CrewAI vs LangGraph vs AutoGen: Multi-Agent Compared

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

TL;DR: CrewAI, LangGraph and AutoGen are open-source Python frameworks that coordinate multiple LLM agents in three different ways. CrewAI assigns roles and tasks to a crew, LangGraph wires agents into an explicit state graph with checkpoints, and AutoGen lets agents talk in a managed group chat. Pick CrewAI for fast role-based pipelines and LangGraph for durable, auditable control flow. AutoGen is now in maintenance mode, so new Microsoft-stack projects should evaluate Microsoft Agent Framework instead.

Looking for the wider landscape, including LangChain, Strands and AgentCore? Start with the AI agent frameworks guide. This page covers only the three frameworks people most often shortlist for multi-agent work.

Key Takeaways

• The three frameworks model coordination differently: CrewAI uses roles and tasks, LangGraph uses nodes, edges and shared state, and AutoGen uses conversations between agents.
• LangGraph gives the most control and the strongest durability: every step is checkpointed, so a run can pause for human approval, survive a restart and be replayed from an earlier state.
• CrewAI is the fastest way to a working multi-agent pipeline. A crew of agents with sequential or hierarchical processes takes a few dozen lines, and CrewAI Flows add event-driven control when you need it.
• AutoGen's group-chat teams (RoundRobinGroupChat, SelectorGroupChat, Swarm, MagenticOneGroupChat) are good for open-ended collaboration. The repository is in maintenance mode, and Microsoft points new feature work to Microsoft Agent Framework.
• The main cost difference comes from how many LLM calls the coordination pattern makes. Selector-style group chats and hierarchical managers spend an extra model call to decide who goes next, while a graph with fixed edges does not.
• Choose by workflow shape: a fixed pipeline fits CrewAI, branching or loops with approvals fit LangGraph, and conversation-driven exploration fits AutoGen or its successor.

What are CrewAI, LangGraph and AutoGen?

All three frameworks solve the same problem: one LLM agent with a long prompt and many tools gets unreliable once a task has distinct stages, so you split the work across specialised agents and need something to coordinate them. Where they differ is in what they treat as the basic unit of coordination.

CrewAI is an MIT-licensed Python framework from CrewAI, Inc. (version 1.15 on PyPI in November 2026). It has its own runtime and does not depend on LangChain. You describe each Agent by role, goal and backstory, give it Tasks with an expected_output, and group them into a Crew that runs under a Process: sequential (tasks in order, each one's output passed on as context) or hierarchical (a manager agent plans, delegates and checks the work). For control flow beyond a single crew, CrewAI Flows add event-driven orchestration with @start, @listen and @router decorators, typed state, @persist for saving state and @human_feedback for review steps.

LangGraph is an MIT-licensed Python and JavaScript library from the LangChain team (version 1.2 on PyPI). It is a low-level orchestration runtime. You define a typed state object, add nodes (functions, LLM calls or whole agents) and connect them with edges, including conditional edges that pick the next node from the current state. A checkpointer saves state after every step, which enables interrupt() for human-in-the-loop pauses, resume after a crash, and time-travel debugging. Multi-agent systems are built as supervisor graphs, handoffs via Command, or subgraphs.

AutoGen is Microsoft's MIT-licensed multi-agent framework. Its 0.4 rewrite split it into autogen-core (an event-driven actor runtime) and autogen-agentchat (a high-level API, version 0.7.5). In AgentChat you create AssistantAgents and put them into a team that runs a group conversation until a termination condition fires. The GitHub repository now says AutoGen is in maintenance mode: contributions are limited to bug fixes, security patches and documentation, and feature work is directed to Microsoft Agent Framework, which the Semantic Kernel and AutoGen teams describe as AutoGen's successor. Separately, some of AutoGen's original creators maintain a community fork called AG2 (Apache-2.0), which continues the older 0.2-style API.

How do CrewAI, LangGraph and AutoGen compare at a glance?

