LangGraph vs AutoGen vs CrewAI 2026: Which One Ships

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Production “agents” are mostly orchestration: LLM calls, tools, memory/state, retries, and guardrails. Three ecosystems lead in 2026—LangGraph, AutoGen, and CrewAI—each with different ergonomics for web data workloads.

Quick Answer

Pick LangGraph 1.0 for production agents that need stateful graphs, retries, and resumable checkpoints — it now powers agents at Uber, LinkedIn, and Klarna. Pick AutoGen 0.4 AgentChat when multi-agent debate is the product. Pick CrewAI for role-based workflows (researcher → editor → analyst) that map to org charts. For web data inside any of them, expose Apify Actors via REST, langchain-apify, or the Apify MCP server.

Comparison table

	LangGraph	AutoGen	CrewAI
Core idea	Stateful graph of nodes and edges	Multi-agent conversations and tool registration	Roles, tasks, and crews
Control flow	Explicit—you draw the machine	Emergent—agents negotiate	Semi-structured—manager/sequential processes
Debugging	Strong (graph + LangSmith)	Harder—chat transcripts	Medium—task logs
Production fit	Highest for long pipelines	Improving; best for exploratory flows	Good for prototypes and marketing ops
Tooling	LangChain tools, custom callables	Function wrappers	LangChain-compatible tools
Web data fit	Excellent (checkpointing, retries)	Good with discipline	Good when roles map to org charts
Learning curve	Medium	Low–medium	Low

LangGraph

LangGraph (LangChain ecosystem) models your agent as a state machine. That matters for scraping: you can persist which URLs succeeded, which failed, and what to retry—instead of hoping a single chat transcript stays coherent. LangGraph 1.0 is now GA and ships durable execution, the new agent middleware API, and a stable Python + JS surface — the snippets below target 1.x.

Best for: Multi-step research, enrichment pipelines, and anything you might need to resume after failure.

Minimal install

pip install langgraph langchain-anthropic requests

Pattern: LangGraph + Apify (REST API tool)

This sketch calls apify/website-content-crawler through the Apify REST API so you are not tied to a specific langchain-apify release. Set APIFY_TOKEN (or pass your token into the helper).

import os
import requests
from typing import TypedDict, List, Annotated
import operator

from langgraph.graph import StateGraph, END
from langchain_anthropic import ChatAnthropic
from langchain_core.tools import tool

APIFY_TOKEN = os.environ["APIFY_TOKEN"]

def apify_dataset_items(actor_id: str, run_input: dict, wait_secs: int = 120) -> list:
    act = actor_id.replace("/", "~")
    r = requests.post(
        f"https://api.apify.com/v2/acts/{act}/runs?fpr=use-apify",
        params={"token": APIFY_TOKEN, "waitForFinish": wait_secs},
        json=run_input,
        timeout=wait_secs + 30,
    )
    r.raise_for_status()
    run = r.json()["data"]
    ds = run["defaultDatasetId"]
    items = requests.get(
        f"https://api.apify.com/v2/datasets/{ds}/items?fpr=use-apify",
        params={"token": APIFY_TOKEN, "clean": True},
        timeout=60,
    )
    items.raise_for_status()
    return items.json()

class WebResearchState(TypedDict):
    query: str
    evidence: Annotated[List[str], operator.add]
    answer: str

@tool
def scrape_start_url(url: str) -> str:
    """Fetch page text via Apify Website Content Crawler (single page)."""
    rows = apify_dataset_items(
        "apify/website-content-crawler",
        {"startUrls": [{"url": url}], "maxCrawlDepth": 0, "maxCrawlPages": 1},
    )
    if not rows:
        return ""
    return rows[0].get("text") or rows[0].get("markdown", "") or str(rows[0])[:8000]

tools = [scrape_start_url]
llm = ChatAnthropic(model="claude-sonnet-4-20250514").bind_tools(tools)

def retrieve(state: WebResearchState):
    doc = scrape_start_url.invoke("https://apify.com/pricing?fpr=use-apify")
    return {"evidence": [doc]}

def answer(state: WebResearchState):
    prompt = f"Question: {state['query']}\n\nEvidence:\n{state['evidence']}"
    resp = llm.invoke(prompt)
    return {"answer": resp.content}

builder = StateGraph(WebResearchState)
builder.add_node("retrieve", retrieve)
builder.add_node("answer", answer)
builder.set_entry_point("retrieve")
builder.add_edge("retrieve", "answer")
builder.add_edge("answer", END)

graph = builder.compile()
print(graph.invoke({"query": "What buckets does Apify pricing mention?", "evidence": [], "answer": ""}))

AutoGen

AutoGen (Microsoft ecosystem) shines when you want multiple personas—for example a “skeptic” and a “researcher”—to argue toward an answer. For scraping, the risk is chatty loops unless you cap rounds and narrow tool surfaces.

Heads-up — v0.4 rewrite. The legacy autogen package (ConversableAgent, UserProxyAgent) is the v0.2 API. AutoGen 0.4 splits into autogen-core, autogen-agentchat, and autogen-ext. The snippet below uses the current AgentChat quickstart pattern.

