{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-xlang-ai--openagents","slug":"xlang-ai--openagents","name":"OpenAgents","type":"agent","url":"https://arxiv.org/abs/2310.10634","page_url":"https://unfragile.ai/xlang-ai--openagents","categories":["coding","chatbot"],"tags":["agent","assistant-chat-bots","code-generation","executable-langauge-grounding","gpt","hacktoberfest","language-model","language-model-agent","llm","semantic-parsing","tool-learning","ui"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"inactive","verified":false},"capabilities":[{"id":"github-xlang-ai--openagents__cap_0","uri":"capability://planning.reasoning.multi.agent.orchestration.with.unified.chat.interface","name":"multi-agent orchestration with unified chat interface","description":"Provides a single Next.js-based web UI that routes user queries to specialized agent implementations (Data, Plugins, Web) through a Flask backend, managing agent selection, state transitions, and real-time streaming responses. The system uses a service-oriented architecture where each agent type is independently deployable but communicates through standardized API endpoints, enabling users to switch between agents within a single conversation context without manual reconfiguration.","intents":["I want to build an AI assistant that can handle data analysis, plugin integration, and web browsing in one interface","I need to deploy multiple specialized agents that share conversation history and context","I want to let users pick which agent capability they need without leaving the chat interface"],"best_for":["teams building production AI assistants with multiple specialized capabilities","developers extending agent frameworks with custom agent types","organizations deploying self-hosted AI platforms with data privacy requirements"],"limitations":["Agent switching requires backend coordination — no client-side agent routing, adding ~100-200ms latency per agent change","Conversation context is stored in MongoDB; no built-in cross-agent memory optimization for large conversation histories","Frontend state management via React Context — doesn't scale beyond ~50 concurrent users without load balancing"],"requires":["Python 3.8+","Node.js 16+ for Next.js frontend","MongoDB instance for persistence","Redis instance for caching","Flask backend running on port 5000 (configurable)","API keys for at least one LLM provider (OpenAI, Anthropic, etc.)"],"input_types":["text (user queries)","file uploads (CSV, JSON, Excel for Data Agent)","URLs (for Web Agent)"],"output_types":["text (chat responses)","structured data (JSON from Data Agent)","visualizations (charts, tables)","web interaction logs (from Web Agent)"],"categories":["planning-reasoning","tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xlang-ai--openagents__cap_1","uri":"capability://data.processing.analysis.data.analysis.agent.with.code.execution.sandbox","name":"data analysis agent with code execution sandbox","description":"Executes Python and SQL code in an isolated environment to perform data manipulation, transformation, and visualization tasks. The Data Agent accepts structured inputs (CSV, JSON, Excel), parses them into pandas DataFrames, executes user-requested operations through a restricted Python/SQL interpreter, and returns results as visualizations, tables, or raw data. This capability integrates with the backend's memory system to cache intermediate results and maintain execution context across multiple queries.","intents":["I want to upload a CSV and ask an AI to analyze trends, filter rows, and generate charts","I need to execute SQL queries on uploaded data without writing the queries myself","I want to chain multiple data transformations (filter → aggregate → visualize) in natural language"],"best_for":["non-technical business analysts exploring datasets","data scientists prototyping analysis workflows before production","teams building internal data exploration tools with LLM interfaces"],"limitations":["Code execution is sandboxed but not fully isolated — malicious code could still access file system within container boundaries","Large datasets (>500MB) may cause memory issues in the execution environment; no built-in streaming for big data","SQL execution limited to in-memory DataFrames via SQLite — no direct database connections to external systems","Visualization output is static (matplotlib/seaborn) — no interactive dashboards or real-time updates"],"requires":["Python 3.8+ with pandas, numpy, matplotlib, seaborn installed","SQLite for SQL execution (included in Python)","File upload handler in Flask backend","MongoDB for caching execution results"],"input_types":["CSV files","JSON files","Excel files (.xlsx, .xls)","natural language queries describing data operations"],"output_types":["pandas DataFrames (as JSON)","matplotlib/seaborn visualizations (PNG/SVG)","summary statistics (JSON)","SQL query results (structured data)"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xlang-ai--openagents__cap_10","uri":"capability://tool.use.integration.extensible.plugin.architecture.for.custom.agents","name":"extensible plugin architecture for custom agents","description":"Provides a framework for developers to create custom agent types by implementing a standard agent interface (inherited from a base Agent class) and registering them with the backend. Custom agents can leverage shared adapters (memory, streaming, callbacks) and integrate with the existing UI without modification. The system uses a plugin discovery mechanism to load agents from the agents/ directory, enabling drop-in extensibility.","intents":["I want to build a custom agent for my domain (e.g., legal document analysis) without forking the codebase","I need to add a new agent type that reuses the chat UI and memory system","I want to share my custom agent with the community through a plugin mechanism"],"best_for":["developers extending OpenAgents with domain-specific agents","teams building internal AI platforms on top of OpenAgents","open-source contributors adding new agent types"],"limitations":["Custom agents must implement the full interface — no partial implementation or mixins","Shared adapters (memory, streaming) may not fit all agent patterns — custom agents may need to bypass them","No built-in versioning or compatibility checking — breaking changes in the base interface affect all custom agents","Documentation for extending is limited — requires reading source code to understand patterns","No sandboxing for custom agents — malicious code can access the full backend"],"requires":["Python 3.8+","Understanding of the Agent base class interface","Knowledge of Flask and the backend API","Familiarity with the shared adapters (memory, streaming, callbacks)"],"input_types":["agent class definition (Python)","configuration (YAML or environment variables)"],"output_types":["agent instance registered in the system","API endpoints for the agent","UI integration (automatic)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xlang-ai--openagents__cap_11","uri":"capability://automation.workflow.docker.based.deployment.with.environment.configuration","name":"docker-based deployment with environment configuration","description":"Provides Docker Compose configuration for deploying OpenAgents as containerized services (frontend, backend, MongoDB, Redis) with environment variable-based configuration. The system supports both local development (docker-compose up) and production deployments with proper networking, volume management, and service dependencies. Configuration is externalized through .env files, enabling easy switching between LLM providers, database backends, and deployment targets.","intents":["I want to deploy OpenAgents locally for testing without installing dependencies manually","I need to deploy to production with proper isolation between services","I want to switch LLM providers or database backends through environment variables"],"best_for":["developers deploying OpenAgents in containerized environments","teams using Kubernetes or Docker Swarm for orchestration","organizations with DevOps practices requiring infrastructure-as-code"],"limitations":["Docker Compose is single-host only — no built-in clustering or high availability","Volume management requires careful configuration — data loss if volumes are deleted","Network isolation between containers is basic — no advanced security policies","Scaling requires manual service replication or Kubernetes — no built-in horizontal scaling","Environment variable limits (typically 64KB total) — complex configurations may exceed limits"],"requires":["Docker 20.10+","Docker Compose 1.29+",".env file with configuration (API keys, database URLs, etc.)","Sufficient disk space for MongoDB and Redis volumes"],"input_types":["docker-compose.yml configuration",".env file with environment variables"],"output_types":["running containers (frontend, backend, MongoDB, Redis)","exposed ports (5000 for backend, 3000 for frontend)","persistent volumes (database data)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xlang-ai--openagents__cap_2","uri":"capability://tool.use.integration.plugin.based.tool.integration.with.auto.selection","name":"plugin-based tool integration with auto-selection","description":"Provides access to 200+ third-party plugins (shopping, weather, scientific tools, etc.) through a plugin registry and automatic selection mechanism. The Plugins Agent uses the LLM to determine which plugins are relevant to a user query, constructs appropriate API calls with parameter binding, and aggregates results. The system maintains a plugin manifest with schemas, descriptions, and authentication requirements, enabling the agent to reason about tool availability without manual configuration per query.","intents":["I want to ask 'what's the