{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-modelscope--ms-agent","slug":"modelscope--ms-agent","name":"ms-agent","type":"agent","url":"https://ms-agent-en.readthedocs.io","page_url":"https://unfragile.ai/modelscope--ms-agent","categories":["ai-agents"],"tags":["agentic-insight","agentic-search","chat-bot","code-generation","deep-research","memory"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-modelscope--ms-agent__cap_0","uri":"capability://tool.use.integration.llm.agnostic.agent.orchestration.with.multi.provider.support","name":"llm-agnostic agent orchestration with multi-provider support","description":"Central LLMAgent class orchestrates execution loops across multiple LLM providers (OpenAI, Anthropic, local models via Ollama) through a unified interface. The framework abstracts provider-specific APIs into a common message-passing protocol, enabling agents to switch backends without code changes. Configuration-driven provider selection allows runtime binding of LLM endpoints.","intents":["Build agents that work with multiple LLM providers without rewriting core logic","Switch between cloud and local LLM backends for cost/latency optimization","Support fallback LLM providers if primary endpoint fails"],"best_for":["Teams building multi-model agent systems","Developers optimizing for cost by switching between GPT-4 and open-source models","Organizations with on-premise LLM requirements"],"limitations":["Provider-specific features (vision, function calling schemas) require adapter code","Token counting and cost estimation varies by provider — no unified metering","Streaming response handling differs across providers, may require provider-specific callbacks"],"requires":["Python 3.9+","API keys for at least one LLM provider (OpenAI, Anthropic, or local Ollama instance)","YAML configuration file specifying LLM provider details"],"input_types":["text prompts","conversation history (message lists)","structured configuration (YAML)"],"output_types":["text responses","structured tool calls","streaming token sequences"],"categories":["tool-use-integration","multi-provider-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-modelscope--ms-agent__cap_1","uri":"capability://tool.use.integration.model.context.protocol.mcp.tool.integration.with.schema.based.function.calling","name":"model context protocol (mcp) tool integration with schema-based function calling","description":"Implements MCP-compliant tool registration and invocation through a schema-based function registry. Tools are defined with JSON schemas describing parameters, return types, and descriptions; the framework automatically marshals function calls from LLM outputs into executable tool invocations with type validation. Supports both built-in tools and external MCP servers.","intents":["Define reusable tools with strict input/output contracts that agents can discover and invoke","Connect agents to external MCP servers (e.g., filesystem, database, API services)","Validate tool inputs against schemas before execution to prevent runtime errors"],"best_for":["Developers building tool-augmented agents with strict API contracts","Teams integrating agents with existing MCP-compatible services","Systems requiring auditable tool execution with input validation"],"limitations":["Schema validation adds ~50-100ms per tool call for complex nested schemas","MCP server discovery requires manual configuration — no automatic service discovery","Tool execution is synchronous by default; parallel tool calls require custom orchestration","Error handling in tool execution doesn't automatically trigger agent recovery — requires explicit error handling in agent logic"],"requires":["Python 3.9+","Tool definitions with valid JSON schemas","MCP server endpoints (if using external tools) with network connectivity"],"input_types":["JSON schemas (tool definitions)","LLM function call outputs","structured parameters matching schema"],"output_types":["tool execution results (JSON-serializable)","error messages with validation details"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-modelscope--ms-agent__cap_10","uri":"capability://automation.workflow.gradio.based.web.ui.with.agent.runner.and.project.discovery","name":"gradio-based web ui with agent runner and project discovery","description":"Web UI layer built with Gradio provides interactive interface for agent execution, project management, and workflow visualization. Implements agent runner subprocess management for isolated execution, project discovery for loading agent configurations from filesystem or registry, and real-time execution monitoring with streaming output.","intents":["Provide non-technical users with web interface for running agents without code","Visualize agent execution progress and intermediate results in real-time","Manage multiple agent projects and configurations through web UI"],"best_for":["Non-technical users running pre-configured agents","Teams deploying agents as web services","Rapid prototyping and testing of agent workflows"],"limitations":["Gradio UI is limited to simple input/output forms — complex workflows require custom UI components","Subprocess isolation adds latency (~500ms-1s per execution) compared