Best for: Brainstorming, multi-perspective analysis, and human-in-the-loop approvals.

pip install -U "autogen-agentchat" "autogen-ext[openai]"

import asyncio
import os
import requests

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

APIFY_TOKEN = os.environ["APIFY_TOKEN"]

def scrape_url(url: str) -> str:
    """Fetch page text via Apify Website Content Crawler (single page)."""
    act = "apify~website-content-crawler"
    r = requests.post(
        f"https://api.apify.com/v2/acts/{act}/runs?fpr=use-apify",
        params={"token": APIFY_TOKEN, "waitForFinish": 120},
        json={"startUrls": [{"url": url}], "maxCrawlDepth": 0, "maxCrawlPages": 1},
        timeout=180,
    )
    r.raise_for_status()
    ds = r.json()["data"]["defaultDatasetId"]
    rows = requests.get(
        f"https://api.apify.com/v2/datasets/{ds}/items?fpr=use-apify",
        params={"token": APIFY_TOKEN, "clean": True},
        timeout=60,
    ).json()
    if not rows:
        return ""
    return rows[0].get("text") or rows[0].get("markdown", "") or str(rows[0])[:8000]

async def main() -> None:
    model_client = OpenAIChatCompletionClient(model="gpt-4.1")
    researcher = AssistantAgent(
        name="researcher",
        model_client=model_client,
        tools=[scrape_url],
        system_message="Fetch evidence with scrape_url, then answer with URL citations.",
    )
    result = await researcher.run(task="Summarize https://apify.com/store?fpr=use-apify in five bullets.")
    print(result.messages[-1].content)
    await model_client.close()

asyncio.run(main())

CrewAI

CrewAI optimizes for roleplay: “researcher,” “editor,” “analyst.” It is fast to demo and maps well to marketing and ops workflows where humans already think in job titles.

Best for: Parallelized tasks with clear hand-offs and templated outputs.

import os
from crewai import Agent, Task, Crew, Process
from crewai_tools import ApifyActorsTool

# Official Apify + CrewAI bridge — `pip install 'crewai[tools]' langchain-apify`.
# See https://docs.crewai.com/en/tools/automation/apifyactorstool
scrape_page = ApifyActorsTool(actor_name="apify/website-content-crawler")

researcher = Agent(
    role="Web researcher",
    goal="Collect factual bullets from official pricing pages",
    tools=[scrape_page],
    llm="claude-sonnet-4-20250514",
)

writer = Agent(
    role="Editor",
    goal="Turn research into a crisp Markdown summary",
    tools=[],
    llm="claude-sonnet-4-20250514",
)

task_research = Task(
    description='Run the crawler on https://apify.com/pricing?fpr=use-apify with maxCrawlDepth 0 and maxCrawlPages 1; list every plan name you see in the captured text.',
    agent=researcher,
    expected_output="Bullet list with quotes only from the page",
)

task_write = Task(
    description="Produce a 120-word plain-English pricing overview for buyers.",
    agent=writer,
    expected_output="Markdown paragraph",
)

crew = Crew(agents=[researcher, writer], tasks=[task_research, task_write], process=Process.sequential)
print(crew.kickoff())

Apify integration (API, LangChain tool, MCP)

Pick the integration that matches where your LLM runs:

Surface	What to use	Why
Python services	langchain-apify (ApifyWrapper, ApifyActorsTool) or REST	Wrapper loads datasets into LangChain; REST stays version-agnostic
Any stack	Apify REST API	Lowest dependency count
Desktop chat (Claude, etc.)	@apify/actors-mcp-server	Zero custom bridge code—see our Claude Desktop MCP setup

For hosted agents, you will still set APIFY_TOKEN secrets, cap Actor inputs, and log run IDs so you can reconcile cost.

Platform entry point: https://apify.com?fpr=use-apify

When to use which framework

You need…	Choose…
Checkpoints, retries, explicit branching	LangGraph
Fast org-chart demos (“researcher + writer”)	CrewAI
Exploratory multi-agent debate	AutoGen
Long-running scrape + validation + summarize	LangGraph first
One script, one tool, one answer	Plain OpenAI/Anthropic SDK may beat a framework

Frequently Asked Questions

Use LangGraph when you need deterministic, testable graphs for production. Use CrewAI for role-based workflows that map to how non-engineers describe work. Use AutoGen when multi-agent conversation is the product feature, not a hidden implementation detail.

LangGraph is usually the best default because you can represent retries, partial failures, and backpressure explicitly. CrewAI works for simpler pipelines. AutoGen can work but often needs extra guards to prevent runaway tool chatter.

Expose Apify Actors as tools—via langchain-apify in Python, via REST in any language, or via the Apify MCP server in desktop clients. The framework orchestrates LLM decisions; Apify executes the expensive browser and proxy work.

Microsoft’s newer AutoGen releases are far more capable than early prototypes, but emergent multi-agent flows are still harder to regression-test than LangGraph graphs. For revenue-critical scraping, prefer explicit state machines.

Not strictly, but LangGraph pairs well with LangSmith tracing for debugging graphs in production. CrewAI and AutoGen benefit from whatever logging you already use.