CrewAI

Role-based 'Crews' of agents; flat abstraction over team-shaped workflows. Architecture: Role-based agents ('Crew') with layered memory (ChromaDB, SQLite, vector embeddings); ideal for automation and quick setup. Best suited: Fast prototyping of role-based team workflows with built-in layered memory. Watch out: High abstraction makes internal state hard to d…

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Content type

Technology

Technical building block: can be automated, integrated, or operated.

Classification level

Element

Concrete cog in the system that works inside larger relationships.

Available perspectives

Open this building block directly in an already curated view.

360°

In brief

CrewAI – in brief

CrewAI is an open-source framework for agentic AI workflows in which specialized agents collaborate as a crew through roles, tasks, and tools.

Where does it come from?

The framework emerged to model team-shaped multi-agent processes above language models through an accessible Python abstraction.

The mental model

Agents receive a role, goal, model, and tools; tasks define expected outputs and context. A crew arranges work sequentially or hierarchically and passes results between agents. Flows add explicit state, events, and control logic for longer processes.

Key building blocks

Agents

Specialized roles bundle instructions, models, and authorized tools.

Tasks

Defined inputs and outputs structure collaboration.

Orchestration

Crew and flow govern order, handoffs, and state transitions.

When is it useful?

CrewAI fits experimental and repeatable multi-agent workflows. Quality, cost, termination, prompt injection, tool permissions, observability, and evaluation remain application responsibilities.

CrewAI – Offizielle Dokumentation