{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-crewaiinc--crewai","slug":"crewaiinc--crewai","name":"crewAI","type":"agent","url":"https://crewai.com","page_url":"https://unfragile.ai/crewaiinc--crewai","categories":["ai-agents"],"tags":["agents","ai","ai-agents","aiagentframework","llms"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-crewaiinc--crewai__cap_0","uri":"capability://planning.reasoning.multi.agent.orchestration.with.role.based.task.delegation","name":"multi-agent orchestration with role-based task delegation","description":"CrewAI orchestrates autonomous agents by assigning them distinct roles, goals, and backstories, then distributing tasks across the crew with hierarchical or sequential execution patterns. Each agent maintains its own LLM context and tool access, coordinating through a message-passing architecture where task outputs feed into subsequent agent inputs. The framework handles agent-to-agent (A2A) protocol communication, enabling agents to request information or delegate sub-tasks to peers without human intervention.","intents":["I want to decompose a complex workflow into specialized agent roles that collaborate autonomously","I need agents to hand off work to each other based on task dependencies and expertise","I want to define agent personalities and constraints that persist across multi-turn interactions"],"best_for":["teams building multi-step automation workflows with distinct functional domains","developers prototyping autonomous agent systems without building orchestration from scratch","enterprises automating research, analysis, or content creation pipelines"],"limitations":["Agent coordination adds latency proportional to crew size; no built-in parallelization across independent tasks","A2A protocol requires explicit task definitions; implicit agent discovery not supported","Memory isolation between agents can lead to redundant context processing if not carefully managed"],"requires":["Python 3.9+","At least one LLM provider API key (OpenAI, Anthropic, Gemini, Azure, or Bedrock)","crewai package installed via pip"],"input_types":["task descriptions (natural language strings)","agent role definitions (structured metadata)","tool/capability registrations (function signatures)"],"output_types":["structured task results (text, JSON, or custom objects)","execution logs with agent reasoning traces","memory artifacts (consolidated knowledge from agent interactions)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-crewaiinc--crewai__cap_1","uri":"capability://automation.workflow.event.driven.flow.composition.with.state.management","name":"event-driven flow composition with state management","description":"CrewAI Flows provide an event-driven orchestration layer built on decorators and state machines, enabling complex workflows that compose crews, conditional branching, and human feedback loops. Flows use a state persistence model where each step's output becomes the next step's input, with built-in support for serialization and resumption. The framework tracks flow execution events (start, step completion, error) through a BaseEventListener interface, enabling observability and custom event handlers without modifying core flow logic.","intents":["I want to build workflows that conditionally route between different crews or processing steps","I need to pause workflows for human review and resume with feedback","I want to visualize and monitor multi-step automation pipelines in real time"],"best_for":["developers building complex automation pipelines with conditional logic and human-in-the-loop checkpoints","teams needing workflow persistence and resumption across service restarts","enterprises requiring audit trails and event-driven monitoring of agent workflows"],"limitations":["State serialization requires all flow variables to be JSON-serializable; custom objects need explicit converters","Event listener callbacks are synchronous; async event handling requires manual threading or async wrapper","Flow visualization requires external tools; no built-in UI for flow design or execution monitoring"],"requires":["Python 3.9+","crewai package with Flow support (0.30.0+)","Optional: external state store for persistence (Redis, database, or file system)"],"input_types":["decorated Python functions (flow steps)","state objects (dictionaries or Pydantic models)","event listener implementations (custom classes)"],"output_types":["flow execution results (final state after all steps)","event logs (structured records of flow lifecycle events)","persisted state snapshots (for resumption)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-crewaiinc--crewai__cap_10","uri":"capability://automation.workflow.enterprise.deployment.with.control.plane.and.monitoring","name":"enterprise deployment with control plane and monitoring","description":"CrewAI AMP (Advanced Management Platform) provides enterprise deployment capabilities including a control plane for managing multiple crew instances, centralized monitoring dashboards, role-based access control (RBAC), and audit logging. The platform enables teams to deploy crews as managed services with automatic scaling, health