AgentFlow vs Microsoft AutoGen: typed graphs vs conversational agents

AgentFlowvsMicrosoft AutoGen

Deterministic graph routing instead of LLM-driven group-chat selectors. Production server included.

Microsoft AutoGen pioneered the conversational multi-agent pattern. Agents talk to each other in chat rooms, often with a UserProxyAgent and one or more AssistantAgents, and the framework drives the conversation forward. AgentFlow stays closer to a classical workflow runtime: a typed StateGraph, explicit nodes and edges, deterministic routing, and a production server.

If you have prototyped multi-agent flows in AutoGen and want a more deterministic, easier-to-debug runtime, with serving and a TypeScript client included. This page compares the two.

TL;DR: AgentFlow vs AutoGen

Both frameworks are open-source. AutoGen 0.4 split into core / agentchat / extensions; this comparison treats AutoGen AgentChat as the closest analogue.

Dimension	AgentFlow	AutoGen
Orchestration	Typed StateGraph with explicit nodes and conditional edges	GroupChat / Selector + agent-to-agent messages
Determinism	▲Routing is a Python function. Easy to log, replay, and test	Selector-driven; behavior depends on the chat manager LLM
State	AgentState + Message stream you can serialize at any point	Per-agent message lists; aggregating state requires plumbing
Persistence	▲Built-in InMemoryCheckpointer / PgCheckpointer with thread IDs	BYO persistence; checkpoint hooks evolving in 0.4
API serving	▲Built-in `agentflow api` REST + SSE server	AutoGen Studio (UI tool); production server is BYO
TypeScript client	Typed `@10xscale/agentflow-client`	No first-party TS client
Best for	Production multi-agent products with web/mobile frontends	Research, exploration, and conversation-style automations
License	MIT	CC-BY-4.0 / MIT (varies by package)

Why teams choose AgentFlow over AutoGen for production

1. Deterministic routing. AgentFlow's add_conditional_edges(node, fn, mapping) is just a function. Easy to unit test, easy to log, easy to reason about in incident review. AutoGen's group chat selector is itself an LLM in many configurations, which makes flows harder to debug under load.
2. One state, one history. Every node sees the same AgentState. In AutoGen, each agent maintains its own message list, and reconstructing "what did the system actually do" is a glue exercise.
3. First-class persistence. Threads, checkpoints, and resumability are wired into the runtime. AutoGen 0.4 is moving toward similar hooks, but production teams typically still write their own.
4. Ship the API, not just the agents. agentflow api plus @10xscale/agentflow-client give you a REST + SSE backend and a typed frontend SDK in one repo. AutoGen Studio is a UI for exploration, not a production server.

Same workflow, both frameworks

A two-agent planner → coder loop, first in AutoGen, then in AgentFlow.

AutoGen (AgentChat)

import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_agentchat.conditions import TextMentionTermination
from autogen_ext.models.openai import OpenAIChatCompletionClient

async def main():
    model = OpenAIChatCompletionClient(model="gpt-4o-mini")

    planner = AssistantAgent(
        "planner",
        model_client=model,
        system_message="Break the task into steps. End with the word DONE when the plan is complete.",
    )
    coder = AssistantAgent(
        "coder",
        model_client=model,
        system_message="Implement the plan in Python. Print the code.",
    )

    team = RoundRobinGroupChat(
        [planner, coder],
        termination_condition=TextMentionTermination("DONE"),
    )

    await team.run_stream(task="Build a script that fetches today's weather for Tokyo.")

asyncio.run(main())

AgentFlow

from agentflow.core.graph import Agent, StateGraph
from agentflow.core.state import AgentState, Message
from agentflow.utils import END

planner = Agent(
    model="google/gemini-2.5-flash",
    system_prompt=[{"role": "system", "content": "Break the task into 3 numbered steps."}],
)

coder = Agent(
    model="google/gemini-2.5-flash",
    system_prompt=[{"role": "system", "content": "Use the plan in context to write Python code that implements it."}],
)

graph = StateGraph(AgentState)
graph.add_node("PLAN", planner)
graph.add_node("CODE", coder)

graph.set_entry_point("PLAN")
graph.add_edge("PLAN", "CODE")
graph.add_edge("CODE", END)

app = graph.compile()

result = app.invoke(
    {"messages": [Message.text_message("Build a script that fetches today's weather for Tokyo.")]},
    config={"thread_id": "plan-code-1"},
)
print(result["messages"][-1].text())

In AgentFlow you state the order with edges. To turn this into a loop ("plan → code → review → re-plan if reviewer says no"), add a conditional edge from a REVIEW node back to PLAN. The control flow stays visible in the graph definition. No termination strings, no group-chat selector to second-guess.

