{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-xagent","slug":"xagent","name":"XAgent","type":"agent","url":"https://github.com/OpenBMB/XAgent","page_url":"https://unfragile.ai/xagent","categories":["ai-agents"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-xagent__cap_0","uri":"capability://planning.reasoning.hierarchical.task.decomposition.with.milestone.based.planning","name":"hierarchical task decomposition with milestone-based planning","description":"XAgent's Planner component breaks down complex user tasks into hierarchical subtasks with explicit milestones using LLM reasoning. The system generates structured task trees where each subtask has defined success criteria and dependencies, enabling the Actor to execute subtasks sequentially or in parallel. This differs from flat task lists by maintaining semantic relationships and allowing the system to validate progress against milestones before proceeding to dependent tasks.","intents":["I need an agent to automatically break down a complex project into executable steps with clear success criteria","I want the agent to understand task dependencies and only proceed when milestones are achieved","I need visibility into how a complex goal was decomposed into actionable subtasks"],"best_for":["teams building autonomous agents for multi-step workflows","developers needing interpretable task planning for complex domains like research, data analysis, or software engineering"],"limitations":["Planning quality depends on LLM reasoning capability — may fail on highly specialized domains without domain-specific prompting","No built-in validation that generated subtasks are actually achievable with available tools","Milestone definitions are LLM-generated and may not align with actual task semantics in edge cases"],"requires":["OpenAI API key or compatible LLM endpoint","Python 3.8+","Task description in natural language"],"input_types":["natural language task description","optional context about available tools"],"output_types":["structured task tree with subtasks and milestones","execution plan with dependencies"],"categories":["planning-reasoning","task-decomposition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-xagent__cap_1","uri":"capability://tool.use.integration.docker.sandboxed.tool.execution.with.multi.tool.orchestration","name":"docker-sandboxed tool execution with multi-tool orchestration","description":"XAgent's ToolServer provides a containerized execution environment where the Actor can safely invoke multiple tool types (file editor, Python notebook, web browser, shell, API client) without risk to the host system. Tools are registered in a schema-based registry that the Actor queries to determine which tools are available for a given subtask. The system handles tool invocation, output capture, and error handling within the container boundary, with results returned to the Agent for further reasoning.","intents":["I need an agent to execute arbitrary code and shell commands safely without compromising my system","I want the agent to use multiple tools (file editing, web search, API calls, Python execution) in a single workflow","I need to audit and replay all tool invocations that the agent performed"],"best_for":["enterprises requiring sandboxed agent execution for security compliance","developers building agents that need filesystem, network, and code execution capabilities","teams needing reproducible agent execution traces"],"limitations":["Docker overhead adds 2-5 second startup latency per task execution","Tool output is captured as text — binary outputs (images, videos) require serialization","No built-in resource limits (CPU, memory, disk) — requires manual Docker configuration","Cross-tool state is not automatically managed — each tool invocation is isolated unless explicitly passed context"],"requires":["Docker daemon running","Docker Compose 1.29+","Python 3.8+","Sufficient disk space for container images"],"input_types":["tool name and parameters","file paths or code snippets","API endpoints and credentials"],"output_types":["tool execution results (text, JSON, file paths)","error messages and stack traces","execution logs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-xagent__cap_10","uri":"capability://search.retrieval.web.browsing.and.information.retrieval.with.headless.browser","name":"web browsing and information retrieval with headless browser","description":"XAgent's ToolServer includes a web browser tool that allows the Agent to search the web, visit URLs, and extract information from web pages. The browser is headless (no GUI) and runs within the container, enabling automated web navigation and scraping. The Agent can search for information, follow links, and parse HTML to extract relevant data. Results are returned as text or structured data for further processing.","intents":["I want the agent to search the web for information to answer questions or gather data","I need the agent to visit specific URLs and extract relevant information","I want the agent to perform multi-step web navigation (search → click → extract)"],"best_for":["research