to in-process execution","Real-time streaming requires WebSocket support — may not work behind certain proxies","Project discovery is filesystem-based — no support for remote project registries","No built-in authentication or multi-user support — requires external reverse proxy for production use"],"requires":["Python 3.9+","Gradio library (installed with ms-agent)","Agent configurations in YAML format","Optional: Docker for containerized deployment"],"input_types":["text inputs (prompts, queries)","file uploads (documents, code)","configuration parameters"],"output_types":["text responses","generated artifacts (code, documents)","execution logs and traces","streaming output (real-time updates)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-modelscope--ms-agent__cap_11","uri":"capability://image.visual.short.video.generation.workflow.with.singularity.cinema.integration","name":"short video generation workflow with singularity cinema integration","description":"Specialized Singularity Cinema workflow generates short videos (~5 minutes) from text prompts through multi-step composition: script generation from prompt, scene planning with visual descriptions, and video synthesis using text-to-video models. Manages video artifacts and enables iterative refinement of generated videos.","intents":["Generate short videos from text descriptions without manual video editing","Automate video content creation for social media or marketing","Iteratively refine generated videos based on feedback"],"best_for":["Content creators automating video generation","Marketing teams producing social media content at scale","Educational platforms generating explanatory videos"],"limitations":["Video quality depends on underlying text-to-video model — current models produce lower quality than professional video","Generation time is significant (~5-10 minutes per video) — not suitable for real-time applications","Video length is limited to ~5 minutes — longer videos require multiple generations and manual stitching","Scene consistency across cuts is not guaranteed — may require manual review and editing","Audio synthesis is separate from video generation — requires additional text-to-speech processing"],"requires":["Python 3.9+","Text-to-video model API (e.g., Runway, Synthesia, or similar)","LLM provider for script and scene generation","Optional: Text-to-speech API for audio generation"],"input_types":["video prompt (text description)","optional style/tone parameters","optional reference images or videos"],"output_types":["generated video file (MP4 or similar)","script and scene descriptions","video metadata (duration, resolution, format)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-modelscope--ms-agent__cap_12","uri":"capability://automation.workflow.yaml.based.configuration.system.with.agent.and.workflow.definitions","name":"yaml-based configuration system with agent and workflow definitions","description":"Configuration system uses YAML files to define agents, tools, workflows, and LLM providers without code. Supports configuration inheritance, variable substitution, and environment-based overrides. AgentLoader factory class parses configurations and instantiates agents/workflows with dependency injection, enabling configuration-driven agent construction.","intents":["Define agents and workflows declaratively without writing Python code","Switch between different agent configurations (e.g., dev vs production) through environment variables","Share agent configurations across teams and projects"],"best_for":["Non-technical users configuring agents without code knowledge","Teams managing multiple agent configurations with environment-specific overrides","Organizations standardizing agent definitions across projects"],"limitations":["Complex logic cannot be expressed in YAML — requires code for custom behaviors","Configuration validation is basic — invalid configurations may only fail at runtime","No built-in configuration versioning — requires external version control","Variable substitution is simple string replacement — no support for complex expressions","Configuration inheritance is single-level — no support for deep inheritance hierarchies"],"requires":["Python 3.9+","YAML configuration files with valid syntax","All referenced tools/agents must be registered in the framework"],"input_types":["YAML configuration files","environment variables for overrides"],"output_types":["instantiated agent objects","workflow definitions","configuration validation errors"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-modelscope--ms-agent__cap_13","uri":"capability://automation.workflow.callback.based.message.flow.with.custom.event.hooks","name":"callback-based message flow with custom event hooks","description":"Message flow architecture implements callback hooks at key execution points (before/after LLM calls, tool execution, task completion) enabling custom event processing without modifying core agent logic. Callbacks receive message context and can modify behavior through return values. Supports both synchronous and asynchronous callbacks.","intents":["Monitor agent execution and log detailed traces for debugging","Implement custom metrics collection and