{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-langgptai--langgpt","slug":"langgptai--langgpt","name":"LangGPT","type":"repo","url":"https://github.com/langgptai","page_url":"https://unfragile.ai/langgptai--langgpt","categories":["prompt-engineering"],"tags":["chatgpt","claude","deeplearning","doubao","framework","gemini","gpt-4","gpt3-prompts","langgpt","meta-prompting","prompt","prompt-engineering","qwen","structured-prompts"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-langgptai--langgpt__cap_0","uri":"capability://text.generation.language.role.based.prompt.templating.with.hierarchical.structure","name":"role-based prompt templating with hierarchical structure","description":"Provides a Markdown-based template system that organizes prompts into discrete sections (Profile, Rules, Workflow, Initialization) using a Role Template pattern. The framework enforces a hierarchical structure similar to object-oriented programming, where each role definition includes metadata (author, version, language), capability descriptions, behavioral constraints, and execution workflows. This enables prompts to be authored, versioned, and maintained as reusable code artifacts rather than ad-hoc text.","intents":["Create reusable prompt templates that can be shared across teams and projects","Version control and iterate on prompts with clear change tracking","Define role-based personas with consistent structure across different LLM providers","Organize complex multi-step prompts into maintainable sections"],"best_for":["teams building production LLM applications requiring prompt consistency","prompt engineers managing libraries of 10+ specialized prompts","organizations standardizing prompt design across GPT-4, Claude, Gemini, and other LLMs"],"limitations":["Markdown/JSON/YAML parsing is manual — no built-in IDE or syntax validation","Template inheritance and composition not natively supported — requires manual duplication","No automatic prompt optimization or A/B testing framework included","Requires manual variable substitution — no built-in templating engine like Jinja2"],"requires":["Basic Markdown knowledge for template authoring","Access to at least one LLM (GPT-4 recommended, Claude/Gemini/Qwen also supported)","Text editor or IDE supporting Markdown (VS Code, Obsidian, etc.)"],"input_types":["Markdown documents","JSON configuration","YAML specifications"],"output_types":["Structured prompt text","Role definitions","Executable prompt templates"],"categories":["text-generation-language","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langgptai--langgpt__cap_1","uri":"capability://text.generation.language.multi.provider.prompt.compatibility.layer","name":"multi-provider prompt compatibility layer","description":"Designs prompts in a provider-agnostic format that can be executed across GPT-4, Claude, Gemini, Qwen, Doubao, and other LLMs without modification. The framework abstracts away provider-specific syntax and API differences, allowing a single Role Template to be deployed to multiple LLM backends. This is achieved through standardized section definitions (Profile, Rules, Workflow) that map to universal LLM instruction patterns rather than provider-specific prompt formats.","intents":["Write a prompt once and deploy it to multiple LLM providers without rewriting","Reduce vendor lock-in by maintaining provider-agnostic prompt definitions","Compare LLM outputs on identical prompts across different providers","Migrate prompts between LLM providers as capabilities or pricing change"],"best_for":["teams evaluating multiple LLM providers and needing consistent prompt behavior","enterprises requiring multi-provider redundancy for critical LLM applications","developers building LLM applications that need to switch providers without prompt refactoring"],"limitations":["Provider-specific features (function calling, vision, tool use) require manual adaptation","No automatic capability detection — developers must manually verify prompt compatibility across providers","Prompt optimization for provider-specific strengths (e.g., Claude's long context) requires manual tuning","No built-in testing framework to validate prompt behavior consistency across providers"],"requires":["API keys for target LLM providers (OpenAI, Anthropic, Google, Alibaba, etc.)","Understanding of each provider's API and response format","Manual testing to verify prompt behavior across providers"],"input_types":["Markdown Role Templates","JSON/YAML prompt definitions"],"output_types":["Provider-agnostic prompt text","Execution results from multiple LLMs"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langgptai--langgpt__cap_10","uri":"capability://planning.reasoning.prompt.chain.composition.and.orchestration","name":"prompt chain composition and orchestration","description":"Enables composition of multiple Role Templates into prompt chains where the output of one prompt becomes the input to the next, creating multi-step reasoning or processing pipelines. Prompt chains are orchestrated sequences of prompts that work together to solve complex problems by breaking them into smaller, manageable steps. This allows complex tasks to be decomposed into reusable