Dimension	CrewAI	LangGraph	AutoGen (AgentChat)
Coordination model	Role-based crew running tasks	Explicit state graph	Multi-agent group conversation
Core abstractions	Agent, Task, Crew, Process, Flow	StateGraph, nodes, edges, checkpointer	AssistantAgent, teams, termination conditions
Who decides the next step	Task order (sequential) or a manager LLM (hierarchical)	Your edges and routing functions	Turn order, an LLM selector, or agent handoffs
Control flow	Linear crews; branching and loops through Flows	Cycles, branches, parallel fan-out, subgraphs	Conversation turns; GraphFlow for directed workflows
State	Task outputs as context; Flow state (dict or Pydantic)	Typed shared state, saved after every step	Message history; teams can save and load state
Durability	@persist on Flows	Checkpointers (in-memory, SQLite, Postgres) with resume and replay	save_state() / load_state() on agents and teams
Human-in-the-loop	human_input on tasks, @human_feedback in Flows	interrupt() + Command(resume=...)	UserProxyAgent, HandoffTermination
Languages	Python	Python, JavaScript/TypeScript	Python (.NET in the core layer)
License	MIT	MIT	MIT
Project status (Nov 2026)	Active	Active	Maintenance mode; successor is Microsoft Agent Framework
Managed platform	CrewAI AMP (commercial)	LangSmith deployment (commercial)	None; Azure AI Foundry targets Agent Framework
Best fit	Business pipelines with clear roles	Production workflows that need control and audit	Research-style collaboration and existing AutoGen systems

How does each framework coordinate agents?

This is the decision that matters most, because the rest of each framework follows from it.

CrewAI coordinates through a job description. You write who each agent is and what each task must produce, and the Process decides the order. In a sequential crew the order is the list of tasks you wrote, so it is predictable and cheap. In a hierarchical crew a manager LLM (set with manager_llm or manager_agent) decides who does what and checks the results, which adapts better to vague goals but adds manager calls and makes runs less repeatable. The role/goal/backstory prompts work well for content, research and analysis pipelines where the stages correspond to job roles.

LangGraph coordinates through a flowchart you write. The LLM still makes decisions inside nodes, but the set of possible next steps is fixed by your edges. A router can send a failed validation back to the writer, a supervisor node can pick a specialist, and a compliance step can be made unskippable simply by having no edge that goes around it. That last property is why LangGraph tends to win when a workflow has to be audited: you can show which paths are possible, and the checkpoint history shows which path a run actually took.

AutoGen coordinates through conversation. Agents share one message thread and take turns. RoundRobinGroupChat rotates through them in a fixed order. SelectorGroupChat asks a model to pick the next speaker from the agents' names and descriptions, optionally narrowed by a candidate_func or a custom selector_func. Swarm lets agents hand off to each other explicitly, and MagenticOneGroupChat runs Microsoft's orchestrator-led Magentic-One pattern. Runs end when a termination condition matches, such as TextMentionTermination("APPROVE") or MaxMessageTermination(20). This suits exploratory tasks where nobody knows the steps in advance. The cost is that the flow is emergent: two runs of the same task can take different paths and different numbers of turns.

A rough way to remember them: CrewAI is an org chart, LangGraph is a state machine, and AutoGen is a meeting.

How much code does a research-and-write pipeline take in each?

The same two-agent job (research a topic, then write a short brief) shows the difference in style. The examples assume a model provider key is set in the environment.

