CrewAI is an open-source Python framework for orchestrating autonomous AI agents to work together on complex tasks. It enables developers to build multi-agent systems by combining the collaborative intelligence of Crews — teams of role-playing agents with specialized goals and tools — with the precise control of Flows, which are event-driven, stateful workflows. The framework is built from scratch independent of LangChain or other agent frameworks, offering both high-level simplicity and low-level customization. It is used by 63% of the Fortune 500 and has over 53,000 GitHub stars with more than 100,000 certified developers through its community courses.
Architecture
CrewAI has two complementary layers. Flows serve as the backbone — they manage state, control execution, and handle event-driven logic with conditional branching, loops, and routing. Crews are the intelligence units: teams of autonomous agents that collaborate through role-based delegation to solve specific tasks. In production applications, a Flow delegates complex work to a Crew, receives results, and continues execution. This separation enables developers to balance autonomy with precision depending on the task.
Key Capabilities
CrewAI supports role-playing agents where each agent has a defined role, goal, backstory, and toolset. Agents can use memory (short-term, long-term, entity, and user memory), knowledge bases, and structured outputs via Pydantic. Task execution follows sequential, hierarchical, or hybrid processes with support for guardrails, callbacks, and human-in-the-loop triggers. The framework includes a built-in planning system that allows agents to break down tasks before execution, plus a testing and evaluation framework for measuring agent performance.
Flows provide production-grade features including state management that persists across steps and executions, checkpointing for resuming after failures, a router decorator for conditional branching, and event listeners that trigger actions based on internal or external events.
Tools and Integrations
CrewAI ships with over 40 built-in tools spanning web search (Tavily, Serper, Exa, Brave, You.com), web scraping (Firecrawl, Scrapfly, Browserbase, Selenium, Spider), file and document processing (CSV, PDF, DOCX, JSON, XML with RAG search), database querying (PostgreSQL, MySQL, MongoDB, Snowflake, Qdrant, Weaviate, SingleStore), cloud storage (S3 reader and writer), and automation (Composio, Zapier, Apify, MultiOn). Agents can also integrate MCP servers as tools using both Stdio and SSE transport protocols, and call external CrewAI Platform automations from within a Crew.
LLM Support
CrewAI connects to any large language model through LiteLLM or native provider integrations. It supports OpenAI, Anthropic, Google Gemini, AWS Bedrock, Azure OpenAI, Ollama (local models), LM Studio, and custom LLM implementations. The framework includes strategic LLM selection guidance and allows per-agent model configuration, enabling cost optimization by routing simple tasks to cheaper models and complex reasoning to more capable ones.
Pricing
CrewAI offers a Free plan that includes a visual editor (Crew Studio), AI copilot, GitHub integration, and 50 workflow executions per month. The Enterprise plan provides custom pricing with SSO (Microsoft Entra, Okta), role-based access control, dedicated infrastructure (including on-premise deployment), 50 hours of development per month, on-site support and training, enterprise connectors, dedicated VPC, SAM certification, and FedRAMP High compliance. The CrewAI AMP platform adds observability with tracing and OpenTelemetry, hallucination guardrails, performance metrics, LLM management, automatic scaling, cron scheduling, and a triggers system that connects Gmail, Slack, Salesforce, HubSpot, Google Drive, Microsoft Teams, Outlook, OneDrive, and Zapier.
Deployment
CrewAI runs locally via the crewai CLI using uv for dependency management (requires Python 3.10 to 3.14). For production, the CrewAI AMP platform offers managed cloud deployment with automatic updates and scaling, while CrewAI Factory provides containerized self-hosted deployment on any hyperscaler or on-premises infrastructure. The enterprise platform supports monorepo deployments, private package registries, secrets management (AWS, Azure, GCP with workload identity federation), and OpenTelemetry export to Datadog, Langfuse, MLflow, and other observability backends.