and information gathering tasks","agents needing to access current information not in training data","applications requiring web scraping and data extraction"],"limitations":["Web scraping is slow — page loads and parsing add 2-10 seconds per request","JavaScript-heavy websites may not render correctly in headless browser","Web scraping may violate terms of service for some websites","HTML parsing is fragile — page structure changes break extraction logic","No built-in handling of CAPTCHAs, authentication, or anti-scraping measures"],"requires":["Docker container with headless browser (Chromium, Firefox)","Network connectivity to the internet","Python 3.8+"],"input_types":["search queries","URLs","CSS selectors or XPath for element extraction"],"output_types":["search results","web page content (HTML, text)","extracted data (tables, lists, text)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-xagent__cap_11","uri":"capability://automation.workflow.shell.command.execution.with.environment.isolation","name":"shell command execution with environment isolation","description":"XAgent's ToolServer provides a bash shell environment where the Agent can execute arbitrary shell commands within the container. The Agent can install packages, run scripts, manage files, and host services. Command execution is isolated to the container, preventing damage to the host system. Output (stdout, stderr) is captured and returned to the Agent. The shell maintains state across multiple commands, allowing the Agent to set environment variables and manage working directories.","intents":["I want the agent to execute system commands (git, npm, pip, etc.) to manage projects","I need the agent to install dependencies and configure environments programmatically","I want the agent to run scripts and manage files on the filesystem"],"best_for":["DevOps and infrastructure automation tasks","agents managing software projects and dependencies","applications requiring system-level operations"],"limitations":["Command execution is synchronous — long-running processes block the agent","No built-in timeout — runaway commands can hang the agent indefinitely","Shell state is not persisted — restarting the container loses environment variables and working directory","No built-in privilege escalation — cannot run commands that require sudo","Output buffering may lose data from commands with large output"],"requires":["Docker container with bash and common utilities (git, curl, etc.)","Sufficient container disk space for package installation"],"input_types":["shell commands","environment variables","file paths"],"output_types":["command output (stdout, stderr)","exit codes","file system changes"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-xagent__cap_12","uri":"capability://code.generation.editing.file.editing.and.management.with.text.based.operations","name":"file editing and management with text-based operations","description":"XAgent's ToolServer includes a file editor tool that allows the Agent to read, write, and modify files within the container. The Agent can create new files, edit existing files, and manage directory structures. File operations are text-based, supporting common formats (code, markdown, JSON, etc.). The editor provides line-level operations (insert, delete, replace) for precise edits. File paths are resolved relative to the working directory, and the Agent can navigate the filesystem.","intents":["I want the agent to create and edit code files, configuration files, and documentation","I need the agent to manage project structure and organize files","I want the agent to read files to understand existing code or data"],"best_for":["code generation and editing tasks","configuration management and infrastructure-as-code","documentation and content creation"],"limitations":["File operations are text-only — binary files (images, executables) cannot be edited","No built-in version control — file changes are not tracked or reversible","Large file operations (reading/writing multi-MB files) may be slow","No built-in conflict resolution — concurrent edits from multiple agents may cause data loss","File permissions are inherited from the container — no fine-grained access control"],"requires":["Docker container with filesystem access","Python 3.8+","Sufficient disk space for file storage"],"input_types":["file paths","file content","line numbers for edits"],"output_types":["file content (text)","file metadata (size, permissions)","edit confirmation"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-xagent__cap_13","uri":"capability://planning.reasoning.human.feedback.integration.for.mid.execution.guidance","name":"human feedback integration for mid-execution guidance","description":"XAgent supports human-in-the-loop execution where the Agent can pause and request human feedback during task execution. When the Agent encounters ambiguity or needs guidance, it can ask clarifying questions and wait for human input. The WebSocket interface enables real-time feedback submission from users. The Agent incorporates human feedback into its reasoning and adjusts its