performance monitoring","Modify agent behavior dynamically based on execution context (e.g., rate limiting, cost control)"],"best_for":["Teams building production agent systems requiring detailed observability","Systems implementing custom cost control or rate limiting","Applications requiring custom logging or analytics integration"],"limitations":["Callback overhead adds latency (~10-50ms per callback) — impacts performance for high-frequency callbacks","Callback execution is synchronous by default — async callbacks may cause race conditions","No built-in callback ordering or prioritization — callbacks execute in registration order","Callback errors can break agent execution — requires careful error handling in callbacks","No callback persistence — callbacks are lost if agent process crashes"],"requires":["Python 3.9+","Custom callback implementations (functions or classes)","Understanding of agent execution flow and message types"],"input_types":["message objects (LLM requests/responses, tool calls)","execution context (agent state, task info)"],"output_types":["callback return values (may modify behavior)","side effects (logging, metrics, external API calls)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-modelscope--ms-agent__cap_2","uri":"capability://planning.reasoning.autonomous.deep.research.with.adaptive.breadth.and.follow.up.question.generation","name":"autonomous deep research with adaptive breadth and follow-up question generation","description":"Specialized workflow (Agentic Insight v2) that decomposes research tasks into iterative exploration phases. The agent autonomously generates follow-up questions, adapts search breadth based on information density, and synthesizes findings into structured reports. Uses web search integration and document processing to gather and analyze information across multiple sources.","intents":["Conduct multi-step research on complex topics with minimal human guidance","Automatically identify knowledge gaps and generate follow-up research questions","Synthesize information from diverse sources into coherent research reports"],"best_for":["Researchers and analysts automating literature reviews and market research","Content creators gathering background information for articles or reports","Teams conducting competitive analysis or due diligence at scale"],"limitations":["Search quality depends on underlying search engine (Bing, Google) — no control over result ranking","Document processing pipeline has token limits (~100k tokens per document) — large PDFs require chunking","Adaptive breadth heuristics are rule-based, not learned — may over/under-explore depending on topic","Synthesis quality varies with LLM capability — weaker models produce less coherent reports","No built-in fact verification — relies on source credibility signals"],"requires":["Python 3.9+","API keys for search engine (Bing Search API or similar)","LLM provider with strong reasoning capabilities (GPT-4 or Claude recommended)","Internet connectivity for web search"],"input_types":["research query (text)","optional initial context or constraints"],"output_types":["structured research report (markdown or JSON)","list of sources with citations","follow-up questions for further research"],"categories":["planning-reasoning","search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-modelscope--ms-agent__cap_3","uri":"capability://code.generation.editing.three.phase.code.generation.with.design.coding.refinement.workflow","name":"three-phase code generation with design-coding-refinement workflow","description":"Specialized Code Genesis workflow decomposes code generation into three distinct phases: Design (architecture planning), Coding (implementation), and Refine (testing and optimization). Each phase uses targeted prompts and tool calls to produce artifacts (design docs, code files, test cases). The framework maintains artifact state across phases and enables iterative refinement based on execution feedback.","intents":["Generate production-quality code with architectural planning before implementation","Create code artifacts with associated design documentation and test cases","Iteratively refine generated code based on execution results or user feedback"],"best_for":["Developers using AI to scaffold complex projects with proper architecture","Teams generating code with mandatory design review and testing phases","Educational contexts teaching structured code generation practices"],"limitations":["Three-phase workflow increases latency by 3-4x vs single-pass generation","Design phase output quality depends on LLM's architectural reasoning — weaker models produce generic designs","Refinement phase requires executable code environment — sandboxed execution may not support all languages/dependencies","No automatic rollback if refinement introduces regressions — requires explicit version management","Artifact size limits (typically ~100k tokens per artifact) constrain project complexity"],"requires":["Python 3.9+","LLM provider with strong code reasoning (GPT-4 or Claude 3.5+)","Code execution sandbox (local or containerized) for testing phase","Language-specific tooling (compilers, interpreters, test runners) for target