prompt components that can be chained together in different combinations.","intents":["Decompose complex tasks into reusable prompt components","Create multi-step reasoning pipelines where each step builds on previous results","Combine multiple specialized prompts to solve complex problems","Enable prompt reuse across different chain compositions"],"best_for":["developers building complex multi-step LLM applications","teams needing to decompose complex tasks into reusable prompt components","applications requiring iterative refinement or multi-stage processing"],"limitations":["No built-in chain orchestration framework — requires manual implementation or external tools","Error handling and recovery in chains requires manual implementation","Token usage compounds across chain steps — can become expensive for long chains","No built-in mechanism to validate that chain outputs are suitable inputs for next step"],"requires":["Clear decomposition of complex tasks into prompt steps","Implementation of chain orchestration logic","Testing to verify chain outputs flow correctly between steps","Token budget management for multi-step chains"],"input_types":["Multiple Role Templates","Chain composition specifications"],"output_types":["Multi-step reasoning outputs","Refined or processed results"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langgptai--langgpt__cap_11","uri":"capability://planning.reasoning.som.prompting.with.sam.specialized.agent.model.integration","name":"som prompting with sam (specialized agent model) integration","description":"Implements SOM (Self-Organizing Map) prompting patterns integrated with SAM (Specialized Agent Model) concepts, enabling prompts to organize and structure information hierarchically. SOM prompting allows prompts to define how information should be organized and processed, while SAM integration enables specialization of agents for specific tasks. This pattern enables complex information organization and agent specialization within the prompt structure itself.","intents":["Organize information hierarchically using self-organizing map patterns","Specialize agents for specific tasks within a prompt","Structure complex information processing pipelines","Enable emergent behavior through organized agent interactions"],"best_for":["developers building complex information organization systems","teams needing hierarchical information processing","applications requiring agent specialization and coordination"],"limitations":["SOM/SAM patterns are advanced and require deep understanding of the concepts","No built-in SOM/SAM implementation — requires manual implementation within prompts","Complexity of SOM/SAM patterns can make prompts difficult to understand and maintain","LLM may not correctly implement SOM/SAM patterns without extensive instruction"],"requires":["Deep understanding of SOM (Self-Organizing Map) concepts","Understanding of SAM (Specialized Agent Model) patterns","Advanced prompt engineering skills","Extensive testing to verify SOM/SAM patterns work correctly"],"input_types":["Complex information structures","Agent specialization specifications"],"output_types":["Hierarchically organized information","Specialized agent outputs"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langgptai--langgpt__cap_12","uri":"capability://planning.reasoning.multi.role.collaboration.and.interaction.patterns","name":"multi-role collaboration and interaction patterns","description":"Enables definition of multiple roles that can interact and collaborate within a single prompt or prompt chain, creating multi-agent scenarios where different roles have different perspectives, capabilities, or responsibilities. Multi-role collaboration patterns allow roles to be composed together to solve problems that require multiple specialized perspectives or capabilities. This enables complex collaborative reasoning where different roles contribute their expertise to reach conclusions.","intents":["Create multi-agent scenarios where different roles collaborate","Enable different perspectives on the same problem","Combine specialized capabilities from multiple roles","Implement debate, discussion, or consensus-building patterns"],"best_for":["developers building multi-perspective reasoning systems","teams needing collaborative problem-solving with multiple specialized roles","applications requiring debate or consensus-building patterns"],"limitations":["No built-in multi-role orchestration framework — requires manual implementation","Coordinating multiple roles can be complex and error-prone","Token usage multiplies with multiple roles — can become expensive","No built-in conflict resolution or consensus mechanisms"],"requires":["Clear definition of each role's perspective and capabilities","Implementation of role interaction and coordination logic","Testing to verify roles interact correctly and produce coherent outputs","Token budget management for multi-role scenarios"],"input_types":["Multiple Role Templates","Role interaction specifications"],"output_types":["Multi-perspective outputs","Collaborative reasoning results","Consensus or debate