{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-paperclipai","slug":"paperclipai","name":"paperclipai","type":"cli","url":"https://github.com/paperclipai/paperclip","page_url":"https://unfragile.ai/paperclipai","categories":["automation"],"tags":["paperclip","ai","agents","orchestration","cli"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-paperclipai__cap_0","uri":"capability://automation.workflow.multi.agent.team.orchestration.via.cli","name":"multi-agent team orchestration via cli","description":"Orchestrates teams of AI agents through a command-line interface, enabling agents to be spawned, coordinated, and managed as autonomous workers. Uses a task-queue-based architecture where agents receive work items, execute them independently, and report results back to a central coordinator. Agents can communicate with each other through a message-passing system to handle interdependent tasks.","intents":["I want to run multiple AI agents in parallel to handle different business functions","I need agents to coordinate on complex multi-step workflows without manual intervention","I want to scale agent capacity by adding more agents to the team dynamically"],"best_for":["teams building autonomous business operations","developers prototyping multi-agent systems","founders automating repetitive business processes at scale"],"limitations":["No built-in persistence layer — agent state is ephemeral unless explicitly saved to external storage","CLI-based interface limits real-time UI feedback; monitoring requires parsing stdout/logs","No native load balancing — task distribution relies on simple queue mechanics without priority or resource awareness"],"requires":["Node.js 16+ (npm package)","API keys for at least one LLM provider (OpenAI, Anthropic, or compatible)","Unix-like shell environment or Windows with WSL for CLI execution"],"input_types":["text prompts","structured task definitions (JSON/YAML)","agent configuration files"],"output_types":["text results","structured task completion reports","agent execution logs"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-paperclipai__cap_1","uri":"capability://planning.reasoning.agent.role.definition.and.specialization","name":"agent role definition and specialization","description":"Allows developers to define specialized agent roles with specific system prompts, capabilities, and behavioral constraints. Each agent role is configured with a unique persona, set of allowed tools, and decision-making parameters. The system enforces role boundaries so agents stay within their domain of responsibility and cannot exceed their defined capabilities.","intents":["I want to create a specialized sales agent that only handles customer inquiries","I need to define what tools and APIs each agent can access for security","I want agents to have different personalities and expertise levels for different tasks"],"best_for":["teams building domain-specific agent teams","organizations with strict role-based access control requirements","developers creating agents for regulated industries"],"limitations":["Role definitions are static at startup — changing agent roles requires redeployment","No runtime role validation — agents can potentially exceed their scope if underlying LLM is manipulated","Limited to text-based role descriptions; no formal capability schema enforcement"],"requires":["Configuration file format (YAML or JSON) defining roles","System prompt templates for each role","Tool/API whitelist definitions per role"],"input_types":["role configuration files","system prompts","tool definitions"],"output_types":["agent instances with enforced role boundaries","role validation reports"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-paperclipai__cap_10","uri":"capability://automation.workflow.error.handling.and.resilience.patterns","name":"error handling and resilience patterns","description":"Provides built-in error handling mechanisms including automatic retries with exponential backoff, circuit breakers for failing services, and fallback agents when primary agents fail. Implements timeout handling to prevent agents from hanging indefinitely. Supports custom error handlers that can implement domain-specific recovery logic. Errors are logged with full context for debugging.","intents":["I want agents to automatically retry failed tasks","I need to handle API failures gracefully without crashing the system","I want to use backup agents when primary agents are unavailable"],"best_for":["teams running agents in production","developers building resilient systems","organizations with strict uptime requirements"],"limitations":["Retry logic is exponential backoff only; no adaptive retry strategies","Circuit breakers are per-service; no cross-service circuit breaking","Fallback agents must be explicitly configured; no automatic fallback discovery"],"requires":["Error handling configuration (retry counts, backoff parameters)","Fallback agent definitions","Timeout values"],"input_types":["error events","retry policies"],"output_types":["retry attempts","fallback execution results","error reports"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-paperclipai__cap_11","uri":"capability://automation.workflow.agent.configuration.management.and.versioning","name":"agent