CrewAI

CrewAI is an open-source Python framework for orchestrating teams of AI agents that collaborate to complete complex tasks. Instead of relying on a single agent to handle everything, you define a crew (a group of specialized agents, each with its own role, goal, and toolset) and let them divide the work and pass results between each other. For developers building production-grade AI systems, CrewAI provides the scaffolding to move beyond simple prompt-response patterns and into workflows where multiple agents reason, delegate, and execute in concert.

How CrewAI works

CrewAI is built around four core concepts that mirror how a real team operates: agents, tasks, crews, and flows.

• Agents: The individual workers. Each agent is defined by three attributes: a role (what it does), a goal (the outcome it is trying to achieve), and a backstory (context that shapes how it reasons and responds). An agent might be a Researcher, a Writer, or a QA Reviewer. The role is what directs its behavior.
• Tasks: The assignments agents execute. A task has a description, an expected output format, and a pointer to the agent responsible for it. Tasks can run sequentially (one after another) or in parallel (multiple agents working at the same time).
• Crews: The team. A crew bundles agents and tasks together and defines how they execute. Two execution modes are available: sequential, where output from one task feeds directly into the next, and hierarchical, where a manager agent delegates and coordinates the work of other agents.
• Flows: The backbone of larger applications. Flows let you chain multiple crews together, manage state across steps, and use conditional logic to decide what runs next. Think of a Flow as the process definition and a Crew as the unit of work inside it.
• Tools: What agents use to interact with the outside world. CrewAI ships with built-in tools for web search, file reading, code execution, and data retrieval. You can also write custom tools that connect to any API or external system.

When a crew runs, each agent picks up its assigned task, uses any tools it needs, and, if delegation is enabled, can pass work to another agent in the crew. The output of one task becomes available as context for the next, so the crew builds toward a final result collaboratively.

What you can build with CrewAI

CrewAI suits builders who need structured collaboration between specialized agents: content teams, research pipelines, automated business workflows, and developer tooling.

• Automated content pipeline: A Strategist Agent selects the topic and target keyword, a Research Agent pulls relevant sources, a Writer Agent drafts the article, and an SEO Agent optimizes it before output. The whole process runs without manual handoffs between steps.
• Lead scoring and outreach system: Agents enrich incoming lead data, score it against ICP criteria, and draft personalized outreach emails for qualified leads. Connect it to a CRM via a custom tool and the whole pipeline runs on new lead intake.
• Agentic software development workflow: A Coder Agent writes the initial implementation, a Code Reviewer Agent checks for bugs and best practices, and a QA Tester Agent generates test cases. A Project Manager Agent coordinates the sequence and resolves dependencies.
• Customer support analysis pipeline: Agents pull support tickets, categorize and analyze them for recurring issues, and generate a structured report with volume trends and recommended fixes, replacing manual ticket review.
• Retail or business intelligence system: A Store Analyst Agent reviews product placement or sales data, an Insights Agent surfaces patterns, and a Strategy Agent produces a prioritized action plan. Real-world examples of this are already in production.
• Research and report generation: A crew of researcher agents, each focused on a different domain or data source, produces a synthesized report on any topic. Useful for competitive analysis, market research, or technical deep-dives.