checks, and failover. Integration with enterprise identity providers (SSO, SAML) and security tools (secrets management, compliance scanning) enables governance at scale.","intents":["I want to deploy and manage multiple agent crews across teams with centralized monitoring","I need role-based access control and audit trails for compliance","I want to scale agent deployments automatically based on demand"],"best_for":["enterprises deploying agents across multiple teams and departments","organizations with strict compliance and governance requirements","teams needing centralized monitoring and cost tracking across agent deployments"],"limitations":["Enterprise features require CrewAI AMP subscription; not available in open-source version","Control plane adds operational complexity; requires dedicated infrastructure or cloud service","Integration with enterprise systems (SAML, secrets management) requires custom configuration"],"requires":["CrewAI AMP subscription","Enterprise identity provider (Okta, Azure AD, or compatible SAML provider)","Kubernetes cluster or cloud platform (AWS, Azure, GCP) for deployment","Optional: secrets management system (HashiCorp Vault, AWS Secrets Manager)"],"input_types":["crew definitions (same as open-source)","deployment configurations (resource limits, scaling policies)","access control policies (RBAC rules, team assignments)"],"output_types":["managed crew instances (deployed and monitored)","monitoring dashboards (metrics, logs, alerts)","audit logs (all user actions and crew executions)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-crewaiinc--crewai__cap_11","uri":"capability://automation.workflow.crew.studio.visual.workflow.designer.and.testing","name":"crew studio visual workflow designer and testing","description":"Crew Studio is a web-based IDE for designing, testing, and debugging agent workflows visually. The tool provides a drag-and-drop interface for composing crews, defining tasks, and configuring agents without writing code. Built-in testing capabilities enable running crews with sample inputs, inspecting execution traces, and iterating on agent behavior. The studio integrates with version control and deployment pipelines, enabling teams to manage agent workflows as code while providing a visual interface for non-technical stakeholders.","intents":["I want to design agent workflows visually without writing code","I need to test crews interactively and debug execution traces","I want to collaborate with non-technical stakeholders on agent behavior"],"best_for":["teams with mixed technical and non-technical members designing agent workflows","developers iterating on agent behavior and needing fast feedback loops","enterprises standardizing agent workflow design across teams"],"limitations":["Visual designer may not support all advanced CrewAI features (custom hooks, complex memory logic)","Code generation from visual designs may require manual refinement for production use","Collaboration features depend on cloud infrastructure; self-hosted deployments have limited support"],"requires":["Web browser (Chrome, Firefox, Safari, Edge)","CrewAI account (free or paid tier)","Optional: Git integration for version control"],"input_types":["visual workflow definitions (drag-and-drop composition)","test inputs (sample data for crew execution)","agent configurations (role, goal, tools)"],"output_types":["generated crew code (Python)","execution traces (visual representation of agent steps)","test results (pass/fail with detailed logs)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-crewaiinc--crewai__cap_12","uri":"capability://tool.use.integration.marketplace.and.agent.repository.for.capability.sharing","name":"marketplace and agent repository for capability sharing","description":"CrewAI Marketplace enables teams to publish, discover, and reuse pre-built agents, crews, and skills from a central repository. The marketplace includes versioning, dependency management, and compatibility checking to ensure agents work across different CrewAI versions. Teams can publish private agents to internal repositories or share public agents with the community, with built-in rating and review systems for quality assurance.","intents":["I want to share reusable agents and crews with my team or the community","I need to discover pre-built agents that solve common problems","I want to manage versions and dependencies for shared agent artifacts"],"best_for":["teams building libraries of reusable agents and crews","enterprises standardizing agent implementations across departments","developers contributing to the open-source agent ecosystem"],"limitations":["Marketplace quality depends on community contributions; no guarantee of production-readiness","Dependency conflicts can arise when combining agents from different publishers","Private marketplace requires additional infrastructure or cloud service"],"requires":["CrewAI account","Published agent or crew (packaged as Python module)","Optional: authentication for private marketplace"],"input_types":["agent/crew definitions (Python code)","metadata (name, description, version, dependencies)","documentation (README, examples)"],"output_types":["published artifacts (available in marketplace)","version history (all published versions)","usage statistics (downloads, ratings)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-crewaiinc--crewai__cap_13","uri":"capability://automation.workflow.automation.triggers.and.event.driven.integration","name":"automation triggers and event-driven integration","description":"CrewAI supports automation triggers that execute crews in response to external events (webhooks, scheduled tasks, message queue events). The trigger system integrates with common platforms (Slack, email, HTTP webhooks) enabling crews to be invoked from external systems without manual intervention. Triggers include filtering and transformation logic to map external events to crew inputs, enabling event-driven automation workflows.","intents":["I want crews to automatically execute when external events occur (e.g., new email, Slack message)","I need to schedule crews to run at specific times or intervals","I want to integrate crews with existing business tools and workflows"],"best_for":["teams automating business processes that are triggered by external events","developers building event-driven agent systems","enterprises integrating agents with existing tools and platforms"],"limitations":["Trigger latency depends on external event delivery; real-time guarantees not provided","Complex event filtering requires custom trigger implementations","Scaling triggers across many external sources requires careful rate limiting"],"requires":["Python 3.9+","crewai package with trigger support","External event source (webhook, message queue, scheduler)","Optional: authentication for external platforms"],"input_types":["trigger configuration (event source, filtering rules)","event payloads (from external systems)","transformation logic (mapping events to crew inputs)"],"output_types":["crew execution (triggered by external events)","execution results (returned to external system)","trigger logs (for debugging and monitoring)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-crewaiinc--crewai__cap_2","uri":"capability://tool.use.integration.unified.llm.provider.abstraction.with.streaming.and.tool.calling","name":"unified llm provider abstraction with streaming and tool calling","description":"CrewAI abstracts LLM interactions through a provider-agnostic interface supporting OpenAI, Azure, Anthropic, Gemini, and Bedrock, with unified handling of streaming responses, function calling, and message formatting. The framework normalizes provider-specific APIs (e.g., OpenAI's function_call vs Anthropic's tool_use) into a common tool-calling schema, enabling agents to switch providers without code changes. LLM hooks allow injection of custom logic (logging, caching, rate limiting) at request/response boundaries without modifying agent code.","intents":["I want to support multiple LLM providers and switch between them without rewriting agent logic","I need to stream LLM responses for real-time feedback in long-running tasks","I want to intercept and log all LLM calls for debugging and cost tracking"],"best_for":["teams building multi-provider LLM applications to avoid vendor lock-in","developers needing streaming responses for interactive agent interfaces","enterprises requiring LLM observability and cost management across crews"],"limitations":["Provider-specific features (e.g., OpenAI's vision, Anthropic's extended thinking) require custom provider implementations","Streaming adds complexity to error handling; partial responses may be lost on connection failure","Tool calling normalization has ~50-100ms overhead per call due to schema translation"],"requires":["Python 3.9+","API keys for at least one supported LLM provider","crewai package with LLM provider modules installed"],"input_types":["provider configuration (model name, API key, parameters)","tool schemas (function signatures for tool calling)","message histories (conversation context)"],"output_types":["streamed text tokens (for real-time display)","tool calls (structured function invocations with arguments)","completion metadata (usage stats, finish reason)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-crewaiinc--crewai__cap_3","uri":"capability://tool.use.integration.schema.based.tool.registration.and.execution.with.mcp.support","name":"schema-based tool registration and execution with mcp support","description":"CrewAI provides a tool registry system where agents declare capabilities via Python functions or classes with type hints, automatically generating JSON schemas for LLM tool calling. The framework supports both native tools (Python functions) and Model Context Protocol (MCP) tools (external processes), with unified invocation through a common interface. Tool execution includes error handling, timeout management, and optional result validation through Pydantic schemas, enabling agents to safely call external APIs and local utilities.","intents":["I want agents to call external APIs and local functions without writing boilerplate integration code","I need to expose tools via MCP so agents can use standardized tool repositories","I want to validate