languages"],"input_types":["code generation request (natural language)","optional requirements document or specification","existing code context or dependencies"],"output_types":["design document (markdown)","source code files (multiple languages supported)","test cases and test results","refinement notes and optimization suggestions"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-modelscope--ms-agent__cap_4","uri":"capability://automation.workflow.dag.based.workflow.execution.with.conditional.branching.and.parallel.task.composition","name":"dag-based workflow execution with conditional branching and parallel task composition","description":"DagWorkflow engine executes tasks as directed acyclic graphs, enabling complex multi-step workflows with conditional branches, parallel execution, and data flow between tasks. Tasks are defined in YAML configuration with dependencies, conditions, and parameter mappings. The engine handles task scheduling, error propagation, and state management across the DAG.","intents":["Compose multi-step agent workflows with explicit task dependencies and data flow","Execute independent tasks in parallel to reduce total execution time","Implement conditional logic (if-then-else) within workflow definitions without code changes"],"best_for":["Teams building complex multi-agent systems with interdependent tasks","Workflows requiring conditional branching based on intermediate results","Systems needing workflow visualization and debugging capabilities"],"limitations":["DAG structure must be acyclic — no support for loops or recursive workflows (must use external orchestration)","Data passing between tasks is JSON-serializable only — complex objects require custom serialization","Conditional branching uses simple expression evaluation — no support for complex boolean logic without custom operators","Error handling is all-or-nothing — task failure stops entire DAG unless explicit error recovery is configured","No built-in workflow persistence — DAG state is in-memory, requires external storage for resumable workflows"],"requires":["Python 3.9+","YAML workflow configuration files with valid DAG structure","All referenced tasks/agents must be registered in the framework"],"input_types":["YAML workflow definition","task input parameters (JSON-serializable)","conditional expressions (simple Python expressions)"],"output_types":["task execution results (JSON)","workflow execution trace with timing","error logs with task failure details"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-modelscope--ms-agent__cap_5","uri":"capability://memory.knowledge.progressive.context.loading.with.anthropic.agent.skills.protocol","name":"progressive context loading with anthropic agent skills protocol","description":"Implements the Anthropic Agent Skills protocol for progressive loading of tool/skill definitions based on task context. Rather than loading all available skills upfront, the framework analyzes the task and loads only relevant skill schemas, reducing token overhead. Skills are organized hierarchically with metadata enabling semantic matching to task requirements.","intents":["Reduce LLM context overhead by loading only task-relevant tool definitions","Organize large tool libraries with semantic discovery based on task context","Implement hierarchical skill organization (e.g., database skills, file skills, API skills)"],"best_for":["Agents with large tool libraries (50+ tools) where context efficiency is critical","Systems with heterogeneous tool sets requiring semantic organization","Cost-sensitive deployments where token usage directly impacts operating costs"],"limitations":["Semantic matching of tasks to skills relies on embedding similarity — may miss relevant skills with poor descriptions","Progressive loading adds latency for initial skill discovery (~100-200ms per discovery phase)","Skill hierarchies must be manually defined — no automatic skill organization","Skills loaded in later phases may not be available for initial planning — requires multi-turn interaction for discovery","No caching of skill discovery results — each new task requires re-evaluation"],"requires":["Python 3.9+","Skill definitions with semantic metadata (descriptions, categories, tags)","Embedding model for semantic matching (local or API-based)","Anthropic Claude API (for Agent Skills protocol compliance)"],"input_types":["task description (text)","skill library with metadata","optional skill hierarchy definition"],"output_types":["loaded skill schemas (JSON)","skill discovery trace showing matching process","context token savings estimate"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-modelscope--ms-agent__cap_6","uri":"capability://memory.knowledge.conversational.memory.management.with.configurable.retention.and.summarization","name":"conversational memory management with configurable retention and summarization","description":"Memory system maintains conversation history with configurable retention policies (sliding window, summarization, or full history). Supports multiple memory backends (in-memory, Redis, database) and implements automatic summarization of old messages to maintain context while reducing token usage. Memory is scoped per agent instance