{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-openagents","slug":"openagents","name":"OpenAgents","type":"agent","url":"https://github.com/xlang-ai/OpenAgents","page_url":"https://unfragile.ai/openagents","categories":["ai-agents"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-openagents__cap_0","uri":"capability://planning.reasoning.multi.agent.orchestration.with.specialized.agent.routing","name":"multi-agent orchestration with specialized agent routing","description":"OpenAgents implements a service-oriented architecture that routes user requests to one of three specialized agent types (Data, Plugins, Web) based on task intent. The backend Flask server maintains a unified message flow interface while each agent type implements its own execution logic, with shared adapters handling stream parsing, memory callbacks, and data models. This modular design allows agents to be independently deployed and scaled while maintaining a consistent interface for the frontend.","intents":["I need to deploy multiple AI agents that handle different task types without building separate systems","I want agents to share common infrastructure (memory, LLM integration, callbacks) while maintaining specialized behaviors","I need to add a new agent type without refactoring the entire backend"],"best_for":["teams building multi-capability AI platforms with heterogeneous agent requirements","developers extending agent systems with new specialized agent types","organizations needing independent scaling of different agent workloads"],"limitations":["Agent routing logic is implicit in frontend/backend communication — no explicit routing engine or decision tree visible in architecture","Shared adapters create tight coupling between agent implementations and core framework patterns","No built-in load balancing or failover between agent instances documented"],"requires":["Flask backend server running","MongoDB for persistence","Redis for caching","Node.js 14+ for Next.js frontend","Python 3.8+ for backend services"],"input_types":["natural language queries","file uploads (CSV, JSON, images)","web URLs"],"output_types":["structured agent responses","streaming text via WebSocket","data visualizations","file downloads"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openagents__cap_1","uri":"capability://data.processing.analysis.data.agent.with.python.sql.code.execution.and.visualization","name":"data agent with python/sql code execution and visualization","description":"The Data Agent provides a specialized toolkit for data manipulation, analysis, and visualization by executing Python and SQL code in a sandboxed environment. It integrates with the backend's memory system to maintain context across multiple data operations, supports file uploads (CSV, JSON, images), and generates visualizations through matplotlib/plotly. The agent uses LLM-guided code generation to translate natural language data requests into executable Python/SQL, with streaming output to provide real-time feedback during long-running computations.","intents":["I want to upload a CSV and ask questions about it in natural language without writing SQL","I need to perform multi-step data transformations and see intermediate results streamed in real-time","I want to generate charts and statistical summaries from raw data automatically"],"best_for":["data analysts and business users who prefer natural language over SQL","teams building data exploration interfaces without custom backend development","organizations needing quick data insights without data engineering overhead"],"limitations":["Code execution is sandboxed but still requires careful input validation — arbitrary Python execution poses security risks in multi-tenant deployments","No explicit query optimization or cost control for large dataset operations","Visualization capabilities limited to matplotlib/plotly — no interactive BI tool integration documented","Memory context is session-based — no cross-session data persistence for analysis history"],"requires":["Python 3.8+ with pandas, numpy, matplotlib, plotly installed","Backend Flask server with code execution sandbox","MongoDB for storing analysis context","File upload capability (max size configurable)"],"input_types":["CSV files","JSON files","image files (for OCR/analysis)","natural language queries","SQL queries (passthrough)"],"output_types":["Python/SQL code generated","data tables (JSON/CSV)","matplotlib/plotly visualizations","statistical summaries","streaming text responses"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openagents__cap_10","uri":"capability://tool.use.integration.plugin.registry.system.with.metadata.driven.discovery","name":"plugin registry system with metadata-driven discovery","description":"OpenAgents maintains a registry of 200+ plugins with structured metadata (name, description, parameters, authentication requirements, category). Plugins are registered with JSON schemas describing their inputs/outputs, enabling the LLM to understand plugin capabilities and select appropriate plugins based on user intent. The registry supports plugin discovery, parameter validation, and authentication management, allowing new plugins to be added without modifying agent code.","intents":["I want to add a new third-party API integration without modifying the Plugins