{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-openagi","slug":"openagi","name":"OpenAGI","type":"repo","url":"https://github.com/agiresearch/OpenAGI","page_url":"https://unfragile.ai/openagi","categories":["automation"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-openagi__cap_0","uri":"capability://planning.reasoning.react.pattern.agent.orchestration.with.tool.aware.reasoning","name":"react-pattern agent orchestration with tool-aware reasoning","description":"Implements the ReAct (Reasoning + Acting) pattern through ReactAgent class that extends BaseAgent, enabling agents to interleave chain-of-thought reasoning with tool invocation. The framework manages the reasoning loop by accepting LLM outputs, parsing tool calls, executing tools, and feeding results back into the reasoning chain. This architecture decouples reasoning logic from tool execution, allowing agents to reason about which tools to use before invoking them.","intents":["Build agents that reason through multi-step problems before taking action","Create agents that can dynamically select and invoke tools based on task requirements","Implement agents that learn from tool execution results and adjust reasoning"],"best_for":["Research teams building experimental agent architectures","Developers creating task-specific agents that require complex reasoning workflows","Teams migrating from simple tool-calling to structured reasoning patterns"],"limitations":["ReAct pattern requires multiple LLM calls per task, increasing latency and token costs compared to single-shot tool calling","No built-in optimization for redundant reasoning steps or tool call caching","Reasoning quality depends entirely on LLM capability; no guardrails for hallucinated tool calls"],"requires":["Python 3.8+","LLM API access (OpenAI, Anthropic, or compatible backend)","Tool implementations extending BaseTool class"],"input_types":["task description (string)","agent configuration (JSON)","tool definitions (Python classes)"],"output_types":["final task result (string/structured)","reasoning trace (intermediate steps)","tool execution logs"],"categories":["planning-reasoning","agent-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openagi__cap_1","uri":"capability://automation.workflow.agentfactory.based.agent.lifecycle.management.and.instantiation","name":"agentfactory-based agent lifecycle management and instantiation","description":"Provides a factory pattern implementation (AgentFactory class) that handles agent creation, configuration loading, activation, and lifecycle coordination. The factory abstracts agent instantiation by loading configuration from JSON files, resolving dependencies, and managing agent state across creation and execution phases. This enables standardized agent deployment and reduces boilerplate for agent setup.","intents":["Quickly instantiate agents from configuration files without manual setup","Manage multiple agent instances with consistent lifecycle handling","Enable agent sharing by standardizing configuration and initialization patterns"],"best_for":["Teams building agent platforms with multiple agent types","Developers who want to separate agent configuration from implementation logic","Organizations building agent marketplaces or hubs"],"limitations":["Factory pattern adds abstraction layer that may obscure agent initialization flow for simple use cases","Configuration-driven approach requires strict schema adherence; schema validation is minimal","No built-in versioning for agent configurations, making backward compatibility manual"],"requires":["Python 3.8+","Agent configuration JSON files in standardized format","Agent implementation files in pyopenagi/agents/{author}/{agent_name}/ structure"],"input_types":["agent configuration (JSON)","agent implementation (Python module)","task specification (string/dict)"],"output_types":["agent instance (BaseAgent or ReactAgent)","execution results (task-dependent)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openagi__cap_10","uri":"capability://automation.workflow.agent.metadata.and.dependency.management.with.standardized.packaging","name":"agent metadata and dependency management with standardized packaging","description":"Implements standardized agent packaging through directory structure (pyopenagi/agents/{author}/{agent_name}/), configuration files (config.json), and dependency specifications (meta_requirements.txt). This enables consistent agent distribution, dependency resolution, and metadata tracking. Agents can be packaged with all dependencies and shared through the Agent Hub.","intents":["Package agents with all dependencies for distribution and reuse","Manage agent metadata and versioning for discovery and tracking","Standardize agent structure across different implementations"],"best_for":["Teams publishing agents to the Agent Hub","Organizations managing internal agent libraries","Developers creating reproducible agent deployments"],"limitations":["Standardized structure is rigid; agents with non-standard requirements don't fit the pattern","Dependency management is manual through meta_requirements.txt; no automatic conflict resolution","No versioning mechanism; agents cannot specify version constraints for dependencies","Metadata is limited to config.json; no rich metadata like author info or usage examples"],"requires":["Python 3.8+","Agent implementation in standardized directory structure","config.json with required metadata fields","meta_requirements.txt with Python dependencies"],"input_types":["agent implementation (Python module)","metadata (JSON config)","dependencies (requirements.txt format)"],"output_types":["packaged agent (directory structure)","agent metadata (searchable)","dependency list"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openagi__cap_11","uri":"capability://automation.workflow.aios.kernel.integration.with.cerebrum.sdk.migration.path","name":"aios kernel integration with cerebrum sdk migration