MAF 1.0, LangGraph or CrewAI? Choosing an Agent Framework in 2026

Microsoft released Agent Framework 1.0 (MAF) on April 3, 2026. It folds AutoGen and Semantic Kernel into one package meant for production, and it landed in a market where LangGraph and CrewAI were already settled choices. Teams that used to ask which framework to pick now ask whether to switch. Here is a comparison aimed at that decision, with all facts current as of April 11, 2026.

Short answer

Prototype in CrewAI. Choose LangGraph for complicated, cloud-neutral Python workflows. Choose MAF if you live on Azure, since it ships for both .NET and Python. If you already have LangGraph code, the langchain-azure-ai package connects it to Azure Foundry and spares you a rewrite.

What shifted since 2025

• AutoGen and Semantic Kernel merged into MAF 1.0, generally available on April 3, 2026.
• LangGraph v1 settled its runtime and tooling.
• CrewAI grew up on flows and checkpointing.
• MCP, A2A and AG-UI went from nice-to-have to expected.

Three different mental models

The frameworks disagree about what an agent even is, and that disagreement matters more than any feature table.

LangGraph models a workflow as a directed graph. Nodes are functions, edges are decisions, and state moves through the graph in plain sight. You think in code.

# LangGraph: explicit graph control
from langgraph.graph import StateGraph
from langgraph.checkpoint.sqlite import SqliteSaver

graph = StateGraph(State)
graph.add_node("classify", classify_doc)
graph.add_node("extract", extract_data)
graph.add_conditional_edges("classify", route_by_type)
app = graph.compile(checkpointer=SqliteSaver("agent.db"))

CrewAI models a team. Each agent has a role, a goal and a backstory, all written in everyday language, so you think in org charts.

# CrewAI: role-based simplicity
from crewai import Agent, Crew, Process

researcher = Agent(role="Researcher", goal="Find accurate data",
                   llm="gpt-4o")
writer = Agent(role="Writer", goal="Draft clear reports",
               llm="gpt-4o")
crew = Crew(agents=[researcher, writer], tasks=[...],
            process=Process.sequential)

MAF offers two styles under one roof: free-form, LLM-driven conversation inherited from AutoGen, and fixed business workflows inherited from Semantic Kernel. You can combine them.

# MS Agent Framework: simple agent in Python
import asyncio
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient

async def main():
    client = AIProjectClient(
        endpoint="https://your-project.services.ai.azure.com/api",
        credential=DefaultAzureCredential()
    )
    agent = client.agents.create_agent(
        model="gpt-4.1",
        instructions="You are a concise data analyst."
    )
asyncio.run(main())

Production concerns, one at a time

State and persistence

This is where a deployment either survives or doesn't. LangGraph is the only one of the three with step-level checkpoint replay built in. If an agent breaks at step 7 of 12, you can look at every earlier state and restart from step 6. In regulated industries that capability is close to mandatory, so LangGraph takes this round.

MAF has a graph workflow engine covering sequential, concurrent, handoff and group-chat patterns, plus Durable Functions for long-running state. Its README mentions time-travel in the graph workflows, but the replay tooling is less polished than what LangSmith offers. The Durable Task integration is still beta in Python, and once it stabilizes the gap could narrow for Azure-native teams. CrewAI needs outside tooling here.

Memory

Memory is the biggest differentiator and also the biggest hole in every framework. MAF is the only one with a fully managed memory layer that scales for enterprises, though it works on Azure only and remains in preview. Foundry's managed memory, in public preview since January 2026, pulls facts out of conversations, settles conflicts between them and brings back relevant context in later sessions, with no infrastructure of your own. It wins if your company has already standardized on Microsoft Foundry.

LangGraph can call Foundry Agent Service agents through langchain-azure-ai, but the managed memory is an MAF feature and doesn't fully cross that bridge. LangGraph does give you durable execution and persisted state through checkpoints. It just doesn't wrap them in a ready-made managed-memory abstraction. For self-hosted setups, CrewAI has the most complete memory out of the box.

Debugging and observability

Picture an agent making a bad call at 3 AM. What does the on-call engineer get to see? LangGraph paired with LangSmith gives the best answer: trace visualization alongside checkpoint replay is the strongest debugging setup for agents right now. MAF traces natively into Application Insights and the new Foundry Agents dashboard, which is production-ready but has no replay. CrewAI depends on third-party tools.

One handy detail: install langchain-azure-ai[opentelemetry] and LangGraph traces also reach Azure Application Insights. That gives you LangSmith while developing and Azure Monitor in production.

MCP and A2A

These protocols decide how agents reach tools and each other. MAF wins here. The Foundry portal's Tools tab brings MCP servers, A2A endpoints, Fabric, SharePoint, Azure AI Search and over 1,400 Logic Apps connectors together in a single place, and nothing else matches that breadth for enterprise integration. CrewAI's native protocol support is solid. LangGraph needs more manual wiring.