plan accordingly. This enables collaborative problem-solving where humans and agents work together.","intents":["I want to guide the agent when it makes incorrect assumptions or needs clarification","I need the agent to ask for help on decisions that require human judgment","I want to correct the agent's course mid-execution without restarting the task"],"best_for":["tasks requiring human judgment or domain expertise","applications where agent autonomy must be balanced with human oversight","teams building collaborative human-AI workflows"],"limitations":["Feedback collection adds latency — agent must wait for human response","No built-in timeout for feedback requests — agent may hang indefinitely","Feedback integration is manual — requires explicit prompting to incorporate feedback","No mechanism to validate that human feedback is correct or beneficial","Feedback history is not automatically used to improve future decisions"],"requires":["XAgentServer with WebSocket support","XAgentWeb or custom client for feedback submission","Human availability to provide timely feedback"],"input_types":["agent questions or clarifications","human feedback or corrections","guidance on next steps"],"output_types":["feedback acknowledgment","adjusted execution plan","continued task execution"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-xagent__cap_14","uri":"capability://planning.reasoning.model.fine.tuning.and.customization.via.xagentgen","name":"model fine-tuning and customization via xagentgen","description":"XAgentGen is a component that enables customization of LLM models specifically for XAgent tasks. It can fine-tune models on domain-specific data or generate specialized model variants optimized for particular task types. The generated models are integrated back into XAgent's LLM provider interface, allowing seamless substitution of base models. This enables organizations to create proprietary models optimized for their specific use cases without modifying XAgent core.","intents":["I want to fine-tune an LLM on my domain-specific data to improve agent performance","I need to create specialized models for specific task types without retraining from scratch","I want to optimize model size and latency for my specific use case"],"best_for":["organizations with domain-specific data wanting to improve agent performance","teams needing specialized models for particular task types","enterprises requiring proprietary models for competitive advantage"],"limitations":["Fine-tuning requires significant training data and compute resources","Model generation is time-consuming — may take hours or days","No built-in evaluation metrics — requires manual validation of generated models","Generated models may not generalize beyond the training domain","Integration with XAgent requires code changes to the provider interface"],"requires":["Training data in appropriate format","GPU compute resources for fine-tuning","Python 3.8+","Base model access (OpenAI, Anthropic, or open-source)"],"input_types":["training data (task-result pairs)","model configuration parameters","task type specifications"],"output_types":["fine-tuned model weights","model evaluation metrics","deployment-ready model artifacts"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-xagent__cap_2","uri":"capability://tool.use.integration.multi.provider.llm.integration.with.dynamic.model.selection","name":"multi-provider llm integration with dynamic model selection","description":"XAgent abstracts LLM interactions through a provider-agnostic interface that supports OpenAI and other compatible endpoints. The system can dynamically select which LLM to use for different components (planning, acting, reasoning) based on configuration, enabling cost-performance tradeoffs. Prompts are templated and versioned, allowing different prompt strategies to be tested without code changes. The integration handles token counting, rate limiting, and retry logic transparently.","intents":["I want to swap between different LLM providers (OpenAI, Anthropic, local models) without rewriting agent code","I need to use cheaper models for simple subtasks and more capable models for complex reasoning","I want to version and A/B test different prompting strategies across the agent"],"best_for":["teams experimenting with multiple LLM providers to optimize cost and latency","developers building agents that need to work with both cloud and self-hosted models","organizations requiring multi-model strategies for different task complexities"],"limitations":["Prompt templates are tightly coupled to specific model capabilities — switching models may require prompt retuning","No built-in prompt caching — each API call sends full context, increasing latency and cost","Token counting is approximate and may not match actual provider billing","Rate limiting is client-side only — no coordination across distributed agent instances"],"requires":["API key for at least one LLM provider (OpenAI, Anthropic, etc.)","Python 3.8+","Network connectivity to LLM endpoints"],"input_types":["task description","context and tool descriptions","previous reasoning