AGENT INCOME .IO

AI agents, agentic coding, and passive income.

AutoGen Tutorial: Build Multi-Agent Workflows in Python in 2026

· 9 min read

This AutoGen tutorial is for developers who want to build a real multi-agent workflow without disappearing into framework archaeology. AutoGen is still useful in 2026, especially if you want Python-first agent patterns, group chats, and MCP-friendly tooling. But there is one thing you should know before you start: Microsoft now points new users toward Microsoft Agent Framework, while AutoGen stays in maintenance mode for bug fixes and security patches.

That sounds more dramatic than it is. AutoGen did not vanish. The repo is still live, the docs are still maintained, and the current stable packages still give you a practical way to build agent systems. You just need to treat it like a framework you can ship with today, not the long-term center of Microsoft’s roadmap.

If you want the short version: learn AutoGen if you keep seeing it in codebases, tutorials, or client projects. Start a brand-new greenfield app with it only if its agent patterns fit your problem better than alternatives like the OpenAI Agents SDK tutorial, Google ADK tutorial, or the graph-heavy approach in LangGraph vs CrewAI.

Why AutoGen matters right now

The search demand is still there, and this week’s timing makes the keyword more interesting.

In the last month, Microsoft publicly pushed Microsoft Agent Framework into Release Candidate and published an official migration guide from AutoGen. At the same time, the main AutoGen GitHub repo added a blunt note near the top: if you’re new to AutoGen, check out Microsoft Agent Framework first. That combination creates a good tutorial window because developers are searching for two things at once:

  • how to use AutoGen as it exists today
  • whether they should keep using it at all

That is exactly the kind of topic worth covering early. People do not just want API docs. They want someone to explain the practical decision.

What AutoGen actually is in 2026

AutoGen is Microsoft’s open-source framework for building agentic applications in Python with a strong focus on multi-agent conversations and orchestration.

The current docs split the project into a few layers:

  • AgentChat for conversational single-agent and multi-agent apps
  • Core for lower-level event-driven multi-agent systems
  • Extensions for model providers, MCP tooling, code execution, and other integrations
  • AutoGen Studio for no-code prototyping in a browser UI

That layering is useful because it tells you how to think about the stack.

If you just want to prototype a useful agent workflow, start with AgentChat. If you need a more explicit runtime for distributed or more complex systems, drop lower into Core. Most indie hackers should stay high-level unless they have a clear reason not to.

AutoGen tutorial: install and run your first agent

AutoGen now uses modular Python packages instead of one vague framework install.

Start with a fresh environment:

python -m venv .venv
source .venv/bin/activate
pip install -U "autogen-agentchat" "autogen-ext[openai]"
export OPENAI_API_KEY=sk-...

You need Python 3.10 or later.

Now create a simple assistant agent:

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


async def main() -> None:
    model_client = OpenAIChatCompletionClient(model="gpt-4.1")

    agent = AssistantAgent(
        "assistant",
        model_client=model_client,
        system_message=(
            "You are a concise operations assistant. "
            "Use short paragraphs and give practical answers."
        ),
    )

    result = await agent.run(task="Give me three AI agent SaaS ideas a solo developer could ship in 30 days.")
    print(result)

    await model_client.close()


asyncio.run(main())

That gives you the basic AutoGen mental model. You create a model client, wire it into an agent, and run the agent on a task. Nothing fancy yet.

What makes AutoGen different is what comes next: multiple agents talking to each other, shared task execution patterns, and a cleaner route to MCP-based tools than a lot of older agent frameworks had.

The part AutoGen is still good at

If you only need one agent and a couple of tools, AutoGen is probably not the simplest choice.

Where it still shines is structured collaboration.

AutoGen became popular because it made multi-agent systems feel normal instead of experimental. Instead of one giant prompt trying to do everything, you can split work into narrower roles: a planner, a researcher, a reviewer, a coder, a compliance checker, whatever your workflow needs.

That design is still useful for products that make money from process-heavy work:

  • lead research and outbound prep
  • support triage with review before reply
  • content research and editing pipelines
  • internal analyst tools that compare data, docs, and logs
  • code agents that need separation between planning and execution

This is also why AutoGen keeps showing up in developer conversations even while Microsoft’s roadmap has shifted. The patterns it popularized are still solid.

A simple multi-agent pattern

Here is the kind of workflow AutoGen is built for: one agent does the draft, another critiques it.

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


async def main() -> None:
    model_client = OpenAIChatCompletionClient(model="gpt-4.1")

    writer = AssistantAgent(
        "writer",
        model_client=model_client,
        system_message="Write clear product copy for technical founders.",
    )

    reviewer = AssistantAgent(
        "reviewer",
        model_client=model_client,
        system_message=(
            "Critique drafts for clarity, hype, and missing specifics. "
            "Be blunt and useful."
        ),
    )

    team = RoundRobinGroupChat([writer, reviewer], max_turns=4)
    result = await team.run(task="Draft a landing page headline and subheadline for an AI support copilot.")
    print(result)

    await model_client.close()


asyncio.run(main())

You do not need to copy this pattern exactly. The main point is architectural: let each agent own one responsibility. The output gets easier to inspect, the prompts get shorter, and debugging becomes less miserable.