with optional shared memory for multi-agent coordination.","intents":["Maintain conversation context across multiple agent turns without unbounded token growth","Implement conversation summarization to preserve key information while reducing context size","Share memory across multiple agents for coordinated multi-agent workflows"],"best_for":["Long-running agent conversations where token efficiency is critical","Multi-agent systems requiring shared context or coordination","Applications with strict token budgets (e.g., cost-sensitive deployments)"],"limitations":["Summarization is lossy — fine-grained details are lost, may impact task accuracy","Memory backend selection is static — cannot switch backends at runtime","Shared memory requires external storage (Redis, database) — adds operational complexity","Memory scoping is per-agent — no automatic memory isolation between concurrent agent instances","Summarization quality depends on LLM capability — weaker models produce less coherent summaries"],"requires":["Python 3.9+","LLM provider for summarization (if using summarization policy)","Optional: Redis or database for persistent memory backends"],"input_types":["conversation messages (text)","memory retention policy configuration","optional memory backend specification"],"output_types":["conversation history (full or summarized)","memory statistics (token count, message count)","summarization results"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-modelscope--ms-agent__cap_7","uri":"capability://planning.reasoning.self.healing.error.recovery.with.automatic.retry.and.fallback.strategies","name":"self-healing error recovery with automatic retry and fallback strategies","description":"Framework implements automatic error detection and recovery through configurable retry policies, fallback LLM providers, and error-specific recovery handlers. When a task fails (tool execution error, LLM timeout, validation failure), the agent automatically attempts recovery using strategies like retrying with modified prompts, switching to fallback providers, or decomposing the task into simpler subtasks.","intents":["Automatically recover from transient failures (API timeouts, rate limits) without human intervention","Implement fallback strategies when primary LLM provider fails or produces invalid output","Decompose failed tasks into simpler subtasks for more reliable execution"],"best_for":["Production agent systems requiring high reliability and minimal human intervention","Long-running agents where transient failures are expected","Cost-sensitive systems using multiple LLM providers with different pricing/reliability tradeoffs"],"limitations":["Retry logic is exponential backoff by default — may not be optimal for all error types","Fallback provider selection is static — no dynamic provider selection based on error type","Task decomposition is heuristic-based — may not always produce valid subtasks","Recovery attempts increase total execution time and token usage — no cost-benefit analysis","Error classification is rule-based — may misclassify errors and apply wrong recovery strategy"],"requires":["Python 3.9+","Multiple LLM providers configured as fallbacks","Error recovery handler definitions for custom error types"],"input_types":["task definition","error recovery policy configuration","fallback provider list"],"output_types":["task result (after recovery attempts)","recovery trace showing retry attempts and strategies used","error logs with detailed failure information"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-modelscope--ms-agent__cap_8","uri":"capability://planning.reasoning.financial.research.multi.agent.workflow.with.quantitative.and.sentiment.analysis","name":"financial research multi-agent workflow with quantitative and sentiment analysis","description":"Specialized FinResearch workflow orchestrates multiple agents for financial market research: a quantitative analysis agent processes financial data and metrics, a sentiment analysis agent evaluates news and social signals, and a synthesis agent combines findings into investment recommendations. Uses DAG-based composition with data flow between agents.","intents":["Conduct comprehensive financial research combining quantitative metrics and sentiment signals","Automate investment research workflows with specialized analysis agents","Generate investment recommendations with supporting quantitative and qualitative evidence"],"best_for":["Financial analysts automating research workflows","Investment firms conducting due diligence at scale","Fintech platforms providing AI-powered investment insights"],"limitations":["Quantitative analysis depends on data source quality and availability — missing data impacts analysis","Sentiment analysis is language-dependent — non-English sources may have lower accuracy","Financial recommendations are informational only — not suitable for autonomous trading without human review","Data freshness is critical but not guaranteed — may use stale data if sources are not updated frequently","Regulatory compliance requirements vary by jurisdiction — framework provides no compliance checking"],"requires":["Python 3.9+","Financial data APIs (e.g., Alpha Vantage, IEX