CrewAI vs LangGraph vs AutoGen: Which Multi-Agent Framework Should You Use in 2026?

from crewai import Agent, Task, Crew

# Agents are like employees — each has a clear role
researcher = Agent(
    role="Market Research Analyst",
    goal="Find accurate data about competitor pricing",
    backstory="10 years of experience in market analysis",
    llm="claude-sonnet-4-20250514",
    tools=[web_search, document_reader]
)

writer = Agent(
    role="Content Writer",
    goal="Write clear, engaging reports from research data",
    backstory="Technical writer who simplifies complex topics",
    llm="claude-sonnet-4-20250514"
)

# Tasks define what needs to be done
research_task = Task(
    description="Research the top 5 competitors and their pricing",
    expected_output="Detailed pricing comparison table",
    agent=researcher
)

writing_task = Task(
    description="Write a one-page summary of the research findings",
    expected_output="Executive summary in markdown",
    agent=writer
)

# Crew orchestrates the team
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, writing_task],
    verbose=True
)

result = crew.kickoff()

from langgraph.graph import StateGraph, END
from typing import TypedDict, Annotated

# Define the shared state
class ResearchState(TypedDict):
    query: str
    research_data: str
    draft: str
    feedback: str
    revision_count: int

# Each node is a function that processes state
def research_node(state: ResearchState) -> ResearchState:
    # Call LLM to research the topic
    data = llm.invoke(f"Research: {state['query']}")
    return {"research_data": data.content}

def write_node(state: ResearchState) -> ResearchState:
    # Write based on research
    draft = llm.invoke(f"Write about: {state['research_data']}")
    return {"draft": draft.content}

def review_node(state: ResearchState) -> ResearchState:
    # Review the draft
    feedback = llm.invoke(f"Review this draft: {state['draft']}")
    return {"feedback": feedback.content, "revision_count": state["revision_count"] + 1}

# Define when to revise vs finish
def should_revise(state: ResearchState) -> str:
    if "approved" in state["feedback"].lower():
        return "end"
    if state["revision_count"] >= 3:
        return "end"  # Max 3 revisions
    return "revise"

# Build the graph
graph = StateGraph(ResearchState)
graph.add_node("research", research_node)
graph.add_node("write", write_node)
graph.add_node("review", review_node)

# Define the flow
graph.set_entry_point("research")
graph.add_edge("research", "write")
graph.add_edge("write", "review")

# Conditional edge — loop back or finish
graph.add_conditional_edges("review", should_revise, {
    "revise": "write",  # Go back to writing
    "end": END          # Done
})

app = graph.compile()
result = app.invoke({"query": "AI trends 2026", "revision_count": 0})

from autogen import AssistantAgent, GroupChat, GroupChatManager

# Agents are conversation participants
researcher = AssistantAgent(
    name="Researcher",
    system_message="""You research topics thoroughly.
    Present findings with data and sources.
    When done, say RESEARCH_COMPLETE.""",
    llm_config=llm_config
)

writer = AssistantAgent(
    name="Writer",
    system_message="""You write clear content based on research.
    Wait for the researcher to finish before writing.
    When done, say DRAFT_COMPLETE.""",
    llm_config=llm_config
)

reviewer = AssistantAgent(
    name="Reviewer",
    system_message="""You review content for accuracy and clarity.
    If changes needed, explain what to fix.
    If approved, say APPROVED.""",
    llm_config=llm_config
)

# Group chat — agents discuss the topic
group_chat = GroupChat(
    agents=[researcher, writer, reviewer],
    messages=[],
    max_round=10,
    speaker_selection_method="round_robin"
)

manager = GroupChatManager(groupchat=group_chat, llm_config=llm_config)

# Start the conversation
result = researcher.initiate_chat(
    manager,
    message="Research and write about AI coding trends in 2026"
)

Researcher → Writer → Editor → Publisher

Classifier Agent → Router → Specialist Agent → Response Agent

Architect → Developer → Tester

Research → Write → Review → (approved? → publish) or (rejected? → rewrite → review)

Process order → Validate → (if payment fails → retry 3x) → (if inventory low → notify) → Ship

AI generates → Human reviews → (approve → deploy) or (reject → AI revises)

Multiple agents discuss a problem, challenge each other, reach consensus

Developer presents code → Reviewer critiques → Developer responds → iterate

Multiple researchers present findings → synthesize into one report

pip install crewai crewai-tools

pip install langgraph langchain-anthropic

pip install autogen-agentchat

What matters most to you?

Speed to prototype → CrewAI
   └── Simple, linear workflows → CrewAI
   └── Need parallel agents → CrewAI

Control and debugging → LangGraph
   └── Complex branching logic → LangGraph
   └── Need state persistence → LangGraph
   └── Production system → LangGraph

Conversational agents → AutoGen
   └── Debate/brainstorming → AutoGen
   └── Iterative refinement → AutoGen
   └── (Check current status — major rewrite in progress)