path","description":"Integrates with AIOS (AI Operating System) kernel as the primary agent creation system, with an explicit migration path to Cerebrum SDK for future versions. The integration enables agents to run within the AIOS environment, accessing kernel services and resources. The architecture supports both current AIOS integration and future Cerebrum SDK compatibility.","intents":["Create agents that run within the AIOS kernel environment","Leverage AIOS kernel services and resource management","Prepare agents for migration to Cerebrum SDK"],"best_for":["Teams building agents for AIOS environments","Organizations planning to migrate to Cerebrum SDK","Developers leveraging AIOS kernel capabilities"],"limitations":["AIOS integration is tightly coupled; agents are not portable to non-AIOS environments","Cerebrum SDK migration path is not yet implemented; future compatibility is uncertain","AIOS kernel documentation is limited; integration details must be inferred from code","No fallback mechanism if AIOS kernel is unavailable; agents cannot run standalone"],"requires":["Python 3.8+","AIOS kernel installation and configuration","Agent implementation compatible with AIOS interfaces"],"input_types":["agent implementation (Python module)","AIOS kernel configuration","task specification"],"output_types":["agent instance (AIOS-compatible)","task results","kernel resource usage"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openagi__cap_2","uri":"capability://tool.use.integration.multi.provider.tool.integration.with.rapidapi.and.custom.tool.adapters","name":"multi-provider tool integration with rapidapi and custom tool adapters","description":"Implements a pluggable tool system through BaseTool abstract class with concrete implementations for RapidAPI, Huggingface, and custom tools. Each tool type has its own adapter that handles API authentication, request formatting, response parsing, and error handling. Tools are registered with agents and invoked through a standardized interface, allowing agents to seamlessly call external APIs without knowing implementation details.","intents":["Integrate external APIs (RapidAPI, Huggingface) into agent workflows without custom code","Create custom tools that agents can invoke with standardized tool calling interface","Manage tool authentication and API credentials centrally"],"best_for":["Developers building agents that need access to diverse external APIs","Teams wanting to abstract API complexity from agent logic","Organizations building internal tool libraries for agent reuse"],"limitations":["Tool system requires explicit tool registration; no automatic API discovery or schema generation","Error handling is tool-specific; no unified error recovery or retry logic across tool types","Tool response parsing is manual; no automatic schema validation or type coercion","RapidAPI integration requires valid API keys and subscription; costs scale with tool usage"],"requires":["Python 3.8+","API credentials for external services (RapidAPI, Huggingface, etc.)","Tool implementation extending BaseTool class","Tool configuration in agent config.json"],"input_types":["tool parameters (dict/kwargs)","API credentials (environment variables or config)","tool schema definition (JSON)"],"output_types":["API response (JSON/structured data)","tool execution status (success/error)","formatted tool output (agent-consumable format)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openagi__cap_3","uri":"capability://automation.workflow.agent.hub.interactor.for.distributed.agent.sharing.and.discovery","name":"agent hub interactor for distributed agent sharing and discovery","description":"Implements the Interactor system that manages downloading and uploading of agent implementations to/from a centralized Agent Hub. The interactor handles agent packaging, versioning, and repository management, enabling community-driven agent sharing. Agents can be published to the hub with metadata and dependencies, then discovered and downloaded by other users for local execution.","intents":["Share custom agent implementations with the broader OpenAGI community","Discover and download pre-built agents from the Agent Hub for reuse","Manage agent versions and dependencies across distributed deployments"],"best_for":["Research teams publishing experimental agents for community feedback","Developers building on top of existing agent implementations","Organizations creating internal agent libraries and sharing across teams"],"limitations":["Agent Hub requires network connectivity; no offline agent discovery or caching","No built-in access control or permission management; all published agents are public","Dependency resolution is manual through meta_requirements.txt; no automatic conflict detection","No versioning or rollback mechanism; updating an agent affects all downstream users"],"requires":["Python 3.8+","Network access to Agent Hub repository","Agent implementation in standardized directory structure","meta_requirements.txt with Python dependencies","config.json with agent metadata"],"input_types":["agent implementation (Python module)","agent metadata (JSON config)","dependencies (requirements.txt format)"],"output_types":["agent package (downloadable from hub)","agent metadata (searchable in hub)","installation instructions"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openagi__cap_4","uri":"capability://automation.workflow.queue.based.llm.backend.request.management.with.multi.provider.support","name":"queue-based llm backend request management with multi-provider support","description":"Implements a Queues system that manages requests to language model backends, handling the flow of prompts and responses between agents and LLM services. The queue system abstracts LLM provider details, allowing agents to submit prompts without knowing which backend processes them. This enables load balancing, request batching, and provider switching without agent code