Microsoft AutoGen & Agent Framework: Tutorial & Evolution
AutoGen is an open-source multi-agent framework originally developed by Microsoft Research that enables multiple AI agents to collaborate through structured conversations to solve complex tasks. Unlike traditional single-agent systems, autogen allows specialized agents to assume different roles,such as coding, reviewing, planning, or executing,and communicate with one another to iteratively improve outcomes. This conversation-driven architecture made it one of the most influential AI agent frameworks for building collaborative, tool-using AI applications across software development, research, data analysis, and enterprise automation.
Since its release, autogen has significantly influenced the evolution of multi-agent AI, with its core orchestration concepts now forming part of Microsoft's newer Microsoft Agent Framework. Although AutoGen itself has moved into maintenance mode, its conversational agent patterns remain widely used for learning, prototyping, and maintaining existing applications. Understanding how AutoGen structures agent interactions, coordinates multi-agent conversations, and integrates external tools provides a strong foundation for working with modern agent ecosystems. As organizations increasingly adopt collaborative AI systems, familiarity with AI agent frameworks like AutoGen helps developers evaluate orchestration strategies, compare framework capabilities, and build scalable, production-ready agentic AI applications.
Microsoft AutoGen & Agent Framework
Search for autogen today and you'll get search results that don't quite agree with each other, some describing it as Microsoft's current flagship multi-agent framework, others mentioning a newer “Microsoft Agent Framework” without explaining how the two relate. Both are right, just at different points in time: autogen was the framework, and as of 2026 it's the foundation that a newer, unified framework was built on top of.
Understanding what is autogen matters whether you're maintaining an existing autogen codebase, evaluating agentic ai frameworks for a new project, or trying to make sense of Microsoft's current agent-tooling story. Autogen popularized a genuinely distinct idea in the agent space, agents that solve problems by conversing with each other, and that idea didn't disappear; it became one of the two foundations Microsoft merged into its newer, production-focused framework.
This module explains what autogen is and how its core agents work, walks through a hands-on autogen tutorial covering the key building blocks, and then covers autogen's 2026 evolution into Microsoft Agent Framework, plus how autogen compares to other ai agent frameworks like LangGraph and CrewAI, so you can make an informed choice for a new project.
This distinction matters more than it might seem at first glance. A search for autogen today surfaces years of tutorials, GitHub stars, and academic citations built on the original framework, all still technically accurate about how autogen works, but silent on what replaced it as Microsoft's actively developed path forward. Getting the full picture, not just the historically popular half of it, is the difference between making an informed framework choice and building on something already in maintenance mode without realizing it.
Stop learning AI in fragments—master a structured AI Engineering Course with hands-on GenAI systems with IIT Roorkee CEC Certification
AI Engineering Course Advanced Certification by IIT-Roorkee CEC
A hands on AI engineering program covering Machine Learning, Generative AI, and LLMs - designed for working professionals & delivered by IIT Roorkee in collaboration with Scaler.
Enrol Now
,,
What Is AutoGen?
AutoGen is an open-source Python framework, released by Microsoft Research in 2023, built around a distinctive idea: instead of a single agent looping through tasks alone, autogen lets multiple LLM-backed agents converse with each other, in a structured message exchange, to jointly solve a problem. That conversational, multi-agent design is what is autogen at its core, and it's what made the framework influential well beyond Microsoft's own ecosystem. To understand why AutoGen became so influential, let's look at the ideas and building blocks that define the framework.
Why AutoGen's Approach Was Distinctive
Most early agent frameworks modeled a single agent reasoning in a loop, plan, act, observe, repeat. Autogen instead modeled agent collaboration as conversation: one agent proposes a solution, another critiques or executes it, and a third might supervise the exchange, with the dialogue itself surfacing the reasoning process in a way that's easy to inspect and debug. This made autogen especially well suited to tasks like code generation and debugging, where having a “Coder” agent and a “Critic” agent talk through a solution mirrors how a human engineering team actually works.
Transform Your Career
Choose from our industry-leading programs designed for career success
Modern Software and AI Engineering Program
Master full-stack development with AI integration
+1000 moreModern Data Science and ML with specialisation in AI
Advanced data science techniques with AI specialization
+1000 moreAdvanced AIML with Specialisation in Agentic AI
Deep dive into AIML with focus on Agentic systems
+1000 moreDevOps, Cloud & AI Platform Engineering
Build and manage AI-powered cloud infrastructure
+1000 moreAI Engineering Advanced Certification by IIT-Roorkee
Premier AI engineering certification from IIT-Roorkee
AutoGen's Influence on the Agent Ecosystem
Released in 2023, autogen became one of the most cited open-source projects in the agent space, both in academic research and enterprise experimentation, well before “agentic AI” became an industry-wide term. Its conversation-first design pattern, agents with distinct personas exchanging structured messages rather than one agent silently looping, influenced how later agentic ai frameworks approached multi-agent design, even ones that eventually chose a different underlying architecture. Understanding autogen's original design is genuinely useful background even for engineers who end up building on a different framework, because so much of the vocabulary and mental model around multi-agent collaboration traces back to it.