weather in Tokyo and show me flights there' and have the agent pick the right plugins","I need to integrate 50+ third-party APIs without writing individual connectors","I want the agent to learn which plugins are available and use them intelligently without explicit instructions"],"best_for":["consumer-facing AI assistants needing broad tool coverage","teams building vertical AI applications (travel, shopping, research) with many integrations","platforms where end-users can add custom plugins without code"],"limitations":["Plugin selection is LLM-driven — hallucination risk if the model misunderstands plugin capabilities or constraints","No built-in rate limiting or quota management — plugins may hit API limits if called repeatedly","Authentication is per-plugin — managing 200+ API keys/OAuth tokens requires secure credential storage (not fully addressed in core)","Plugin schemas must be manually maintained; no automatic schema discovery from OpenAPI specs","Latency compounds with multiple plugin calls — sequential execution adds 500ms-2s per plugin"],"requires":["Plugin manifest file (JSON) with schema definitions","API keys/credentials for each plugin provider","Network access to plugin endpoints","LLM with function-calling capability (OpenAI, Anthropic, etc.)"],"input_types":["natural language queries","structured parameters (location, date, search terms)"],"output_types":["aggregated plugin results (JSON)","formatted text summaries","structured data (flights, weather, products)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xlang-ai--openagents__cap_3","uri":"capability://automation.workflow.autonomous.web.browsing.with.chrome.extension","name":"autonomous web browsing with chrome extension","description":"Enables agents to autonomously navigate websites, extract information, and interact with web pages through a Chrome extension that captures page state and DOM interactions. The Web Agent receives high-level instructions (e.g., 'find the cheapest flight'), translates them into browser actions (click, scroll, fill form), and uses vision/OCR capabilities to interpret page content. The extension maintains a session context and screenshot history, allowing the agent to reason about page state changes and plan multi-step navigation sequences.","intents":["I want the agent to browse a website, find information, and extract it without me manually navigating","I need to automate form filling and multi-step web interactions (search → filter → checkout)","I want the agent to take screenshots and understand page layouts to interact with dynamic content"],"best_for":["automation engineers building web scraping and RPA solutions","e-commerce platforms automating price comparison and competitor monitoring","research teams gathering data from multiple websites programmatically"],"limitations":["Chrome extension requires manual installation and user approval — not suitable for fully headless automation","JavaScript-heavy sites may render incorrectly; no built-in Selenium/Playwright fallback for complex SPAs","Vision-based interaction (OCR, element detection) has ~5-10% error rate on complex layouts","Session management is stateful — concurrent browsing sessions require separate Chrome instances, limiting scalability","No built-in handling of CAPTCHAs, authentication flows, or JavaScript challenges","Page load times and network latency directly impact agent response time (no caching of page snapshots)"],"requires":["Chrome/Chromium browser with extension support","Chrome extension installed and enabled","Vision/OCR model (e.g., Claude's vision API or local model) for page interpretation","Network access to target websites","Backend service to coordinate browser actions and state"],"input_types":["natural language instructions (e.g., 'find hotels in Paris')","URLs to navigate to","form data to fill"],"output_types":["extracted text and structured data","screenshots of pages","interaction logs (clicks, form submissions)","final results (prices, product details, etc.)"],"categories":["automation-workflow","search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xlang-ai--openagents__cap_4","uri":"capability://memory.knowledge.conversation.memory.management.with.mongodb.persistence","name":"conversation memory management with mongodb persistence","description":"Manages conversation history, user context, and agent state across sessions using MongoDB as the primary store and Redis for caching frequently accessed data. The system stores messages, execution results, file uploads, and agent-specific state in structured collections, enabling users to resume conversations, reference past interactions, and maintain context across multiple agent switches. Memory is indexed by conversation ID and user ID, with TTL policies for automatic cleanup of old sessions.","intents":["I want users to close the app and resume their conversation later with full context","I need