{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-foundationagents--metagpt","slug":"foundationagents--metagpt","name":"MetaGPT","type":"agent","url":"https://atoms.dev/","page_url":"https://unfragile.ai/foundationagents--metagpt","categories":["ai-agents"],"tags":["agent","gpt","llm","metagpt","multi-agent"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-foundationagents--metagpt__cap_0","uri":"capability://planning.reasoning.multi.role.agent.orchestration.with.software.company.simulation","name":"multi-role agent orchestration with software company simulation","description":"MetaGPT assigns distinct LLM-powered roles (Product Manager, Architect, Engineer, QA) to collaborate as a simulated software company. Each role executes domain-specific actions sequentially, with message passing between roles enabling task decomposition and workflow coordination. The framework uses a Role base class with action queues and memory systems to maintain role-specific context across multi-turn interactions, simulating realistic software development workflows where roles depend on outputs from upstream roles.","intents":["I want to decompose a complex software task into specialized agent roles that collaborate like a real team","I need agents to execute tasks in a specific order with dependencies (e.g., design before implementation)","I want to simulate a software company workflow where different agents have different responsibilities"],"best_for":["teams building autonomous software development systems","developers prototyping multi-agent workflows with role specialization","researchers studying emergent behavior in agent collaboration"],"limitations":["Role coordination is sequential by default — parallel role execution requires custom environment setup","No built-in conflict resolution when roles produce contradictory outputs","Role memory is in-process only — requires external persistence for long-running systems","Scaling beyond 5-10 roles becomes expensive due to cumulative LLM calls per workflow step"],"requires":["Python 3.9+","API key for at least one LLM provider (OpenAI, Anthropic, or compatible)","metagpt package installed via pip"],"input_types":["natural language task descriptions","structured requirements (YAML/JSON)","code snippets or project context"],"output_types":["code files","design documents","test cases","project artifacts in git repository"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-foundationagents--metagpt__cap_1","uri":"capability://code.generation.editing.actionnode.based.structured.output.generation.with.dynamic.validation","name":"actionnode-based structured output generation with dynamic validation","description":"ActionNode is a declarative system for defining LLM output schemas with automatic prompt generation, parsing, and validation. Each ActionNode specifies expected output fields with types, descriptions, and validation rules. MetaGPT generates prompts that guide the LLM to produce structured outputs (JSON, code, markdown), then parses and validates responses against the schema. If validation fails, the system can trigger automatic revision loops where the LLM corrects its output based on validation errors.","intents":["I need the LLM to produce structured outputs (JSON, code) that I can reliably parse and validate","I want to automatically retry LLM outputs when they don't match expected schemas","I need to define domain-specific output formats (e.g., design documents, test cases) with validation rules"],"best_for":["developers building reliable LLM pipelines that require structured outputs","teams implementing code generation with strict format requirements","systems that need automatic error correction and retry logic for LLM outputs"],"limitations":["Complex nested schemas can produce verbose prompts that exceed token limits","Validation errors don't always lead to successful corrections — LLM may repeat same mistakes","No support for conditional schema validation (e.g., different fields required based on input type)","Revision loops add latency — each failed validation triggers a new LLM call"],"requires":["Python 3.9+","Pydantic for schema definition","LLM provider with function calling or structured output support"],"input_types":["natural language prompts","context strings","previous LLM outputs for revision"],"output_types":["JSON objects","Python code","markdown documents","structured text with validation"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-foundationagents--metagpt__cap_10","uri":"capability://automation.workflow.mock.llm.and.response.caching.for.testing.and.development","name":"mock llm and response caching for testing and development","description":"MetaGPT includes a MockLLM class that simulates LLM responses for testing without making actual API calls. The system also implements response caching where real LLM responses are cached and replayed in subsequent runs. This enables fast iteration during development and reproducible testing. Cache is stored in JSON files and can be versioned with git.","intents":["I want to test agent workflows without spending money on LLM API calls","I need reproducible test results that don't depend on LLM randomness","I want to develop and debug agents quickly without waiting for LLM responses"],"best_for":["developers building and testing agent systems","teams with limited LLM API budgets","CI/CD pipelines that need fast, reproducible tests"],"limitations":["Mock responses are static — don't reflect LLM behavior changes","Cache invalidation is manual — stale responses can cause incorrect