Core Building Blocks
Every autogen application is built from a small set of agent types, all subclasses of a general ConversableAgent class capable of exchanging messages and taking action.
| Agent Type | Role | Typical Use |
|---|---|---|
| AssistantAgent | LLM-backed agent that reasons and proposes solutions or code | Generating code, plans, or analysis |
| UserProxyAgent | Represents the human or executes code on their behalf | Running generated code, gating human approval |
| GroupChatManager | Coordinates a multi-agent conversation, selecting the next speaker | Orchestrating 3+ agents working together |
A typical autogen setup pairs an AssistantAgent, which generates a proposed solution or code, with a UserProxyAgent, which can execute that code (often inside a Docker container for safety) and report the result back, the two go back and forth until the task is resolved or a human steps in.
Common AutoGen Conversation Patterns
Autogen supports several ways of structuring how agents talk to each other, and picking the right pattern matters as much as picking the right agents. Two-agent chat is the simplest: an AssistantAgent and a UserProxyAgent going back and forth directly, well suited to straightforward code-generation tasks. Group chat, coordinated by a GroupChatManager, brings three or more agents into a shared conversation where the manager decides who speaks next based on the discussion so far, useful when a task benefits from multiple perspectives, like a coder, a critic, and a product-manager agent debating an approach. Nested chats let one agent spin up a separate, self-contained conversation with another agent to handle a sub-task, then return the result to the main conversation, which keeps complex workflows from turning into one unwieldy group chat.
Real-World AutoGen Use Cases
-
Automated code generation and debugging, where a coder agent and a critic agent iterate until tests pass
-
Data analysis pipelines, where one agent writes analysis code and another validates the output against the original question
-
Research assistance, where multiple agents gather, summarize, and cross-check information on a topic
-
Adversarial evaluation, where a “red team” agent probes a solution proposed by another agent for weaknesses before it ships
What ties these use cases together is a task that genuinely benefits from more than one perspective or role, a single agent working alone can write code, but a coder-critic pair working in autogen's conversational format tends to catch more bugs and edge cases, closer to how pair programming or code review works among humans. This is the pattern worth internalizing from an autogen tutorial even if you eventually build on a different framework: identify where a task benefits from a second, differently-instructed agent's perspective, and design the conversation flow around that division of labor rather than routing everything through a single do-everything agent.
AutoGen Tutorial: Core Concepts in Practice
The clearest way to understand autogen is to see its agents defined and wired together. This autogen tutorial snippet shows the classic pattern: a coder agent, a reviewer agent, and a human proxy collaborating in a group chat.
import autogen llm_config = {"config_list": config_list} user_proxy = autogen.UserProxyAgent( name="User_proxy", code_execution_config={"work_dir": "coding", "use_docker": True}, human_input_mode="TERMINATE", ) coder = autogen.AssistantAgent(name="Coder", llm_config=llm_config) critic = autogen.AssistantAgent( name="Critic", system_message="Review the code for bugs and best practices.", llm_config=llm_config, ) groupchat = autogen.GroupChat(agents=[user_proxy, coder, critic], messages=[], max_round=12) manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config) user_proxy.initiate_chat(manager, message="Write and test a function that reverses a linked list.") Reading the AutoGen Tutorial Example
In this autogen tutorial pattern, the UserProxyAgent kicks off the conversation with a task. The Coder agent, an AssistantAgent, generates Python code to solve it. Because code_execution_config is set, the UserProxyAgent can run that code and report back the output or any errors. The Critic agent reviews the exchange and flags issues, and the GroupChatManager decides which agent speaks next based on the conversation so far, the whole exchange continues until the task is resolved or max_round is reached.
AutoGen Studio and AgentChat
Beyond the core Python API, autogen includes AutoGen Studio, a no-code interface for building and testing agent workflows visually, useful for prototyping without writing the full agent-definition code shown above. Version 0.4 and later introduced AgentChat, a more structured, event-driven API layer with better support for async execution, cancellation, and long-running tasks, addressing some of the debugging and reliability gaps developers hit with the original conversational API at production scale.
