{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-crewai","slug":"crewai","name":"CrewAI","type":"framework","url":"https://github.com/joaomdmoura/crewai","page_url":"https://unfragile.ai/crewai","categories":["ai-agents"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-crewai__cap_0","uri":"capability://planning.reasoning.role.based.agent.instantiation.with.behavioral.configuration","name":"role-based agent instantiation with behavioral configuration","description":"Creates autonomous agents with defined roles, goals, and backstories through a declarative Agent class that encapsulates identity, expertise, and behavioral constraints. Each agent is initialized with a role string, goal statement, and optional backstory that shapes how the LLM interprets the agent's persona and decision-making context. The framework uses these attributes to construct system prompts that guide agent behavior without explicit instruction engineering.","intents":["Define specialized agents with distinct expertise areas (researcher, analyst, writer) for multi-agent workflows","Create agents with consistent personalities and behavioral patterns across multiple interactions","Assign agents specific goals that constrain their actions within a larger task context"],"best_for":["teams building multi-agent systems where role separation improves task decomposition","developers prototyping agent-based workflows without complex prompt engineering","applications requiring consistent agent personas across conversations"],"limitations":["Agent behavior depends entirely on LLM interpretation of role/goal text — no formal behavioral guarantees","No built-in conflict resolution when agent goals contradict each other","Backstory and role descriptions are unstructured text, making it difficult to programmatically verify agent constraints"],"requires":["Python 3.9+","LLM API access (OpenAI, Anthropic, or local Ollama)","CrewAI framework installed via pip"],"input_types":["text (role description)","text (goal statement)","text (backstory/context)"],"output_types":["Agent object with configured identity","System prompt context for LLM"],"categories":["planning-reasoning","agent-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-crewai__cap_1","uri":"capability://planning.reasoning.task.to.agent.assignment.with.sequential.execution.orchestration","name":"task-to-agent assignment with sequential execution orchestration","description":"Defines discrete tasks with descriptions and expected outputs, then assigns them to specific agents for execution in a configurable sequence. Tasks are encapsulated as Task objects with a description, expected_output specification, and assigned_agent reference. The framework orchestrates execution order through a Crew object that manages task dependencies and ensures agents execute tasks sequentially or in parallel based on configuration, handling context passing between tasks.","intents":["Break complex workflows into discrete subtasks assigned to specialized agents","Define expected outputs for each task to guide agent behavior and enable validation","Control task execution order and dependencies in multi-agent workflows"],"best_for":["complex workflows requiring task decomposition across multiple specialized agents","applications where output validation between steps is critical","teams building deterministic multi-agent pipelines with clear task boundaries"],"limitations":["Task dependencies are implicit (through execution order) rather than explicit DAG-based — no automatic cycle detection","No built-in retry logic for failed tasks — requires manual error handling","Expected output specifications are text descriptions, not formal schemas — validation is LLM-dependent","Sequential execution can be slow for independent tasks that could run in parallel"],"requires":["Python 3.9+","CrewAI framework","Assigned agents already instantiated"],"input_types":["text (task description)","text (expected output specification)","Agent object reference"],"output_types":["Task execution result","Structured output matching expected_output specification"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-crewai__cap_10","uri":"capability://data.processing.analysis.structured.output.parsing.and.validation","name":"structured output parsing and validation","description":"Parses and validates agent outputs against expected schemas or formats, ensuring outputs match task specifications. The framework can extract structured data from agent responses (JSON, key-value pairs, etc.) and validate against defined schemas. This enables downstream systems to reliably consume agent outputs without manual parsing or error handling.","intents":["Extract structured data from agent responses for downstream processing","Validate agent outputs match expected formats before passing to next agent","Enable reliable integration between agents and external systems"],"best_for":["workflows requiring structured outputs for downstream processing","applications where output validation is critical for reliability","systems integrating agent outputs with databases or APIs"],"limitations":["Output parsing is LLM-dependent — agents may not produce valid structured output","No automatic schema