test results","Large caches can slow down test startup","Cached responses may not cover all code paths or edge cases"],"requires":["Python 3.9+","metagpt.mock.mock_llm module","JSON cache files (optional)"],"input_types":["prompts","messages","function schemas"],"output_types":["mock responses","cached responses","test results"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-foundationagents--metagpt__cap_11","uri":"capability://memory.knowledge.context.serialization.and.recovery.for.workflow.persistence","name":"context serialization and recovery for workflow persistence","description":"MetaGPT supports serializing the entire execution context (roles, messages, artifacts, configuration) to enable workflow resumption from checkpoints. The Context class manages runtime state and can be serialized to JSON or other formats. This enables long-running workflows to be paused and resumed, or migrated across systems. Context recovery reconstructs the full agent state including memory and message history.","intents":["I want to pause a long-running agent workflow and resume it later","I need to migrate agent workflows between systems or environments","I want to checkpoint agent state for debugging or auditing"],"best_for":["teams running long-duration agent workflows","developers debugging complex agent interactions","systems that need workflow persistence across restarts"],"limitations":["Serialization can be slow for large contexts with extensive message history","Deserialization may fail if schema changes between versions","LLM provider credentials must be handled carefully during serialization","Context recovery doesn't guarantee identical behavior — LLM responses may differ"],"requires":["Python 3.9+","JSON serialization support","sufficient storage for context snapshots"],"input_types":["Context objects","serialized context data"],"output_types":["JSON context snapshots","recovered Context objects"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-foundationagents--metagpt__cap_12","uri":"capability://tool.use.integration.function.calling.with.schema.based.tool.integration.across.multiple.llm.providers","name":"function calling with schema-based tool integration across multiple llm providers","description":"MetaGPT implements a schema-based function calling system where tools are defined with Pydantic models or JSON schemas, and the framework translates these to provider-specific function calling formats (OpenAI, Anthropic, etc.). The system handles function call parsing, validation, and execution. Tools can be registered globally or per-role, and the framework manages the function calling loop (LLM calls function → execute → return result → LLM continues).","intents":["I want agents to call external functions or APIs reliably across different LLM providers","I need to define tools once and use them with multiple LLM providers","I want automatic function call validation and error handling"],"best_for":["developers building tool-using agents","teams implementing multi-provider function calling","systems that need reliable tool integration across LLM backends"],"limitations":["Schema translation between providers can lose information or cause incompatibilities","Function call parsing is provider-specific — some providers are more reliable than others","No built-in timeout or rate limiting for function execution","Complex function signatures may not translate well to all providers"],"requires":["Python 3.9+","Pydantic for schema definition","LLM provider with function calling support"],"input_types":["function definitions (Pydantic models or JSON schemas)","function implementations","tool registry"],"output_types":["function calls","function results","execution logs"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-foundationagents--metagpt__cap_13","uri":"capability://image.visual.multi.modal.capabilities.with.image.input.and.vision.model.support","name":"multi-modal capabilities with image input and vision model support","description":"MetaGPT supports multi-modal inputs including images and vision models. Agents can process images, extract information, and generate descriptions or code based on visual content. The framework integrates vision capabilities with the standard LLM provider system, enabling agents to analyze screenshots, diagrams, or other visual artifacts. Vision model responses are integrated into the message stream and can be used by downstream agents.","intents":["I want agents to analyze images or screenshots and extract information","I need agents to generate code or documentation based on visual designs","I want to use vision models alongside text models in agent workflows"],"best_for":["teams building visual analysis into agent workflows","developers implementing design-to-code systems","systems that need to process images or screenshots"],"limitations":["Vision model support varies by provider — not all providers support images equally","Image processing adds latency and cost compared to text-only workflows","Vision model outputs are less structured than text — may require additional parsing","Large images can exceed token limits or cause processing failures"],"requires":["Python 3.9+","LLM provider with vision capabilities (GPT-4V, Claude 3, etc.)","image files in supported formats (PNG, JPEG, etc.)"],"input_types":["images (PNG, JPEG, etc.)","image URLs","base64-encoded images"],"output_types":["image descriptions","extracted information","generated code or