If your current setup is one prompt with twelve instructions and five tools, splitting that into specialized agents is often the first real improvement.

Adding MCP tools to an AutoGen agent

One reason AutoGen still feels modern is that it does not force you into bespoke tool wrappers for everything. The current docs include MCP support through McpWorkbench, which means you can expose tools through a standard interface instead of inventing your own function-calling mess every time.

That matters because MCP keeps spreading across the ecosystem. A tool surface built once can often be reused in other runtimes too. If you have not worked with MCP before, read the full MCP server tutorial after this.

Here is the shape of an AutoGen agent using an MCP server:

import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.ui import Console
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StdioServerParams


async def main() -> None:
    model_client = OpenAIChatCompletionClient(model="gpt-4.1")

    server = StdioServerParams(
        command="npx",
        args=["@playwright/mcp@latest", "--headless"],
    )

    async with McpWorkbench(server) as mcp:
        agent = AssistantAgent(
            "web_assistant",
            model_client=model_client,
            workbench=mcp,
            model_client_stream=True,
            max_tool_iterations=10,
        )

        await Console(
            agent.run_stream(task="Open the pricing page for Railway and summarize the cheapest paid plan.")
        )

    await model_client.close()


asyncio.run(main())

This is the practical takeaway: AutoGen is not just old group chat demos. It still works as a decent Python harness for tool-using agents, especially if your workflow needs browsers, external systems, or standard MCP servers.

Should you start a new project with AutoGen?

Usually, only if you have a good reason.

The official Microsoft position is pretty clear now. Microsoft Agent Framework is the successor to both AutoGen and Semantic Kernel, and Microsoft has published a dedicated migration guide from AutoGen. The Agent Framework repo also highlights graph-based workflows, checkpointing, human-in-the-loop flows, A2A, MCP, OpenTelemetry, and a unified Python and .NET story.

That does not mean AutoGen is bad. It means the roadmap energy moved.

My honest rule of thumb looks like this:

  • Use AutoGen if you are inheriting an AutoGen codebase, you want its AgentChat patterns specifically, or you need to move fast in Python and the current APIs fit your problem.
  • Use Microsoft Agent Framework if you want the Microsoft-backed long-term path, better migration support, workflow features, and a more future-proof starting point.
  • Use the OpenAI Agents SDK if your app is simpler and you want the leanest possible mental model.
  • Use LangGraph if you want maximum control and can tolerate more ceremony.

That last point matters. Framework choice is mostly about failure modes. Pick the thing you will still understand when it breaks at 2 AM.

What changes if you migrate later

The good news is that learning AutoGen is not wasted effort.

The official migration material makes it clear that many of the concepts carry over:

  • model clients still matter
  • agent creation still starts from instructions plus tools
  • multi-agent orchestration still exists, but under a newer workflow model
  • observability and human-in-the-loop patterns get more first-class support in Agent Framework

So the real risk is not that AutoGen knowledge becomes useless. The risk is building too much framework-specific glue when you should have kept your business logic separate.

If you think migration is likely, do three things from day one:

  1. keep prompts and tool logic outside framework internals
  2. isolate model and provider configuration behind small wrappers
  3. treat orchestration as replaceable infrastructure, not your product moat

That sounds boring. It is also how you avoid rewriting your whole app because a vendor changes direction.

A realistic way to make money with AutoGen

If you are building for revenue, do not start with a giant autonomous swarm.

Start with one bounded workflow where multiple roles genuinely help:

  • inbound lead qualification
  • account research before sales calls
  • support ticket triage with suggested replies
  • content research plus first draft plus review
  • internal audit workflows where one agent gathers evidence and another checks it

Those are good business use cases because the output can be inspected and priced. You can charge for time saved, not for vague “AI magic.”

The best AutoGen product is usually not a chat app. It is a boring workflow that used to take a human thirty minutes and now takes five, with human review still in the loop where it matters.

The bottom line

AutoGen is still worth learning in 2026, but you should learn it with eyes open.

It remains a useful Python framework for multi-agent workflows, especially when you want AgentChat patterns and MCP-friendly tooling. But it is no longer the center of Microsoft’s strategy. If you are brand new and choosing a long-term stack, Microsoft Agent Framework is the safer bet. If you already have AutoGen code or want to prototype quickly with its current APIs, AutoGen is still perfectly capable.

That is the real answer behind the keyword. Not “AutoGen is dead” and not “ignore the roadmap.” Just use the framework for what it is: a still-usable tool with a clear successor.

Sources