LangGraph and AutoGen are two of the most-used frameworks for building multi-agent systems, and they take opposite design bets. LangGraph models a workflow as an explicit graph you control; AutoGen lets agents converse and let structure emerge. Neither is better in the abstract—the right choice depends on how much of your control flow you can specify in advance. This guide compares the two on architecture and on a runnable example, then covers the production concerns that outlast any framework decision. It was last technically reviewed on July 16, 2026. Because both projects change quickly across major versions, treat specific APIs here as a starting point and confirm them against current docs.

Key Takeaways

  • LangGraph makes control flow explicit (nodes, edges, a shared state object); AutoGen makes it emergent (agents exchange messages until a termination condition).
  • Choose LangGraph when the workflow is known and you need determinism, state inspection, and human-in-the-loop checkpoints. Choose AutoGen when the task is open-ended and you want to prototype fast.
  • Both need a hard loop cap. Neither halts on its own by default.
  • Any agent that executes code must run it in a sandbox—use_docker=False on a host with real credentials is a security hole, not a convenience.
  • The framework does not enforce permissions. Per-agent tool scope and server-side access control do.

Why Multi-Agent, and When Not

Splitting a job across specialized agents can help: a coder agent that only writes code and a tester agent that only runs it each keep a narrower context than one generalist trying to hold everything at once. When a test fails, the tester can feed the error back to the coder, forming an automatic repair loop.

But be clear-eyed about the trade-off. As the multi-agent systems guide in this series argues, splitting into multiple agents is worth its coordination cost only when roles need different tools or permissions, when work can genuinely run in parallel, or when different owners evaluate different steps. A coder/tester loop qualifies because the tester needs an execution tool the coder should not have—but a task that is simply "multi-step" often runs better as a single agent, or as a deterministic pipeline with no model in the routing at all.

Two Architectures

LangGraph: a graph-based state machine

LangGraph abstracts the workflow into a directed graph:

  • Nodes are execution steps—an agent, a tool call, or a plain function.
  • Edges define the flow, including conditional edges (for example, "if the test passed, end; otherwise, go back to the coder").
  • State is a single object passed between nodes. Each node reads it and returns an update.

The philosophy is control: the flow is one you draw, state is inspectable, and you can add checkpoints and human approval. This suits workflows whose shape is known in advance.

AutoGen: conversation-driven agents

AutoGen's core is the conversable agent. Agents collaborate by exchanging messages, and progress depends on that conversation plus a termination condition rather than a predefined graph. The philosophy is flexibility: define each agent's role and tools, put them in a conversation, and let the interaction find the path. This suits open-ended, exploratory tasks—at the cost of less predictable control flow.

A Coder-and-Tester Loop in Both

We will build the same two-agent system: a coder that writes Python, and a tester that runs it and reports errors back until it works.

LangGraph

Note that llm and run_python_code are placeholders you supply—run_python_code in particular must execute in a sandbox (see below). The loop cap in should_continue is not optional; without it the graph can cycle forever.

python 
import os
from typing import TypedDict, Annotated
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
import operator

llm = ChatOpenAI(model=os.environ.get("OPENAI_MODEL", "gpt-4o"))

class AgentState(TypedDict):
    messages: Annotated[list, operator.add]
    code: str
    test_result: str
    iterations: int

def coder_node(state: AgentState):
    result = llm.invoke("Write or fix the code:\n" + str(state["messages"]))
    return {"code": result.content, "iterations": state["iterations"] + 1}

def tester_node(state: AgentState):
    # run_python_code MUST run in a sandbox, never on the host directly
    result = run_python_code(state["code"])
    return {"test_result": result}

def should_continue(state: AgentState):
    if "SUCCESS" in state["test_result"]:
        return END
    if state["iterations"] > 3:  # hard loop cap — required
        return END
    return "coder"

workflow = StateGraph(AgentState)
workflow.add_node("coder", coder_node)
workflow.add_node("tester", tester_node)
workflow.set_entry_point("coder")
workflow.add_edge("coder", "tester")
workflow.add_conditional_edges("tester", should_continue)

app = workflow.compile()