documentation"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-foundationagents--metagpt__cap_2","uri":"capability://automation.workflow.projectrepo.based.artifact.management.with.git.integration","name":"projectrepo-based artifact management with git integration","description":"ProjectRepo is a file system abstraction that manages code artifacts, design documents, and project metadata with automatic git integration. It provides methods to write files, commit changes, and maintain project structure. The system tracks file modifications, enables incremental development by reading previous outputs, and integrates with git for version control. Artifacts are organized by type (code, docs, tests) and can be retrieved for downstream processing or review.","intents":["I want agents to write code and documentation files that are automatically version-controlled","I need to track what files were created/modified during an agent workflow","I want agents to read previous project artifacts to understand context for incremental development"],"best_for":["teams building autonomous code generation systems with version control","developers implementing incremental development where agents refine previous outputs","systems that need to persist agent-generated artifacts across sessions"],"limitations":["Git integration requires local git repository — no support for remote-only workflows","File conflicts are not automatically resolved — requires manual intervention or custom merge logic","No built-in support for large binary artifacts (images, models)","Commit messages are auto-generated and may not be semantically meaningful"],"requires":["Python 3.9+","git installed and configured locally","write permissions to project directory"],"input_types":["file paths","code strings","document content","metadata objects"],"output_types":["file system artifacts","git commits","project structure metadata"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-foundationagents--metagpt__cap_3","uri":"capability://tool.use.integration.llm.provider.abstraction.with.multi.provider.support.and.token.management","name":"llm provider abstraction with multi-provider support and token management","description":"MetaGPT implements a BaseLLM abstract class with concrete implementations for OpenAI, Anthropic, Azure, AWS Bedrock, and OpenAI-compatible providers (Ollama, vLLM). The system includes a provider registry that routes requests to the appropriate LLM backend based on configuration. Token counting and cost tracking are built-in, with support for streaming responses and function calling across different provider APIs. Configuration is centralized and can be overridden per-request.","intents":["I want to switch between different LLM providers without changing agent code","I need to track token usage and costs across multiple LLM calls","I want to use local LLMs (Ollama) or self-hosted models alongside cloud providers"],"best_for":["teams building multi-provider LLM applications","developers who want cost visibility and control over LLM usage","organizations using both cloud and self-hosted LLMs"],"limitations":["Token counting is approximate for some providers — actual usage may differ","Not all providers support all features (e.g., function calling, streaming) equally","Provider-specific parameters (temperature, top_p) may not map 1:1 across providers","Switching providers mid-workflow can cause inconsistent outputs due to model differences"],"requires":["Python 3.9+","API keys for desired providers (OpenAI, Anthropic, etc.)","metagpt.config with provider configuration"],"input_types":["prompts (strings)","messages (list of dicts)","function schemas (for function calling)"],"output_types":["text responses","function calls (structured)","streaming tokens"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-foundationagents--metagpt__cap_4","uri":"capability://planning.reasoning.rolezero.dynamic.agent.system.with.tool.integration","name":"rolezero dynamic agent system with tool integration","description":"RoleZero is a generalized agent system that dynamically generates agent behaviors based on task descriptions and available tools. Unlike pre-defined roles (Engineer, PM), RoleZero agents are created on-the-fly with tool bindings, memory management, and adaptive prompting. The system includes an Editor tool for code manipulation, a tool registry for function calling, and experience retrieval that learns from previous task executions. RoleZero agents can handle diverse tasks without pre-built role definitions.","intents":["I want to create agents dynamically for tasks that don't fit pre-defined roles","I need agents to use external tools (APIs, code editors, file systems) to accomplish tasks","I want agents to learn from previous executions and improve over time"],"best_for":["developers building flexible, task-specific agents","teams implementing tool-using agents with dynamic behavior","systems that need to handle diverse, unpredictable tasks"],"limitations":["Dynamic agent generation can be unpredictable — behavior varies based on task description quality","Tool integration requires explicit tool definitions — no automatic tool discovery","Experience retrieval is basic — no sophisticated learning or transfer learning","No built-in safety guardrails for dynamically generated agents"],"requires":["Python 3.9+","Tool definitions (functions with signatures)","LLM provider with function calling support"],"input_types":["task descriptions","tool definitions","previous execution history"],"output_types":["tool calls","task results","execution