changes.","intents":["Route agent prompts to different LLM providers (OpenAI, Anthropic, local models) transparently","Manage LLM request queues to handle concurrent agent executions","Switch LLM providers without modifying agent code"],"best_for":["Teams running multiple agents concurrently with shared LLM resources","Organizations wanting to switch between LLM providers without code changes","Developers building LLM-agnostic agent frameworks"],"limitations":["Queue system adds latency for request processing; no priority queue or SLA guarantees","No built-in request batching or prompt caching; each agent request is processed independently","Provider switching requires configuration changes; no automatic failover between providers","Queue state is not persisted; agent requests are lost if system crashes"],"requires":["Python 3.8+","LLM provider API keys (OpenAI, Anthropic, or compatible)","Queue backend configuration (in-memory or external queue service)","Agent integration with queue-based LLM calls"],"input_types":["prompt (string)","model specification (string)","generation parameters (dict)"],"output_types":["LLM response (string)","token usage (dict)","request status (success/error)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openagi__cap_5","uri":"capability://automation.workflow.configuration.driven.agent.customization.with.json.schema.validation","name":"configuration-driven agent customization with json schema validation","description":"Enables agents to be customized through JSON configuration files (config.json) that specify agent parameters, tool selections, and execution settings. The BaseAgent class loads and validates configurations, allowing non-developers to customize agent behavior without modifying code. Configuration includes tool selections, model parameters, and agent-specific settings that control runtime behavior.","intents":["Customize agent behavior through configuration files without code changes","Enable non-technical users to adjust agent parameters and tool selections","Create multiple agent variants from a single implementation with different configurations"],"best_for":["Teams wanting to separate agent configuration from implementation","Organizations deploying agents with environment-specific settings","Non-technical users needing to customize agent behavior"],"limitations":["Configuration validation is minimal; invalid configurations may cause runtime errors","No schema versioning; configuration format changes break existing configs","Configuration options are agent-specific; no standardized configuration schema across agents","No configuration hot-reloading; agents must be restarted to apply configuration changes"],"requires":["Python 3.8+","config.json file in agent directory with required fields","Understanding of agent-specific configuration options"],"input_types":["JSON configuration file","environment variables (for secrets)","command-line arguments (optional)"],"output_types":["validated configuration (dict)","agent instance with applied settings"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openagi__cap_6","uri":"capability://planning.reasoning.agent.process.execution.with.task.decomposition.and.step.by.step.execution","name":"agent process execution with task decomposition and step-by-step execution","description":"Implements AgentProcess class that manages agent execution flow, breaking down complex tasks into steps and executing them sequentially. The process system tracks execution state, handles step transitions, and manages context between steps. This enables agents to handle multi-step tasks where each step depends on previous results, with visibility into execution progress.","intents":["Execute multi-step tasks where each step depends on previous results","Track agent execution progress and intermediate results","Implement task decomposition and step-by-step execution strategies"],"best_for":["Developers building agents that handle complex multi-step workflows","Teams needing visibility into agent execution progress","Organizations implementing task decomposition strategies"],"limitations":["Process execution is sequential; no parallel step execution or branching logic","No built-in step recovery or rollback; failed steps terminate execution","Process state is not persisted; execution progress is lost on system failure","Step dependencies must be explicitly defined; no automatic dependency resolution"],"requires":["Python 3.8+","Agent implementation with step definitions","Task specification with clear step boundaries"],"input_types":["task specification (string/dict)","step definitions (list of steps)","context from previous steps (dict)"],"output_types":["step results (task-dependent)","execution trace (list of steps executed)","final task result"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openagi__cap_7","uri":"capability://data.processing.analysis.prompt.compression.and.context.optimization.for.token.efficiency","name":"prompt compression and context optimization for token efficiency","description":"Provides a compressor utility (pyopenagi/utils/compressor.py) that optimizes prompts and context for LLM consumption, reducing token usage while preserving semantic meaning. The compressor applies techniques like summarization, deduplication, and selective context inclusion to minimize prompt size. This reduces LLM API costs and latency while maintaining agent reasoning quality.","intents":["Reduce token usage and LLM API costs for agents with large context windows","Optimize prompts for faster LLM response times","Maintain reasoning quality while minimizing prompt size"],"best_for":["Teams running agents with large context requirements and high API costs","Developers optimizing agents for latency-sensitive applications","Organizations managing multiple concurrent agents with shared LLM budgets"],"limitations":["Compression may lose semantic information; aggressive compression can degrade reasoning