configuration management and versioning","description":"Enables version control of agent configurations, allowing teams to track changes, rollback to previous versions, and compare configurations across versions. Configurations include agent prompts, tool bindings, role definitions, and execution parameters. Supports configuration templates for creating similar agents with minimal duplication. Enables environment-specific configurations (dev, staging, production).","intents":["I want to track changes to agent configurations over time","I need to rollback to a previous agent configuration if something breaks","I want to manage different configurations for different environments"],"best_for":["teams managing multiple agent configurations","developers implementing CI/CD for agents","organizations with change control requirements"],"limitations":["Versioning is file-based; no built-in Git integration","No automatic conflict resolution for concurrent configuration changes","Configuration validation is basic; complex schema validation requires custom logic"],"requires":["Configuration files (YAML or JSON)","Version control system (Git recommended)","Configuration schema definitions"],"input_types":["configuration files","version identifiers"],"output_types":["versioned configurations","configuration diffs","rollback confirmations"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-paperclipai__cap_2","uri":"capability://tool.use.integration.tool.and.api.binding.for.agent.execution","name":"tool and api binding for agent execution","description":"Provides a registry system for binding external tools and APIs to agents, enabling them to take actions beyond text generation. Tools are registered with schemas defining inputs, outputs, and execution logic. Agents can discover available tools, invoke them with appropriate parameters, and handle results. Supports both synchronous and asynchronous tool execution with error handling and retry logic.","intents":["I want my agents to call external APIs like Stripe or Salesforce","I need agents to execute code or shell commands as part of their workflow","I want to add custom tools that agents can use without modifying core agent logic"],"best_for":["developers integrating agents with existing business systems","teams building agents that need to take real-world actions","organizations with custom internal tools and APIs"],"limitations":["Tool execution is synchronous by default — long-running operations block agent execution","No built-in rate limiting or quota management per tool","Tool schemas must be manually defined; no automatic schema inference from API specs"],"requires":["Tool definitions with input/output schemas","API credentials or authentication tokens for external services","JavaScript/TypeScript runtime for custom tool implementations"],"input_types":["tool schema definitions (JSON)","API credentials","custom JavaScript functions"],"output_types":["tool execution results","error messages with retry information","execution logs"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-paperclipai__cap_3","uri":"capability://automation.workflow.agent.communication.and.message.passing","name":"agent communication and message passing","description":"Enables agents to send and receive messages from other agents in the team, facilitating coordination on complex tasks. Messages are routed through a central message broker that handles delivery, ordering, and acknowledgment. Agents can subscribe to message types and react to incoming messages, enabling event-driven workflows where one agent's output triggers another agent's action.","intents":["I want one agent to notify another agent when a task is complete","I need agents to collaborate on a task by passing intermediate results","I want to build workflows where agent actions trigger other agents automatically"],"best_for":["teams building complex multi-agent workflows","developers implementing event-driven agent systems","organizations automating cross-functional business processes"],"limitations":["No message persistence — messages are lost if agents are offline or crash","No built-in message ordering guarantees across multiple agents","Message routing is simple broadcast/subscribe; no content-based routing or filtering"],"requires":["Message broker implementation (in-memory or external)","Message schema definitions","Agent subscription configuration"],"input_types":["text messages","structured message payloads (JSON)"],"output_types":["message delivery confirmations","message logs","agent reaction outputs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-paperclipai__cap_4","uri":"capability://automation.workflow.task.queue.and.work.distribution","name":"task queue and work distribution","description":"Manages a task queue where work items are submitted and distributed to available agents for execution. Tasks are enqueued with metadata (priority, deadline, dependencies) and assigned to agents based on availability and capability matching. The queue system tracks task status, handles retries for failed tasks, and provides visibility into queue depth and agent utilization.","intents":["I want to submit a batch of tasks and have agents process