Limitations to Know Before Building on AutoGen
An honest autogen tutorial should cover where the framework struggles, not just where it shines. Group chats with many agents can become expensive and slow, since every turn is a full model call, and the GroupChatManager's speaker-selection logic can occasionally loop or stall on ambiguous conversations without careful prompt design. Code execution via a UserProxyAgent needs proper sandboxing (Docker is strongly recommended) since generated code runs with real system access. And because autogen predates many modern production concerns, built-in observability, hosted deployment, standardized tool protocols, teams shipping to production increasingly reach for Microsoft Agent Framework or another of the more recently matured agentic ai frameworks instead.
AutoGen's Evolution: From AutoGen to Microsoft Agent Framework
The most important thing to understand about autogen in 2026 is that it is no longer Microsoft's actively developed, flagship agent framework. In October 2025, Microsoft announced Microsoft Agent Framework (MAF), described as unifying “the enterprise-ready foundations of Semantic Kernel with the innovative orchestration of AutoGen” into a single SDK spanning both .NET and Python. MAF reached Release Candidate in February 2026 and hit 1.0 general availability on April 2–3, 2026.
This timeline matters for anyone researching autogen right now, because a large share of existing tutorials, courses, and blog posts, including several written before this transition, still describe autogen as Microsoft's current, actively developed framework without mentioning what has changed since. That's not necessarily wrong about how autogen works technically, but it's an incomplete picture of where Microsoft's actual investment and roadmap now sit, and it's worth knowing before basing a new project's framework choice on an older tutorial.
What Happened to AutoGen
As of 2026, autogen itself is in maintenance mode: it still works, but it receives no new features from Microsoft. Its lineage split two ways. AG2 (formerly AutoGen) is a community-driven, Apache 2.0-licensed continuation maintained outside Microsoft, adding interoperability features across agent frameworks. Separately, Microsoft's own successor path is Microsoft Agent Framework, which carries forward autogen's multi-agent orchestration concepts alongside Semantic Kernel's enterprise-focused session management, tools, and middleware, as a single, actively developed, production-supported SDK.
What Microsoft Agent Framework Adds
Microsoft Agent Framework isn't simply autogen renamed. It adds a consistent programming model across .NET and Python, chat clients, tools, MCP integrations, context providers, and middleware, plus production concerns autogen didn't natively address: an “agent harness” layer for shell and filesystem access with human-in-the-loop approval, and Hosted Agents in Azure AI Foundry for deploying an agent as a managed, auto-scaling service that scales to zero when idle. For teams already invested in autogen's conversational multi-agent patterns, Microsoft provides direct migration guidance rather than requiring a rewrite from scratch.
The billing model reflects this production focus too: Hosted Agents in Foundry Agent Service began metered billing in April 2026 based on consumption, with the notable feature that a deployed agent scales to zero and costs nothing while idle, only incurring charges once a request comes in. This is a meaningfully different operating model from a typical autogen deployment, where a developer is usually responsible for provisioning and managing their own compute regardless of whether the agent is actively handling requests.
Scaler Placement Report and Statistics
Scaler learners achieved 2.5x salary growth with average post-Scaler CTC reaching ₹23L.
Google
Amazon
Microsoft
Flipkart
Adobe1200+ moreMigrating from AutoGen to Microsoft Agent Framework
For teams with an existing autogen codebase, Microsoft's migration path is designed to be incremental rather than a full rewrite. The multi-agent orchestration patterns, conversation-driven collaboration, group chats, agent roles, carry over conceptually, since the same engineering team built both frameworks. What changes is mostly the surrounding infrastructure: session and state management, tool registration, and deployment move to Microsoft Agent Framework's unified model, which also opens up .NET support for teams that need it. Microsoft's official guidance recommends starting new projects directly on Microsoft Agent Framework, and migrating existing autogen systems opportunistically rather than treating it as an urgent deadline, since autogen continues to run in maintenance mode.
Should You Still Use AutoGen Today?
For new projects on the Microsoft/Azure ecosystem, Microsoft Agent Framework is the recommended starting point rather than autogen directly. That said, autogen (or its AG2 fork) remains a reasonable choice for research, teaching, and prototyping conversational multi-agent patterns, given how widely documented and cited it is, and existing autogen codebases don't need to migrate immediately, since the framework continues to run, just without new feature development.
A useful rule of thumb: if you're starting a brand-new production system today, especially one that will run on Azure, build on Microsoft Agent Framework. If you're studying multi-agent design patterns, extending an existing autogen codebase, or need the specific conversational research patterns autogen popularized and the AG2 community continues to develop, staying on autogen or moving to AG2 remains a defensible choice, just go in aware that you're choosing a framework in maintenance mode over its actively developed successor.
