no business logic implementation","Language support depends on LLM training data; may produce non-idiomatic code for less common languages","No validation of generated code syntax; requires compilation/linting before use","Cannot generate complex build configurations or CI/CD pipelines"],"requires":["Python 3.9+","Completed design document and task breakdown","API key for LLM provider","Target programming language specified (Python, JavaScript, Go, etc.)"],"input_types":["structured text (design document, task breakdown)"],"output_types":["code (Python, JavaScript, Go, Java, etc.)","file structure (directory tree with file stubs)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-metagpt__cap_4","uri":"capability://tool.use.integration.multi.agent.orchestration.with.message.passing","name":"multi-agent orchestration with message-passing","description":"Coordinates multiple specialized LLM agents (Product Manager, Architect, Engineer, QA) through a message-passing system where agents publish outputs to a shared context and subscribe to relevant messages. The framework manages agent lifecycle, conversation history, state persistence, and message routing based on agent roles and capabilities. Agents can trigger other agents asynchronously, creating a workflow DAG that executes the full requirement-to-code pipeline.","intents":["I want to orchestrate multiple AI agents to work together on a complex task","I need agents to collaborate with clear role boundaries and message contracts","I want to build custom agent workflows that chain multiple LLM calls with state management"],"best_for":["teams building multi-agent AI systems and workflows","developers extending MetaGPT with custom agents","organizations automating complex, multi-step processes with AI"],"limitations":["Message-passing adds latency (100-500ms per message) compared to direct function calls","No built-in persistence for agent state; requires external storage for long-running workflows","Agent coordination is synchronous by default; asynchronous patterns require custom implementation","Debugging multi-agent workflows is complex; limited visibility into agent decision-making"],"requires":["Python 3.9+","MetaGPT framework installed","API keys for LLM providers (OpenAI, Anthropic, or compatible)","Understanding of agent design patterns and role-based architecture"],"input_types":["text (initial requirement or task)","structured data (agent configuration, message schemas)"],"output_types":["structured data (agent outputs, message logs)","text/code (final deliverables from agent pipeline)"],"categories":["tool-use-integration","planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-metagpt__cap_5","uri":"capability://code.generation.editing.context.aware.code.completion.with.codebase.understanding","name":"context-aware code completion with codebase understanding","description":"Provides code completion suggestions that understand the full codebase structure, design patterns, and existing implementations. The framework indexes the generated code repository and uses semantic understanding of class hierarchies, module dependencies, and API contracts to suggest contextually relevant completions. Completions respect the design specification and maintain consistency with existing code patterns.","intents":["I want code completion that understands my project's architecture and design patterns","I need suggestions that respect the module structure and API contracts from the design","I want to maintain code consistency across the codebase during implementation"],"best_for":["developers implementing code generated from MetaGPT designs","teams maintaining consistency across large codebases","organizations using AI-assisted development with design-aware completion"],"limitations":["Requires full codebase indexing; slow for very large projects (>100k LOC)","Completion quality depends on design document accuracy; poor designs produce poor suggestions","No real-time completion; requires background indexing and may have stale suggestions","Limited to Python and JavaScript; other languages require custom indexing"],"requires":["Python 3.9+","Generated code repository from code-skeleton-generation capability","API key for LLM provider","IDE integration (VS Code plugin or language server)"],"input_types":["code (partial code snippet, cursor position)"],"output_types":["code (completion suggestions with context)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-metagpt__cap_6","uri":"capability://code.generation.editing.automated.test.case.generation.from.requirements.and.code","name":"automated test case generation from requirements and code","description":"Generates unit tests, integration tests, and acceptance tests from the PRD, design document, and implementation code. A QA agent analyzes the requirements and code to identify test scenarios, edge cases, and acceptance criteria, then generates test code with proper assertions and mocking. The framework produces test files in language-specific formats (pytest for Python, Jest for JavaScript) with coverage analysis.","intents":["I want to generate test cases automatically from requirements without manual test writing","I need acceptance tests that validate the PRD requirements are met","I want to ensure code coverage matches the design specification"],"best_for":["teams implementing AI-generated code and needing automated test generation","QA teams validating that implementations meet requirements","organizations adopting test-driven development with AI assistance"],"limitations":["Generated tests may miss domain-specific edge cases or business logic nuances","Test quality depends on requirement clarity; vague requirements produce generic tests","No integration with actual test runners; requires manual setup of test infrastructure","Cannot generate performance or load tests; limited to functional testing"],"requires":["Python 3.9+","PRD, design document, and implementation code","API key for LLM provider","Test framework installed (pytest, Jest, etc.)"],"input_types":["structured text (PRD, design document)","code (implementation code)"],"output_types":["code (test files in language-specific format)","structured data (test coverage report)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-metagpt__cap_7","uri":"capability://text.generation.language.documentation.generation.from.code.and.design","name":"documentation generation from code and design","description":"Automatically generates API documentation, architecture guides, and implementation guides from the design document and source code. A Documentation agent analyzes code structure, docstrings, and design rationale to produce markdown/HTML documentation with code examples, architecture diagrams (as ASCII or Mermaid), and usage guides. Documentation is kept in sync with code through