UitbreidenOS CrewAI Multi-Agent Framework
When to activate
Building role-based multi-agent workflows in Python; user mentions CrewAI; need a working multi-agent prototype fast; the problem maps naturally to job roles (researcher, writer, reviewer, analyst); sequential pipeline logic with Claude as the underlying model.
When NOT to use
TypeScript projects — use Mastra instead; complex conditional routing or branching logic required — use LangGraph; production systems that need checkpointing, failure recovery, and resumable runs — use LangGraph; single-agent tasks where the role abstraction adds overhead without benefit.
Instructions
Core model:
CrewAI organizes work as a Crew of Agents running Tasks with Tools. The framework handles agent-to-agent communication, output passing, and process orchestration.
Installation:
pip install crewai crewai-tools
Three core concepts:
Agent— a role with a goal, backstory, LLM, and tool list. Defines who does the work.Task— a description, expected output, and assigned agent. Defines what gets done.Crew— the collection of agents and tasks with a process type. Defines how it runs.
Process types:
Process.sequential— tasks run in order, each output available to the next (default, simplest)Process.hierarchical— a manager agent reads all tasks and delegates to specialist agents dynamically
Claude integration:
from langchain_anthropic import ChatAnthropic
llm = ChatAnthropic(model="claude-sonnet-4-6")
Use Sonnet for most agent roles. Use Haiku for high-volume, low-complexity steps (extraction, formatting). Reserve Opus for reasoning-heavy roles that justify the cost.
Memory: Pass memory=True to Crew to enable agents to retain context across tasks in the same run.
Full example:
from crewai import Agent, Task, Crew, Process
from langchain_anthropic import ChatAnthropic
llm = ChatAnthropic(model="claude-sonnet-4-6")
researcher = Agent(
role="Research Analyst",
goal="Find accurate, up-to-date information on the given topic",
backstory="You specialize in fast, thorough research and structured summaries.",
llm=llm,
verbose=True,
)
writer = Agent(
role="Content Writer",
goal="Write clear, engaging content based on research input",
backstory="You turn technical research into readable prose for developer audiences.",
llm=llm,
verbose=True,
)
research_task = Task(
description="Research {topic} and summarize the key findings in bullet points.",
agent=researcher,
expected_output="Bullet-point summary of 5-10 key facts with sources noted.",
)
write_task = Task(
description="Write a 500-word article based on the research output.",
agent=writer,
expected_output="Complete article, ready to publish.",
)
crew = Crew(
agents=[researcher, writer],
tasks=[research_task, write_task],
process=Process.sequential,
memory=True,
)
result = crew.kickoff(inputs={"topic": "Claude Code agent frameworks"})
print(result)
When CrewAI beats LangGraph: When roles map naturally to the problem, when you need a working prototype in under an hour, when the workflow is sequential and predictable. CrewAI is optimized for readability and fast iteration — not for complex state machines.
Example
Content pipeline for a developer blog: a ResearchAgent gathers technical facts about a new library, a WriterAgent drafts the article, a ReviewerAgent checks for accuracy and tone. Sequential process, Claude Sonnet on all three agents, done in under 50 lines of code.