When you actually need conversation

AutoGen's strength is multi-agent conversation. Agents debating, critiquing, and refining. AgentFlow models that the same way with a single graph plus a router:

from agentflow.core.graph import Agent, StateGraph, ToolNode
from agentflow.prebuilt.tools import create_handoff_tool

# Critic + author with explicit handoffs
author_tools = ToolNode([create_handoff_tool("critic", "Send draft to critic")])
critic_tools = ToolNode([
    create_handoff_tool("author", "Return to author with feedback"),
    create_handoff_tool("done", "Approve the draft"),
])

author = Agent(model="gemini-2.5-flash", provider="google",
    system_prompt=[{"role": "system", "content": "Draft and revise."}],
    tool_node="AUTHOR_TOOLS",
)
critic = Agent(model="gemini-2.5-flash", provider="google",
    system_prompt=[{"role": "system", "content": "Critique strictly."}],
    tool_node="CRITIC_TOOLS",
)

The handoff is an explicit tool call you can log, rate-limit, and recursion-cap. AutoGen's selector achieves a similar outcome but at the cost of an extra LLM call per turn and less observable routing.

Persistence and resumable threads

AgentFlow checkpoints the whole graph after every node:

from agentflow.storage.checkpointer import PgCheckpointer

app = graph.compile(checkpointer=PgCheckpointer(
    db_url="postgresql+asyncpg://user:password@localhost/agentflow",
    redis_url="redis://localhost:6379/0",
))

# Resume the same thread later
app.invoke(
    {"messages": [Message.text_message("Continue from the last revision.")]},
    config={"thread_id": "session-42"},
)

AutoGen 0.4 is adding "memory" extensions, but a production team building a chat product or long-running automation usually still rolls its own thread storage and replay logic on AutoGen.

Serving as an API

pip install 10xscale-agentflow-cli
agentflow init
agentflow api --host 0.0.0.0 --port 8000

Endpoints out of the box:

• POST /v1/graph/invoke. Run the graph and return final messages
• POST /v1/graph/stream. Server-sent events for token-level streaming
• GET /v1/graph/threads/{thread_id}. Fetch persisted state

AutoGen Studio is a great exploration UI but not the production server you put behind a load balancer. With AgentFlow, the same binary you used in dev is what runs in production.

TypeScript client

import {AgentFlowClient, Message} from "@10xscale/agentflow-client";

const client = new AgentFlowClient({baseUrl: "http://127.0.0.1:8000"});

for await (const chunk of client.stream(
  [Message.text_message("Plan and write a Python script for fetching weather.")],
  {config: {thread_id: "ts-stream-1"}},
)) {
  if (chunk.type === "message_chunk") process.stdout.write(chunk.content ?? "");
}

Migrating from AutoGen

The mental model translation is the main work:

1. Each AssistantAgent becomes an agentflow.core.graph.Agent with the same system_message content.
2. RoundRobinGroupChat → a chain of add_edge calls.
3. SelectorGroupChat → a router node with a deterministic Python function or an LLM router (your choice).
4. TextMentionTermination and friends → either a conditional edge that returns END when a flag is set, or a recursion_limit in the invoke config.
5. AutoGen tools → ToolNode([fn, fn, ...]) with regular Python functions.
6. AutoGen's per-agent message lists → AgentFlow's shared AgentState.messages.

A typical 3-agent AutoGen example ports over in a single sitting.

When AutoGen is still the right pick

• Research and benchmarking. AutoGen 0.4's actor model and extension system are excellent for academic experiments and novel multi-agent patterns.
• Microsoft / Azure ecosystem. First-party Azure OpenAI integration and AutoGen Studio are nicely tuned for that stack.
• You want emergent conversation. If your application is a multi-agent debate or critique session and you want the LLM-driven selector to surprise you, AutoGen leans into that.

For most product teams shipping agents behind a real frontend with paying users, the deterministic-graph + persistent-thread + built-in-API combination of AgentFlow tends to win on operability.

Frequently asked questions

Is AutoGen 0.4 still actively developed?

Yes. AutoGen split into autogen-core, autogen-agentchat, and extensions in 0.4. Both AgentFlow and AutoGen are evolving frameworks; for the latest AutoGen surface, refer to Microsoft's GitHub.

Can I get AutoGen-style 'agents that talk to each other' in AgentFlow?

Yes. Implement the conversation as a router + handoff tools. Each handoff is a tool call you can log and inspect, which is usually easier to operate than a chat manager LLM.

Does AgentFlow support OpenAI, Azure OpenAI, and Anthropic like AutoGen does?

Yes. AgentFlow ships first-party providers for OpenAI, Anthropic, Google (Gemini and Vertex AI), and others. See the providers section of the docs.

How does AgentFlow's API server compare to AutoGen Studio?

AutoGen Studio is a UI for visually building and chatting with agent setups. AgentFlow's CLI is a production server (REST + SSE) that exposes a compiled graph behind authenticated endpoints. Different products with different goals.

Can AgentFlow handle human-in-the-loop reviews?

Yes. The graph supports interrupts and resumable threads. You can pause execution at a node, surface state to a human reviewer, and resume with their input on the same thread_id.

Next steps

Related guides

• guideGet started with AgentFlowInstall, build, expose, and connect a typed client.
• guideHandoff between agentsExplicit, debuggable agent-to-agent control flow.
• conceptStreaming and async patternsHow async streams work end-to-end.
• blog7 multi-agent patternsSequential, parallel, router, handoff, supervisor, and human-in-the-loop.