steps"],"output_types":["LLM-generated plans","tool invocation decisions","reasoning traces"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-xagent__cap_3","uri":"capability://automation.workflow.execution.trace.recording.and.replay.with.full.auditability","name":"execution trace recording and replay with full auditability","description":"XAgent's Running Recorder captures all task execution events (planning decisions, tool invocations, results, errors) into a structured log that can be persisted and replayed. The recording system maintains the full decision tree including failed attempts and backtracking, enabling post-hoc analysis of agent behavior. Traces are stored in a database and can be queried to understand why the agent made specific decisions or to debug failures.","intents":["I need to audit exactly what actions an autonomous agent took and why for compliance or debugging","I want to replay an agent's execution to understand failure modes without re-running the task","I need to extract training data from successful agent executions to improve prompts or fine-tune models"],"best_for":["enterprises requiring full audit trails for autonomous systems","researchers analyzing agent behavior and decision-making patterns","teams debugging complex multi-step agent failures"],"limitations":["Recording overhead adds ~5-10% latency per execution step","Trace storage grows linearly with task complexity — long-running tasks can generate multi-MB logs","Replay functionality is read-only — cannot modify and re-execute branches of the trace","Sensitive data (API keys, credentials) may be captured in traces unless explicitly filtered"],"requires":["Database backend (PostgreSQL, MySQL, SQLite)","Python 3.8+","Sufficient disk space for trace storage"],"input_types":["execution events from Dispatcher, Planner, and Actor","tool invocation parameters and results","LLM prompts and responses"],"output_types":["structured execution traces (JSON or database records)","execution timeline and decision tree","failure analysis reports"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-xagent__cap_4","uri":"capability://tool.use.integration.websocket.based.real.time.agent.client.communication","name":"websocket-based real-time agent-client communication","description":"XAgentServer exposes a WebSocket interface that enables real-time bidirectional communication between the agent and client applications. Clients can stream task progress updates, receive intermediate results, and optionally provide human feedback to guide the agent during execution. The WebSocket protocol allows long-lived connections that persist across multiple task executions, reducing connection overhead. The server handles connection management, message routing, and state synchronization between multiple concurrent clients.","intents":["I want to monitor agent progress in real-time as it executes a task","I need to provide human feedback or corrections to the agent mid-execution without restarting","I want to build a web UI that shows live agent reasoning and tool invocations"],"best_for":["teams building interactive agent UIs with real-time progress visualization","applications requiring human-in-the-loop agent execution","developers needing low-latency agent-client communication"],"limitations":["WebSocket connections are stateful — server restarts disconnect all clients","No built-in message queuing — messages sent while client is disconnected are lost","Bandwidth overhead for streaming large execution traces in real-time","Client-side buffering required to handle message bursts from fast agent execution"],"requires":["Python 3.8+","XAgentServer running","WebSocket client library (e.g., websockets, socket.io)"],"input_types":["task descriptions","human feedback and corrections","configuration updates"],"output_types":["real-time execution events","intermediate results","progress updates","error notifications"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-xagent__cap_5","uri":"capability://planning.reasoning.dynamic.agent.instantiation.based.on.task.type","name":"dynamic agent instantiation based on task type","description":"XAgent's Dispatcher component analyzes incoming tasks and dynamically instantiates specialized agent implementations suited to the task type. Rather than using a single monolithic agent, the system can route different task categories (e.g., data analysis, web research, code generation) to agents optimized for those domains. This allows task-specific prompting, tool selection, and reasoning strategies without code duplication. The Dispatcher maintains a registry of available agent types and their applicability conditions.","intents":["I want different agent behaviors for different task types without maintaining separate agent implementations","I need task-specific tool selection — some tasks need web search, others need code execution","I want to optimize prompting and reasoning strategies per task category"],"best_for":["platforms supporting diverse task types (research, coding, analysis, writing)","teams building multi-domain agent systems","developers