quality","Compression algorithms are heuristic-based; no guarantees on compression ratio or quality","No adaptive compression; compression strategy is fixed regardless of task requirements","Compression adds latency; token savings may be offset by compression processing time"],"requires":["Python 3.8+","Prompt or context to compress (string)","Compression configuration (optional)"],"input_types":["prompt (string)","context (string or list)","compression parameters (dict)"],"output_types":["compressed prompt (string)","compression ratio (float)","compression metadata (dict)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openagi__cap_8","uri":"capability://automation.workflow.structured.logging.and.execution.tracing.for.agent.debugging","name":"structured logging and execution tracing for agent debugging","description":"Implements a logging system (pyopenagi/utils/logger.py) that provides structured logging for agent execution, tool calls, and LLM interactions. The logger captures execution traces with timestamps, step details, and error information, enabling debugging and monitoring of agent behavior. Logs can be exported in multiple formats for analysis and auditing.","intents":["Debug agent execution by tracing tool calls and reasoning steps","Monitor agent behavior in production with structured logs","Audit agent decisions and tool usage for compliance"],"best_for":["Developers debugging complex agent behaviors","Teams monitoring agents in production environments","Organizations requiring audit trails for agent decisions"],"limitations":["Logging adds overhead to agent execution; verbose logging can impact performance","Log storage is unbounded; no automatic log rotation or cleanup","Log format is fixed; no customization for different logging needs","No built-in log analysis or visualization tools"],"requires":["Python 3.8+","Logging configuration (optional)","Log output destination (file, stdout, or external service)"],"input_types":["log events (from agent execution)","log level (DEBUG, INFO, WARNING, ERROR)","log metadata (dict)"],"output_types":["structured log entries (JSON or text)","execution traces (list of events)","error reports"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openagi__cap_9","uri":"capability://code.generation.editing.extensible.agent.framework.with.baseagent.inheritance.pattern","name":"extensible agent framework with baseagent inheritance pattern","description":"Provides BaseAgent abstract class that serves as the foundation for all agent implementations, defining core interfaces for configuration loading, tool management, and task execution. Agents extend BaseAgent and implement specific reasoning patterns (e.g., ReactAgent implements ReAct). This inheritance-based architecture enables code reuse and standardized agent development patterns.","intents":["Create custom agents by extending BaseAgent with specific reasoning patterns","Reuse common agent functionality across multiple agent implementations","Standardize agent development patterns across teams"],"best_for":["Teams building multiple agent types with shared functionality","Developers creating custom reasoning patterns for specialized tasks","Organizations standardizing agent development practices"],"limitations":["Inheritance-based design limits flexibility for agents with non-standard patterns","BaseAgent interface is fixed; extending with new methods requires subclassing","No composition-based alternative; agents cannot mix multiple reasoning patterns","Documentation of BaseAgent interface is minimal; developers must read source code"],"requires":["Python 3.8+","Understanding of BaseAgent interface and methods","Implementation of required abstract methods"],"input_types":["agent configuration (JSON)","task specification (string/dict)","tool definitions (Python classes)"],"output_types":["agent instance (custom agent class)","task results (agent-specific)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","LLM API access (OpenAI, Anthropic, or compatible backend)","Tool implementations extending BaseTool class","Agent configuration JSON files in standardized format","Agent implementation files in pyopenagi/agents/{author}/{agent_name}/ structure","Agent implementation in standardized directory structure","config.json with required metadata fields","meta_requirements.txt with Python dependencies","AIOS kernel installation and configuration","Agent implementation compatible with AIOS interfaces"],"failure_modes":["ReAct pattern requires multiple LLM calls per task, increasing latency and token costs compared to single-shot tool calling","No built-in optimization for redundant reasoning steps or tool call caching","Reasoning quality depends entirely on LLM capability; no guardrails for hallucinated tool calls","Factory pattern adds abstraction layer that may obscure agent initialization flow for simple use cases","Configuration-driven approach requires strict schema adherence; schema validation is minimal","No built-in versioning for agent configurations, making backward compatibility manual","Standardized structure is rigid; agents with non-standard requirements don't fit the pattern","Dependency management is manual through meta_requirements.txt; no automatic conflict resolution","No versioning mechanism; agents cannot specify version constraints for dependencies","Metadata is limited to config.json; no rich metadata like author info or usage examples","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"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=openagi","compare_url":"https://unfragile.ai/compare?artifact=openagi"}},"signature":"4Ivktk7PagJGcQyX6fXNurJ2WPULyMx0QFgJz4riPgpyut2wANUJ2NH9jwbuCbx0B+Bme9T+aAnQr+pvwfcVDg==","signedAt":"2026-06-22T16:26:41.819Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openagi","artifact":"https://unfragile.ai/openagi","verify":"https://unfragile.ai/api/v1/verify?slug=openagi","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"}}