{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-openkrew","slug":"openkrew","name":"openkrew","type":"agent","url":"https://openkrew.com","page_url":"https://unfragile.ai/openkrew","categories":["ai-agents"],"tags":["ai","agents","multi-agent","distributed","slack","discord","teams","llm","claude","openai","developer-tools"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-openkrew__cap_0","uri":"capability://automation.workflow.distributed.multi.agent.orchestration.across.machines","name":"distributed multi-agent orchestration across machines","description":"Coordinates execution of multiple AI agents across geographically distributed machines using a message-passing architecture. Agents communicate through a central coordination layer that handles task distribution, state synchronization, and result aggregation without requiring shared memory or databases. Each machine runs an agent instance that can independently process tasks while maintaining consistency through event-driven coordination patterns.","intents":["I need to scale AI agent workloads across multiple servers without centralizing all computation","I want agents on different machines to collaborate on complex tasks by passing work between them","I need fault tolerance where if one machine goes down, other agents can continue operating"],"best_for":["teams building large-scale AI agent systems requiring horizontal scaling","organizations with distributed infrastructure wanting to leverage idle compute across machines","developers building resilient multi-agent systems that need to survive individual node failures"],"limitations":["Network latency between machines adds overhead to agent-to-agent communication — typical 50-500ms per message depending on network topology","No built-in consensus mechanism for distributed state — requires external coordination service for strong consistency guarantees","Message ordering across machines is eventual-consistent only, not strictly ordered","Requires manual configuration of machine discovery and network topology"],"requires":["Node.js 16+ or equivalent runtime on each machine","Network connectivity between all agent machines (TCP/IP)","Message broker or pub/sub system for inter-machine communication (can use Redis, RabbitMQ, or custom implementation)","Shared configuration or service discovery mechanism (DNS, Consul, or hardcoded endpoints)"],"input_types":["task definitions (JSON/structured)","agent configuration (YAML/JSON)","inter-agent messages (serialized objects)"],"output_types":["task results (JSON/structured data)","agent execution logs (text)","distributed state snapshots (JSON)"],"categories":["automation-workflow","distributed-systems"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-openkrew__cap_1","uri":"capability://tool.use.integration.slack.discord.teams.chat.integration.with.agent.deployment","name":"slack/discord/teams chat integration with agent deployment","description":"Deploys AI agents directly into chat platforms (Slack, Discord, Microsoft Teams) using native bot APIs and webhook handlers. Agents receive messages as events, process them through LLM inference, and respond back through the chat platform's message API. Integration handles authentication via OAuth/tokens, message parsing, thread context preservation, and rate limiting per platform's constraints.","intents":["I want to deploy an AI agent into our Slack workspace without building a separate UI","I need agents to respond to @mentions and commands in Discord channels","I want to maintain conversation history and context within Teams threads"],"best_for":["teams already using Slack/Discord/Teams who want to add AI capabilities without new tools","developers building internal chatbots for team productivity","organizations wanting to deploy agents with minimal infrastructure changes"],"limitations":["Chat platform rate limits restrict agent response frequency — typically 1-5 messages per second per channel depending on platform","Message length constraints (Slack: 4000 chars, Discord: 2000 chars) require response truncation or threading","No direct access to platform-specific rich features like modals or interactive components without additional SDK integration","Authentication tokens require manual rotation and secure storage"],"requires":["Bot application created in target chat platform (Slack App, Discord Bot, Teams App)","OAuth credentials or API tokens with appropriate scopes (chat:write, chat:read, etc.)","Webhook URL or event subscription configured in chat platform settings","Network accessibility for webhook endpoints (public HTTPS URL or relay service)"],"input_types":["chat messages (text)","message metadata (user ID, channel, timestamp)","thread context (previous messages in conversation)"],"output_types":["formatted chat messages (text with markdown/formatting)","threaded replies (nested messages)","reactions and status updates"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-openkrew__cap_10","uri":"capability://tool.use.integration.agent.capability.discovery.and.dynamic.registration","name":"agent capability discovery and dynamic registration","description":"Allows agents to discover available capabilities (functions, tools, other agents) at runtime through a registry system. New capabilities can be registered dynamically without restarting agents, enabling hot-loading of new functions and tools. Provides introspection APIs for agents to query available capabilities, their parameters, and usage examples.","intents":["I want to add new tools to agents without restarting them","I need agents to discover what capabilities are available