logs"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-foundationagents--metagpt__cap_5","uri":"capability://memory.knowledge.message.based.inter.agent.communication.with.memory.persistence","name":"message-based inter-agent communication with memory persistence","description":"MetaGPT uses a message-passing architecture where agents communicate through a shared message pool. Messages are typed objects containing sender, receiver, content, and metadata. The system includes a BrainMemory component that persists agent memories to Redis or S3, enabling agents to retrieve context from previous interactions. Messages are indexed and searchable, supporting both direct role-to-role communication and broadcast patterns.","intents":["I want agents to communicate asynchronously without tight coupling","I need agents to remember context from previous interactions across sessions","I want to query what agents have communicated about specific topics"],"best_for":["teams building loosely-coupled multi-agent systems","developers implementing long-running agent workflows with persistence","systems that need audit trails of agent communication"],"limitations":["Message ordering is not guaranteed in distributed setups","Memory persistence adds latency — Redis/S3 calls block agent execution","No built-in message encryption — sensitive data should be handled carefully","Message schema is flexible but not validated — malformed messages can cause downstream errors"],"requires":["Python 3.9+","Redis (for BrainMemory) or S3 credentials (for cloud persistence)","metagpt.context for message routing"],"input_types":["message objects","sender/receiver role identifiers","message content (text, structured data)"],"output_types":["message history","memory retrieval results","indexed message queries"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-foundationagents--metagpt__cap_6","uri":"capability://code.generation.editing.software.development.workflow.automation.with.pre.built.roles","name":"software development workflow automation with pre-built roles","description":"MetaGPT includes pre-built roles (Product Manager, Architect, Engineer, QA) with domain-specific ActionNodes that automate software development workflows. The Product Manager generates requirements, the Architect designs system architecture, the Engineer implements code, and QA creates test cases. Each role has specialized prompts and output schemas. The workflow is orchestrated through a SoftwareCompany class that coordinates role execution and artifact generation.","intents":["I want to generate a complete software project from a natural language description","I need agents to create design documents, code, and tests in a coordinated workflow","I want to automate the software development process end-to-end"],"best_for":["teams prototyping software projects quickly","developers building code generation tools","researchers studying AI-driven software development"],"limitations":["Generated code quality varies significantly based on task complexity and LLM capability","Pre-built roles are opinionated — customization requires subclassing and prompt engineering","No built-in testing or validation of generated code — outputs may not be executable","Workflow is sequential — cannot parallelize role execution without custom environment setup","Cost scales with project complexity — large projects can require hundreds of LLM calls"],"requires":["Python 3.9+","LLM provider with strong code generation capability (GPT-4 recommended)","metagpt package with software development roles"],"input_types":["natural language project descriptions","requirements documents","technology stack specifications"],"output_types":["Python/JavaScript code files","design documents (markdown)","test cases","project structure"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-foundationagents--metagpt__cap_7","uri":"capability://code.generation.editing.incremental.development.with.context.recovery.and.artifact.reuse","name":"incremental development with context recovery and artifact reuse","description":"MetaGPT supports incremental development where agents read previous project artifacts to understand context and build upon prior work. The system can serialize and recover execution context, enabling workflows to resume from checkpoints. Agents can query the ProjectRepo to retrieve previous code, design documents, or test cases, then modify or extend them. This enables iterative refinement where each workflow step builds on previous outputs.","intents":["I want agents to refine previous code or designs rather than regenerating from scratch","I need to resume agent workflows from checkpoints without losing context","I want agents to understand project history and make decisions based on previous work"],"best_for":["teams building iterative code generation systems","developers implementing long-running agent workflows","systems that need to handle project evolution over time"],"limitations":["Context recovery is manual — requires explicit serialization/deserialization","Large project histories can exceed LLM context windows","No automatic conflict resolution when agents modify the same artifacts","Incremental changes may accumulate errors — no built-in validation across iterations"],"requires":["Python 3.9+","ProjectRepo with git integration","sufficient LLM context window for project history"],"input_types":["previous artifact paths","modification requests","context snapshots"],"output_types":["modified artifacts","context snapshots","change logs"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-foundationagents--metagpt__cap_8","uri":"capability://code.generation.editing.mermaid.diagram.generation.for.architecture.and.workflow.visualization","name":"mermaid