inference — schemas must be manually defined","Validation failures don't trigger retries — invalid outputs are passed through","Complex nested schemas may confuse LLMs — parsing accuracy decreases with schema complexity","No built-in format conversion — only validates against expected format"],"requires":["Python 3.9+","CrewAI framework","Schema definition (JSON Schema, Pydantic model, etc.)"],"input_types":["Agent response text","Output schema specification"],"output_types":["Parsed structured data","Validation results","Formatted output"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-crewai__cap_11","uri":"capability://automation.workflow.async.execution.and.concurrent.task.processing","name":"async execution and concurrent task processing","description":"Supports asynchronous execution of crews and tasks, enabling concurrent processing of independent tasks and non-blocking I/O for tool calls. The framework provides async versions of core methods (async kickoff, async task execution) that integrate with Python's asyncio event loop. This allows crews to execute multiple tasks concurrently when they don't have dependencies, improving throughput for I/O-bound operations.","intents":["Execute independent tasks concurrently to reduce overall execution time","Handle I/O-bound operations (API calls, database queries) without blocking","Build responsive applications that don't freeze during long-running crew executions"],"best_for":["applications with many independent tasks that can run in parallel","I/O-heavy workflows with multiple external API calls","web applications or services requiring non-blocking execution"],"limitations":["Async execution adds complexity — requires understanding of asyncio and event loops","Task dependencies must be explicitly managed — no automatic dependency detection","Concurrent execution may increase LLM API costs — multiple agents calling LLM simultaneously","Debugging async execution is harder than synchronous — race conditions and timing issues","Not all tools support async — some tool calls may still block"],"requires":["Python 3.9+","CrewAI framework","Understanding of Python asyncio","Async-compatible tool implementations"],"input_types":["Async crew configuration","Task definitions with dependency information"],"output_types":["Concurrent execution results","Performance metrics"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-crewai__cap_12","uri":"capability://planning.reasoning.custom.agent.behavior.through.inheritance.and.overrides","name":"custom agent behavior through inheritance and overrides","description":"Allows developers to extend Agent class behavior through inheritance and method overrides, enabling custom reasoning logic, decision-making, or tool selection. Developers can override methods like think(), act(), or _call() to implement custom agent behavior while maintaining integration with the crew framework. This enables advanced use cases like custom planning algorithms or specialized reasoning patterns.","intents":["Implement custom reasoning or planning algorithms for specialized agents","Override default agent behavior for domain-specific requirements","Create agent variants with different decision-making strategies"],"best_for":["advanced developers needing custom agent behavior beyond role-based configuration","domain-specific applications requiring specialized reasoning patterns","research projects exploring novel agent architectures"],"limitations":["Requires deep understanding of CrewAI internals — API stability not guaranteed","Custom implementations may break with framework updates","No documentation of internal methods — requires reading source code","Custom behavior is not portable across different LLM providers","Testing custom agents is complex — requires mocking LLM responses"],"requires":["Python 3.9+","CrewAI framework","Deep understanding of CrewAI architecture","Python OOP knowledge"],"input_types":["Agent subclass definition","Overridden methods"],"output_types":["Custom agent behavior","Modified execution flow"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-crewai__cap_2","uri":"capability://memory.knowledge.inter.agent.communication.and.context.propagation","name":"inter-agent communication and context propagation","description":"Automatically passes task outputs from one agent to the next agent in the execution sequence, maintaining a shared context window that each agent can reference. The framework implements context propagation by storing task results in memory and injecting them into subsequent agent prompts, enabling agents to build on previous work without explicit message passing. This allows agents to reference earlier findings, analyses, or outputs when executing their assigned tasks.","intents":["Enable agents to reference findings from previous agents without manual context management","Build multi-step workflows where each agent refines or extends previous work","Maintain conversation history and task outputs across agent handoffs"],"best_for":["research workflows where agents build on each other's findings (researcher → analyst → writer)","iterative refinement pipelines where each agent improves previous outputs","applications requiring full