CrewAI
A Python framework for orchestrating multi-agent systems where agents are organized into crews with defined roles, goals, and task assignments. CrewAI emphasizes role-based agent design and sequential or parallel task execution.
CrewAI provides a high-level abstraction for building multi-agent workflows. You define agents with specific roles (like "Senior Data Analyst" or "Content Strategist"), assign them goals and tools, then organize tasks into crews that execute collaboratively. The framework handles inter-agent communication and task delegation, letting you focus on defining what each agent should accomplish.
For product teams evaluating agent frameworks, CrewAI offers a gentle learning curve and intuitive mental model. The role-based design maps naturally to how human teams work, making it easy to prototype complex workflows. It integrates with LangChain tools and supports multiple LLM providers. The main consideration is that CrewAI's abstractions can feel limiting for highly custom orchestration logic. It works best for workflows that follow predictable patterns, like research-then-write or analyze-then-recommend. For more dynamic agent interactions, you may need lower-level frameworks.
Related Terms
Model Context Protocol (MCP)
An open standard that defines how AI models connect to external tools, data sources, and services through a unified interface. MCP enables agents to dynamically discover and invoke capabilities without hardcoded integrations.
Tool Use
The ability of an AI model to invoke external functions, APIs, or services during a conversation to perform actions beyond text generation. Tool use transforms language models from passive responders into active problem solvers.
Function Calling
A model capability where the AI generates structured JSON arguments for predefined functions rather than free-form text. Function calling provides a reliable bridge between natural language understanding and programmatic execution.
Agentic Workflow
A multi-step process where an AI agent autonomously plans, executes, and iterates on tasks using tools, reasoning, and feedback loops. Agentic workflows go beyond single-turn interactions to accomplish complex goals.
ReAct Pattern
An agent architecture that interleaves Reasoning and Acting steps, where the model thinks about what to do next, takes an action, observes the result, and repeats. ReAct combines chain-of-thought reasoning with tool use in a unified loop.
Chain of Thought
A prompting technique that instructs the model to break down complex problems into sequential reasoning steps before producing a final answer. Chain of thought significantly improves accuracy on math, logic, and multi-step tasks.