and use them appropriately","I want to dynamically enable/disable capabilities based on agent permissions or context"],"best_for":["teams building extensible agent systems where new tools are added frequently","developers wanting to decouple agent code from tool definitions","organizations with dynamic tool availability based on user permissions or context"],"limitations":["Dynamic registration adds complexity to agent code — requires handling capability changes at runtime","No built-in permission system — requires manual implementation of access control","Registry lookups add latency — typically 5-20ms per capability discovery","Capability versioning can cause compatibility issues if not carefully managed"],"requires":["Capability registry system (in-memory, database, or service)","Capability metadata format (JSON schema with name, parameters, description)","Introspection APIs for agents to query registry"],"input_types":["capability definitions (JSON schema with metadata)","capability registration requests (add/remove/update)","capability queries (list available, get details)"],"output_types":["capability registry (list of available capabilities)","capability metadata (parameters, descriptions, examples)","registration confirmations (success/failure)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-openkrew__cap_11","uri":"capability://automation.workflow.agent.performance.optimization.and.cost.tracking","name":"agent performance optimization and cost tracking","description":"Monitors and optimizes agent resource usage including token consumption, API call frequency, and execution time. Tracks costs per agent execution and aggregates across teams. Provides recommendations for optimization (e.g., use cheaper models, reduce context size, batch requests). Implements cost controls like token budgets and rate limiting to prevent runaway spending.","intents":["I need to track how much each agent execution costs","I want to optimize agents to use fewer tokens and reduce API costs","I need to set spending limits to prevent unexpected bills"],"best_for":["organizations running agents at scale with significant API costs","teams wanting to optimize cost/performance tradeoffs","developers building cost-aware agent systems"],"limitations":["Cost tracking requires accurate pricing data from all LLM providers — pricing changes require manual updates","Optimization recommendations are heuristic-based and may not apply to all use cases","Token counting varies by model and provider, making cost estimates approximate","Rate limiting can degrade agent responsiveness if limits are too strict"],"requires":["LLM provider pricing data (token costs per model)","Token counting logic for each model","Cost tracking storage (database or logging service)"],"input_types":["agent execution metrics (tokens used, API calls, execution time)","model pricing data (cost per token)","cost control policies (budgets, rate limits)"],"output_types":["cost reports (per-agent, per-team, aggregated)","optimization recommendations (model suggestions, context reduction ideas)","cost alerts (budget exceeded, unusual spending patterns)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-openkrew__cap_2","uri":"capability://tool.use.integration.llm.provider.abstraction.with.multi.model.support","name":"llm provider abstraction with multi-model support","description":"Provides a unified interface for calling multiple LLM providers (OpenAI, Anthropic Claude, local Ollama, etc.) with automatic request/response translation. Abstracts differences in API schemas, token counting, model naming conventions, and parameter mappings so agents can switch providers or models without code changes. Handles provider-specific features like function calling, vision capabilities, and streaming responses through a common abstraction layer.","intents":["I want to switch from OpenAI to Claude without rewriting agent code","I need to run agents on local Ollama models for privacy while keeping the same agent logic","I want to use different models for different tasks (fast model for simple tasks, powerful model for complex reasoning)"],"best_for":["developers building multi-model agent systems to avoid vendor lock-in","teams wanting to experiment with different LLMs without refactoring","organizations with privacy requirements needing to switch between cloud and local models"],"limitations":["Abstraction layer adds 10-50ms latency per request due to translation and normalization overhead","Not all provider features map cleanly — some model-specific capabilities (e.g., Claude's extended thinking) may not be fully exposed through abstraction","Token counting varies by model and provider, making cost estimation approximate","Streaming response handling differs between providers, potentially causing buffering delays"],"requires":["API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","Node.js 16+ runtime","Network access to LLM provider endpoints or local Ollama instance running on accessible network"],"input_types":["prompt text (string)","system instructions (string)","structured messages (JSON with role/content)","function/tool definitions (JSON schema)"],"output_types":["completion text (string)","structured responses (JSON)","streaming tokens (event stream)","usage metadata (token counts, cost estimates)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-openkrew__cap_3","uri":"capability://planning.reasoning.agent.task.decomposition.and.sequential.execution.planning","name":"agent task decomposition and sequential execution planning","description":"Breaks down complex user requests into subtasks that agents