to track what data was uploaded and what analyses were run in previous sessions","I want to implement conversation search so users can find past interactions"],"best_for":["production AI assistants requiring persistent user sessions","teams building multi-turn conversation systems with long-running analysis","platforms where conversation history is a feature (not just implementation detail)"],"limitations":["MongoDB queries add ~50-100ms latency per memory lookup; no built-in optimization for large conversation histories (>10k messages)","Redis caching is optional — without it, every message retrieval hits MongoDB","No built-in conversation compression or summarization — memory grows linearly with conversation length","TTL policies are database-level — no application-level control over retention per user or conversation type","Cross-agent memory sharing requires manual synchronization — each agent type may store state differently"],"requires":["MongoDB 4.0+ instance with network access","Redis instance (optional but recommended)","PyMongo driver for Python backend","Proper indexing on conversation_id and user_id fields"],"input_types":["chat messages (text)","file metadata (uploads)","agent execution results (JSON)","user preferences and settings"],"output_types":["conversation history (paginated)","session state (JSON)","search results (matching messages)","analytics (conversation metrics)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xlang-ai--openagents__cap_5","uri":"capability://tool.use.integration.llm.provider.abstraction.with.multi.model.support","name":"llm provider abstraction with multi-model support","description":"Abstracts interactions with multiple LLM providers (OpenAI, Anthropic, local models via Ollama) through a unified interface, handling API key management, request formatting, streaming response parsing, and error handling. The system maintains provider-specific adapters that translate between OpenAgents' internal message format and each provider's API schema, enabling users to swap LLM backends without changing agent code. Configuration is environment-based, allowing runtime provider selection.","intents":["I want to switch from OpenAI to Anthropic without rewriting my agent code","I need to use a local LLM (Ollama) for privacy-sensitive data","I want to compare agent performance across different LLM providers"],"best_for":["teams evaluating multiple LLM providers for cost/performance tradeoffs","organizations with data privacy requirements needing local model support","developers building LLM-agnostic agent frameworks"],"limitations":["Provider adapters must be manually maintained — new LLM APIs require code changes, not configuration","Streaming response parsing differs per provider — no unified streaming interface (OpenAI vs Anthropic have different token formats)","Function-calling schemas vary by provider (OpenAI tools vs Anthropic tool_use) — agents must handle provider-specific differences","Error handling is provider-specific — rate limits, authentication, and timeout behaviors differ","No built-in fallback or retry logic across providers — if primary provider fails, no automatic failover"],"requires":["API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","Environment variables configured (OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.)","Network access to LLM provider endpoints or local Ollama instance","Python requests library for HTTP calls"],"input_types":["chat messages (text)","system prompts","function/tool definitions (JSON schema)"],"output_types":["text completions","streaming tokens","function calls (structured)","usage metrics (tokens, cost)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xlang-ai--openagents__cap_6","uri":"capability://automation.workflow.streaming.response.handling.with.real.time.ui.updates","name":"streaming response handling with real-time ui updates","description":"Implements server-sent events (SSE) and WebSocket-based streaming to deliver agent responses to the frontend in real-time, enabling users to see intermediate results, code execution progress, and tool calls as they happen. The backend streams tokens from the LLM, execution logs from the Data Agent, and plugin results as they complete, with the frontend rendering updates incrementally. This avoids blocking on long-running operations and provides perceived performance improvements.","intents":["I want users to see agent thinking and tool calls in real-time, not wait for the full response","I need to show progress bars for long data analysis or web browsing tasks","I want to stream code execution logs as the Data Agent runs Python/SQL"],"best_for":["consumer-facing AI assistants where perceived latency matters","applications with long-running agent tasks (web browsing, data analysis)","teams building interactive AI experiences with real-time feedback"],"limitations":["Streaming adds complexity to