outcomes"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langgptai--langgpt__cap_13","uri":"capability://text.generation.language.prompt.design.principles.and.best.practices.documentation","name":"prompt design principles and best practices documentation","description":"Provides comprehensive documentation of prompt design principles, common patterns, and anti-patterns that guide effective prompt engineering within the LangGPT framework. This includes guidance on structuring prompts, avoiding common pitfalls, and applying proven patterns for different use cases. The documentation serves as a knowledge base that helps users apply the framework effectively and avoid common mistakes.","intents":["Learn prompt design principles and best practices","Understand common patterns and anti-patterns in prompt engineering","Apply proven techniques to improve prompt quality","Avoid common mistakes and pitfalls in prompt design"],"best_for":["prompt engineers learning the LangGPT framework","teams establishing prompt engineering standards","developers new to structured prompt design"],"limitations":["Documentation is static — may not reflect latest LLM capabilities or best practices","Principles and patterns are guidelines, not enforced rules","No automated validation that prompts follow documented principles","Documentation quality depends on community contributions and maintenance"],"requires":["Time to read and understand documentation","Willingness to apply documented principles","Testing to verify principles improve prompt quality"],"input_types":["Documentation and guides","Example prompts"],"output_types":["Knowledge and understanding","Improved prompt designs"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langgptai--langgpt__cap_14","uri":"capability://text.generation.language.example.applications.and.use.case.templates","name":"example applications and use case templates","description":"Provides pre-built example prompts and templates for common use cases including content generation, code generation, fitness planning, and other domains. These examples serve as starting points for users to understand how to apply the LangGPT framework to their specific problems, reducing the learning curve and enabling faster prompt development. Examples demonstrate best practices and patterns in action.","intents":["Get started quickly with pre-built templates for common use cases","Learn by example how to structure prompts for specific domains","Reduce time to first working prompt","Understand how to apply LangGPT patterns to real problems"],"best_for":["developers new to LangGPT looking for quick-start templates","teams building applications in common domains (content, code, planning)","users wanting to understand LangGPT through concrete examples"],"limitations":["Examples may not cover all use cases or domains","Examples may need customization for specific requirements","Example quality depends on community contributions","Examples may become outdated as LLM capabilities evolve"],"requires":["Understanding of the example's domain","Ability to customize examples for specific needs","Testing to verify examples work with target LLM"],"input_types":["Example prompt templates","Use case descriptions"],"output_types":["Customized prompts","Domain-specific implementations"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langgptai--langgpt__cap_2","uri":"capability://text.generation.language.dynamic.variable.substitution.and.templating","name":"dynamic variable substitution and templating","description":"Supports variable placeholders within prompts that can be dynamically substituted at runtime, enabling parameterized prompt generation without manual text editing. Variables are defined using a syntax that integrates with the Role Template structure, allowing prompts to accept user input, context data, or system parameters. This enables the same prompt template to be reused across different inputs and contexts by simply changing variable values rather than rewriting the entire prompt.","intents":["Create parameterized prompts that accept dynamic user input or context","Reuse the same prompt template across multiple similar tasks with different data","Build prompt chains where output from one step becomes input variables for the next","Generate personalized prompts based on user profile or session context"],"best_for":["developers building LLM applications with dynamic user inputs","teams creating prompt chains or multi-step workflows","applications requiring personalization or context-aware prompts"],"limitations":["Variable syntax and substitution logic must be manually implemented — no built-in templating engine provided","No type checking or validation for variable values — incorrect types can degrade prompt quality","Complex conditional logic based on variables requires manual implementation","No built-in escaping or sanitization for variable values — injection attacks possible if not handled carefully"],"requires":["Understanding of variable syntax and substitution patterns","Manual implementation of variable replacement logic in application code","Testing to ensure variable substitution produces valid prompts"],"input_types":["Markdown templates with variable placeholders","Variable values (strings, numbers, lists)"],"output_types":["Substituted prompt text","Parameterized prompt definitions"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langgptai--langgpt__cap_3","uri":"capability://planning.reasoning.workflow.based.prompt.execution.sequencing","name":"workflow-based prompt execution sequencing","description":"Defines a Workflow section within Role Templates that specifies the sequence of steps an LLM should follow when executing a prompt. The workflow section acts as an execution plan, breaking down complex tasks into ordered steps with clear input/output expectations for each step. This enables multi-step reasoning patterns (similar to chain-of-thought) to be encoded directly in the prompt structure, making complex reasoning processes explicit and reproducible across different LLM invocations.","intents":["Define multi-step reasoning processes that LLMs should follow","Break down complex tasks into ordered steps with clear dependencies","Encode chain-of-thought patterns directly in prompt templates","Create reproducible execution plans that produce consistent results across LLM invocations"],"best_for":["developers building complex reasoning tasks requiring multiple steps","teams needing reproducible multi-step LLM workflows","applications where step-by-step reasoning improves output quality"],"limitations":["Workflow execution is not enforced — LLMs may skip steps or deviate from the defined sequence","No built-in monitoring or validation that LLM followed the workflow correctly","Complex conditional branching within workflows requires manual implementation","No automatic step-by-step output extraction — requires post-processing to parse step results"],"requires":["Clear understanding of task decomposition and step sequencing","Manual design of workflow steps appropriate for the target LLM","Testing to verify LLM follows the defined workflow"],"input_types":["Markdown workflow definitions","Step descriptions with input/output specifications"],"output_types":["Step-by-step execution plans","Structured reasoning outputs"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langgptai--langgpt__cap_4","uri":"capability://safety.moderation.rule.based.constraint.and.behavior.definition","name":"rule-based constraint and behavior definition","description":"Provides a Rules section within Role Templates that explicitly defines behavioral constraints, operating principles, and guardrails that the LLM must follow when assuming a role. Rules are structured as a list of constraints that guide LLM behavior without requiring complex prompt engineering tricks. This enables consistent behavior enforcement across different LLM providers and invocations by making constraints explicit and machine-readable within the template structure.","intents":["Define explicit behavioral constraints that LLMs must follow","Enforce consistent output format and style across multiple LLM invocations","Prevent unwanted behaviors or off-topic responses through guardrails","Make role-specific constraints explicit and maintainable"],"best_for":["teams building role-based LLM assistants with specific behavioral requirements","applications requiring consistent output formatting or style","systems needing guardrails to prevent unwanted LLM behaviors"],"limitations":["Rule enforcement depends on LLM instruction-following capability — not guaranteed","Complex conditional rules are difficult to express in the Rules section","No built-in validation that LLM output actually follows defined rules","Rules may conflict with each other or with the LLM's base training, causing unpredictable behavior"],"requires":["Clear articulation of behavioral constraints and operating principles","Understanding of LLM instruction-following limitations","Testing to verify rules are actually followed"],"input_types":["Markdown rule definitions","Constraint descriptions"],"output_types":["Structured rule lists","Constrained LLM outputs"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langgptai--langgpt__cap_5","uri":"capability://memory.knowledge.profile.based.role.metadata.and.capability.declaration","name":"profile-based role metadata and capability declaration","description":"Defines a Profile section that serves as a 'resume' for a role, containing essential metadata (author, version, language, description) and explicit capability declarations organized by skill category. The Profile section acts as machine-readable documentation of what a role can do, enabling role discovery, versioning, and capability-based selection. This allows prompts to be catalogued and selected based on declared capabilities rather than requiring manual inspection of prompt text.","intents":["Document role capabilities and metadata in a structured, machine-readable format","Enable version control and tracking of prompt evolution","Support role discovery and selection based on declared capabilities","Create role libraries with clear capability documentation"],"best_for":["teams building prompt libraries with multiple roles","organizations needing role versioning and change tracking","applications requiring capability-based role selection"],"limitations":["Profile metadata is not automatically