tool results and handle failures gracefully without breaking agent execution"],"best_for":["developers building agents that integrate with existing APIs and services","teams adopting MCP for standardized tool sharing across multiple agent frameworks","enterprises needing tool governance (approval workflows, audit logging)"],"limitations":["Tool schema generation from Python type hints may be incomplete for complex nested types; manual schema overrides required","MCP tool latency depends on subprocess startup time; cold starts can add 500ms+ per tool call","No built-in tool versioning; breaking changes to tool signatures require agent retraining"],"requires":["Python 3.9+","crewai-tools package for built-in tools","Optional: MCP server implementation for external tool integration","Optional: Pydantic 2.0+ for result validation"],"input_types":["Python functions with type hints","Tool class definitions (inheriting from BaseTool)","MCP server configurations (stdio or HTTP transport)"],"output_types":["tool execution results (any Python type)","error messages (with stack traces for debugging)","tool metadata (schema, description, required parameters)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-crewaiinc--crewai__cap_4","uri":"capability://memory.knowledge.unified.memory.architecture.with.rag.and.consolidation","name":"unified memory architecture with rag and consolidation","description":"CrewAI implements a unified memory system with three scopes (short-term task context, long-term crew knowledge, entity-level facts) that automatically consolidates agent interactions into structured knowledge. The framework uses embeddings and vector search for semantic retrieval, enabling agents to recall relevant past interactions without explicit memory management. Memory consolidation runs asynchronously, extracting key facts from conversations and deduplicating similar entries to prevent memory bloat while maintaining retrieval accuracy.","intents":["I want agents to remember facts and decisions from previous tasks without manual context passing","I need semantic search over agent interactions to find relevant past work","I want to prevent memory from growing unbounded while preserving important knowledge"],"best_for":["teams building long-running agent systems that accumulate knowledge over time","developers needing semantic search over agent interaction history","enterprises requiring knowledge retention and reuse across multiple crews"],"limitations":["Memory consolidation adds latency (100-500ms per consolidation cycle) and requires background processing","Embedding quality depends on the embedding model; poor embeddings lead to irrelevant retrieval results","No built-in memory expiration; old facts persist indefinitely unless manually pruned","Memory scopes are isolated; cross-crew knowledge sharing requires explicit export/import"],"requires":["Python 3.9+","Embedding model (OpenAI, Ollama, or custom implementation)","Vector store backend (in-memory, Chroma, Pinecone, or custom)","Optional: external database for persistent memory storage"],"input_types":["agent interactions (messages, tool calls, results)","manual memory entries (facts, decisions)","semantic search queries (natural language or embeddings)"],"output_types":["retrieved memories (ranked by relevance)","consolidated facts (deduplicated knowledge)","memory metadata (timestamps, source agents, confidence scores)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-crewaiinc--crewai__cap_5","uri":"capability://safety.moderation.task.guardrails.and.validation.with.agent.evaluation","name":"task guardrails and validation with agent evaluation","description":"CrewAI provides task-level validation through guardrails that enforce constraints (output format, content policies, quality thresholds) before task completion. The framework includes an agent evaluation system that measures task success using custom metrics or LLM-based scoring, enabling automated quality checks and retry logic. Guardrails are composable and can be chained to enforce multiple constraints (e.g., format validation → content moderation → quality scoring) with early exit on failure.","intents":["I want to ensure agent outputs meet quality standards before returning them to users","I need to automatically retry failed tasks with different approaches or parameters","I want to measure agent performance and identify tasks that consistently fail"],"best_for":["teams deploying agents in production where output quality is critical","developers building self-improving agent systems with automated evaluation","enterprises needing compliance checks (content moderation, data privacy) on agent outputs"],"limitations":["LLM-based evaluation adds 1-5 seconds per task due to additional model calls","Guardrails are task-specific; no cross-task constraint propagation","Retry logic can lead to exponential cost growth if guardrails are too strict"],"requires":["Python 3.9+","crewai package with guardrails support","Optional: custom evaluation metrics (Python functions or classes)"],"input_types":["task outputs (text, JSON, or structured data)","evaluation criteria (natural language descriptions