Cloud, or similar)","News/sentiment data sources (e.g., NewsAPI, Twitter API)","LLM provider with strong financial domain knowledge (GPT-4 or Claude recommended)"],"input_types":["ticker symbol or company identifier","research parameters (time period, analysis depth)","optional constraints or focus areas"],"output_types":["quantitative analysis report (metrics, trends, ratios)","sentiment analysis summary (news sentiment, social signals)","investment recommendation with confidence score","supporting evidence and data sources"],"categories":["planning-reasoning","data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-modelscope--ms-agent__cap_9","uri":"capability://memory.knowledge.document.processing.pipeline.with.rag.enabled.retrieval.and.summarization","name":"document processing pipeline with rag-enabled retrieval and summarization","description":"Document processing pipeline ingests PDFs, web pages, and other documents, extracts text with OCR support, chunks content into semantic units, generates embeddings, and stores in vector database for retrieval-augmented generation (RAG). Supports both dense retrieval (semantic similarity) and sparse retrieval (keyword matching) with configurable ranking strategies.","intents":["Index large document collections for semantic search and retrieval","Extract information from unstructured documents (PDFs, web pages) for agent use","Implement RAG to ground agent responses in retrieved document content"],"best_for":["Knowledge-intensive applications requiring document grounding","Teams building document search and Q&A systems","Agents requiring access to large document collections without loading all content into context"],"limitations":["OCR quality depends on document quality — scanned PDFs with poor quality have high error rates","Chunking strategy is heuristic-based — may split semantic units incorrectly, impacting retrieval quality","Vector database requires external infrastructure (Pinecone, Weaviate, or self-hosted) — adds operational complexity","Retrieval quality depends on embedding model and chunk size — requires tuning for specific domains","No automatic document update detection — stale documents remain in index unless explicitly refreshed"],"requires":["Python 3.9+","Document files (PDF, HTML, markdown, etc.)","Embedding model (local or API-based)","Vector database (Pinecone, Weaviate, Milvus, or similar)","Optional: OCR engine (Tesseract or cloud-based) for scanned documents"],"input_types":["document files (PDF, HTML, markdown, plain text)","optional document metadata (title, author, date)","search queries (text)"],"output_types":["retrieved document chunks with relevance scores","document summaries","structured extracted information (if using extraction)"],"categories":["memory-knowledge","search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":45,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","API keys for at least one LLM provider (OpenAI, Anthropic, or local Ollama instance)","YAML configuration file specifying LLM provider details","Tool definitions with valid JSON schemas","MCP server endpoints (if using external tools) with network connectivity","Gradio library (installed with ms-agent)","Agent configurations in YAML format","Optional: Docker for containerized deployment","Text-to-video model API (e.g., Runway, Synthesia, or similar)","LLM provider for script and scene generation"],"failure_modes":["Provider-specific features (vision, function calling schemas) require adapter code","Token counting and cost estimation varies by provider — no unified metering","Streaming response handling differs across providers, may require provider-specific callbacks","Schema validation adds ~50-100ms per tool call for complex nested schemas","MCP server discovery requires manual configuration — no automatic service discovery","Tool execution is synchronous by default; parallel tool calls require custom orchestration","Error handling in tool execution doesn't automatically trigger agent recovery — requires explicit error handling in agent logic","Gradio UI is limited to simple input/output forms — complex workflows require custom UI components","Subprocess isolation adds latency (~500ms-1s per execution) compared to in-process execution","Real-time streaming requires WebSocket support — may not work behind certain proxies","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.576881833091424,"quality":0.35,"ecosystem":0.5800000000000001,"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":"2026-05-24T12:16:22.062Z","last_scraped_at":"2026-05-03T13:58:37.060Z","last_commit":"2026-04-15T06:21:35Z"},"community":{"stars":4224,"forks":493,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=modelscope--ms-agent","compare_url":"https://unfragile.ai/compare?artifact=modelscope--ms-agent"}},"signature":"1V9GApJlwy3LcbfWbYNY5vTDsG2YbES7yjrMhoHHb2Zxby4Z8iM0sIpsNILEbK947S8buk6sD5cAKNVYqpQmAg==","signedAt":"2026-06-21T04:49:28.629Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/modelscope--ms-agent","artifact":"https://unfragile.ai/modelscope--ms-agent","verify":"https://unfragile.ai/api/v1/verify?slug=modelscope--ms-agent","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"}}