task history and context visibility"],"limitations":["Context window grows with each task — may exceed LLM token limits in long workflows","No selective context filtering — all previous outputs are included, potentially introducing noise","Context propagation is unidirectional (forward only) — agents cannot request information from previous steps","No built-in context summarization or compression for long-running crews"],"requires":["Python 3.9+","CrewAI framework","LLM with sufficient context window for accumulated outputs"],"input_types":["Task output from previous agent","Current task description"],"output_types":["Agent response incorporating previous context","Updated shared context for next agent"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-crewai__cap_3","uri":"capability://tool.use.integration.tool.use.integration.with.function.calling.abstraction","name":"tool-use integration with function calling abstraction","description":"Enables agents to invoke external tools and APIs through a unified function-calling interface that abstracts provider differences. Tools are registered as Python functions with type hints and docstrings, which CrewAI converts into function schemas compatible with OpenAI, Anthropic, and other LLM providers. The framework handles tool invocation, result parsing, and error handling, allowing agents to call tools as part of their reasoning process without manual API orchestration.","intents":["Give agents access to external APIs, databases, or computation without hardcoding API calls","Enable agents to search the web, query databases, or perform calculations as part of task execution","Support multi-provider tool calling without rewriting tool definitions for each LLM"],"best_for":["agents that need real-time data access (web search, database queries, API calls)","workflows combining LLM reasoning with deterministic computation or external services","teams using multiple LLM providers and needing portable tool definitions"],"limitations":["Tool schemas are inferred from Python type hints — complex types may not translate cleanly to all LLM providers","No built-in tool result validation — agents can misinterpret tool outputs","Tool execution errors are passed back to the agent as text — no structured error handling","Tool calling is sequential — agents cannot parallelize multiple tool calls","Requires explicit tool registration — no automatic discovery of available functions"],"requires":["Python 3.9+","CrewAI framework","Python functions with type hints and docstrings","LLM provider supporting function calling (OpenAI, Anthropic, Ollama)"],"input_types":["Python function with type hints","Function docstring describing tool behavior","Tool parameters as typed arguments"],"output_types":["Tool execution result","Structured function call schema","Tool invocation logs"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-crewai__cap_4","uri":"capability://automation.workflow.crew.level.execution.and.result.aggregation","name":"crew-level execution and result aggregation","description":"Orchestrates the complete execution of a multi-agent workflow by managing task sequencing, agent assignment, and final result collection. The Crew class coordinates all agents and tasks, executing them in the specified order while maintaining shared context and collecting outputs. It provides a single entry point (kickoff method) that runs the entire workflow and returns aggregated results, handling errors and managing the execution lifecycle.","intents":["Execute a complete multi-agent workflow with a single function call","Collect and aggregate results from all agents into a final output","Monitor and control the execution of complex multi-agent systems"],"best_for":["applications requiring end-to-end multi-agent workflows (research, content generation, analysis)","teams building production systems where workflow orchestration must be reliable and observable","use cases where final output aggregation and validation are critical"],"limitations":["No built-in error recovery — if any task fails, the entire crew execution stops","No task-level parallelization — all tasks execute sequentially even if independent","Limited observability — no built-in logging or monitoring of agent decisions","No workflow persistence — crew state is not saved, making resumption impossible","Final output format is unstructured text — requires post-processing for structured results"],"requires":["Python 3.9+","CrewAI framework","At least one Agent and one Task defined","LLM API access configured"],"input_types":["List of Task objects","List of Agent objects","Crew configuration (verbose mode, memory settings)"],"output_types":["Final aggregated result as text","Execution logs","Task-by-task outputs"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-crewai__cap_5","uri":"capability://tool.use.integration.llm.provider.abstraction.and.multi.model.support","name":"llm provider abstraction and multi-model support","description":"Abstracts LLM provider differences through a unified interface that supports OpenAI, Anthropic, Ollama, and other providers. Agents can be configured with different LLM models independently, allowing crews to use different models