wanting to specialize agent behavior without code duplication"],"limitations":["Task classification is LLM-based and may misclassify edge cases","Adding new agent types requires code changes to the Dispatcher","No automatic fallback if the selected agent type fails — requires explicit error handling","Agent specialization benefits depend on having truly different prompting strategies per type"],"requires":["Python 3.8+","Task description with sufficient context for classification","Registered agent implementations for supported task types"],"input_types":["task description","optional task metadata or tags"],"output_types":["selected agent type","task routing decision","specialized execution plan"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-xagent__cap_6","uri":"capability://memory.knowledge.file.based.task.persistence.and.state.management","name":"file-based task persistence and state management","description":"XAgent persists task state, execution traces, and intermediate results to the filesystem and database, enabling task resumption after interruptions. The system stores task metadata, planning decisions, and tool outputs in structured formats that can be queried and analyzed. State is synchronized between the agent core and persistent storage, allowing the agent to recover from failures and continue from the last successful checkpoint. The persistence layer abstracts storage details, supporting multiple backend options.","intents":["I need an agent to resume long-running tasks after system failures without losing progress","I want to store and query historical task executions for analysis and debugging","I need to checkpoint agent state at milestones to enable efficient task resumption"],"best_for":["systems running long-duration agent tasks that may be interrupted","teams needing historical task data for analysis and compliance","applications requiring fault-tolerant agent execution"],"limitations":["Checkpoint frequency is fixed — no adaptive checkpointing based on task progress","State recovery requires manual intervention to resume from checkpoints","Storage overhead grows with task complexity — no automatic cleanup of old traces","Distributed agent instances may have stale state if checkpoints are not synchronized"],"requires":["Filesystem with write permissions","Database backend (PostgreSQL, MySQL, SQLite)","Python 3.8+"],"input_types":["task state snapshots","execution events","tool results"],"output_types":["persisted task metadata","execution traces","checkpoint files"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-xagent__cap_7","uri":"capability://automation.workflow.web.based.chat.interface.with.task.management.ui","name":"web-based chat interface with task management ui","description":"XAgentWeb provides a React-based web interface that enables users to interact with XAgent through a chat-like conversation. Users can submit tasks, monitor real-time execution progress, upload files, and view detailed execution traces. The UI maintains task history, allows filtering and searching past executions, and provides visualization of the task decomposition tree. Authentication is integrated to support multi-user scenarios. The interface communicates with XAgentServer via WebSocket for real-time updates.","intents":["I want non-technical users to interact with an autonomous agent through a familiar chat interface","I need to visualize how the agent decomposed a task and what actions it took","I want to manage multiple concurrent tasks and view their execution status"],"best_for":["non-technical users interacting with autonomous agents","teams needing a unified interface for task submission and monitoring","applications requiring multi-user task management"],"limitations":["UI is tightly coupled to XAgentServer — cannot be used with other agent backends without modification","Real-time updates depend on WebSocket connectivity — disconnections cause UI staleness","File upload size is limited by server configuration","Task visualization is limited to the execution trace structure — custom visualizations require code changes"],"requires":["Node.js 14+","React 17+","XAgentServer running and accessible","Modern web browser with WebSocket support"],"input_types":["natural language task descriptions","file uploads","configuration parameters"],"output_types":["chat messages","execution progress updates","task visualization","execution trace details"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-xagent__cap_8","uri":"capability://tool.use.integration.api.based.tool.integration.with.rapidapi.support","name":"api-based tool integration with rapidapi support","description":"XAgent's ToolServer includes a RapidAPI integration that allows the Agent to discover and invoke third-party APIs without pre-configuration. The system queries the RapidAPI marketplace to find relevant APIs for a given subtask, retrieves API specifications, and constructs appropriate API calls. Results are parsed and returned to the Agent for further reasoning. This enables access to thousands of APIs (weather, translation, data lookup, etc.) without