or metric functions)","retry configurations (max attempts, backoff strategy)"],"output_types":["validation results (pass/fail with reasons)","evaluation scores (numeric or categorical)","retry decisions (proceed, retry, escalate)"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-crewaiinc--crewai__cap_6","uri":"capability://tool.use.integration.agent.skills.and.capability.composition","name":"agent skills and capability composition","description":"CrewAI enables agents to acquire skills dynamically through a skill registry, where skills are reusable, composable units of functionality that can be shared across multiple agents. Skills encapsulate domain expertise (e.g., web research, data analysis) as Python functions or classes with metadata (description, required tools, dependencies). The framework automatically injects skills into agent context and tool registries, enabling agents to discover and use skills without explicit configuration.","intents":["I want to build a library of reusable agent capabilities that multiple agents can leverage","I need to compose complex agent behaviors from simpler, testable skill units","I want to version and manage skills independently from agent definitions"],"best_for":["teams building agent systems with shared domain expertise across multiple crews","developers creating skill marketplaces or agent capability libraries","enterprises standardizing agent behaviors across different departments"],"limitations":["Skill discovery is manual; no automatic skill recommendation based on task requirements","Skill dependencies must be explicitly declared; circular dependencies can cause runtime errors","No built-in skill versioning; breaking changes require manual migration"],"requires":["Python 3.9+","crewai package with skill support","Skill definitions (Python functions or classes)"],"input_types":["skill implementations (Python functions with metadata)","skill metadata (description, required tools, dependencies)","agent configurations (skill assignments)"],"output_types":["injected tool registries (skills added to agent tools)","skill metadata (for discovery and documentation)","execution results (from skill invocation)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-crewaiinc--crewai__cap_7","uri":"capability://automation.workflow.liteagent.lightweight.execution.with.minimal.overhead","name":"liteagent lightweight execution with minimal overhead","description":"CrewAI provides LiteAgent as a minimal agent implementation that strips away orchestration overhead (memory, hooks, evaluation) for scenarios where simple, fast agent execution is needed. LiteAgent uses the same LLM and tool interfaces as full agents but skips state management and event tracking, reducing per-agent memory footprint and latency. This enables high-throughput agent deployments where individual agents are stateless and short-lived.","intents":["I want to run many lightweight agents in parallel without memory overhead","I need fast agent execution for real-time applications with strict latency budgets","I want to use agents as stateless functions in serverless or containerized environments"],"best_for":["developers building high-throughput agent services (e.g., API endpoints, batch processing)","teams deploying agents in resource-constrained environments (edge, serverless)","applications requiring sub-100ms agent response times"],"limitations":["LiteAgent has no memory or state persistence; each invocation is independent","No hooks or event tracking; debugging and observability are limited","No task validation or guardrails; output quality must be managed externally"],"requires":["Python 3.9+","crewai package with LiteAgent support","LLM provider API key"],"input_types":["agent role and goal (simple strings)","task description (single task per invocation)","tool registry (same as full agents)"],"output_types":["task result (text or structured data)","minimal metadata (no execution logs or memory)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-crewaiinc--crewai__cap_8","uri":"capability://automation.workflow.built.in.tracing.and.telemetry.with.observability.integrations","name":"built-in tracing and telemetry with observability integrations","description":"CrewAI includes native tracing that captures agent execution traces (LLM calls, tool invocations, reasoning steps) and exports them to observability platforms via OpenTelemetry. The framework provides a console formatter for local debugging and integrations with third-party tools (e.g., Langfuse, DataDog) for production monitoring. Traces include structured metadata (agent name, task ID, timestamps, token usage) enabling cost analysis, performance profiling, and error debugging.","intents":["I want to monitor agent execution in production and identify performance bottlenecks","I need to track LLM costs and token usage across crews","I want to debug agent failures by replaying execution traces"],"best_for":["teams deploying agents in production with observability requirements","developers debugging complex multi-agent workflows","enterprises needing cost tracking and compliance auditing"],"limitations":["Tracing adds ~5-10% latency