automated regeneration.","intents":["I want to generate API documentation automatically from code without manual writing","I need architecture documentation that explains design decisions and trade-offs","I want to create developer guides that help new team members understand the codebase"],"best_for":["teams documenting AI-generated code and designs","open-source projects needing comprehensive documentation","organizations maintaining documentation as code"],"limitations":["Generated documentation quality depends on code quality and docstring completeness","Cannot generate visual architecture diagrams; limited to ASCII/Mermaid text-based diagrams","No integration with documentation platforms (Confluence, Notion); requires manual publishing","Regeneration may overwrite manual documentation updates"],"requires":["Python 3.9+","Design document and implementation code","API key for LLM provider","Markdown or HTML output format specified"],"input_types":["code (source code with docstrings)","structured text (design document)"],"output_types":["markdown (API documentation, guides)","HTML (rendered documentation)","text (ASCII diagrams, architecture guides)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-metagpt__cap_8","uri":"capability://planning.reasoning.iterative.refinement.through.agent.feedback.loops","name":"iterative refinement through agent feedback loops","description":"Enables agents to review and refine each other's outputs through feedback loops, where downstream agents (Engineer, QA) can request changes from upstream agents (PM, Architect) if outputs are incomplete or inconsistent. The framework manages feedback routing, tracks refinement iterations, and maintains version history of artifacts. Refinement continues until quality thresholds are met or max iterations reached.","intents":["I want agents to validate each other's work and request refinements automatically","I need to ensure PRD, design, and code are consistent through automated validation","I want to track how artifacts evolved through multiple refinement iterations"],"best_for":["teams using multi-agent workflows that need quality gates","organizations automating the full requirement-to-code pipeline with validation","projects requiring artifact consistency across multiple stages"],"limitations":["Feedback loops add significant latency (multiple LLM calls per iteration); can take minutes for complex refinements","No guarantee of convergence; feedback loops may cycle indefinitely without proper termination conditions","Refinement quality depends on agent ability to identify and fix issues; may miss subtle inconsistencies","Version history grows quickly; requires storage management for long-running workflows"],"requires":["Python 3.9+","Multi-agent orchestration capability enabled","API keys for LLM providers","Feedback validation rules defined (consistency checks, quality metrics)"],"input_types":["structured data (agent outputs, validation rules)"],"output_types":["structured data (refined artifacts, refinement history)","text (feedback logs, iteration summaries)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-metagpt__cap_9","uri":"capability://tool.use.integration.custom.agent.creation.and.extension.framework","name":"custom agent creation and extension framework","description":"Provides a framework for developers to define custom agents with specific roles, capabilities, and reasoning patterns. Agents are implemented as Python classes inheriting from a base Agent class, with configurable system prompts, tool bindings, and message handlers. The framework handles agent registration, lifecycle management, and integration into existing workflows without modifying core code.","intents":["I want to create custom agents for domain-specific tasks (e.g., Security Agent, DevOps Agent)","I need to extend MetaGPT with agents that understand my organization's processes","I want to build specialized agents that use custom tools and APIs"],"best_for":["developers extending MetaGPT with custom agents","organizations building domain-specific AI workflows","teams that need agents for specialized roles (security, compliance, DevOps)"],"limitations":["Requires Python programming knowledge; not suitable for non-technical users","Custom agents must be tested and validated; framework provides no automated testing","Tool integration requires manual implementation; no automatic tool discovery","Agent reasoning patterns are limited to LLM capabilities; complex logic requires custom code"],"requires":["Python 3.9+","MetaGPT framework installed","Understanding of agent design patterns and LLM prompting","API keys for LLM providers"],"input_types":["Python code (agent class definition)","structured data (agent configuration, system prompts)"],"output_types":["agent instance (registered and ready to use in workflows)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"low","permissions":["Python 3.9+","API key for OpenAI GPT-4 or compatible LLM provider","MetaGPT framework installed with dependencies","Completed PRD from requirement-to-PRD capability","API key for LLM provider","Design document and task breakdown","Target programming languages specified","Initial requirement text","Completed design document from design-generation capability","Completed design document and task breakdown"],"failure_modes":["PRD quality depends on LLM model capability; weaker models produce generic, non-specific documents","No domain-specific knowledge injection — generates generic PRDs without industry context","Requires multiple LLM API calls (3-5 per requirement), increasing latency and cost","No human-in-the-loop refinement loop built-in; outputs require manual review","Design quality depends on LLM's understanding of architectural patterns; may suggest suboptimal designs for complex systems","No validation against actual performance constraints or infrastructure capabilities","Cannot generate executable architecture diagrams; outputs are text-based descriptions","Limited to common architectural patterns; novel or domain-specific designs may be generic","Code quality varies by language; better support for mainstream languages (Python, JavaScript) than niche languages","Generated code may not follow all language-specific best practices; requires linting and review","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.578Z","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=metagpt","compare_url":"https://unfragile.ai/compare?artifact=metagpt"}},"signature":"UlIdZH3r3WIOLS+eL5J4VXpkeXuGMQvB3tDJetrTn7OQPhPN1Yu80n5odM2UyGXLJn1+ySVfuYwvQMFCZ6fqCA==","signedAt":"2026-06-21T17:07:20.259Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/metagpt","artifact":"https://unfragile.ai/metagpt","verify":"https://unfragile.ai/api/v1/verify?slug=metagpt","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"}}