hardcoding integrations.","intents":["I want the agent to automatically discover and use relevant APIs for tasks without pre-integration","I need the agent to access external data sources (weather, news, financial data) dynamically","I want to extend agent capabilities to any API available on RapidAPI without code changes"],"best_for":["agents needing access to diverse external APIs","applications requiring dynamic API discovery and integration","teams wanting to avoid hardcoding API integrations"],"limitations":["RapidAPI discovery is slow — API lookup and specification retrieval adds 2-5 seconds per API call","API selection is LLM-based and may choose inappropriate APIs for a task","Authentication credentials for APIs must be provided or stored securely","API rate limits are not enforced by XAgent — requires external rate limiting","API response parsing is fragile — inconsistent response formats may cause failures"],"requires":["RapidAPI account and API key","Python 3.8+","Network connectivity to RapidAPI"],"input_types":["task description","API search queries","API parameters"],"output_types":["API discovery results","API responses (JSON, XML, text)","parsed data"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-xagent__cap_9","uri":"capability://code.generation.editing.python.notebook.execution.with.interactive.code.validation","name":"python notebook execution with interactive code validation","description":"XAgent's ToolServer includes a Python notebook environment that allows the Agent to write, execute, and validate code interactively. The Agent can use this for data analysis, visualization, hypothesis testing, and algorithm development. Code execution is sandboxed within the Docker container, and outputs (including plots and tables) are captured and returned to the Agent. The notebook maintains state across multiple code cells, enabling iterative development and debugging.","intents":["I want the agent to write and execute Python code to solve analytical or computational tasks","I need the agent to validate ideas through code execution before committing to a solution","I want the agent to generate visualizations and data summaries programmatically"],"best_for":["data analysis and scientific computing tasks","agents needing to validate hypotheses through code execution","tasks requiring custom algorithms or data transformations"],"limitations":["Code execution is synchronous — long-running computations block the agent","No built-in resource limits — runaway code can consume all container resources","Notebook state is not persisted — restarting the container loses all variables and outputs","Large data outputs (plots, tables) may exceed message size limits when returned to the agent","Python version and installed packages are fixed — cannot dynamically install new dependencies"],"requires":["Docker container with Python 3.8+ and common data science libraries (pandas, numpy, matplotlib)","Sufficient container memory for data processing"],"input_types":["Python code snippets","data files or URLs","parameters for analysis"],"output_types":["code execution results","plots and visualizations","data summaries","error messages and tracebacks"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key or compatible LLM endpoint","Python 3.8+","Task description in natural language","Docker daemon running","Docker Compose 1.29+","Sufficient disk space for container images","Docker container with headless browser (Chromium, Firefox)","Network connectivity to the internet","Docker container with bash and common utilities (git, curl, etc.)","Sufficient container disk space for package installation"],"failure_modes":["Planning quality depends on LLM reasoning capability — may fail on highly specialized domains without domain-specific prompting","No built-in validation that generated subtasks are actually achievable with available tools","Milestone definitions are LLM-generated and may not align with actual task semantics in edge cases","Docker overhead adds 2-5 second startup latency per task execution","Tool output is captured as text — binary outputs (images, videos) require serialization","No built-in resource limits (CPU, memory, disk) — requires manual Docker configuration","Cross-tool state is not automatically managed — each tool invocation is isolated unless explicitly passed context","Web scraping is slow — page loads and parsing add 2-10 seconds per request","JavaScript-heavy websites may not render correctly in headless browser","Web scraping may violate terms of service for some websites","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":"2026-06-17T09:51:04.690Z","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=xagent","compare_url":"https://unfragile.ai/compare?artifact=xagent"}},"signature":"mg5l6NcnM5elcL+4rkPdYji9dsJa3t+lUKrRyUOeISaTRfiayn6IQA7nFYRgmyIb6s0uaP8+z2yDnRsN0HDNBw==","signedAt":"2026-06-20T14:06:23.441Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/xagent","artifact":"https://unfragile.ai/xagent","verify":"https://unfragile.ai/api/v1/verify?slug=xagent","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"}}