can execute sequentially or in parallel, with dependency tracking and result aggregation. Uses LLM-based reasoning to determine task order, identify dependencies, and decide which agent should handle each subtask. Maintains execution state across tasks, passes outputs from one task as inputs to dependent tasks, and handles failures with retry logic and fallback strategies.","intents":["I want an agent to automatically break down a complex request into steps it can execute","I need agents to understand task dependencies and execute in the right order","I want to reuse agent capabilities by composing them into larger workflows"],"best_for":["teams building complex multi-step AI workflows that require reasoning about task order","developers wanting to compose simple agents into sophisticated systems","organizations needing transparent task execution with clear step-by-step logging"],"limitations":["LLM-based planning adds latency — typically 1-3 seconds per decomposition depending on request complexity","Task decomposition quality depends on LLM reasoning ability — complex interdependencies may be missed","No built-in optimization for task ordering — may execute tasks in suboptimal order","Requires explicit task definitions and agent capabilities to be well-documented for LLM to understand"],"requires":["LLM provider configured (OpenAI, Anthropic, or local Ollama)","Agent definitions with clear capability descriptions","Task schema defining available operations and their parameters"],"input_types":["user request (natural language text)","agent capability definitions (JSON schema)","task constraints (JSON)"],"output_types":["execution plan (structured task list with dependencies)","task results (aggregated outputs from all subtasks)","execution trace (step-by-step log of what was executed)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-openkrew__cap_4","uri":"capability://memory.knowledge.agent.state.persistence.and.context.management","name":"agent state persistence and context management","description":"Maintains agent state across multiple interactions, including conversation history, task progress, and learned information. Stores state in configurable backends (in-memory, file-based, or external databases) with automatic serialization and deserialization. Provides context windowing to manage token limits by selectively including relevant historical context in LLM prompts while discarding less relevant information.","intents":["I want agents to remember previous conversations and build on past context","I need to persist agent state so it survives restarts","I want to manage token usage by intelligently selecting which context to include in prompts"],"best_for":["teams building long-running agents that need to maintain state across sessions","developers wanting agents to learn from past interactions","organizations with token budget constraints needing smart context management"],"limitations":["In-memory storage doesn't persist across process restarts — requires external backend for durability","Context windowing heuristics may discard important information if not carefully tuned","No built-in encryption for sensitive state — requires manual implementation of security","Large conversation histories can cause memory bloat if not actively pruned"],"requires":["Storage backend (in-memory, file system, or database like PostgreSQL/MongoDB)","Serialization format (JSON, MessagePack, or custom)","Memory management strategy for long-running agents"],"input_types":["conversation messages (text)","agent state objects (JSON/structured)","context relevance scores (numeric)"],"output_types":["persisted state snapshots (JSON)","context-windowed prompts (text with selected history)","state recovery data (for resuming after failures)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-openkrew__cap_5","uri":"capability://tool.use.integration.function.calling.and.tool.use.with.schema.validation","name":"function calling and tool use with schema validation","description":"Enables agents to call external functions and APIs by generating structured function calls from LLM outputs. Defines available functions through JSON schemas that describe parameters, return types, and constraints. Validates function calls against schemas before execution, executes the function, and feeds results back to the LLM for further reasoning. Supports both synchronous and asynchronous function execution with error handling and retry logic.","intents":["I want agents to call external APIs and tools to accomplish tasks","I need to ensure agents only call functions with valid parameters","I want agents to use tool results to refine their reasoning and take next steps"],"best_for":["developers building agents that need to interact with external systems","teams wanting to safely expose APIs to LLM-powered agents with parameter validation","organizations building agent-driven automation that requires tool integration"],"limitations":["Schema validation adds latency — typically 10-50ms per function call depending on schema complexity","LLM function calling quality varies by model — some models hallucinate function calls that don't exist","Error handling requires explicit definition of error types and recovery strategies","Asynchronous function execution complicates agent reasoning flow and requires careful state management"],"requires":["Function definitions with JSON schema describing parameters and return types","LLM provider supporting function calling (OpenAI, Anthropic, etc.)","Actual function implementations (JavaScript/TypeScript functions or API endpoints)"],"input_types":["function