diagram generation for architecture and workflow visualization","description":"MetaGPT includes utilities to generate Mermaid diagrams (flowcharts, sequence diagrams, class diagrams) from agent-generated content. The Architect role produces system design diagrams, and the framework can convert these to Mermaid syntax for visualization. Diagrams are stored as artifacts and can be rendered in documentation or dashboards. This enables visual representation of software architecture and workflows.","intents":["I want to generate architecture diagrams automatically from design descriptions","I need to visualize software workflows and system interactions","I want to include diagrams in generated documentation"],"best_for":["teams generating technical documentation automatically","developers building architecture visualization tools","systems that need to communicate design decisions visually"],"limitations":["Mermaid syntax is limited — complex diagrams may not render correctly","LLM-generated diagrams may have syntax errors requiring manual correction","No support for custom diagram types beyond Mermaid's built-in formats","Diagram complexity is limited by Mermaid's rendering capabilities"],"requires":["Python 3.9+","Mermaid rendering tool (CLI or web-based)","LLM provider for diagram generation"],"input_types":["architecture descriptions","workflow specifications","system design documents"],"output_types":["Mermaid diagram syntax","rendered diagram images","diagram artifacts"],"categories":["code-generation-editing","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-foundationagents--metagpt__cap_9","uri":"capability://automation.workflow.configuration.management.with.yaml.environment.variables.and.programmatic.overrides","name":"configuration management with yaml, environment variables, and programmatic overrides","description":"MetaGPT's configuration system uses a layered approach where settings can be loaded from YAML files, environment variables, or programmatic configuration objects. The Config class implements priority-based merging where later sources override earlier ones. Configuration includes LLM provider settings, role definitions, and runtime parameters. The system supports configuration caching for performance and enables per-request overrides.","intents":["I want to configure MetaGPT through YAML files without code changes","I need to override configuration at runtime for different environments","I want to manage LLM provider settings centrally"],"best_for":["teams managing multiple MetaGPT deployments","developers building configurable agent systems","operations teams deploying MetaGPT in different environments"],"limitations":["Configuration validation is minimal — invalid settings may not be caught until runtime","No built-in support for configuration versioning or rollback","Complex nested configurations can be hard to manage in YAML","Per-request overrides require programmatic access — not available through CLI"],"requires":["Python 3.9+","YAML files (optional)","environment variables (optional)"],"input_types":["YAML configuration files","environment variables","Config objects"],"output_types":["merged configuration","provider-specific settings","runtime parameters"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":50,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","API key for at least one LLM provider (OpenAI, Anthropic, or compatible)","metagpt package installed via pip","Pydantic for schema definition","LLM provider with function calling or structured output support","metagpt.mock.mock_llm module","JSON cache files (optional)","JSON serialization support","sufficient storage for context snapshots","LLM provider with function calling support"],"failure_modes":["Role coordination is sequential by default — parallel role execution requires custom environment setup","No built-in conflict resolution when roles produce contradictory outputs","Role memory is in-process only — requires external persistence for long-running systems","Scaling beyond 5-10 roles becomes expensive due to cumulative LLM calls per workflow step","Complex nested schemas can produce verbose prompts that exceed token limits","Validation errors don't always lead to successful corrections — LLM may repeat same mistakes","No support for conditional schema validation (e.g., different fields required based on input type)","Revision loops add latency — each failed validation triggers a new LLM call","Mock responses are static — don't reflect LLM behavior changes","Cache invalidation is manual — stale responses can cause incorrect test results","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8660369603266433,"quality":0.35,"ecosystem":0.55,"match_graph":0.25,"freshness":0.6,"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":"2026-05-03T13:57:19.180Z","last_commit":"2026-01-21T10:12:33Z"},"community":{"stars":67651,"forks":8597,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=foundationagents--metagpt","compare_url":"https://unfragile.ai/compare?artifact=foundationagents--metagpt"}},"signature":"qRBJVk/BwMsdVDNVuoqlfmC1Xs7Gb1LBGKczXDF6PJoEUWy1wn45vV+c0AK+xHOWFAJYtwqqmlxCVYDttU+BCA==","signedAt":"2026-06-22T21:16:55.955Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/foundationagents--metagpt","artifact":"https://unfragile.ai/foundationagents--metagpt","verify":"https://unfragile.ai/api/v1/verify?slug=foundationagents--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"}}