{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-the-open-agent--openagent","slug":"the-open-agent--openagent","name":"openagent","type":"agent","url":"https://www.openagentai.org/","page_url":"https://unfragile.ai/the-open-agent--openagent","categories":["ai-agents","rag-knowledge"],"tags":["a2a","agent","agentic","agentic-ai","agi","chatbot","chatgpt","gpt","harness","knowledge-base","langchain","llama","llm","mcp","model-context-protocol","multi-agent","openagent","openai","openclaw","rag"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-the-open-agent--openagent__cap_0","uri":"capability://planning.reasoning.multi.agent.orchestration.with.agent.loops","name":"multi-agent orchestration with agent loops","description":"Coordinates multiple specialized agents through iterative loop patterns, enabling task decomposition and delegation across agents with shared context. Implements agent-to-agent (a2a) communication patterns where agents can spawn sub-agents, share state, and coordinate on complex multi-step tasks without requiring centralized orchestration logic.","intents":["I need to break down a complex task across multiple specialized agents that can work together","I want agents to delegate subtasks to other agents and aggregate results","I need to implement hierarchical agent structures where parent agents spawn child agents"],"best_for":["teams building complex agentic systems requiring task decomposition","developers implementing multi-agent workflows with interdependent tasks","builders prototyping AGI-adjacent systems with agent hierarchies"],"limitations":["Agent loop depth and complexity can lead to exponential context growth without pruning strategies","No built-in deadlock detection or circular dependency prevention between agents","State synchronization across agents requires explicit message passing — no automatic distributed state management"],"requires":["Go runtime (primary implementation language)","LLM API access (OpenAI, Anthropic, or compatible)","Message queue or event bus for inter-agent communication (optional but recommended for scalability)"],"input_types":["natural language task descriptions","structured task definitions with subtask specifications","agent configuration schemas"],"output_types":["aggregated results from multiple agents","execution traces showing agent interactions","structured task completion reports"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-the-open-agent--openagent__cap_1","uri":"capability://tool.use.integration.computer.use.and.browser.automation.agent","name":"computer-use and browser automation agent","description":"Enables agents to interact with desktop environments and web browsers through screen perception and action execution, allowing agents to take screenshots, parse visual elements, click UI components, type text, and navigate web pages. Implements a perception-action loop where agents receive visual feedback and execute browser/desktop commands to accomplish user goals without requiring explicit API integrations.","intents":["I want an agent to autonomously browse the web and fill out forms","I need an agent to interact with desktop applications by understanding and clicking UI elements","I want to automate repetitive browser tasks like data scraping or account management"],"best_for":["developers automating web-based workflows and RPA tasks","teams building agents for legacy system integration without APIs","builders creating AI assistants that need to interact with any web application"],"limitations":["Visual perception relies on screenshot quality and resolution — low-DPI or complex UI layouts may cause misidentification","No built-in CAPTCHA handling or anti-bot detection circumvention","Action execution latency includes screenshot capture, LLM inference, and DOM manipulation — typically 2-5 seconds per action","Limited to applications accessible via browser or desktop — cannot interact with native OS dialogs or system-level operations"],"requires":["Browser automation framework (Playwright, Selenium, or similar)","Desktop environment with display server (X11, Wayland, or Windows)","Vision-capable LLM (Claude 3.5 Sonnet, GPT-4V, or equivalent)","Sufficient GPU/CPU for real-time screenshot processing"],"input_types":["natural language task descriptions","target URLs or application names","visual screenshots from browser/desktop"],"output_types":["executed browser actions (clicks, text input, navigation)","extracted data from web pages","task completion status with visual evidence"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-the-open-agent--openagent__cap_10","uri":"capability://automation.workflow.logging.monitoring.and.observability.for.agent.execution","name":"logging, monitoring, and observability for agent execution","description":"Provides comprehensive logging and monitoring of agent execution including action traces, LLM calls, tool invocations, and performance metrics. Agents emit structured logs that can be aggregated and analyzed to understand behavior, debug issues, and optimize performance. Integrates with observability platforms for real-time monitoring.","intents":["I want to understand what agents are doing and