Three frameworks dominate the conversation about building AI agents in 2026: LangGraph, AutoGen, and CrewAI. All three can be used to build multi-step AI systems. All three have active communities and production deployments. Choosing between them without understanding what each one actually does leads to a lot of refactoring.
This guide explains what each framework is built to solve, gives you a decision matrix, and covers the one thing all three frameworks share: they are abstractions on top of the same underlying patterns, which means you should understand those patterns before you commit to any of them.
What LangGraph Actually Does
LangGraph is a state machine framework for building controllable, stateful AI workflows. It models your agent as a directed graph where nodes are processing steps and edges define how the workflow transitions between them — including conditional branches.
The key concept is explicit state. You define a TypedDict that represents everything your agent knows at any point, and every node reads from and writes to that state. The graph then controls how that state evolves.
from langgraph.graph import StateGraph, END
from typing import TypedDict
class AgentState(TypedDict):
messages: list
next_action: str
result: str
def analyze(state: AgentState) -> AgentState:
# LLM call to analyze the task
return {"next_action": "search" if needs_search(state) else "answer"}
def search(state: AgentState) -> AgentState:
# Tool call
return {"result": run_search(state["messages"][-1])}
graph = StateGraph(AgentState)
graph.add_node("analyze", analyze)
graph.add_node("search", search)
graph.add_conditional_edges("analyze", route_based_on_action)
LangGraph does not assume a specific agent architecture. You can build a simple single-agent loop, a supervisor with workers, or a complex multi-agent pipeline. The framework gives you the primitives; you assemble the architecture.
Best for: Workflows where you need fine-grained control over execution flow, human-in-the-loop checkpointing, or custom multi-agent architectures. Production systems where you need to debug, observe, and control exactly what the agent is doing.
What AutoGen Actually Does
AutoGen (from Microsoft) is built around the concept of conversational agents — AI components that communicate with each other through message passing to complete tasks collaboratively.
The mental model is a group of agents talking to each other. An AssistantAgent generates responses using an LLM. A UserProxyAgent can execute code, run tools, and interact with humans. A GroupChat coordinates multiple agents in a conversation loop.
from autogen import AssistantAgent, UserProxyAgent, GroupChat, GroupChatManager
assistant = AssistantAgent(
name="Analyst",
llm_config={"model": "claude-sonnet-4-6"}
)
executor = UserProxyAgent(
name="Executor",
code_execution_config={"work_dir": "workspace"},
human_input_mode="NEVER"
)
chat = GroupChat(agents=[assistant, executor], messages=[], max_round=10)
manager = GroupChatManager(groupchat=chat, llm_config={"model": "claude-sonnet-4-6"})
executor.initiate_chat(manager, message="Analyze the sales data in data.csv and plot the top 10 products")
AutoGen's strength is code execution. The UserProxyAgent can actually run the code the AssistantAgent generates, see the output, and feed it back into the conversation. For data analysis, code generation, and research workflows that involve writing and running programs, AutoGen handles this naturally.
Best for: Code generation and execution workflows, research tasks, anything where you want agents that write and run code as part of the task. Prototyping multi-agent systems quickly.
What CrewAI Actually Does
CrewAI takes a role-based approach. You define agents by their role, goal, and backstory, assign them tools, then assemble them into a crew with a process (sequential or hierarchical). Agents are opinionated about what they do; the framework handles the coordination.
from crewai import Agent, Task, Crew, Process
researcher = Agent(
role="Senior Research Analyst",
goal="Find accurate, up-to-date information about AI developments",
backstory="You have 10 years of experience in AI research.",
tools=[search_tool],
verbose=True
)
writer = Agent(
role="Technical Writer",
goal="Write clear, accurate technical content",
backstory="You translate complex AI topics into developer-friendly explanations.",
verbose=True
)
research_task = Task(description="Research the latest LLM benchmarks", agent=researcher)
write_task = Task(description="Write a 500-word summary", agent=writer, context=[research_task])
crew = Crew(agents=[researcher, writer], tasks=[research_task, write_task], process=Process.sequential)
result = crew.kickoff()
CrewAI is the most beginner-friendly of the three. The role-based abstraction maps naturally to how people think about work. You do not need to think about state machines or message passing — you just define who does what.
Best for: Content pipelines, research and synthesis workflows, tasks that map cleanly to distinct roles. Good for teams new to agent development who want to move fast.
Decision Matrix
| Criteria | LangGraph | AutoGen | CrewAI | |---|---|---|---| | Control and observability | Highest | Medium | Lower | | Code execution support | Manual | Built-in | Via tools | | Learning curve | Steeper | Medium | Gentlest | | Multi-agent coordination | Explicit (graph) | Conversational | Role-based | | Production readiness | High | Medium | Growing | | Debugging experience | Best | Harder | Harder |
Choose LangGraph if: You are building a production system, you need checkpointing and human-in-the-loop, you want full control over the execution graph, or you are building something complex enough that you need to debug individual steps.
Choose AutoGen if: Your workflow involves generating and running code, you are doing data analysis or research that requires actual code execution, or you are building a quick prototype of a multi-agent system.
Choose CrewAI if: Your task maps cleanly to distinct roles, you want to move quickly, or the team building the system is less experienced with agent architecture.
Migration Paths
Moving from CrewAI to LangGraph is the most common upgrade path as teams build more complex systems and hit CrewAI's limits. The pattern is: identify the implicit workflow in your crew's sequential tasks, map each task to a graph node, and make the state explicit. The logic transfers; the structure changes.
Moving from AutoGen to LangGraph usually means replacing the conversational coordination model with explicit graph edges. AutoGen's GroupChat is a loop; LangGraph lets you express the same logic as a controlled state machine with conditional routing.
The Case for Raw First
All three frameworks are abstractions on the same underlying patterns: LLM calls, tool execution, result routing, state management. If you have not built a working agent from scratch — just Python, an API client, and a loop — you will not understand what the frameworks are actually doing.
That gap creates real problems in production. When LangGraph throws a cryptic error during graph compilation, when AutoGen's GroupChat gets stuck in a loop, when CrewAI's sequential process skips a task — you cannot debug what you do not understand.
The pattern of learning raw first and then adopting frameworks is intentional. The frameworks save real time and handle real problems. But they are most useful when you already know what problem they are solving.
Going Deeper
If you want to go from framework comparisons to production-grade multi-agent systems, Phase 5 of the Agentic AI course at MindloomHQ covers LangGraph, AutoGen, and CrewAI in depth — after you have built the raw patterns they abstract.
The 12 lessons cover the supervisor pattern, parallel agent execution, agent memory and state management, and how to evaluate multi-agent systems. Phase 0 and Phase 1 are completely free to start.