schema definitions (JSON schema)","LLM-generated function calls (structured JSON)","function parameters (typed values matching schema)"],"output_types":["function execution results (typed return values)","validation errors (structured error messages)","execution logs (trace of function calls and results)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-openkrew__cap_6","uri":"capability://planning.reasoning.agent.team.coordination.with.role.based.task.assignment","name":"agent team coordination with role-based task assignment","description":"Organizes multiple agents into teams with defined roles and responsibilities, automatically routing tasks to appropriate agents based on their capabilities. Uses agent metadata (skills, expertise, availability) to make routing decisions, handles inter-agent communication for collaboration, and aggregates results from multiple agents. Supports hierarchical team structures where some agents coordinate others.","intents":["I want to organize agents by role (researcher, analyst, writer) and route tasks appropriately","I need agents to collaborate on complex tasks by passing work between specialized agents","I want a manager agent to coordinate work across multiple specialist agents"],"best_for":["teams building complex multi-agent systems with specialized agents for different domains","organizations wanting to model real team structures in AI systems","developers building agent systems that require coordination and collaboration"],"limitations":["Role-based routing requires accurate capability metadata — misclassified agents lead to poor task assignment","Inter-agent communication overhead increases with team size — communication complexity is O(n²) for n agents","No built-in load balancing — agents may become bottlenecks if not manually configured","Hierarchical coordination adds latency as requests flow through manager agents"],"requires":["Agent definitions with role and capability metadata","Task routing rules or LLM-based routing logic","Inter-agent communication mechanism (message queue, direct API calls, etc.)"],"input_types":["task requests (natural language or structured)","agent role definitions (JSON metadata)","capability descriptions (text)"],"output_types":["routed task assignments (which agent handles which task)","aggregated results from multiple agents (combined output)","coordination logs (trace of inter-agent communication)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-openkrew__cap_7","uri":"capability://automation.workflow.agent.monitoring.and.execution.logging.with.observability","name":"agent monitoring and execution logging with observability","description":"Tracks agent execution in real-time, logging all decisions, function calls, LLM interactions, and results. Provides structured logs with timestamps, execution traces, and performance metrics (latency, token usage, cost). Enables debugging by replaying execution traces and identifying where agents made decisions. Integrates with observability platforms for centralized monitoring of distributed agent teams.","intents":["I need to debug why an agent made a particular decision or took a wrong action","I want to monitor agent performance and identify bottlenecks","I need to track token usage and costs across agent executions"],"best_for":["teams operating production agent systems requiring visibility into agent behavior","developers debugging complex multi-agent interactions","organizations tracking AI costs and optimizing agent efficiency"],"limitations":["Comprehensive logging adds 5-15% overhead to agent execution time","Large-scale logging can generate significant storage requirements — typical agent execution generates 1-10MB of logs","Real-time monitoring requires streaming log infrastructure, not suitable for simple file-based logging","Privacy concerns with logging sensitive data — requires careful configuration of what gets logged"],"requires":["Logging infrastructure (file system, cloud logging service, or observability platform)","Structured logging format (JSON, OpenTelemetry, etc.)","Optional: observability platform integration (Datadog, New Relic, etc.)"],"input_types":["agent execution events (decisions, function calls, LLM interactions)","performance metrics (latency, token counts)","error events (failures, exceptions)"],"output_types":["structured execution logs (JSON with timestamps and metadata)","execution traces (replay-able sequence of events)","performance dashboards (aggregated metrics and visualizations)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-openkrew__cap_8","uri":"capability://automation.workflow.agent.error.handling.and.recovery.with.fallback.strategies","name":"agent error handling and recovery with fallback strategies","description":"Implements automatic error detection and recovery for agent failures, including LLM API errors, function call failures, and timeout handling. Defines fallback strategies (retry with backoff, use alternative function, escalate to human, etc.) that execute when primary operations fail. Tracks error patterns and adapts recovery strategies based on error type and frequency.","intents":["I want agents to automatically retry failed API calls with exponential backoff","I need agents to use alternative functions when primary tools fail","I want to escalate to human review when agents encounter errors they can't recover from"],"best_for":["teams running production agents that need resilience to transient failures","developers building agents that depend on unreliable external services","organizations wanting to minimize manual intervention in agent workflows"],"limitations":["Retry