why they made specific decisions","I need to debug agent failures by examining execution traces","I want to monitor agent performance and identify bottlenecks"],"best_for":["developers debugging complex agent behaviors","teams operating agents in production with observability requirements","builders creating agents that need to be auditable and explainable"],"limitations":["Comprehensive logging adds 5-10% overhead to agent execution time","Structured logging requires careful schema design to capture relevant information","Log volume can be substantial for long-running agents — requires log aggregation and retention policies","Real-time monitoring requires integration with observability platforms (Datadog, New Relic, etc.)"],"requires":["Logging framework (structured logging library)","Log storage and aggregation (file system, cloud logging, or log aggregation service)","Observability platform (optional but recommended for production)","Log schema and standardization"],"input_types":["agent execution events","performance metrics","error conditions"],"output_types":["structured logs","execution traces","performance dashboards","alerts and anomalies"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-the-open-agent--openagent__cap_11","uri":"capability://safety.moderation.security.and.access.control.for.agent.operations","name":"security and access control for agent operations","description":"Implements security controls and access management for agent operations including authentication, authorization, and sandboxing. Agents operate within defined security boundaries with restricted permissions for tool access and resource usage. Provides audit trails for compliance and prevents unauthorized agent actions.","intents":["I want to restrict which tools agents can access based on user roles","I need to prevent agents from accessing sensitive data or performing dangerous operations","I want to audit agent actions for compliance and security monitoring"],"best_for":["teams deploying agents in regulated environments with compliance requirements","developers building multi-tenant agent systems with isolation requirements","builders creating agents that interact with sensitive systems or data"],"limitations":["Fine-grained access control adds complexity to agent configuration and management","Security policies may conflict with agent autonomy — overly restrictive policies limit agent effectiveness","Audit logging adds overhead and storage requirements for compliance","No built-in secret management — requires integration with external secret stores"],"requires":["Authentication system (OAuth, API keys, or similar)","Authorization framework (RBAC, ABAC, or policy-based)","Sandboxing environment for code execution","Audit logging infrastructure","Secret management system"],"input_types":["user credentials","access policies","resource permissions"],"output_types":["authorization decisions","audit logs","security events"],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-the-open-agent--openagent__cap_2","uri":"capability://code.generation.editing.coding.agent.with.code.generation.and.execution","name":"coding agent with code generation and execution","description":"Enables agents to generate, analyze, and execute code in multiple programming languages as part of task completion. Agents can write code snippets, execute them in sandboxed environments, interpret results, and iterate on code based on execution feedback. Integrates with language-specific runtimes and provides error handling and output capture for code execution loops.","intents":["I need an agent to write and execute Python scripts to solve computational problems","I want an agent to generate code, test it, and refine it based on test failures","I need to automate data processing tasks where an agent writes and runs analysis code"],"best_for":["developers building agents for data science and analysis workflows","teams automating code generation and testing pipelines","builders creating AI assistants that need to solve programming problems autonomously"],"limitations":["Sandbox execution adds 500ms-2s latency per code execution due to container/process startup","No persistent state between code executions unless explicitly managed through file I/O or database connections","Limited to languages with available runtimes in the sandbox environment","Security model relies on sandbox isolation — complex privilege escalation attacks may bypass protections"],"requires":["Sandboxed code execution environment (Docker, Firecracker, or similar)","Language runtimes for supported languages (Python 3.9+, Node.js 18+, etc.)","File system access for code artifact storage","Resource limits (CPU, memory, timeout) to prevent runaway executions"],"input_types":["natural language problem descriptions","code snippets for analysis or modification","data files for processing"],"output_types":["generated source code","code execution results and output","error messages and stack traces","refined code based on execution feedback"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-the-open-agent--openagent__cap_3","uri":"capability://memory.knowledge.rag.powered.knowledge.retrieval.and.context.injection","name":"rag-powered