validated — incorrect or outdated information can persist","No built-in role discovery mechanism — requires manual cataloguing or external indexing","Capability declarations are not executable or testable — they are documentation only","No automatic capability verification that declared skills actually work as described"],"requires":["Clear understanding of role purpose and capabilities","Discipline in maintaining accurate metadata and version numbers","Documentation of skills and capabilities"],"input_types":["Markdown profile definitions","Metadata (author, version, language)","Skill descriptions"],"output_types":["Structured role metadata","Capability declarations","Role documentation"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langgptai--langgpt__cap_6","uri":"capability://text.generation.language.initialization.based.prompt.context.setup","name":"initialization-based prompt context setup","description":"Provides an Initialization section within Role Templates that specifies the initial context, system state, or setup information that should be provided to the LLM before executing the main prompt. The Initialization section enables prompts to be self-contained by encoding all necessary context and setup within the template itself, rather than requiring external context management. This allows prompts to be portable and executable in different environments without additional setup steps.","intents":["Define initial context or system state that the LLM needs to know","Make prompts self-contained and portable across different execution environments","Specify startup instructions or initialization steps for the LLM","Encode environment-specific configuration within the prompt template"],"best_for":["developers building portable, self-contained prompts","teams needing prompts that work across different execution environments","applications requiring consistent initialization across multiple LLM invocations"],"limitations":["Initialization context is static — cannot be dynamically updated based on runtime conditions","Large initialization sections can consume significant token budget","No built-in mechanism to verify initialization was successful","Complex initialization logic requires manual implementation"],"requires":["Clear understanding of what context the LLM needs to know","Careful management of initialization size to avoid token waste","Testing to verify initialization is sufficient for prompt execution"],"input_types":["Markdown initialization definitions","Context descriptions","Setup instructions"],"output_types":["Initialized prompt context","Setup-ready prompts"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langgptai--langgpt__cap_7","uri":"capability://planning.reasoning.conditional.logic.and.branching.in.prompts","name":"conditional logic and branching in prompts","description":"Supports conditional statements and branching logic within prompts, enabling different execution paths based on input conditions or LLM state. Conditional logic allows prompts to adapt their behavior dynamically, executing different Rules, Workflows, or outputs based on specified conditions. This enables single prompt templates to handle multiple scenarios without requiring separate prompt definitions for each case.","intents":["Create adaptive prompts that behave differently based on input conditions","Handle multiple scenarios within a single prompt template","Implement conditional branching in workflows based on intermediate results","Reduce prompt duplication by consolidating conditional logic into single templates"],"best_for":["developers building adaptive LLM applications with multiple scenarios","teams needing to consolidate multiple related prompts into single templates","applications requiring conditional behavior based on user input or context"],"limitations":["Conditional syntax and evaluation logic must be manually implemented — no built-in conditional engine","Complex nested conditionals can become difficult to maintain and understand","LLM may not correctly evaluate or follow conditional logic","No built-in testing framework for conditional branches"],"requires":["Clear definition of conditions and branching logic","Manual implementation of conditional evaluation","Testing to verify conditional branches work correctly"],"input_types":["Markdown templates with conditional syntax","Condition definitions","Branch specifications"],"output_types":["Conditionally-executed prompts","Branched execution paths"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langgptai--langgpt__cap_8","uri":"capability://tool.use.integration.command.based.prompt.interaction.patterns","name":"command-based prompt interaction patterns","description":"Defines a Commands feature that enables prompts to specify explicit commands or actions that the LLM should recognize and execute. Commands are structured directives that the LLM can interpret and act upon, enabling prompt-driven control of LLM behavior without requiring complex natural language instructions. This allows prompts to define a command vocabulary that the LLM should understand and respond to consistently.","intents":["Define explicit commands that the LLM should recognize