error handling — partial responses may be sent before errors occur","Frontend must handle out-of-order or duplicate messages in streaming context","SSE connections are unidirectional — no client-to-server streaming (only server-to-client)","Connection timeouts and network interruptions require client-side reconnection logic","Streaming overhead (framing, parsing) adds ~5-10% latency compared to single response","Browser support for SSE is good but requires fallback for older clients"],"requires":["Flask backend with streaming response support","Next.js frontend with fetch API or EventSource for SSE","Network that supports long-lived HTTP connections","Proper CORS configuration for streaming endpoints"],"input_types":["user queries","agent execution requests"],"output_types":["streaming text tokens","JSON events (tool calls, execution logs)","progress updates (percentage complete)","error messages (mid-stream)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xlang-ai--openagents__cap_7","uri":"capability://code.generation.editing.semantic.parsing.of.natural.language.to.executable.operations","name":"semantic parsing of natural language to executable operations","description":"Translates natural language queries into executable operations (Python code, SQL queries, API calls, browser actions) by using the LLM to understand intent and generate appropriate code or commands. The system maintains a library of operation templates and examples, uses few-shot prompting to guide code generation, and validates generated code before execution. This enables users to express complex data operations, plugin calls, and web interactions in plain English without learning syntax.","intents":["I want to say 'show me the top 5 products by sales' and have it generate SQL automatically","I need to express complex data transformations in natural language without writing pandas code","I want to tell the agent 'find flights cheaper than $500' and have it construct the right plugin calls"],"best_for":["non-technical users building data queries and automations","rapid prototyping of data analysis workflows","teams building natural language interfaces to existing systems"],"limitations":["Semantic parsing is LLM-dependent — ambiguous queries may generate incorrect code (e.g., 'top 5' could mean max or min)","Generated code is not formally verified — may contain logical errors that only appear at runtime","Context window limits prevent handling very complex multi-step operations (>5 steps)","No built-in handling of domain-specific terminology — requires custom prompt engineering per domain","Hallucination risk: LLM may generate code for non-existent columns, functions, or plugins"],"requires":["LLM with strong code generation capability (GPT-4, Claude 3+)","Few-shot examples in system prompt for target operation type","Code execution environment (Python, SQL, etc.)","Validation logic to catch syntax errors before execution"],"input_types":["natural language queries","schema information (column names, available functions)","context from previous operations"],"output_types":["Python code (pandas operations)","SQL queries","API call specifications","browser action sequences"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xlang-ai--openagents__cap_8","uri":"capability://data.processing.analysis.file.upload.and.data.ingestion.with.format.detection","name":"file upload and data ingestion with format detection","description":"Handles file uploads (CSV, JSON, Excel) through the web UI, automatically detects file format and schema, parses data into structured representations (pandas DataFrames), and stores metadata in MongoDB for later reference. The system validates file size, checks for encoding issues, and provides users with a preview of parsed data before analysis. Uploaded files are cached in Redis for quick access across multiple queries.","intents":["I want to upload a CSV and immediately start analyzing it without manual format specification","I need to handle Excel files with multiple sheets and detect the right sheet to analyze","I want to see a preview of my data before running analysis to catch import errors"],"best_for":["business users uploading datasets for ad-hoc analysis","data exploration workflows where format varies","teams building data ingestion pipelines with user uploads"],"limitations":["File size limits (typically 100MB-1GB depending on deployment) — no streaming ingestion for very large files","Format detection is heuristic-based — ambiguous files (e.g., CSV vs TSV) may be misdetected","Excel parsing doesn't automatically handle merged cells, formulas, or complex formatting","No built-in data validation or schema enforcement — garbage data is parsed as-is","Uploaded files are stored temporarily — no long-term file versioning or audit trail"],"requires":["Flask file upload handler with size limits configured","pandas and openpyxl libraries for