AutoGen

The AutoGen version is more declarative—you define the agents and start a conversation. The single most important line is the execution config: run generated code in Docker, not directly on the host.

python 
import os
from autogen import AssistantAgent, UserProxyAgent

config_list = [{"model": os.environ.get("OPENAI_MODEL", "gpt-4o")}]

coder = AssistantAgent(
    name="Coder",
    llm_config={"config_list": config_list},
    system_message=(
        "You are a senior Python engineer. Write code to meet the request. "
        "If the tester reports an error, fix it and output again."
    ),
)

tester = UserProxyAgent(
    name="Tester",
    human_input_mode="NEVER",
    max_consecutive_auto_reply=3,  # hard loop cap — required
    is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),
    code_execution_config={
        "work_dir": "coding",
        "use_docker": True,  # sandbox generated code; do not disable in production
    },
)

tester.initiate_chat(
    coder,
    message="Write a function to compute the Fibonacci sequence, with assertion tests.",
)

AutoGen's API surface has shifted across major versions—the imports and config shape above match the classic autogen package, but confirm against the version you install.

Production Concerns That Outlast the Choice

The framework decides ergonomics; these decide whether the system is safe to run.

  • Sandbox anything that executes code. A coder/tester loop generates and runs untrusted code by design. Run it in an isolated container with no production credentials. use_docker=False on a developer laptop is one thing; the same setting in production is a remote code execution surface.
  • Cap the loop, everywhere. LangGraph needs the iteration check in should_continue; AutoGen needs max_consecutive_auto_reply. Add wall-clock and cost limits too—agents that pay per token can burn budget in a tight loop.
  • The framework does not enforce permissions. Neither a graph edge nor a system prompt restricts what an agent can reach. Scope each agent's tools to its role, and enforce object-level and tenant-level access control server-side, on every call.
  • Treat tool output and inter-agent messages as untrusted input. A test log, a tool result, or another agent's message can carry text crafted to steer the model. Validate and attribute it; never let it act as an authorized instruction.

Choosing

LangGraph fits when:

  • The workflow is well-defined and mostly fixed (intake → intent → retrieval → reply).
  • You need state inspection and time-travel-style backtracking.
  • You need human-in-the-loop approval at specific nodes.
  • You are already invested in the LangChain ecosystem.

AutoGen fits when:

  • The task is exploratory and open-ended (several agents debating an approach).
  • You want a fast prototype without writing explicit state management.
  • You need flexible topologies—nested group chats, dynamic speaker selection.

For a broader look at role-based frameworks alongside these two, see the CrewAI workflow guide.

FAQ

How do the two handle runaway loops?

Neither halts on its own. AutoGen relies on max_consecutive_auto_reply to cut off endless back-and-forth; LangGraph relies on an iteration count in the shared state, checked in a conditional edge. Treat both as required, and add cost and time budgets on top.

Can I mix them?

Yes—either can be wrapped as a node or tool inside the other. A common pattern is a deterministic LangGraph shell that invokes an AutoGen group chat for one exploratory sub-step, keeping the overall flow controllable.

Which is "better"?

Neither. LangGraph is a precise pipeline; AutoGen is an open seminar. The real question is how much of your control flow you can specify up front—and, whichever you pick, whether you have sandboxing, loop caps, and per-agent permissions in place.

Conclusion

LangGraph and AutoGen sit at opposite ends of a single spectrum: explicit control versus emergent conversation. Pick the one whose bet matches your task, use a single AI agent or a deterministic pipeline when neither multi-agent trade-off applies, and remember that the framework is the easy part. Sandboxed execution, hard loop limits, and least-privilege permissions are what keep the system safe—no framework grants them for free.

Primary Sources