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The system tracks agent configurations, previous decisions, and accumulated knowledge to enable agents to build on prior work and maintain consistency across long-running workflows without requiring full context re-injection on each step.","intents":["I want agents to remember previous decisions and context across multiple interactions","I need to preserve agent state and execution history for audit and debugging purposes","I want agents to build on prior work without losing context between task handoffs"],"best_for":["applications requiring long-running agent workflows with persistent context","teams needing audit trails and execution history for compliance","developers building stateful agent systems with memory requirements"],"limitations":["State persistence mechanism not documented — unclear if in-memory, database-backed, or distributed","No information on state serialization format or compatibility with external storage systems","Unknown limits on context window growth and state cleanup policies"],"requires":["Node.js runtime","Storage backend (type and configuration unspecified)","LLM provider with sufficient context window for state injection"],"input_types":["agent execution events","task results","context updates"],"output_types":["agent state snapshots","execution history","context summaries"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-agents-shire__cap_2","uri":"capability://tool.use.integration.llm.provider.abstraction.and.multi.model.support","name":"llm provider abstraction and multi-model support","description":"Abstracts underlying LLM provider APIs (OpenAI, Anthropic, local models, etc.) behind a unified interface, allowing agents to switch between different language models without code changes. The abstraction layer handles provider-specific request formatting, response parsing, and error handling, enabling flexible model selection based on task requirements, cost, or latency constraints.","intents":["I want to switch between different LLM providers without rewriting agent code","I need to use different models for different agents based on task complexity","I want to fall back to alternative models if primary provider is unavailable"],"best_for":["teams wanting flexibility to switch LLM providers","cost-conscious builders needing to optimize model selection per task","developers building resilient systems with provider fallback capabilities"],"limitations":["Supported providers not explicitly documented in available materials","No information on handling provider-specific features (vision, function calling, streaming)","Unknown support for local/self-hosted model deployment"],"requires":["API keys for at least one supported LLM provider","Node.js runtime","npm package installation"],"input_types":["provider configuration objects","model identifiers","prompt text"],"output_types":["normalized LLM responses","token usage metrics","provider-agnostic result objects"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-agents-shire__cap_3","uri":"capability://planning.reasoning.task.decomposition.and.workflow.definition","name":"task decomposition and workflow definition","description":"Provides mechanisms to define complex workflows as sequences or DAGs of tasks that agents can execute. Tasks can specify dependencies, success/failure conditions, and parameter passing between steps. The system decomposes high-level goals into executable subtasks and manages task scheduling, execution order, and result aggregation across the workflow.","intents":["I want to define a multi-step workflow where each step is handled by an agent","I need to specify task dependencies and execution order","I want to decompose a complex goal into smaller, manageable subtasks"],"best_for":["developers building complex automation workflows","teams implementing multi-step business processes with AI","builders prototyping task-based agent systems"],"limitations":["Workflow definition format and DSL not documented","No information on supported task dependency patterns (sequential, parallel, conditional)","Unknown support for dynamic task generation or runtime workflow modification"],"requires":["Node.js runtime","Task definition schema (format unspecified)","Agent implementations for each task type"],"input_types":["workflow definitions","task specifications","parameter objects"],"output_types":["task execution results","workflow completion status","aggregated task outputs"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-agents-shire__cap_4","uri":"capability://tool.use.integration.agent.to.tool.binding.and.function.calling","name":"agent-to-tool binding and function calling","description":"Enables agents to invoke external tools and APIs through a structured function-calling interface. 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Agents can be configured with specific system prompts, role definitions, tool access, model preferences, and behavioral constraints. The configuration system enables reproducible agent creation and allows agents to be instantiated with consistent behavior across multiple deployments.","intents":["I want to define an agent with specific role, capabilities, and constraints","I need to configure agents with custom system prompts and behavioral rules","I want to create reusable agent templates that can be instantiated multiple times"],"best_for":["teams building standardized agent templates","developers needing reproducible agent configurations","builders implementing agent factories or agent pools"],"limitations":["Configuration schema and supported parameters not documented","No information on configuration validation or constraint enforcement","Unknown support for configuration inheritance or composition"],"requires":["Node.js runtime","Configuration object or file format (unspecified)","LLM provider credentials"],"input_types":["configuration objects","agent definitions","parameter specifications"],"output_types":["initialized agent instances","configuration validation results"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-agents-shire__cap_6","uri":"capability://automation.workflow.agent.communication.and.message.passing","name":"agent communication and message passing","description":"Implements inter-agent communication through a message-passing system that allows agents to send structured messages to each other, broadcast to multiple agents, or communicate through a shared message bus. Messages can carry task requests, results, status updates, or arbitrary data, enabling loose coupling between agents while maintaining coordination.","intents":["I want agents to send messages to each other to coordinate work","I need a way for agents to request help from other agents","I want to implement pub/sub patterns where agents broadcast updates"],"best_for":["teams building loosely-coupled multi-agent systems","developers implementing agent collaboration patterns","builders creating systems where agents need to discover and communicate dynamically"],"limitations":["Message format, routing, and delivery