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

from crewai import Agent, Task, Crew, Process
from crewai_tools import SerperDevTool

search_tool = SerperDevTool()

researcher = Agent(
    role="Senior Research Analyst",
    goal="Find the latest market trends in AI agent frameworks",
    backstory="You are an expert at discovering cutting-edge tech trends.",
    tools=[search_tool],
    verbose=True,
)

writer = Agent(
    role="Tech Content Strategist",
    goal="Write a concise summary report from the research",
    backstory="You distill complex technical research into founder-friendly insights.",
    verbose=True,
)

research_task = Task(
    description="Research the top 3 AI agent frameworks in 2026 and their adoption trends.",
    expected_output="A bullet-point brief with key stats and GitHub star counts.",
    agent=researcher,
)

write_task = Task(
    description="Turn the research brief into a 300-word executive summary.",
    expected_output="A polished executive summary ready for a VC update.",
    agent=writer,
)

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

result = crew.kickoff()
print(result)

from typing import TypedDict, Annotated
from langgraph.graph import StateGraph, END
from langgraph.checkpoint.memory import MemorySaver
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage

llm = ChatOpenAI(model="gpt-4o", temperature=0)

# 1. Define the state schema — everything that flows through the graph
class ResearchState(TypedDict):
    query: str
    research_notes: str
    draft_summary: str
    approved: bool
    final_output: str

# 2. Define nodes (each is a pure function that receives and returns state)
def research_node(state: ResearchState) -> ResearchState:
    """Simulate research — in production, call a search tool here."""
    response = llm.invoke([
        HumanMessage(content=f"Research this topic and return key bullet points: {state['query']}")
    ])
    return {"research_notes": response.content}

def write_node(state: ResearchState) -> ResearchState:
    """Write a summary from the research notes."""
    response = llm.invoke([
        HumanMessage(content=f"Turn these notes into a 300-word executive summary:
{state['research_notes']}")
    ])
    return {"draft_summary": response.content}

def approval_node(state: ResearchState) -> ResearchState:
    """Human-in-the-loop checkpoint — graph pauses here until approved."""
    # In LangGraph Cloud / production, this triggers an interrupt
    # and waits for a human to call graph.update_state() with approved=True
    print(f"
--- DRAFT FOR REVIEW ---
{state['draft_summary']}
")
    return {}  # State update happens externally

def finalize_node(state: ResearchState) -> ResearchState:
    """Finalize the approved draft."""
    return {"final_output": state["draft_summary"]}

# 3. Route based on approval status
def should_finalize(state: ResearchState) -> str:
    return "finalize" if state.get("approved") else "approval"

# 4. Build the graph
builder = StateGraph(ResearchState)
builder.add_node("research", research_node)
builder.add_node("write", write_node)
builder.add_node("approval", approval_node)
builder.add_node("finalize", finalize_node)

builder.set_entry_point("research")
builder.add_edge("research", "write")
builder.add_conditional_edges("write", should_finalize)
builder.add_edge("approval", END)   # Waits for external approval
builder.add_edge("finalize", END)

# 5. Compile with a checkpointer (enables persistence + human interruption)
checkpointer = MemorySaver()
graph = builder.compile(
    checkpointer=checkpointer,
    interrupt_before=["approval"]
)

# 6. Run
config = {"configurable": {"thread_id": "run-001"}}
result = graph.invoke({"query": "Top AI agent frameworks in 2026", "approved": False}, config)
print("State after write:", result)

import asyncio
from autogen_agentchat.agents import AssistantAgent, UserProxyAgent
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_agentchat.conditions import MaxMessageTermination
from autogen_ext.models.openai import OpenAIChatCompletionClient

# Model client
model_client = OpenAIChatCompletionClient(model="gpt-4o")

# Define agents
researcher = AssistantAgent(
    name="Researcher",
    model_client=model_client,
    system_message=(
        "You are a research analyst. When given a topic, find key facts "
        "and return bullet-point notes. Be concise and factual."
    ),
)

writer = AssistantAgent(
    name="Writer",
    model_client=model_client,
    system_message=(
        "You are a content strategist. When given research notes, write "
        "a 300-word executive summary. End your message with TERMINATE."
    ),
)

# Group chat: agents take turns in round-robin order
team = RoundRobinGroupChat(
    participants=[researcher, writer],
    termination_condition=MaxMessageTermination(max_messages=4),
)

async def run():
    result = await team.run(
        task="Research the top AI agent frameworks in 2026 and write an executive summary."
    )
    print(result.messages[-1].content)

asyncio.run(run())

from langgraph.graph import StateGraph, END
from crewai import Agent, Task, Crew, Process
from typing import TypedDict

class PipelineState(TypedDict):
    input_data: str
    research_output: str
    final_report: str

# CrewAI crew used as a single LangGraph node
def research_crew_node(state: PipelineState) -> PipelineState:
    researcher = Agent(
        role="Research Analyst",
        goal="Analyze the given data and extract key insights",
        backstory="Expert at turning raw data into structured findings.",
    )
    task = Task(
        description=f"Analyze: {state['input_data']}",
        expected_output="Structured bullet-point insights",
        agent=researcher,
    )
    crew = Crew(agents=[researcher], tasks=[task], process=Process.sequential)
    result = crew.kickoff()
    return {"research_output": str(result)}

# LangGraph owns the overall flow
def report_writer_node(state: PipelineState) -> PipelineState:
    # Your LLM call to write the final report from research_output
    return {"final_report": f"Report based on: {state['research_output'][:100]}..."}

builder = StateGraph(PipelineState)
builder.add_node("research_crew", research_crew_node)
builder.add_node("report_writer", report_writer_node)
builder.set_entry_point("research_crew")
builder.add_edge("research_crew", "report_writer")
builder.add_edge("report_writer", END)

graph = builder.compile()
result = graph.invoke({"input_data": "Q1 2026 sales data...", "research_output": "", "final_report": ""})
print(result["final_report"])