CrewAI vs AutoGen vs LangGraph: frameworks compared

Picking a multi-agent orchestration library is now a real architecture decision, not a toy choice. This crewai vs autogen vs langgraph comparison cuts through the marketing by evaluating the three on control flow, cost surface, and failure handling that you’ll hit once you ship.

Capabilities

CrewAI models agents as role-playing workers with assigned tasks and a crew coordinator. It assumes a sequential or hierarchical pipeline where each agent completes a task before the next runs. Memory is scoped per agent, and tools attach to an agent declaration.

from crewai import Agent, Task, Crew

researcher = Agent(
    role="Researcher",
    goal="Find recent benchmarks",
    tools=[],
    llm="gpt-4o"
)
writer = Agent(role="Writer", goal="Draft report", llm="gpt-4o")
task = Task(description="Summarize findings", agent=writer)
crew = Crew(agents=[researcher, writer], tasks=[task])
crew.kickoff()

AutoGen centers on conversational agents that negotiate via messages. You define an AssistantAgent and a UserProxyAgent that executes code or solicits human input. Termination is controlled by a max_consecutive_auto_reply or a custom function.

from autogen import AssistantAgent, UserProxyAgent

assistant = AssistantAgent("assistant", llm_config={"model": "gpt-4o"})
user = UserProxyAgent("user", human_input_mode="NEVER",
                      max_consecutive_auto_reply=5)
user.initiate_chat(assistant, message="Plan a trip")

LangGraph treats orchestration as a state machine. You define typed state, nodes that transform it, and edges that decide routing. Cycles and conditional branches are first-class.

from langgraph.graph import StateGraph
from typing import TypedDict

class State(TypedDict):
    input: str
    output: str

def node_a(state: State):
    return {"output": state["input"] + "!"}

g = StateGraph(State)
g.add_node("a", node_a)
g.add_edge("__start__", "a")
g.add_edge("a", "__end__")
app = g.compile()

Control flow

CrewAI hides flow behind role semantics. AutoGen uses implicit chat termination that can drift. LangGraph forces you to draw the graph, which is annoying until you need a loop with a guard.

In this crewai vs autogen vs langgraph comparison, the defining split is who owns the control flow: the framework, the conversation, or you.

Cost Model

All three are open-source (MIT or Apache-2.0), so framework cost is zero. The real spend is LLM tokens, and each framework drives that differently.

CrewAI emits roughly one prompt per task per agent, plus role priming that repeats on every call. AutoGen can loop many messages before termination; a five-agent group chat with ten rounds is fifty LLM calls. LangGraph lets you constrain transitions, but you pay in engineering time, not tokens.

If you route model calls through a gateway such as n4n.ai, you get per-token usage metering and automatic fallback when a provider is rate-limited, which caps surprise bills when AutoGen goes chatty. The framework doesn’t know about provider degradation; the gateway does.

Latency and Throughput

CrewAI runs tasks sequentially unless you manually parallelize with sequential=False or custom flows. Wall-clock scales linearly with agent count. AutoGen’s chat rounds add latency per turn; a 10-message negotiation is normal and blocks on the slowest model. LangGraph’s compiled graph executes predictably, and independent nodes can run concurrently if you wrap them in async.

None of these frameworks batch LLM requests internally. You must implement async calls yourself.

# LangGraph async invocation
import asyncio
result = asyncio.run(app.ainvoke({"input": "go"}))

Throughput is gated by your model provider’s RPM, not the framework. The crewai vs autogen vs langgraph comparison shows no framework gives you free concurrency.

Ergonomics

CrewAI wins on readability for simple pipelines. You declare roles in English and the framework fills prompts. AutoGen requires understanding message schemas, termination conditions, and proxy agents. LangGraph is the most verbose but least magical—you see every state transition.

Learning curve

• CrewAI: hours to first crew.
• AutoGen: days to tune group chats without infinite loops.
• LangGraph: days to internalize reducers and compile steps.

CrewAI’s YAML config is approachable but becomes unmaintainable past a dozen tasks. AutoGen’s Python API is flexible but scattered across autogen and autogen.agentchat. LangGraph reads like a normal program, which helps testing.

Ecosystem

CrewAI ships standalone but reuses LangChain tools via adapters. AutoGen has Microsoft backing and tight Azure OpenAI alignment. LangGraph is part of the LangChain ecosystem, so you inherit thousands of document loaders, vector stores, and tool wrappers.

Version stability differs: LangGraph changes APIs less frequently than AutoGen’s experimental group chat modules. CrewAI moves fast and breaks minor versions.

Limits

CrewAI breaks down when you need dynamic branching; you fight the framework to insert a conditional. AutoGen’s group chat can deadlock with more than five agents because termination conditions conflict. LangGraph is flexible but you write boilerplate for every edge case, including error recovery.

Error handling

LangGraph supports checkpointing and retry policies on edges:

from langgraph.checkpoint import MemorySaver
app = g.compile(checkpointer=MemorySaver())

CrewAI expects you to wrap kickoff() in try/except. AutoGen surfaces errors inside chat messages, which are easy to miss in logs.

Comparison Table

Which to Choose

Prototyping a role-based pipeline

Use CrewAI. You’ll have a working demo in an afternoon and can explain it to a non-ML stakeholder.

Building a negotiable multi-agent system with human-in-the-loop

AutoGen fits if you need code execution and conversational termination. Set max_consecutive_auto_reply low and log every message.

Production workflow with strict state and auditability

LangGraph. You own the graph, can checkpoint, and reason about each transition. This matters when compliance asks why agent B ran after agent A.

Cost-sensitive at scale

Any of the three, but put an inference gateway with metering and fallback in front. The framework choice matters less than the retry policy you enforce. In this crewai vs autogen vs langgraph comparison, the winner on cost is the one you can instrument.

Research experiments with many agents

None of the above handle >10 agents cleanly. Use a custom loop with explicit message queues; these frameworks will fight you.