CrewAI: agents and tasks are declared, and the crew runs them in order.

from crewai import Agent, Task, Crew, Process

researcher = Agent(
    role="Market Researcher",
    goal="Find accurate, current facts about {topic}",
    backstory="You verify every claim against a primary source.",
)
writer = Agent(
    role="Technical Writer",
    goal="Turn research into a clear 300-word brief",
    backstory="You write for engineering leaders who skim.",
)

research = Task(
    description="Research {topic}. List key facts with sources.",
    expected_output="A bullet list of facts, each with a URL.",
    agent=researcher,
)
brief = Task(
    description="Write a brief from the research.",
    expected_output="A 300-word brief in Markdown.",
    agent=writer,
    context=[research],
)

crew = Crew(agents=[researcher, writer], tasks=[research, brief],
            process=Process.sequential)
result = crew.kickoff(inputs={"topic": "multi-agent frameworks"})
print(result.raw)

LangGraph: the same pipeline as an explicit graph, with a human approval gate and a loop back to the writer when the draft is rejected.

from typing import TypedDict
from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, START, END
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.types import interrupt, Command

llm = init_chat_model("openai:gpt-4.1")

class State(TypedDict, total=False):
    topic: str
    research: str
    draft: str
    feedback: str

def research(state: State):
    msg = llm.invoke(f"List key facts with sources about {state['topic']}.")
    return {"research": msg.content}

def write(state: State):
    prompt = f"Write a 300-word brief from:\n{state['research']}"
    if state.get("feedback"):
        prompt += f"\nRevise for this feedback: {state['feedback']}"
    return {"draft": llm.invoke(prompt).content}

def review(state: State):
    decision = interrupt({"draft": state["draft"]})  # pauses the run
    return {"feedback": "" if decision == "approve" else decision}

def route(state: State):
    return END if not state["feedback"] else "write"

builder = StateGraph(State)
builder.add_node("research", research)
builder.add_node("write", write)
builder.add_node("review", review)
builder.add_edge(START, "research")
builder.add_edge("research", "write")
builder.add_edge("write", "review")
builder.add_conditional_edges("review", route, ["write", END])
graph = builder.compile(checkpointer=InMemorySaver())

config = {"configurable": {"thread_id": "brief-1"}}
graph.invoke({"topic": "multi-agent frameworks"}, config)  # stops at review
graph.invoke(Command(resume="approve"), config)            # continues

AutoGen: the same agents as a team in a conversation, with a model picking who speaks next.

import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import SelectorGroupChat
from autogen_agentchat.conditions import TextMentionTermination, MaxMessageTermination
from autogen_agentchat.ui import Console
from autogen_ext.models.openai import OpenAIChatCompletionClient

async def main():
    model = OpenAIChatCompletionClient(model="gpt-4.1")
    researcher = AssistantAgent(
        "researcher", model_client=model,
        description="Finds facts with sources.",
        system_message="Research the topic and list facts with URLs.",
    )
    writer = AssistantAgent(
        "writer", model_client=model,
        description="Writes and revises the brief.",
        system_message="Write a 300-word brief. Reply APPROVE when it is final.",
    )
    team = SelectorGroupChat(
        [researcher, writer], model_client=model,
        termination_condition=TextMentionTermination("APPROVE") | MaxMessageTermination(12),
    )
    await Console(team.run_stream(task="Write a brief on multi-agent frameworks."))

asyncio.run(main())

The CrewAI version is the shortest and the easiest for a non-specialist to read. The LangGraph version is the longest, but it is the only one of the three where the human approval step and the revise loop are part of the structure rather than prompt instructions, and where the paused run survives a process restart once you swap InMemorySaver for a Postgres or SQLite checkpointer. The AutoGen version is compact but relies on the selector model and the APPROVE keyword to end the run, which is why the MaxMessageTermination backstop is not optional.

How do they handle state, memory and long-running workflows?

LangGraph treats state as the main thing. Every node reads and writes a typed state object, and a checkpointer stores a snapshot after each step, keyed by thread_id. That one mechanism provides conversation memory, pause and resume, recovery after a crash, and the ability to fork a run from an earlier checkpoint for debugging. A separate store holds long-term memory that is shared across threads. For workflows that run for hours or wait days for an approval, this is the strongest model of the three.

CrewAI splits state across two layers. Inside a crew, task outputs flow forward as context, and agents can turn on CrewAI's unified memory, which uses an LLM to decide what to save and recall and stores it in a pluggable backend such as LanceDB. Across crews, a Flow holds structured state, either a dict or a Pydantic model, and @persist saves it so a Flow can be reloaded later. It works well, but durability is something you opt into per Flow, not a property of every step.