guarantees not documented","No information on message ordering, persistence, or replay capabilities","Unknown support for message filtering, transformation, or middleware"],"requires":["Node.js runtime","Message broker or bus implementation (type unspecified)","Agent message handlers"],"input_types":["message objects","agent identifiers","message payloads"],"output_types":["message delivery confirmations","message responses","communication logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-agents-shire__cap_7","uri":"capability://automation.workflow.execution.monitoring.and.logging","name":"execution monitoring and logging","description":"Provides comprehensive logging and monitoring of agent execution, including task progress, decision points, tool invocations, and error conditions. The system captures execution traces that can be used for debugging, auditing, and performance analysis. Logs can be streamed in real-time or aggregated for post-execution analysis.","intents":["I want to monitor agent execution in real-time to see what they're doing","I need detailed logs for debugging agent behavior and failures","I want to audit agent decisions and actions for compliance purposes"],"best_for":["teams requiring visibility into agent execution for debugging","developers building production systems needing comprehensive logging","organizations with compliance requirements for agent action auditing"],"limitations":["Logging format, verbosity levels, and output destinations not documented","No information on log aggregation, filtering, or search capabilities","Unknown support for structured logging or integration with external logging systems"],"requires":["Node.js runtime","Logging backend or file system","Optional: external logging service (type unspecified)"],"input_types":["execution events","agent actions","error conditions"],"output_types":["log entries","execution traces","performance metrics"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-agents-shire__cap_8","uri":"capability://automation.workflow.error.handling.and.recovery.strategies","name":"error handling and recovery strategies","description":"Implements error handling mechanisms that allow agents to gracefully handle failures, implement retry logic, and execute recovery strategies. The system can catch exceptions from tool invocations, LLM calls, or task execution, and apply configured recovery actions such as retries with backoff, fallback agents, or alternative approaches.","intents":["I want agents to retry failed operations with exponential backoff","I need agents to handle API failures gracefully without crashing","I want to define fallback strategies when primary approaches fail"],"best_for":["developers building resilient agent systems for production","teams implementing agents that interact with unreliable external services","builders creating fault-tolerant automation workflows"],"limitations":["Error classification, retry policies, and recovery strategies not documented","No information on circuit breaker patterns or failure thresholds","Unknown support for custom error handlers or recovery callbacks"],"requires":["Node.js runtime","Error handling configuration","Fallback agent or strategy definitions"],"input_types":["error objects","exception types","retry configuration"],"output_types":["recovery action results","error logs","failure notifications"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-agents-shire__cap_9","uri":"capability://automation.workflow.agent.performance.metrics.and.analytics","name":"agent performance metrics and analytics","description":"Collects and analyzes performance metrics for agent execution including latency, token usage, success rates, and cost. The system tracks metrics across individual tasks, agents, and entire workflows, enabling performance optimization and cost analysis. Metrics can be aggregated over time to identify trends and bottlenecks.","intents":["I want to understand how much each agent execution costs in terms of tokens and API calls","I need to identify performance bottlenecks in my agent workflows","I want to track success rates and failure patterns across agents"],"best_for":["teams optimizing agent costs and performance","developers building production systems needing performance visibility","organizations tracking AI operational expenses"],"limitations":["Metrics collection, aggregation, and reporting mechanisms not documented","No information on metric retention, archival, or historical analysis","Unknown support for custom metrics or integration with external analytics"],"requires":["Node.js runtime","Metrics storage backend (type unspecified)","Optional: external analytics service"],"input_types":["execution events","token counts","latency measurements"],"output_types":["performance reports","cost summaries","metric aggregations"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":30,"verified":false,"data_access_risk":"high","permissions":["Node.js runtime (version unspecified in available docs)","LLM provider API access (OpenAI, Anthropic, or compatible)","npm package manager for installation","Node.js runtime","Storage backend (type and configuration unspecified)","LLM provider with sufficient context window for state injection","API keys for at least one supported LLM provider","npm package installation","Task definition schema (format unspecified)","Agent implementations for each task type"],"failure_modes":["DeepWiki analysis incomplete — specific orchestration patterns and inter-agent communication protocols not fully documented","No explicit information on agent state synchronization across distributed deployments","Unknown support for dynamic agent discovery or runtime agent registration","State persistence mechanism not documented — unclear if in-memory, database-backed, or distributed","No information on state serialization format or compatibility with external storage systems","Unknown limits on context window growth and state cleanup policies","Supported providers not explicitly documented in available materials","No information on handling provider-specific features (vision, function calling, streaming)","Unknown support for local/self-hosted model deployment","Workflow definition format and DSL not documented","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.12289134059946881,"quality":0.3,"ecosystem":0.52,"match_graph":0.25,"freshness":0.6,"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":"2026-05-24T12:16:23.328Z","last_scraped_at":"2026-04-22T08:08:13.651Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":847,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=agents-shire","compare_url":"https://unfragile.ai/compare?artifact=agents-shire"}},"signature":"nTo1DQXcEvPy00IvDefspyCgSVJrdC11R52SGyWpf88EH7jJZOUkcOG8bIghKSfar0eR2yQz8TFXcfXCTh2FCA==","signedAt":"2026-06-20T11:26:34.352Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/agents-shire","artifact":"https://unfragile.ai/agents-shire","verify":"https://unfragile.ai/api/v1/verify?slug=agents-shire","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"}}