Agent code","I need the system to automatically discover and select relevant plugins based on user requests","I want to manage authentication for 200+ plugins centrally"],"best_for":["teams maintaining large plugin ecosystems (200+ integrations)","platforms needing extensible third-party API support","organizations building plugin marketplaces"],"limitations":["Plugin metadata quality directly impacts LLM selection accuracy — poorly described plugins may be ignored","No plugin versioning or deprecation strategy documented","Authentication management for 200+ plugins requires careful credential handling — no documented secrets management","Plugin discovery relies on LLM reasoning — no explicit cost control or rate-limit awareness","No plugin performance metrics or reliability tracking documented"],"requires":["Plugin registry (JSON or database)","Plugin metadata with descriptions and parameter schemas","Authentication credentials for third-party services","LLM with function-calling capability for plugin selection"],"input_types":["plugin definitions (JSON schema)","authentication credentials"],"output_types":["plugin registry","plugin selection results","API responses"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openagents__cap_11","uri":"capability://code.generation.editing.code.generation.and.execution.sandbox.for.data.operations","name":"code generation and execution sandbox for data operations","description":"The Data Agent generates executable Python and SQL code from natural language requests using the LLM, then executes the code in a sandboxed environment with access to uploaded data. The sandbox provides a controlled execution context with access to common data libraries (pandas, numpy, matplotlib, plotly) while isolating dangerous operations. Generated code is logged and can be reviewed before execution, providing transparency into what the agent is doing.","intents":["I want to generate Python code from natural language without writing it manually","I need to execute data analysis code safely without exposing the full system","I want to see the generated code before it runs for transparency and debugging"],"best_for":["data analysts who prefer natural language over manual coding","teams building data exploration tools with code transparency","organizations needing safe code execution in multi-tenant environments"],"limitations":["Sandbox security depends on implementation details not documented — arbitrary Python execution still poses risks","No explicit timeout or resource limits documented — long-running code may hang the agent","Generated code quality depends on LLM capability — complex data operations may generate incorrect code","No code optimization or performance analysis — generated code may be inefficient","Limited to Python/SQL — no support for R, Julia, or other data languages"],"requires":["Python 3.8+ with data libraries (pandas, numpy, matplotlib, plotly)","Sandboxed execution environment (Docker, subprocess isolation, or similar)","LLM with code generation capability","Uploaded data files (CSV, JSON, etc.)"],"input_types":["natural language data requests","uploaded data files","optional SQL queries"],"output_types":["generated Python/SQL code","execution results","visualizations","error messages"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openagents__cap_12","uri":"capability://image.visual.vision.language.model.integration.for.web.page.understanding","name":"vision-language model integration for web page understanding","description":"The Web Agent integrates vision-language models (GPT-4V, Claude Vision) to interpret screenshots of web pages and understand their visual layout, content, and interactive elements. The agent captures screenshots during browsing, sends them to the vision model with a task description, and receives natural language descriptions of page content and recommended actions. This enables the agent to interact with websites without relying on DOM parsing or explicit selectors, making it adaptable to varied website designs.","intents":["I want the agent to understand complex web page layouts without explicit selectors","I need to extract information from websites with dynamic or non-standard UI","I want the agent to interact with websites by understanding visual content"],"best_for":["web automation tasks requiring visual understanding of complex layouts","organizations scraping websites with dynamic or JavaScript-heavy content","teams building web agents that need to adapt to varied website designs"],"limitations":["Vision model interpretation can be unreliable on complex layouts or non-standard UI patterns","Screenshot-based understanding adds latency per action cycle (capture → LLM reasoning → execution)","No explicit handling of JavaScript-heavy SPAs — page state detection may lag behind actual DOM updates","Vision models have token limits — large or complex pages may exceed context windows","Cost per action cycle is higher than DOM-based approaches due to vision model API calls"],"requires":["Vision-language model API (GPT-4V, Claude Vision, etc.)","Chrome browser with screenshot capability","Backend service for vision model integration","API credentials for vision model provider"],"input_types":["screenshots from web pages","task descriptions","page URLs"],"output_types":["page