them automatically","I need to prioritize urgent tasks over routine ones in the agent queue","I want to see which tasks are pending, in-progress, or completed"],"best_for":["teams processing high volumes of similar tasks","developers building batch processing systems with agents","organizations with variable workload patterns"],"limitations":["No persistent queue storage — tasks in memory are lost on restart","Priority handling is basic; no complex scheduling algorithms","No task dependency resolution — circular dependencies can cause deadlocks"],"requires":["Task definitions with required fields (type, parameters)","Agent pool with at least one available agent","Queue configuration (max size, timeout values)"],"input_types":["task objects (JSON)","task batches (CSV or JSON array)"],"output_types":["task status updates","completion reports","queue metrics"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-paperclipai__cap_5","uri":"capability://automation.workflow.agent.execution.monitoring.and.logging","name":"agent execution monitoring and logging","description":"Provides real-time monitoring of agent execution with detailed logging of actions, decisions, and outcomes. Each agent execution generates logs capturing the agent's reasoning process, tool calls, and results. Logs are structured and queryable, enabling debugging and auditing of agent behavior. Includes metrics collection for performance analysis and error tracking.","intents":["I want to debug why an agent made a particular decision","I need to audit agent actions for compliance and security","I want to track agent performance metrics like success rate and execution time"],"best_for":["teams operating agents in production","developers debugging agent behavior","organizations with audit and compliance requirements"],"limitations":["Logs are stored in-memory by default; no built-in log persistence to external systems","No log aggregation or centralized logging support","Limited filtering and search capabilities for large log volumes"],"requires":["Logging configuration (verbosity level, output format)","Optional external logging service integration"],"input_types":["agent execution events","tool call results","error messages"],"output_types":["structured logs (JSON)","execution metrics","performance reports"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-paperclipai__cap_6","uri":"capability://memory.knowledge.agent.state.persistence.and.recovery","name":"agent state persistence and recovery","description":"Enables agents to save and restore their state across executions, allowing long-running workflows to survive restarts and failures. Agent state includes memory of past interactions, learned preferences, and execution context. State can be persisted to local storage or external backends. Recovery mechanisms restore agent state on restart, allowing workflows to resume from the last checkpoint.","intents":["I want agents to remember context from previous conversations","I need workflows to resume after a system failure without losing progress","I want agents to learn and adapt based on past interactions"],"best_for":["teams running long-lived agent workflows","developers building agents with memory requirements","organizations needing fault-tolerant agent systems"],"limitations":["State persistence is optional and must be explicitly configured","No built-in state synchronization across multiple agent instances","State schema changes require manual migration logic"],"requires":["Storage backend (local file system or external service)","State serialization format (JSON)","Checkpoint configuration (frequency, retention)"],"input_types":["agent state objects","checkpoint triggers"],"output_types":["persisted state files","recovery confirmations"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-paperclipai__cap_7","uri":"capability://automation.workflow.agent.performance.profiling.and.optimization","name":"agent performance profiling and optimization","description":"Analyzes agent execution performance to identify bottlenecks and optimization opportunities. Collects metrics on execution time, token usage, API call latency, and success rates. Provides recommendations for improving agent efficiency, such as caching results, reducing context size, or parallelizing independent tasks. Enables A/B testing of different agent configurations.","intents":["I want to understand why my agents are slow","I need to optimize token usage to reduce API costs","I want to compare performance of different agent configurations"],"best_for":["teams optimizing agent costs and performance","developers fine-tuning agent behavior","organizations scaling agents to production"],"limitations":["Profiling adds overhead to agent execution","Recommendations are heuristic-based; not all suggestions may be applicable","No automatic optimization — recommendations require manual implementation"],"requires":["Profiling configuration (metrics to collect)","Baseline performance data for comparison"],"input_types":["agent execution metrics","configuration variations"],"output_types":["performance reports","optimization recommendations","comparison analyses"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-paperclipai__cap_8","uri":"capability://automation.workflow.workflow.definition.and.execution","name":"workflow