Python on Azure: two real options

Many comparisons treat Azure as a .NET-only world. It isn't. Python teams have two good routes.

Option one: MAF for Python

MAF 1.0 reached general availability for Python on April 3, 2026, the same day as .NET. It is not a preview.

# MAF Python: working agent in a few lines
import asyncio
from agent_framework import Agent
from agent_framework.openai import OpenAIChatClient

async def main():
    agent = Agent(
        client=OpenAIChatClient(),
        instructions="You are a helpful data analyst.",
        tools=[get_weather, get_menu_specials]
    )
    response = await agent.run("What's the weather in Amsterdam?")
asyncio.run(main())

It brings several things LangGraph can't match:

• Foundry managed memory, which extracts, consolidates and retrieves across sessions
• Agent 365, for central governance and direct deployment to Microsoft 365 (generally available May 1)
• The Voice Live API, for native real-time speech-to-speech agents
• Foundry IQ, which offers dynamic RAG behind a single grounding API
• Foundry Control Plane, covering identity, policy and observability together

The price is early-adopter friction. Python GA was only eight days old when this was written. Several integrations, namely A2A, AG-UI, Azure Functions, Cosmos DB and Mem0, are still beta and need the --pre flag. Community material such as tutorials and Stack Overflow answers is thin next to LangGraph's, and the beta packages will probably break things over the coming months.

Option two: LangGraph plus langchain-azure-ai

Microsoft itself maintains langchain-azure-ai (v1.2.1, released March 26, 2026) and recommends it as the way to run LangChain and LangGraph on Azure. It is no community workaround.

# LangGraph + Azure Foundry via the bridge
from langchain_azure_ai.chat_models import AzureAIOpenAIApiChatModel
from azure.identity import DefaultAzureCredential

model = AzureAIOpenAIApiChatModel(
    project_endpoint="https://myproject.services.ai.azure.com/api",
    credential=DefaultAzureCredential(),
    model="gpt-5.2"
)

You get LangGraph's proven debugging together with Azure's models and services. What you give up is everything exclusive to MAF: Foundry managed memory, Voice Live, Agent 365 governance and Foundry IQ. The bridge links Azure's infrastructure, not its agent-specific features.

Picking between them

For a new Azure project that values deep integration, managed memory, governance and voice, take MAF Python and accept being early. If you care most about battle-tested reliability, checkpoint debugging or portability, take LangGraph with the bridge. Both are sound production paths, and neither is a mistake.

What it costs to run

All three frameworks are open source under the MIT license, but total cost of ownership drifts apart quickly. The rough magnitudes below depend on GPT-class pricing, an average conversation length and a moderate tool-call rate, and they include hosting but not engineering time. Treat them as orders of magnitude, not promises.

CrewAI is the cheapest way to start. LangGraph gives the best ratio of price to flexibility. MAF's managed services can save months of engineering, but they also tie you more tightly to Microsoft.

A three-question decision path

1. Are you on Azure? With .NET, MAF is the clear pick. With Python, you have the two routes above: MAF for the deepest integration, or LangGraph plus the bridge for debugging and portability. If you're not on Azure, move on.
2. Do you need complex branching, loops or checkpoint-based debugging? Then LangGraph, whose graph model and time travel have no real rival.
3. Do you need a working multi-agent prototype this week? Then CrewAI, which gets a working crew in about 20 lines. If not, LangGraph is the cloud-neutral default: more work now, fewer regrets at scale.

How enterprise projects usually unfold

A common path is to prototype with CrewAI in the first two weeks to prove the use case, rebuild in MAF or LangGraph during months one to three, and scale on managed infrastructure after that, whether Foundry Agent Service, LangGraph Cloud or both.

Azure shops tend to split in two. Teams starting fresh lean toward MAF Python, because managed memory, Agent 365 and the Foundry Control Plane are hard to reproduce elsewhere. Teams with existing LangGraph code use langchain-azure-ai to reach Azure without rewriting, which is a migration bridge and not a greenfield recommendation. For .NET shops MAF 1.0 wins outright, and outside Azure LangGraph stays the default.

What decides whether the project ships

After three years of agent deployments, the framework explains perhaps 20% of the outcome. The remaining 80% rests on five things no SDK handles for you.

Workflow design counts for more than architecture. If a person can't explain the decision logic clearly, an agent won't invent it, so map the human process end to end before writing any agent code.

Data quality is still the bottleneck. Agents magnify whatever you feed them, so clean the inputs first.

Prompt writing separates good from bad. Elegant LangGraph systems fail on vague instructions, while plain CrewAI prototypes succeed because someone spent two days refining prompts. Most teams underinvest here by a wide margin.

Human review is a design decision, not an option. Decide ahead of time which confidence level triggers a review and which actions need explicit approval.

Change management is the hardest piece. Nobody budgets for it, and it sinks more projects than a poor technology choice ever does.

How far your deployment scales depends much more on these basics than on the SDK you import. Choose the framework that suits your team's skills and your organization's stack, then put the serious effort into everything the framework can't do for you.