content descriptions","recommended actions","extracted data","browser commands"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openagents__cap_13","uri":"capability://memory.knowledge.conversation.history.and.context.management.with.file.references","name":"conversation history and context management with file references","description":"OpenAgents maintains a conversation history within each session that includes user messages, agent responses, and file references. The system allows agents to access previous messages and uploaded files throughout a conversation, enabling multi-turn interactions where agents build on prior context. File uploads are stored with metadata (filename, upload time, size) and can be referenced in subsequent requests without re-uploading, improving user experience for iterative analysis.","intents":["I want the agent to remember previous messages and use them for context in new requests","I need to upload a file once and reference it across multiple requests","I want to see the full conversation history including what files were uploaded when"],"best_for":["conversational AI applications with multi-turn interactions","data analysis platforms where users upload files and perform iterative analysis","systems where conversation context is critical for accurate responses"],"limitations":["Conversation history is session-scoped — no cross-session context or persistent memory","No automatic context summarization — long conversations accumulate tokens and may exceed LLM context limits","File references are session-specific — files cannot be shared across sessions or users","No conversation search or filtering — users cannot easily find previous messages","Memory size limits not documented — very long conversations may hit storage constraints"],"requires":["MongoDB for storing conversation history","Session management (cookies or tokens)","File storage (local filesystem or cloud storage)","Backend API for history retrieval"],"input_types":["user messages","file uploads","agent responses"],"output_types":["conversation history (JSON)","file metadata","context for agent reasoning"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openagents__cap_2","uri":"capability://tool.use.integration.plugins.agent.with.200.third.party.api.integrations.and.auto.selection","name":"plugins agent with 200+ third-party api integrations and auto-selection","description":"The Plugins Agent provides access to 200+ third-party APIs (shopping, weather, scientific tools, etc.) through a unified plugin registry system. The agent uses LLM-based reasoning to automatically select relevant plugins based on user intent, constructs appropriate API calls with parameter binding, and handles response parsing/formatting. Plugins are registered with metadata (description, parameters, authentication requirements) that the LLM uses for selection, enabling the agent to discover and invoke APIs without explicit user specification.","intents":["I want to ask for weather, shopping prices, or scientific data without knowing which APIs to call","I need to integrate 200+ external services without building custom connectors for each","I want the agent to automatically choose the right plugin based on my request intent"],"best_for":["consumer-facing applications needing broad third-party integrations","teams building AI assistants that need access to diverse external data sources","platforms where users expect natural language access to many services"],"limitations":["Plugin selection relies on LLM reasoning — no explicit cost control or rate-limit awareness, risking expensive API calls","Authentication management for 200+ plugins requires careful credential handling — no documented secrets management strategy","Plugin metadata quality directly impacts selection accuracy — poorly described plugins may be ignored or misused","No built-in caching or deduplication of plugin calls — repeated requests may hit APIs multiple times"],"requires":["Plugin registry with 200+ pre-configured integrations","API keys/credentials for third-party services","LLM with function-calling capability (OpenAI, Anthropic, etc.)","Backend service for plugin orchestration and response parsing"],"input_types":["natural language queries","structured plugin parameters (optional)"],"output_types":["parsed API responses","formatted data (JSON, text, tables)","streaming text summaries"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openagents__cap_3","uri":"capability://automation.workflow.web.agent.with.autonomous.browser.control.and.information.extraction","name":"web agent with autonomous browser control and information extraction","description":"The Web Agent enables autonomous web browsing through a Chrome extension that allows the agent to navigate websites, extract information, and interact with web pages (clicking, form filling, scrolling). The agent receives visual feedback (screenshots) from the browser, uses vision-language models to understand page content, and generates browser commands (navigate, click, extract text) to accomplish user goals. This creates a closed-loop system where the agent observes page state, reasons about next actions, and executes them iteratively until the task completes.","intents":["I want to scrape information from websites that require interaction (login, pagination, dynamic content)","I need the agent to autonomously navigate complex websites and extract specific data","I want to automate web-based tasks like form filling or price comparison across multiple sites"],"best_for":["teams building web automation platforms without custom Selenium/Playwright code","organizations needing to extract data from interactive websites with dynamic content","applications requiring autonomous web navigation without explicit step-by-step instructions"],"limitations":["Chrome extension dependency creates browser compatibility constraints and deployment complexity","Vision-language model interpretation of screenshots can be unreliable on complex layouts or non-standard UI patterns","No explicit handling of JavaScript-heavy SPAs — page state detection may lag behind actual DOM updates","Rate limiting and bot detection evasion not documented — sites may block automated access","Latency per action cycle (screenshot → LLM reasoning → command execution) makes real-time interaction slow"],"requires":["Chrome browser with OpenAgents extension installed","Vision-language model (GPT-4V, Claude Vision, etc.) for page understanding","Backend service for browser command orchestration","WebSocket connection for real-time browser communication"],"input_types":["natural language task descriptions","target URLs","extraction criteria (optional)"],"output_types":["extracted text/data from web pages","screenshots of page states","structured data (JSON from tables/lists)","action logs showing navigation history"],"categories":["automation-workflow","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openagents__cap_4","uri":"capability://automation.workflow.streaming.message.flow.with.real.time.feedback","name":"streaming message flow with real-time feedback","description":"OpenAgents implements a streaming architecture where agent responses are sent to the frontend in real-time via WebSocket connections rather than waiting for complete execution. The backend uses streaming callbacks and adapters to capture intermediate outputs (code execution results, API responses, reasoning steps) and forward them to the frontend as they occur. This enables users to see progress during long-running operations (data analysis, web scraping) without waiting for final results, improving perceived responsiveness and allowing early termination of slow operations.","intents":["I want to see intermediate results while an agent is processing my request","I need to cancel long-running operations if they're taking too long","I want real-time visibility into what the agent is doing (code execution, API calls, reasoning)"],"best_for":["interactive applications where user experience depends on real-time feedback","long-running agent operations (data analysis, web scraping) where progress visibility matters","teams building responsive AI interfaces without batch-oriented processing"],"limitations":["Streaming adds complexity to error handling — partial results may be sent before failures occur","WebSocket connection management required on frontend — no fallback to polling documented","Memory overhead from maintaining streaming state across multiple concurrent requests","Ordering guarantees for streamed messages not explicitly documented — out-of-order delivery possible"],"requires":["WebSocket support on frontend (Next.js with socket.io or similar)","Backend streaming adapters configured for each agent type","Redis for message queuing (optional but recommended for reliability)"],"input_types":["agent execution requests"],"output_types":["streaming text chunks","intermediate data (code results, API responses)","progress indicators","error messages"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openagents__cap_5","uri":"capability://memory.knowledge.unified.memory.management.across.agent.sessions","name":"unified memory management across agent sessions","description":"OpenAgents provides a session-based memory system where conversation history, file uploads, and agent execution context are persisted in MongoDB and cached in Redis. The memory system is shared across all three agent types through common adapters, allowing agents to reference previous messages, uploaded files, and past analysis results within a session. The backend manages memory lifecycle (creation, updates, cleanup) and provides APIs for agents to read/write context, enabling multi-turn conversations where agents build on prior interactions.","intents":["I want the agent to remember previous messages and context within a conversation","I need to upload a file once and reference it across multiple agent interactions","I want the agent to maintain state across different agent types (e.g., analyze data, then search web for related info)"],"best_for":["conversational AI applications requiring multi-turn context","platforms where users upload files and expect agents to reference them across multiple requests","systems needing to maintain analysis context across different agent types"],"limitations":["Session-based memory is not cross-session — no persistent memory across different conversations","Memory size limits not documented — large file uploads or long conversations may hit storage constraints","No explicit memory pruning or summarization — old context accumulates and may impact LLM token