definition and execution","description":"Enables definition of complex multi-step workflows where agents execute tasks in sequence, parallel, or conditional branches. Workflows are defined declaratively using a configuration format (YAML or JSON) that specifies task dependencies, branching logic, and error handling. The execution engine interprets workflow definitions and orchestrates agent execution according to the specified flow, handling retries and fallbacks.","intents":["I want to define a multi-step business process that agents execute automatically","I need conditional logic where different agents run based on previous results","I want to handle errors gracefully with fallback agents or retry logic"],"best_for":["teams automating complex business processes","developers building orchestration systems","organizations with repeatable multi-step workflows"],"limitations":["Workflow definitions are static; dynamic workflow generation is not supported","No visual workflow editor — workflows must be written in configuration format","Limited loop support; complex iterative workflows are difficult to express"],"requires":["Workflow definition file (YAML or JSON)","Agent definitions for each workflow step","Tool definitions for agent actions"],"input_types":["workflow definitions","input parameters","context data"],"output_types":["workflow execution results","step-by-step execution logs","final output"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-paperclipai__cap_9","uri":"capability://automation.workflow.agent.team.scaling.and.resource.management","name":"agent team scaling and resource management","description":"Manages scaling of agent teams based on workload, automatically spawning new agent instances when queue depth exceeds thresholds and terminating idle agents to conserve resources. Implements resource limits per agent (memory, execution time) and enforces them to prevent runaway processes. Provides visibility into resource utilization across the team and recommendations for capacity planning.","intents":["I want to automatically scale agents up when there's more work","I need to prevent agents from consuming excessive resources","I want to understand resource usage patterns to plan capacity"],"best_for":["teams with variable workload patterns","developers building cost-conscious agent systems","organizations managing large agent teams"],"limitations":["Scaling decisions are based on simple queue depth metrics; no ML-based prediction","Resource limits are process-level; no fine-grained memory or CPU allocation","Scaling is local to a single machine; no distributed scaling across multiple hosts"],"requires":["Scaling configuration (min/max agents, thresholds)","Resource limit definitions","Monitoring infrastructure"],"input_types":["workload metrics","scaling policies"],"output_types":["scaling decisions","resource utilization reports","capacity recommendations"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":35,"verified":false,"data_access_risk":"high","permissions":["Node.js 16+ (npm package)","API keys for at least one LLM provider (OpenAI, Anthropic, or compatible)","Unix-like shell environment or Windows with WSL for CLI execution","Configuration file format (YAML or JSON) defining roles","System prompt templates for each role","Tool/API whitelist definitions per role","Error handling configuration (retry counts, backoff parameters)","Fallback agent definitions","Timeout values","Configuration files (YAML or JSON)"],"failure_modes":["No built-in persistence layer — agent state is ephemeral unless explicitly saved to external storage","CLI-based interface limits real-time UI feedback; monitoring requires parsing stdout/logs","No native load balancing — task distribution relies on simple queue mechanics without priority or resource awareness","Role definitions are static at startup — changing agent roles requires redeployment","No runtime role validation — agents can potentially exceed their scope if underlying LLM is manipulated","Limited to text-based role descriptions; no formal capability schema enforcement","Retry logic is exponential backoff only; no adaptive retry strategies","Circuit breakers are per-service; no cross-service circuit breaking","Fallback agents must be explicitly configured; no automatic fallback discovery","Versioning is file-based; no built-in Git integration","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.2855567609810649,"quality":0.34,"ecosystem":0.55,"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":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-05-03T14:04:47.473Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":35854,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=paperclipai","compare_url":"https://unfragile.ai/compare?artifact=paperclipai"}},"signature":"kcJHuxrqY1wf0XJ9NMMdcRmgiSETKkfDqww+dgHX+mfRPM3zMDhW9bTWFBpdYFune28m+4hp4uMReg4JDJUtBg==","signedAt":"2026-06-21T13:44:14.759Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/paperclipai","artifact":"https://unfragile.ai/paperclipai","verify":"https://unfragile.ai/api/v1/verify?slug=paperclipai","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"}}