parsing","MongoDB for metadata storage","Temporary file storage (local disk or cloud storage)"],"input_types":["CSV files","JSON files","Excel files (.xlsx, .xls)"],"output_types":["pandas DataFrame (in-memory)","data preview (first N rows as JSON)","schema information (column names, types)","metadata (file size, row count, encoding)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xlang-ai--openagents__cap_9","uri":"capability://memory.knowledge.agent.specific.state.and.context.management","name":"agent-specific state and context management","description":"Maintains isolated state for each agent type (Data Agent execution context, Plugins Agent tool registry, Web Agent session state) while sharing common context (conversation history, user preferences) through a unified backend. Each agent has its own state store in MongoDB, with adapters that translate between agent-specific formats and the common interface. This enables agents to maintain specialized context (e.g., Data Agent's DataFrame cache) without polluting shared state.","intents":["I want the Data Agent to remember which DataFrames were loaded in previous queries","I need the Web Agent to maintain browser session state across multiple navigation steps","I want agents to share conversation history but maintain separate execution contexts"],"best_for":["multi-agent systems where agents need specialized state","applications where agent state must persist across sessions","teams building extensible agent platforms with custom state requirements"],"limitations":["State synchronization between agents is manual — no automatic propagation of state changes","Large state objects (e.g., cached DataFrames) consume MongoDB storage and Redis memory","No built-in state versioning or rollback — state mutations are permanent","Cross-agent state sharing requires explicit adapters — tight coupling between agent implementations","State cleanup is manual — no automatic garbage collection of old agent contexts"],"requires":["MongoDB collections for each agent type's state","Redis for caching frequently accessed state","Adapters to translate between agent-specific and common formats","Unique agent session IDs for state isolation"],"input_types":["agent execution results","user interactions","configuration changes"],"output_types":["agent state (JSON)","shared context (conversation history, user preferences)","state metadata (timestamps, versions)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","Node.js 16+ for Next.js frontend","MongoDB instance for persistence","Redis instance for caching","Flask backend running on port 5000 (configurable)","API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","Python 3.8+ with pandas, numpy, matplotlib, seaborn installed","SQLite for SQL execution (included in Python)","File upload handler in Flask backend","MongoDB for caching execution results"],"failure_modes":["Agent switching requires backend coordination — no client-side agent routing, adding ~100-200ms latency per agent change","Conversation context is stored in MongoDB; no built-in cross-agent memory optimization for large conversation histories","Frontend state management via React Context — doesn't scale beyond ~50 concurrent users without load balancing","Code execution is sandboxed but not fully isolated — malicious code could still access file system within container boundaries","Large datasets (>500MB) may cause memory issues in the execution environment; no built-in streaming for big data","SQL execution limited to in-memory DataFrames via SQLite — no direct database connections to external systems","Visualization output is static (matplotlib/seaborn) — no interactive dashboards or real-time updates","Custom agents must implement the full interface — no partial implementation or mixins","Shared adapters (memory, streaming) may not fit all agent patterns — custom agents may need to bypass them","No built-in versioning or compatibility checking — breaking changes in the base interface affect all custom agents","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5878684228696502,"quality":0.24,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.27,"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":"inactive","updated_at":"2026-05-06T15:12:23.810Z","last_scraped_at":"2026-05-03T13:58:37.060Z","last_commit":"2024-11-18T09:39:29Z"},"community":{"stars":4785,"forks":527,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=xlang-ai--openagents","compare_url":"https://unfragile.ai/compare?artifact=xlang-ai--openagents"}},"signature":"qzD+H9b8oAilZd66ij2kTxtxAOz36kzqmCxEKG/bIlO8beZEHu3f+fnnNrX5t2qJR6pH1Bt5HUE9j22WDW4KDw==","signedAt":"2026-06-21T12:02:47.757Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/xlang-ai--openagents","artifact":"https://unfragile.ai/xlang-ai--openagents","verify":"https://unfragile.ai/api/v1/verify?slug=xlang-ai--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"}}