usage","Redis caching adds latency for memory lookups — no documented cache hit rates or performance impact","No multi-user session isolation documented — potential data leakage in shared deployments"],"requires":["MongoDB for persistent session storage","Redis for caching frequently accessed memory","Backend API for memory read/write operations","Session management in frontend (cookie or token-based)"],"input_types":["conversation messages","file uploads","agent execution results"],"output_types":["session context (JSON)","file references","conversation history"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openagents__cap_6","uri":"capability://tool.use.integration.llm.provider.abstraction.with.multi.model.support","name":"llm provider abstraction with multi-model support","description":"OpenAgents abstracts LLM interactions through a provider-agnostic interface that supports multiple LLM backends (OpenAI, Anthropic, Ollama, etc.). The backend maintains LLM configuration (model selection, temperature, max tokens) and routes agent requests to the appropriate provider based on configuration. This allows users to switch LLM providers without changing agent code, and enables cost optimization by using different models for different tasks (e.g., cheaper models for simple tasks, GPT-4 for complex reasoning).","intents":["I want to use different LLM providers (OpenAI, Anthropic, local Ollama) without rewriting agent code","I need to optimize costs by using cheaper models for simple tasks and expensive models for complex reasoning","I want to add support for a new LLM provider without modifying the core agent logic"],"best_for":["teams building LLM applications that want provider flexibility","organizations optimizing LLM costs across different workload types","developers extending OpenAgents with new LLM providers"],"limitations":["Provider abstraction may hide provider-specific capabilities (e.g., vision, function calling) — not all models support all features","No automatic fallback or retry logic across providers documented","Configuration management for multiple providers adds operational complexity","Token counting and cost estimation varies by provider — no unified cost tracking"],"requires":["API keys for selected LLM providers (OpenAI, Anthropic, etc.)","Backend configuration for LLM provider selection","Environment variables for API credentials"],"input_types":["prompts","conversation context","function definitions (for function-calling models)"],"output_types":["LLM completions","function calls","streaming text"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openagents__cap_7","uri":"capability://automation.workflow.next.js.based.chat.interface.with.file.management.and.agent.selection","name":"next.js-based chat interface with file management and agent selection","description":"OpenAgents provides a web-based chat interface built with Next.js that allows users to select agents, upload files, and interact with agents through a conversational UI. The frontend manages application state (current agent, conversation history, uploaded files) and communicates with the backend via REST APIs and WebSocket connections. The interface includes file upload/download capabilities, agent selection dropdowns, and streaming message display, creating a unified entry point for all three agent types.","intents":["I want a user-friendly web interface to interact with AI agents without command-line tools","I need to upload files and manage them within the chat interface","I want to switch between different agent types (Data, Plugins, Web) from a single interface"],"best_for":["non-technical end users who need a web UI for agent interaction","teams building consumer-facing AI applications","organizations deploying OpenAgents as a SaaS platform"],"limitations":["Next.js frontend adds deployment complexity — requires Node.js runtime and build process","File upload size limits not documented — large files may timeout or fail","State management complexity increases with more agents and concurrent conversations","No built-in authentication/authorization — requires external identity provider integration","Browser compatibility may vary — no documented minimum browser versions"],"requires":["Node.js 14+ for Next.js runtime","Backend API endpoints for agent communication","WebSocket support for streaming messages","Modern web browser (Chrome, Firefox, Safari, Edge)"],"input_types":["text messages","file uploads (CSV, JSON, images, etc.)","agent selection"],"output_types":["chat messages","file downloads","visualizations","streaming text"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openagents__cap_8","uri":"capability://automation.workflow.docker.based.deployment.with.environment.configuration","name":"docker-based deployment with environment configuration","description":"OpenAgents provides Docker containerization for both frontend and backend services, enabling consistent deployment across development, staging, and production environments. The deployment uses environment variables for configuration (API keys, LLM provider selection, database connections), allowing the same Docker images to be deployed with different configurations. Docker Compose orchestration is provided for local development, simplifying setup of the full stack (frontend, backend, MongoDB, Redis).","intents":["I