for different agents based on cost, capability, or latency requirements. The framework handles provider-specific API calls, token counting, and response parsing transparently.","intents":["Use different LLM models for different agents based on task requirements","Switch between LLM providers without rewriting agent or task definitions","Optimize cost by using cheaper models for simple tasks and expensive models for complex reasoning"],"best_for":["teams using multiple LLM providers and wanting to avoid vendor lock-in","cost-conscious applications that need to balance model capability with expense","workflows where different agents have different computational requirements"],"limitations":["Model capabilities vary significantly — agents may behave differently with different models","No automatic model selection based on task complexity — requires manual configuration","Token counting and rate limiting are provider-specific — no unified quota management","Function calling support varies by provider — not all models support all tool types","Response quality and consistency differ across models — no normalization layer"],"requires":["Python 3.9+","CrewAI framework","API keys for desired LLM providers","Provider-specific client libraries (openai, anthropic, etc.)"],"input_types":["LLM provider name (string)","Model identifier (string)","Provider API credentials"],"output_types":["LLM response","Token usage metrics","Provider-specific metadata"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-crewai__cap_6","uri":"capability://memory.knowledge.memory.and.context.management.across.crew.executions","name":"memory and context management across crew executions","description":"Maintains agent memory and context across multiple crew executions, allowing agents to learn from previous interactions and maintain state. The framework provides memory storage mechanisms that persist task outputs, agent decisions, and execution history, enabling agents to reference past work and build on accumulated knowledge. Memory can be configured per-agent or shared across the crew.","intents":["Enable agents to reference previous executions and learn from past interactions","Maintain long-term context about ongoing projects or research","Build iterative workflows where agents refine outputs based on historical context"],"best_for":["long-running applications where agents need to maintain state across sessions","research or analysis workflows that span multiple days or weeks","applications requiring audit trails and historical context"],"limitations":["Memory storage is not persistent by default — requires external database integration","No built-in memory summarization — memory grows unbounded and may exceed token limits","Memory retrieval is not semantic — no similarity-based context selection","No memory privacy or isolation — all agents can access all memory by default","Memory management is manual — developers must implement cleanup and archival strategies"],"requires":["Python 3.9+","CrewAI framework","Optional: external storage backend (database, vector store)"],"input_types":["Task outputs","Agent decisions","Execution metadata"],"output_types":["Retrieved memory context","Memory summaries","Historical execution logs"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-crewai__cap_7","uri":"capability://planning.reasoning.hierarchical.agent.delegation.and.sub.crew.composition","name":"hierarchical agent delegation and sub-crew composition","description":"Enables agents to delegate tasks to other agents or sub-crews, creating hierarchical multi-level agent structures. Agents can spawn sub-crews to handle complex subtasks, with results flowing back to the parent agent. This allows for recursive task decomposition where high-level agents break work into smaller pieces and assign them to specialized sub-agents, creating tree-structured execution hierarchies.","intents":["Decompose complex tasks into hierarchical subtasks managed by specialized agents","Create manager agents that delegate work to specialist agents","Build scalable multi-level agent systems without flat task lists"],"best_for":["complex projects requiring multiple levels of task decomposition","organizations with hierarchical team structures that map to agent hierarchies","workflows where high-level planning agents delegate to execution agents"],"limitations":["Hierarchical delegation adds latency — each level of delegation requires separate LLM calls","No automatic load balancing across sub-crews — uneven task distribution possible","Context propagation through hierarchies can be lossy — information may be lost at delegation boundaries","Debugging hierarchical executions is complex — error traces span multiple levels","No built-in resource limits — sub-crews can spawn unbounded sub-sub-crews"],"requires":["Python 3.9+","CrewAI framework","Multiple agents and crews defined"],"input_types":["Parent task description","Sub-crew configuration","Delegation criteria"],"output_types":["Sub-crew execution results","Aggregated parent-level output"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-crewai__cap_8","uri":"capability://automation.workflow.verbose.execution.logging.and.debugging.output","name":"verbose