AutoGen keeps state as the conversation. Agents and teams expose save_state() and load_state(), which serialise message history and team position so you can stop and continue later. There is no step-level checkpoint history comparable to LangGraph's, so replaying from the middle of a run is not a built-in operation.

How do they support human-in-the-loop approvals?

Approval gates are where production requirements usually separate the three.

• LangGraph: call interrupt(payload) inside any node. The graph saves its state and returns the payload to your application; later, graph.invoke(Command(resume=value), config) continues from exactly that point, even in another process. Because the gate is a node in the graph, there is no path around it.
• CrewAI: set human_input=True on a task to have the agent ask for feedback before finishing it, or use the @human_feedback decorator in a Flow to put a review step between stages. Combine it with @persist if the approval may take longer than the process stays alive.
• AutoGen: add a UserProxyAgent to the team so a human takes a turn in the conversation, or end the run with HandoffTermination when an agent hands off to the user, then start the team again with the person's reply. This works for interactive chat, but for asynchronous approvals you have to save and restore state yourself.

If a consequential action, such as sending money, emailing a customer or merging code, must never run without sign-off, LangGraph's interrupt model is the easiest to verify, because you can check the graph structure instead of trusting a prompt.

Which framework is cheapest and fastest to run?

None of the frameworks charges for the open-source library, so cost comes almost entirely from model calls, and the number of calls depends on the coordination pattern rather than the framework's brand.

• Fixed routing is cheapest. A sequential CrewAI crew or a LangGraph graph with plain edges makes roughly one LLM call per agent step plus tool calls.
• Dynamic routing adds a call per decision. CrewAI's hierarchical manager, AutoGen's SelectorGroupChat and a LangGraph supervisor node each spend a model call on deciding who works next. A cheaper model for routing usually works well here.
• Open-ended conversations are the hardest to budget. A group chat ends when its termination condition matches, so a run that never says APPROVE uses turns until MaxMessageTermination stops it. Always set a turn cap, and on any framework set max_iter, recursion or turn limits.
• Shared history grows. In a group chat every agent sees the whole thread, so input tokens grow with each turn. LangGraph and CrewAI let you pass each agent only the state or context it needs, which keeps prompts smaller.

Measure on your own workload before choosing on cost: log tokens per run for the same task in each candidate, over several runs, because dynamic patterns vary from run to run.

Is AutoGen still a good choice in 2026?

For a new project, usually not as a first choice. The microsoft/autogen repository states that it is in maintenance mode, limited to bug fixes, security patches and documentation, and directs feature work to Microsoft Agent Framework (agent-framework on PyPI, version 1.24 in November 2026). Microsoft describes Agent Framework as the successor to both Semantic Kernel and AutoGen, built by the same teams, and publishes a migration guide from AutoGen. It keeps AutoGen's agent and multi-agent patterns and adds graph-based workflows, state management and enterprise integrations.

AutoGen still makes sense in two cases. First, if you already run AutoGen 0.4+ teams in production, they keep working and still receive security fixes, so plan a migration rather than a rewrite under pressure. Second, if you specifically want the community-driven 0.2-style API, AG2 is an actively maintained fork, but it is a different project with its own roadmap, so evaluate it separately and do not treat it as the same codebase.

So the realistic 2026 comparison for Microsoft-leaning teams is CrewAI vs LangGraph vs Microsoft Agent Framework, with AutoGen's group-chat concepts carried into the successor.

When should you choose CrewAI, LangGraph or AutoGen?

Choose CrewAI when:

• The work divides naturally into roles, such as researcher, analyst, writer and reviewer.
• You want a working multi-agent prototype within a day and readable code that non-specialists can follow.
• The pipeline is mostly linear, and you can add a Flow later for routing and persistence.