logic adds latency — exponential backoff can delay recovery by 10-60 seconds for persistent failures","Fallback strategies require manual definition — no automatic fallback discovery","Error classification may be imprecise, leading to inappropriate recovery strategies","Escalation to humans requires manual workflow integration"],"requires":["Error type definitions and classification logic","Fallback strategy definitions (retry, alternative function, escalation, etc.)","Timeout and retry configuration (backoff parameters, max retries, etc.)"],"input_types":["error events (exceptions, API errors, timeouts)","error context (which operation failed, what was attempted)","fallback strategy definitions (JSON configuration)"],"output_types":["recovery actions (retry, fallback, escalation)","error logs (what failed and why)","recovery results (success or final failure)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-openkrew__cap_9","uri":"capability://text.generation.language.agent.prompt.engineering.and.template.management","name":"agent prompt engineering and template management","description":"Provides a templating system for constructing LLM prompts with variable substitution, conditional sections, and dynamic content injection. Stores prompt templates with version control, allowing A/B testing of different prompts and tracking which template version produced which results. Supports prompt optimization through feedback loops where agent performance metrics inform template refinement.","intents":["I want to manage multiple prompt variations and test which works best","I need to inject dynamic context into prompts based on agent state","I want to version control prompts and track which version was used for each agent execution"],"best_for":["teams iterating on agent behavior through prompt optimization","developers wanting to experiment with different prompting strategies","organizations tracking prompt performance and optimizing for cost/quality tradeoffs"],"limitations":["Prompt quality is highly subjective — no automated metric for optimal prompts","A/B testing requires significant execution volume to achieve statistical significance","Template complexity can make prompts harder to debug","No built-in integration with prompt optimization tools — requires manual feedback loop implementation"],"requires":["Prompt template storage (file system, database, or version control)","Template syntax and variable substitution logic","Optional: A/B testing framework and metrics collection"],"input_types":["prompt templates (text with variable placeholders)","template variables (context data to inject)","performance metrics (feedback on prompt effectiveness)"],"output_types":["rendered prompts (final text sent to LLM)","template versions (tracked history of prompt changes)","performance reports (which templates performed best)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":34,"verified":false,"data_access_risk":"high","permissions":["Node.js 16+ or equivalent runtime on each machine","Network connectivity between all agent machines (TCP/IP)","Message broker or pub/sub system for inter-machine communication (can use Redis, RabbitMQ, or custom implementation)","Shared configuration or service discovery mechanism (DNS, Consul, or hardcoded endpoints)","Bot application created in target chat platform (Slack App, Discord Bot, Teams App)","OAuth credentials or API tokens with appropriate scopes (chat:write, chat:read, etc.)","Webhook URL or event subscription configured in chat platform settings","Network accessibility for webhook endpoints (public HTTPS URL or relay service)","Capability registry system (in-memory, database, or service)","Capability metadata format (JSON schema with name, parameters, description)"],"failure_modes":["Network latency between machines adds overhead to agent-to-agent communication — typical 50-500ms per message depending on network topology","No built-in consensus mechanism for distributed state — requires external coordination service for strong consistency guarantees","Message ordering across machines is eventual-consistent only, not strictly ordered","Requires manual configuration of machine discovery and network topology","Chat platform rate limits restrict agent response frequency — typically 1-5 messages per second per channel depending on platform","Message length constraints (Slack: 4000 chars, Discord: 2000 chars) require response truncation or threading","No direct access to platform-specific rich features like modals or interactive components without additional SDK integration","Authentication tokens require manual rotation and secure storage","Dynamic registration adds complexity to agent code — requires handling capability changes at runtime","No built-in permission system — requires manual implementation of access control","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.14955443375464483,"quality":0.34,"ecosystem":0.6000000000000001,"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":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-05-03T14:04:47.473Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":1565,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=openkrew","compare_url":"https://unfragile.ai/compare?artifact=openkrew"}},"signature":"SZUxfFZ2P8XGCXltDWqI5ksBnnWKbmJnKdvHXaeEKdfodtGqC8L788f61lGTybwvFPr+lMV9q0Z+wMUvEnkXAQ==","signedAt":"2026-06-22T17:25:13.084Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openkrew","artifact":"https://unfragile.ai/openkrew","verify":"https://unfragile.ai/api/v1/verify?slug=openkrew","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"}}