knowledge retrieval and context injection","description":"Integrates retrieval-augmented generation (RAG) to augment agent reasoning with external knowledge sources. Agents can query vector databases, knowledge bases, or document collections to retrieve relevant context before generating responses. Implements semantic search over indexed documents and injects retrieved context into the LLM prompt to ground agent reasoning in factual information.","intents":["I want agents to answer questions grounded in my company's documentation and knowledge base","I need agents to retrieve relevant context from large document collections before reasoning","I want to build agents that can cite sources and provide evidence for their answers"],"best_for":["teams building domain-specific agents with proprietary knowledge bases","developers creating customer support or documentation agents","builders implementing fact-grounded AI assistants that need to cite sources"],"limitations":["Retrieval quality depends on embedding model quality and indexing strategy — poor embeddings lead to irrelevant context injection","Vector database queries add 100-500ms latency per retrieval operation","No automatic knowledge base updates — requires explicit re-indexing when documents change","Context window limitations may prevent injecting all relevant documents for complex queries"],"requires":["Vector database (Pinecone, Weaviate, Milvus, or similar) or in-memory vector store","Embedding model (OpenAI embeddings, Hugging Face, or local model)","Document indexing pipeline for knowledge base population","Document storage (file system, S3, or database)"],"input_types":["natural language queries","document collections for indexing","metadata for filtering and ranking"],"output_types":["retrieved document chunks with relevance scores","augmented prompts with injected context","source citations and references"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-the-open-agent--openagent__cap_4","uri":"capability://tool.use.integration.model.context.protocol.mcp.integration.for.tool.standardization","name":"model-context protocol (mcp) integration for tool standardization","description":"Implements support for the Model-Context Protocol (MCP) standard, enabling agents to discover, invoke, and compose tools through a standardized interface. Agents can dynamically load MCP servers, understand tool schemas, handle tool responses, and chain tool calls together. Provides a unified abstraction over heterogeneous tool implementations (APIs, local functions, external services).","intents":["I want agents to use tools from multiple providers through a standardized interface","I need agents to discover available tools dynamically without hardcoding integrations","I want to build extensible agents where new tools can be added without modifying agent code"],"best_for":["developers building extensible agent platforms with pluggable tools","teams standardizing tool integration across multiple agents","builders creating agent ecosystems where third-party developers can contribute tools"],"limitations":["MCP server discovery and initialization adds 200-500ms overhead per new tool provider","Tool schema validation and type checking may reject valid but non-conformant tool implementations","Error handling across heterogeneous MCP servers requires explicit fallback and retry logic","No built-in rate limiting or quota management across MCP tool calls"],"requires":["MCP server implementations for desired tools","MCP client library compatible with OpenAgent","Network connectivity to MCP servers (local or remote)","Tool schema definitions in MCP format"],"input_types":["tool names and parameters","MCP server configurations","tool schema definitions"],"output_types":["tool execution results","structured tool responses","error messages from tool invocations"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-the-open-agent--openagent__cap_5","uri":"capability://text.generation.language.llm.provider.abstraction.with.multi.model.support","name":"llm provider abstraction with multi-model support","description":"Provides a unified interface for interacting with multiple LLM providers (OpenAI, Anthropic, Ollama, and others) with automatic provider selection and fallback logic. Agents can switch between models based on task requirements, cost constraints, or provider availability. Handles provider-specific API differences, authentication, and response formatting transparently.","intents":["I want agents to use different LLM providers based on cost or performance requirements","I need agents to fall back to alternative models if the primary provider is unavailable","I want to run agents with local models (Ollama) for privacy while maintaining compatibility with cloud providers"],"best_for":["teams optimizing LLM costs across multiple providers","developers building agents that need provider flexibility","builders creating privacy-conscious agents with local model support"],"limitations":["Provider abstraction adds 50-100ms latency per LLM call due to request translation and response normalization","Model capabilities vary significantly across providers — agents