AutoGen vs Other AI Agent Frameworks
Autogen was never the only option among ai agent frameworks, and understanding where it fits relative to the alternatives, now including its own successor, matters for choosing the right tool.
| Framework | Core Paradigm | Best Fit |
|---|---|---|
| AutoGen / AG2 | Conversation-driven multi-agent chat | Research, adversarial/critic setups, code-heavy tasks |
| LangGraph | Stateful graph of nodes and edges | Fine-grained control over complex, cyclical workflows |
| CrewAI | Role-based agent teams (roles, tasks, crews) | Business-process automation with clear role division |
| Microsoft Agent Framework | Unified SDK (chat clients, tools, workflows) | Enterprise/Azure-native production deployment |
Choosing Between Agentic AI Frameworks
Among today's agentic ai frameworks, the right choice depends less on raw capability and more on fit. LangGraph suits teams that want explicit, fine-grained control over a workflow's state machine, especially for complex, cyclical logic. CrewAI suits teams that think in organizational terms, roles, tasks, teams, and want that abstraction handled for them. Autogen and its AG2 fork remain strong for conversation-driven, adversarial, or critic-based patterns, particularly in research settings. Microsoft Agent Framework is the clear choice for teams building on Azure who want autogen's orchestration ideas with enterprise-grade deployment, observability, and support built in from day one.
Getting Started with AutoGen and Modern Agentic AI Frameworks
If you're new to this space, the most practical path is to spend a short amount of time with an autogen tutorial to understand the conversational multi-agent pattern, the concepts transfer directly to nearly every other framework you'll encounter, and then evaluate current ai agent frameworks against your actual production requirements rather than defaulting to whichever framework has the most tutorials online. For teams already on Azure, that likely means starting with Microsoft Agent Framework directly rather than autogen. For research, prototyping, or learning purposes, working through autogen or its AG2 fork remains one of the clearest ways to internalize how multi-agent collaboration actually works under the hood. Scaler's Advanced AIML program with a specialisation in Agentic AI structures this progression deliberately, covering autogen's core patterns before moving into the production frameworks built on top of similar ideas.
Conclusion
Autogen introduced an idea that reshaped how the field thinks about multi-agent systems: agents that solve problems by talking to each other rather than looping alone. Understanding what is autogen, how its core agents fit together, and working through a hands-on autogen tutorial gives you the foundation to read almost any multi-agent codebase, since so many other agentic ai frameworks borrowed from its conversational pattern.
Just as important is understanding where autogen sits today: no longer Microsoft's actively developed framework, but the foundation, alongside Semantic Kernel, of Microsoft Agent Framework, its production-ready successor. Whether you're maintaining an existing autogen system, comparing ai agent frameworks for a new build, or evaluating Microsoft's current agent stack, that context changes the decision. Scaler's Advanced AIML program with a specialisation in Agentic AI covers this hands-on, from autogen's conversational patterns through building on Microsoft Agent Framework in production.
The broader lesson extends past autogen itself: in a field moving this fast, knowing when a framework you learned two years ago has been superseded is as important as knowing how to use it in the first place, and that's true across the entire landscape of agentic ai frameworks, not just Microsoft's.
Turn Learning into Career Growth
FAQs
What is AutoGen?
AutoGen is Microsoft's open-source Python framework where multiple LLM agents converse with each other to solve tasks together.
Is AutoGen still maintained?
AutoGen is in maintenance mode as of 2026; new development continues via the community AG2 fork and Microsoft Agent Framework.
What is Microsoft Agent Framework?
Microsoft Agent Framework is the production SDK that unifies AutoGen's orchestration with Semantic Kernel, reaching 1.0 in April 2026.
How do I start an AutoGen tutorial?
A basic AutoGen tutorial starts with an AssistantAgent and a UserProxyAgent exchanging messages to solve a simple coding task.
Should I learn AutoGen or Microsoft Agent Framework?
Learn AutoGen's concepts for understanding multi-agent patterns, but build new projects on Microsoft Agent Framework or other current agentic AI frameworks.
How does AutoGen compare to other AI agent frameworks?
AutoGen focuses on conversational multi-agent collaboration, while other AI agent frameworks like LangGraph favor explicit state-machine control.
Is AutoGen free to use?
Yes, AutoGen and its AG2 fork are open source and free; Microsoft Agent Framework is also open source under an MIT license.
What is the difference between AutoGen and AG2?
AG2 is the community-maintained fork of AutoGen, adding new features independently while original AutoGen stays in maintenance mode.
Can I deploy AutoGen agents to production?
You can, but Microsoft recommends Microsoft Agent Framework for new production deployments given its hosted, enterprise-ready infrastructure.
