CrewAI vs LangChain Agents: which should you use for multi-agent systems?

from crewai import Agent, Task, Crew
from langchain_openai import ChatOpenAI
import os

# CrewAI agents with built-in roles and collaboration
researcher = Agent(
    role='Senior Researcher',
    goal='Find the most relevant information on the given topic',
    backstory='You are an expert research analyst with access to the internet.',
    llm=ChatOpenAI(model='gpt-4o', api_key=os.environ['OPENAI_API_KEY']),
)

writer = Agent(
    role='Content Writer',
    goal='Write engaging summaries based on research findings',
    backstory='You are a professional writer who turns research into clear narratives.',
    llm=ChatOpenAI(model='gpt-4o', api_key=os.environ['OPENAI_API_KEY']),
)

# Tasks with delegated ownership: CrewAI handles agent routing automatically
research_task = Task(
    description='Research the current state of LLM inference optimization.',
    agent=researcher,
    expected_output='A comprehensive research report with key findings.'
)

write_task = Task(
    description='Write a 500-word summary based on the research findings.',
    agent=writer,
    expected_output='A polished summary article.'
)

crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    verbose=True
)

result = crew.kickoff()
print(result)

from langchain_openai import ChatOpenAI
from langchain.agents import initialize_agent, Tool, AgentType
from langchain.memory import ConversationBufferMemory
import os

llm = ChatOpenAI(model='gpt-4o', api_key=os.environ['OPENAI_API_KEY'])

# LangChain agents are tool-based; you define behavior manually
def research_tool(query: str) -> str:
    """Simulates research lookup."""
    return f"Research findings on {query}: [simulated research data]"

def write_summary_tool(findings: str) -> str:
    """Simulates content generation."""
    return f"Summary based on: {findings[:50]}..."

tools = [
    Tool(name='Research', func=research_tool, description='Search for research on a topic'),
    Tool(name='WriteSummary', func=write_summary_tool, description='Create a summary from findings')
]

# LangChain requires explicit memory and orchestration: no built-in role concept
memory = ConversationBufferMemory(memory_key='chat_history')

agent = initialize_agent(
    tools,
    llm,
    agent=AgentType.OPENAI_FUNCTIONS,
    memory=memory,
    verbose=True
)

# You manually sequence tasks; no Task or Crew abstraction
research_result = agent.run('Research the current state of LLM inference optimization.')
write_result = agent.run(f'Based on this research: {research_result}, write a 500-word summary.')
print(write_result)