want to deploy OpenAgents consistently across multiple environments without manual configuration","I need to containerize the frontend and backend separately for independent scaling","I want to quickly set up a local development environment with all dependencies"],"best_for":["teams deploying OpenAgents to cloud platforms (AWS, GCP, Azure, Kubernetes)","organizations needing reproducible deployments across environments","developers setting up local development environments quickly"],"limitations":["Docker adds operational complexity — requires Docker/Docker Compose knowledge","Image size not documented — may impact deployment speed and storage costs","No Kubernetes manifests provided — requires custom k8s configuration for production","Environment variable management at scale requires external secrets management (Vault, AWS Secrets Manager)","No documented health checks or readiness probes for container orchestration"],"requires":["Docker 20.10+","Docker Compose 1.29+ (for local development)","Environment variables for configuration (API keys, database URLs, etc.)","MongoDB and Redis (can be containerized or external)"],"input_types":["environment variables","Docker configuration files"],"output_types":["running containers","deployed services"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openagents__cap_9","uri":"capability://code.generation.editing.extensible.agent.framework.with.custom.agent.creation","name":"extensible agent framework with custom agent creation","description":"OpenAgents provides a framework for creating custom agents by extending base agent classes and implementing required methods (execute, parse_response, etc.). The framework defines a common interface that all agents must implement, allowing new agents to be added without modifying core backend logic. Custom agents inherit shared infrastructure (memory, callbacks, streaming adapters) automatically, reducing boilerplate and ensuring consistency with existing agents.","intents":["I want to create a custom agent for a specific domain (e.g., medical research, financial analysis) without building from scratch","I need to add a new agent type to OpenAgents without modifying the core framework","I want my custom agent to inherit streaming, memory, and LLM integration from the framework"],"best_for":["developers extending OpenAgents with domain-specific agents","teams building specialized AI capabilities on top of OpenAgents","organizations customizing OpenAgents for internal use cases"],"limitations":["Custom agent interface not fully documented — requires reading existing agent implementations as examples","No validation of custom agent implementations — broken agents may fail silently at runtime","Shared adapters create implicit dependencies — custom agents may break if core adapters change","No plugin system for agents — custom agents require code changes and redeployment"],"requires":["Python 3.8+ for backend development","Understanding of OpenAgents agent interface and shared adapters","Knowledge of Flask for backend integration","Access to OpenAgents source code"],"input_types":["agent class definitions","configuration"],"output_types":["custom agent implementations","integrated with OpenAgents backend"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Flask backend server running","MongoDB for persistence","Redis for caching","Node.js 14+ for Next.js frontend","Python 3.8+ for backend services","Python 3.8+ with pandas, numpy, matplotlib, plotly installed","Backend Flask server with code execution sandbox","MongoDB for storing analysis context","File upload capability (max size configurable)","Plugin registry (JSON or database)"],"failure_modes":["Agent routing logic is implicit in frontend/backend communication — no explicit routing engine or decision tree visible in architecture","Shared adapters create tight coupling between agent implementations and core framework patterns","No built-in load balancing or failover between agent instances documented","Code execution is sandboxed but still requires careful input validation — arbitrary Python execution poses security risks in multi-tenant deployments","No explicit query optimization or cost control for large dataset operations","Visualization capabilities limited to matplotlib/plotly — no interactive BI tool integration documented","Memory context is session-based — no cross-session data persistence for analysis history","Plugin metadata quality directly impacts LLM selection accuracy — poorly described plugins may be ignored","No plugin versioning or deprecation strategy documented","Authentication management for 200+ plugins requires careful credential handling — no documented secrets management","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:03.579Z","last_scraped_at":"2026-05-03T14:00:10.321Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=openagents","compare_url":"https://unfragile.ai/compare?artifact=openagents"}},"signature":"ARk8xqJTxi95K7Tft3MiX6EsUqtRS7OSpV1g3St1oWT1GA8VDjnnfXyX2+URB4nM1+zFGQTvZS22YI0Koen/DQ==","signedAt":"2026-06-20T19:41:20.768Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openagents","artifact":"https://unfragile.ai/openagents","verify":"https://unfragile.ai/api/v1/verify?slug=openagents","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"}}