execution logging and debugging output","description":"Provides detailed logging of agent reasoning, tool calls, and task execution for debugging and observability. When verbose mode is enabled, the framework logs agent thoughts, decisions, tool invocations, and results in human-readable format. This enables developers to understand agent behavior, identify reasoning errors, and debug multi-agent workflows without instrumenting code.","intents":["Debug agent behavior and understand why agents made specific decisions","Monitor multi-agent execution in real-time to identify bottlenecks or failures","Audit agent reasoning for compliance or quality assurance purposes"],"best_for":["development and testing phases where understanding agent behavior is critical","debugging complex multi-agent workflows with unexpected outputs","compliance-sensitive applications requiring audit trails of agent decisions"],"limitations":["Verbose logging can be extremely verbose for long-running crews — difficult to parse","No structured logging format — output is human-readable text, not machine-parseable","Logging overhead can slow execution — verbose mode adds latency","No log filtering or selective logging — all details are logged or none","Logs are not persisted by default — require manual capture or redirection"],"requires":["Python 3.9+","CrewAI framework","verbose=True flag in Crew configuration"],"input_types":["Crew execution state","Agent decisions","Tool invocations"],"output_types":["Human-readable execution logs","Agent reasoning traces","Tool call details"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-crewai__cap_9","uri":"capability://automation.workflow.callback.hooks.for.execution.events.and.custom.processing","name":"callback hooks for execution events and custom processing","description":"Allows developers to register callback functions that execute at specific points in the crew lifecycle (task start, task completion, agent decision, tool call, etc.). Callbacks receive execution context and can perform custom processing, logging, monitoring, or side effects without modifying core agent logic. This enables extensibility for metrics collection, custom notifications, or integration with external systems.","intents":["Collect metrics and monitoring data from multi-agent executions","Trigger notifications or side effects when specific events occur","Integrate crew execution with external systems (logging, analytics, webhooks)"],"best_for":["production systems requiring monitoring and alerting","applications needing custom metrics or analytics","workflows that must trigger external actions based on agent decisions"],"limitations":["Callback execution is synchronous — slow callbacks block agent execution","No callback ordering guarantees — multiple callbacks may execute in unpredictable order","Callback errors can crash the entire crew execution if not handled","Limited context passed to callbacks — may not have access to full execution state","No built-in callback persistence — callbacks are lost if process crashes"],"requires":["Python 3.9+","CrewAI framework","Python functions for callback handlers"],"input_types":["Execution event type","Event context (agent, task, tool, result)"],"output_types":["Custom side effects","Metrics or logs","External system updates"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","LLM API access (OpenAI, Anthropic, or local Ollama)","CrewAI framework installed via pip","CrewAI framework","Assigned agents already instantiated","Schema definition (JSON Schema, Pydantic model, etc.)","Understanding of Python asyncio","Async-compatible tool implementations","Deep understanding of CrewAI architecture","Python OOP knowledge"],"failure_modes":["Agent behavior depends entirely on LLM interpretation of role/goal text — no formal behavioral guarantees","No built-in conflict resolution when agent goals contradict each other","Backstory and role descriptions are unstructured text, making it difficult to programmatically verify agent constraints","Task dependencies are implicit (through execution order) rather than explicit DAG-based — no automatic cycle detection","No built-in retry logic for failed tasks — requires manual error handling","Expected output specifications are text descriptions, not formal schemas — validation is LLM-dependent","Sequential execution can be slow for independent tasks that could run in parallel","Output parsing is LLM-dependent — agents may not produce valid structured output","No automatic schema inference — schemas must be manually defined","Validation failures don't trigger retries — invalid outputs are passed through","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.037Z","last_scraped_at":"2026-05-03T14:00:10.321Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=crewai","compare_url":"https://unfragile.ai/compare?artifact=crewai"}},"signature":"KKewRmgY9kQz2RxDR9Wa1swiVj2TPdDzZmAsntdFQ/Jsle9V3Xee4BEb74Fa7NNEvhAVZ/P2uC/r4zxp8uQlCw==","signedAt":"2026-06-23T07:18:31.796Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/crewai","artifact":"https://unfragile.ai/crewai","verify":"https://unfragile.ai/api/v1/verify?slug=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"}}