Choose LangGraph when:

• The workflow has loops, conditional branches, retries or parallel fan-out.
• You need durable execution: approvals that wait, runs that survive restarts, and replay for debugging.
• You must show auditors or reviewers exactly which paths an agent system can take.
• You want Python and TypeScript implementations of the same orchestration model.

Choose AutoGen (or Microsoft Agent Framework) when:

• The task is exploratory and the steps cannot be known in advance, such as research, brainstorming or code review debates.
• You already have AutoGen teams in production (stay on AutoGen and plan a migration), or you are building new on the Microsoft and Azure stack (start with Agent Framework).

Many teams combine them: a LangGraph graph as the outer, durable workflow, with a CrewAI crew or a conversational team called inside one node for the open-ended part. Keep each agent's business logic in plain functions and tools, so moving between frameworks means rewriting the orchestration rather than the work.

How should you evaluate a multi-agent framework before committing?

Run one realistic workflow end to end in each shortlisted framework and measure the same things:

1. Success rate over at least ten runs, not one demo run, because dynamic coordination varies from run to run.
2. Tokens and latency per run, including routing and manager calls.
3. Failure behaviour: kill the process mid-run and check what can be resumed.
4. Approval behaviour: confirm the consequential step cannot run without a human, with the gate in structure rather than in a prompt where possible.
5. Observability: check that you can see every agent turn, tool call and decision. LangGraph pairs with LangSmith, CrewAI and AutoGen emit OpenTelemetry traces, and all three can export to tools you already use.

Single-agent first is still good advice. Add a second agent only when a single agent with good tools measurably fails, because each extra agent adds coordination cost and new ways to fail.

Frequently Asked Questions

What is the difference between CrewAI and LangGraph?

CrewAI coordinates agents by role: you define agents with roles and goals, assign them tasks, and a sequential or hierarchical process runs the crew. LangGraph coordinates agents through an explicit state graph of nodes and edges, with a checkpoint after every step. CrewAI is faster to start with; LangGraph gives more control over branching, loops, durability and human approval.

Is LangGraph better than AutoGen?

For production workflows that need predictable paths, durable state and approval gates, LangGraph is usually the better fit, and it is actively developed. AutoGen is stronger for open-ended conversational collaboration between agents, but it is now in maintenance mode. For new Microsoft-stack projects, compare LangGraph with Microsoft Agent Framework, AutoGen's successor, rather than with AutoGen itself.

Is CrewAI better than AutoGen?

CrewAI is better for structured pipelines with clear roles and is under active development, with Flows for event-driven control and persistence. AutoGen is better when agents need to talk freely to reach an answer, using group chat teams such as SelectorGroupChat and Swarm. Because AutoGen receives only maintenance updates, CrewAI is the safer default for a new role-based project.

Does CrewAI use LangChain or LangGraph?

No. CrewAI is a standalone framework with its own agent runtime, task engine and Flows, and its current package does not depend on LangChain. You can still call LangChain components, such as retrievers or document loaders, from inside a CrewAI tool, because tools are ordinary Python functions.

Is AutoGen deprecated?

AutoGen is not deleted, but its repository is in maintenance mode: it receives bug fixes, security patches and documentation updates only. Microsoft directs new feature development to Microsoft Agent Framework and publishes a migration guide. AG2 is a separate community fork that continues the older API under its own roadmap.

Can you use CrewAI, LangGraph and AutoGen together?

Yes. A common pattern is a LangGraph graph as the outer workflow for routing, checkpoints and approvals, with a CrewAI crew or a conversational agent team called inside a single node. Keep tools and business logic in plain functions so they can be shared, and log tokens per framework so the combined system stays within budget.

Sources

• CrewAI GitHub repository
• CrewAI processes documentation
• CrewAI Flows documentation
• LangGraph overview
• LangGraph persistence documentation
• LangGraph interrupts documentation
• AutoGen documentation
• AutoGen SelectorGroupChat guide
• AutoGen repository and maintenance notice
• Microsoft Agent Framework overview
• Microsoft Agent Framework migration guide from AutoGen
• AG2 GitHub repository

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