and execute","Create consistent command vocabularies across multiple prompts","Enable prompt-driven control of LLM behavior","Reduce ambiguity in LLM instruction interpretation"],"best_for":["developers building command-driven LLM interfaces","teams needing consistent command vocabularies across prompts","applications requiring explicit control over LLM behavior"],"limitations":["Command recognition depends on LLM instruction-following capability","No built-in command parsing or validation — LLM may misinterpret commands","Complex command hierarchies or parameters are difficult to express","No built-in command execution framework — requires external implementation"],"requires":["Clear definition of command vocabulary and syntax","Understanding of LLM command recognition limitations","Testing to verify commands are recognized and executed correctly"],"input_types":["Markdown command definitions","Command syntax specifications"],"output_types":["Command vocabularies","Command-driven prompts"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langgptai--langgpt__cap_9","uri":"capability://text.generation.language.reminder.based.prompt.reinforcement","name":"reminder-based prompt reinforcement","description":"Supports a Reminders feature that enables prompts to include periodic reinforcement of key instructions or constraints throughout the execution. Reminders are structured directives that reinforce important rules or behaviors at strategic points in the prompt execution, helping ensure the LLM maintains focus on critical requirements. This enables multi-part prompts to reinforce key instructions without requiring constant repetition of the same text.","intents":["Reinforce critical instructions or constraints throughout prompt execution","Maintain LLM focus on key requirements in long or complex prompts","Reduce the need for constant repetition of important rules","Improve consistency in multi-part or multi-step prompts"],"best_for":["developers building long or complex prompts requiring consistent focus","teams needing to reinforce critical constraints throughout execution","applications where LLM attention drift is a problem"],"limitations":["Reminder effectiveness depends on LLM attention and instruction-following capability","Too many reminders can increase token usage and reduce efficiency","No built-in mechanism to determine optimal reminder placement or frequency","Reminders may be perceived as repetitive or annoying by the LLM"],"requires":["Identification of critical instructions that need reinforcement","Strategic placement of reminders throughout the prompt","Testing to verify reminders improve consistency without excessive token waste"],"input_types":["Markdown reminder definitions","Reinforcement instructions"],"output_types":["Reinforced prompts","Consistent execution outputs"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":50,"verified":false,"data_access_risk":"low","permissions":["Basic Markdown knowledge for template authoring","Access to at least one LLM (GPT-4 recommended, Claude/Gemini/Qwen also supported)","Text editor or IDE supporting Markdown (VS Code, Obsidian, etc.)","API keys for target LLM providers (OpenAI, Anthropic, Google, Alibaba, etc.)","Understanding of each provider's API and response format","Manual testing to verify prompt behavior across providers","Clear decomposition of complex tasks into prompt steps","Implementation of chain orchestration logic","Testing to verify chain outputs flow correctly between steps","Token budget management for multi-step chains"],"failure_modes":["Markdown/JSON/YAML parsing is manual — no built-in IDE or syntax validation","Template inheritance and composition not natively supported — requires manual duplication","No automatic prompt optimization or A/B testing framework included","Requires manual variable substitution — no built-in templating engine like Jinja2","Provider-specific features (function calling, vision, tool use) require manual adaptation","No automatic capability detection — developers must manually verify prompt compatibility across providers","Prompt optimization for provider-specific strengths (e.g., Claude's long context) requires manual tuning","No built-in testing framework to validate prompt behavior consistency across providers","No built-in chain orchestration framework — requires manual implementation or external tools","Error handling and recovery in chains requires manual implementation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.671191797130553,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:59:50.673Z","last_commit":"2026-01-30T00:02:22Z"},"community":{"stars":12020,"forks":922,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=langgptai--langgpt","compare_url":"https://unfragile.ai/compare?artifact=langgptai--langgpt"}},"signature":"+CDh0WzGQIQoiQpm3/FzM/j+70nGewZQTuFPTq0XgvIZhIaRfAc+DFwp/9HPJ7fXnP0xP1stiQtSG7MNUR+PAQ==","signedAt":"2026-06-20T21:34:17.516Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/langgptai--langgpt","artifact":"https://unfragile.ai/langgptai--langgpt","verify":"https://unfragile.ai/api/v1/verify?slug=langgptai--langgpt","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"}}