CREWAI

CrewAI Python crews role-based agents.

Definition

CrewAI key concepts: (1) Agent: defined by role (e.g., 'Senior Research Analyst'), goal (e.g., 'find latest AI news'), backstory (LLM character context), LLM (any LangChain LLM compatible), tools (LangChain tools or custom). (2) Task: assigned to specific agent, description (what agent should do), expected_output (what should produce), tools_required. (3) Crew: collection of agents + tasks, process type (sequential = tasks executed in order, hierarchical = manager LLM coordinates other agents). (4) Memory: ShortTermMemory (current task), LongTermMemory (across tasks in crew), EntityMemory (entities mentioned). (5) Process types: (a) Sequential: tasks executed in defined order, each agent works on their task with potentially results from previous tasks as context. (b) Hierarchical: crew has a manager agent that delegates tasks, automatic coordination, more flexible. (6) Custom Tools: Tool class wraps Python functions, integrates with LangChain tools ecosystem (Tavily Web Search, Wikipedia, file reading, etc.). CrewAI v0.x focus iteration on agent role-clarity (vs free-form agent conversations AutoGen). Customers: ~50000+ deployments 2024, focus startups + content marketing + research workflows automation.

Origin

CrewAI initial release September 2023 by Joao Moura ; CrewAI Inc. founded 2024 ; Seed $18M 2024 (Insight Partners + Boldstart Ventures) ; ~20000+ GitHub stars 2024.

Example in context

CrewAI content marketing crew: agents = ResearchAnalyst (researches trending topics Tavily Web Search), ContentWriter (writes draft article based research), Editor (edits + polishes draft) ; tasks sequential: 1) ResearchAnalyst finds trending AI agentic frameworks topic, 2) ContentWriter writes 1500-word draft, 3) Editor polishes ; output: publishable blog article in ~5 minutes via OpenAI GPT-4.

Related terms

• AutoGen — alternative multi-agent.