overhead due to serialization and export","Third-party integrations require additional API keys and configuration","Trace retention depends on external platform limits; long-term storage requires custom archival"],"requires":["Python 3.9+","crewai package with telemetry support","Optional: OpenTelemetry collector for centralized trace aggregation","Optional: API keys for third-party observability platforms"],"input_types":["agent execution events (LLM calls, tool invocations, task completions)","observability platform configuration (endpoint, API key)"],"output_types":["structured traces (JSON with agent, task, and LLM metadata)","formatted logs (human-readable console output)","metrics (token usage, latency, error rates)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-crewaiinc--crewai__cap_9","uri":"capability://automation.workflow.cli.driven.project.scaffolding.and.deployment","name":"cli-driven project scaffolding and deployment","description":"CrewAI provides a CLI tool that scaffolds new crew, flow, and tool projects with boilerplate code, dependency management via UV, and pre-configured project structures. The CLI includes deployment commands that package agents for cloud platforms (AWS, Azure, GCP) and manage authentication, environment variables, and secrets. Project templates follow best practices (modular structure, type hints, testing) enabling teams to start with production-ready code.","intents":["I want to quickly scaffold a new agent project without manually setting up dependencies and structure","I need to deploy agents to cloud platforms with minimal configuration","I want to manage secrets and environment variables securely across environments"],"best_for":["teams starting new agent projects and wanting to follow best practices","developers deploying agents to cloud platforms without DevOps expertise","enterprises standardizing agent project structure across teams"],"limitations":["CLI templates are opinionated; customization requires manual editing after scaffolding","Deployment support is limited to major cloud platforms; custom infrastructure requires manual setup","UV dependency management is new; some teams may prefer pip or Poetry"],"requires":["Python 3.9+","crewai CLI installed (pip install crewai)","UV package manager (installed automatically with crewai)","Optional: cloud platform credentials (AWS, Azure, GCP) for deployment"],"input_types":["project name and type (crew, flow, tool)","deployment target (local, AWS, Azure, GCP)","configuration parameters (environment, secrets)"],"output_types":["scaffolded project directory with boilerplate code","pyproject.toml with dependencies","deployment configuration files (CloudFormation, Terraform, etc.)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","At least one LLM provider API key (OpenAI, Anthropic, Gemini, Azure, or Bedrock)","crewai package installed via pip","crewai package with Flow support (0.30.0+)","Optional: external state store for persistence (Redis, database, or file system)","CrewAI AMP subscription","Enterprise identity provider (Okta, Azure AD, or compatible SAML provider)","Kubernetes cluster or cloud platform (AWS, Azure, GCP) for deployment","Optional: secrets management system (HashiCorp Vault, AWS Secrets Manager)","Web browser (Chrome, Firefox, Safari, Edge)"],"failure_modes":["Agent coordination adds latency proportional to crew size; no built-in parallelization across independent tasks","A2A protocol requires explicit task definitions; implicit agent discovery not supported","Memory isolation between agents can lead to redundant context processing if not carefully managed","State serialization requires all flow variables to be JSON-serializable; custom objects need explicit converters","Event listener callbacks are synchronous; async event handling requires manual threading or async wrapper","Flow visualization requires external tools; no built-in UI for flow design or execution monitoring","Enterprise features require CrewAI AMP subscription; not available in open-source version","Control plane adds operational complexity; requires dedicated infrastructure or cloud service","Integration with enterprise systems (SAML, secrets management) requires custom configuration","Visual designer may not support all advanced CrewAI features (custom hooks, complex memory logic)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8385432150831784,"quality":0.5,"ecosystem":0.55,"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":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:57:06.483Z","last_commit":"2026-05-03T06:50:46Z"},"community":{"stars":50518,"forks":6966,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=crewaiinc--crewai","compare_url":"https://unfragile.ai/compare?artifact=crewaiinc--crewai"}},"signature":"s06mgOkcft8eXiKG3GEXNKBPp1g+adwtYzPBDlue0dcselThUm/OCQSwKNYCltKmdGP9AFeEYppSBaYWitgiCg==","signedAt":"2026-06-21T10:46:14.308Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/crewaiinc--crewai","artifact":"https://unfragile.ai/crewaiinc--crewai","verify":"https://unfragile.ai/api/v1/verify?slug=crewaiinc--crewai","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"}}