may behave differently with different models","No automatic prompt optimization for provider-specific capabilities (e.g., Claude's tool use vs OpenAI's function calling)","Fallback logic requires explicit configuration and may not handle all provider-specific errors gracefully"],"requires":["API keys for desired LLM providers (OpenAI, Anthropic, etc.)","Ollama installation for local model support (optional)","Provider-specific client libraries or HTTP clients","Configuration management for provider selection and fallback rules"],"input_types":["prompts and messages","model selection criteria","provider configuration"],"output_types":["LLM responses normalized to common format","provider metadata and usage statistics","fallback provider information"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-the-open-agent--openagent__cap_6","uri":"capability://memory.knowledge.agent.state.management.and.context.persistence","name":"agent state management and context persistence","description":"Manages agent execution state, conversation history, and context across multiple interactions. Implements state serialization, persistence mechanisms, and context window management to maintain agent continuity. Agents can resume from previous states, maintain long-term memory of interactions, and manage context size to fit within LLM token limits.","intents":["I want agents to remember previous interactions and maintain conversation context","I need agents to persist state across restarts or failures","I want to manage context window size to prevent token limit exceeded errors"],"best_for":["developers building conversational agents with long-term memory","teams implementing stateful agent systems with persistence requirements","builders creating agents that need to resume from failures or interruptions"],"limitations":["State serialization and deserialization adds 100-300ms overhead per state transition","No automatic context pruning — requires explicit strategies for managing context window size","Persistence layer requires external storage (database, file system) — no built-in state store","State consistency across distributed agents requires explicit synchronization mechanisms"],"requires":["Persistent storage backend (database, file system, or cloud storage)","Serialization format (JSON, Protocol Buffers, or similar)","State schema definition for agent-specific state","Cleanup and archival strategy for old state data"],"input_types":["agent execution state","conversation history","context metadata"],"output_types":["serialized state snapshots","context summaries","state recovery information"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-the-open-agent--openagent__cap_7","uri":"capability://planning.reasoning.agent.reasoning.with.chain.of.thought.and.planning","name":"agent reasoning with chain-of-thought and planning","description":"Enables agents to decompose complex tasks into reasoning steps using chain-of-thought (CoT) patterns and explicit planning. Agents can generate intermediate reasoning steps, create task plans before execution, and validate reasoning against task requirements. Implements structured prompting techniques to improve agent decision-making and transparency.","intents":["I want agents to show their reasoning process and explain decisions","I need agents to plan complex multi-step tasks before execution","I want to improve agent reliability by having them reason through problems step-by-step"],"best_for":["developers building agents for complex reasoning tasks","teams requiring explainable agent decisions for compliance or debugging","builders creating agents for domains where reasoning transparency is critical"],"limitations":["Chain-of-thought reasoning increases token usage by 2-5x compared to direct answers","Planning overhead adds 1-3 seconds per task due to additional LLM calls","Reasoning quality depends on prompt engineering and model capability — weaker models may produce incoherent reasoning","No automatic validation that reasoning actually led to correct conclusions"],"requires":["LLM with strong reasoning capabilities (GPT-4, Claude 3.5 Sonnet, or equivalent)","Structured prompt templates for reasoning and planning","Task specification format for planning"],"input_types":["task descriptions","reasoning prompts","planning templates"],"output_types":["reasoning traces with intermediate steps","task plans with subtasks","decision justifications"],"categories":["planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-the-open-agent--openagent__cap_8","uri":"capability://text.generation.language.conversational.interface.with.natural.language.interaction","name":"conversational interface with natural language interaction","description":"Provides a chat-based interface for users to interact with agents through natural language. Implements message parsing, intent recognition, and response generation to create conversational experiences. Supports multi-turn conversations with context preservation and handles user clarifications and follow-up questions.","intents":["I want users to interact with agents through natural conversation","I need to handle multi-turn conversations where context carries across messages","I want agents to ask clarifying questions when user intent is ambiguous"],"best_for":["developers building chatbot interfaces","teams creating conversational AI assistants","builders implementing user-friendly agent frontends"],"limitations":["Natural language ambiguity may lead to misinterpreted user intent","No built-in intent classification — relies on LLM to infer user goals","Conversation context grows unbounded — requires explicit pruning or summarization","No multi-language support beyond what the underlying LLM provides"],"requires":["LLM for natural language understanding and response generation","Message storage for conversation history","User session management"],"input_types":["natural language messages","user metadata"],"output_types":["natural language responses","conversation history","clarification questions"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-the-open-agent--openagent__cap_9","uri":"capability://automation.workflow.error.handling.and.recovery.with.retry.logic","name":"error handling and recovery with retry logic","description":"Implements robust error handling and recovery mechanisms for agent execution failures. Agents can catch errors from tool calls, LLM failures, or execution timeouts and automatically retry with backoff strategies. Provides fallback paths and graceful degradation when primary execution paths fail.","intents":["I want agents to automatically retry failed operations with exponential backoff","I need agents to gracefully handle API rate limits and temporary failures","I want agents to provide meaningful error messages when recovery is impossible"],"best_for":["developers building production agents that need high reliability","teams operating agents in environments with transient failures","builders creating agents that interact with unreliable external services"],"limitations":["Retry logic adds latency (exponential backoff can delay recovery by 10-30 seconds)","No automatic detection of non-retryable errors — requires explicit error classification","Retry exhaustion may leave agents in inconsistent states without explicit rollback logic","Circuit breaker patterns require configuration and monitoring to prevent cascading failures"],"requires":["Error classification strategy (retryable vs non-retryable)","Backoff strategy configuration (exponential, linear, etc.)","Timeout and retry limits","Logging and monitoring for error tracking"],"input_types":["error conditions","retry policies","fallback strategies"],"output_types":["retry attempts with timing","error logs and diagnostics","fallback execution results"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":50,"verified":false,"data_access_risk":"high","permissions":["Go runtime (primary implementation language)","LLM API access (OpenAI, Anthropic, or compatible)","Message queue or event bus for inter-agent communication (optional but recommended for scalability)","Browser automation framework (Playwright, Selenium, or similar)","Desktop environment with display server (X11, Wayland, or Windows)","Vision-capable LLM (Claude 3.5 Sonnet, GPT-4V, or equivalent)","Sufficient GPU/CPU for real-time screenshot processing","Logging framework (structured logging library)","Log storage and aggregation (file system, cloud logging, or log aggregation service)","Observability platform (optional but recommended for production)"],"failure_modes":["Agent loop depth and complexity can lead to exponential context growth without pruning strategies","No built-in deadlock detection or circular dependency prevention between agents","State synchronization across agents requires explicit message passing — no automatic distributed state management","Visual perception relies on screenshot quality and resolution — low-DPI or complex UI layouts may cause misidentification","No built-in CAPTCHA handling or anti-bot detection circumvention","Action execution latency includes screenshot capture, LLM inference, and DOM manipulation — typically 2-5 seconds per action","Limited to applications accessible via browser or desktop — cannot interact with native OS dialogs or system-level operations","Comprehensive logging adds 5-10% overhead to agent execution time","Structured logging requires careful schema design to capture relevant information","Log volume can be substantial for long-running agents — requires log aggregation and retention policies","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5845023166184817,"quality":0.49,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:56:59.048Z","last_commit":"2026-05-03T07:12:14Z"},"community":{"stars":4527,"forks":536,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=the-open-agent--openagent","compare_url":"https://unfragile.ai/compare?artifact=the-open-agent--openagent"}},"signature":"VFT1mXIjLC/MhnUw+Uwtf1M/jdOllmT+yHw5GM+metM7b25mL2+/wXeNLzf1lTEvjp/i9+/V2PSLWEdJZ0mwDQ==","signedAt":"2026-06-19T23:06:28.276Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/the-open-agent--openagent","artifact":"https://unfragile.ai/the-open-agent--openagent","verify":"https://unfragile.ai/api/v1/verify?slug=the-open-agent--openagent","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"}}