Skip to main content

AutoGen: Conversational Multi‑Agent Framework

Full framework comparison: Guide to choosing a framework

What Is AutoGen​

AutoGen is an open‑source Python framework, originally from Microsoft Research, for building multi‑agent systems as conversations. Agents talk to each other—they can exchange messages, delegate subtasks, call tools, write and execute code, and ask humans for input. The entire workflow unfolds as a dialogue, with agents deciding what to do next based on what was just said.

Unlike frameworks built around task graphs or role‑based crews, AutoGen’s mental model is simple: you put agents in a conversation (two‑agent chat or group chat), give them a message to start with, and let them talk until they decide they are done. The conversation handles tool calls, code execution, human feedback, and termination, all without a central coordinator needing to know the plan in advance.

from autogen import AssistantAgent, UserProxyAgent

assistant = AssistantAgent(
    name="assistant",
    system_message="You are a helpful Python engineer.",
    llm_config={"model": "gpt-4o"}
)

user_proxy = UserProxyAgent(
    name="user_proxy",
    code_execution_config={"work_dir": "coding", "use_docker": False},
    human_input_mode="NEVER"
)

user_proxy.initiate_chat(
    assistant,
    message="Write a Python class that implements a stack with push and pop."
)

At its peak, AutoGen was the most widely used multi‑agent research framework, with approximately 50,000 GitHub stars and hundreds of published papers built on top of it. It pioneered the idea that teams of LLM experts can solve problems together through dialogue.

Why AutoGen Was Created​

Before AutoGen (2023–2024), multi‑agent AI meant either linear LLM chains (LangChain style) or simple ReAct loops where one agent called tools, thought, and called more tools. Both approaches assumed a central controller knew the entire plan in advance. Real collaboration—agents negotiating, critiquing each other, dividing work dynamically—was impossible.

AutoGen introduced a completely different mental model: agents as participants in a conversation. No central controller. No predetermined plan. Agents decide what to do next based on the conversation history, just like a team of humans.

What made AutoGen revolutionary:

  1. Agents can delegate work. An engineer agent can ask a reviewer agent to check their code before finalizing.

  2. Agents can use tools and execute code. An agent can call a search API, write Python code, run it, and incorporate the result—all within the conversation.

  3. Humans can participate seamlessly. A human proxy agent can step in to provide input, approve actions, or answer questions, without breaking the conversation flow.

  4. No single point of failure. If one agent gets stuck, other agents can help, critique, or take over.

Early viral demonstrations—a coder + reviewer + executor team solving math problems, a network research team collaborating on investigations, a stock analysis group producing reports—showed 2–10× performance improvements over single‑agent systems. This gap between conversational multi‑agent collaboration and single‑agent reasoning turned AutoGen into the default choice for researchers and developers experimenting with multi‑agent AI in 2024–2025.

Key Features of AutoGen​

FeatureDescription
Conversable AgentsLLM‑powered, tool‑enabled, or human agents that participate in conversations
Two‑Agent ChatSimple dialogue between two agents (e.g., assistant + user proxy)
Group ChatMulti‑agent conversation with three or more agents; a manager (LLM or rule‑based) decides who speaks next
Code ExecutionBuilt‑in code executor (local or Docker) for agents that write and run Python
Human ParticipationHuman proxy agent provides input, approvals, or conversation turns
Tool CallingAgents can use any function/tool through the LLM’s tool‑calling interface
MCP IntegrationNative support for Model Context Protocol servers via autogen_ext.tools.mcp
ObservabilityOpenTelemetry tracing and event logging built into the v0.4 runtime
Modular ArchitectureThree‑layer design (Core → AgentChat → Extensions) for customisation

The v0.4 architecture (2025) introduced a completely redesigned, event‑driven runtime based on the actor model, enabling better scalability, asynchronous execution, and native observability.

How AutoGen Works​

AutoGen’s execution model is organised around conversations and a three‑layer architecture.

The execution flow works like this:

  1. You create agents (AssistantAgent, UserProxyAgent) and optionally a GroupChat with a manager that decides the speaking order.

  2. You call initiate_chat() to start the conversation. The first agent (or the user proxy) sends an initial message.

  3. Agents reply to each other using LLM calls, tool executions, code runs, or human input—each reply is a turn in the conversation.

  4. The conversation continues until a termination condition is met: a message containing a specific word, reaching a maximum number of turns, or a guardrail condition.

  5. The ChatResult object contains the complete conversation history and the final output.

What makes v0.4 different: The Core layer (autogen-core) provides an event‑driven, asynchronous actor framework (RoutedAgent, pub/sub messaging) that replaces v0.2’s synchronous, blocking group chat. This unlocks better scalability, modularity, and observability through OpenTelemetry.

Core Concepts​

Conversable Agents​

In AutoGen, everything is a ConversableAgent—an agent that can send and receive messages. There are three main types:

  • AssistantAgent – An LLM‑powered agent. It decides what to say, whether to call tools, and when to stop. It uses the LLM’s native function‑calling interface.

  • UserProxyAgent – An agent that executes code, runs tools, or forwards messages to a human. It wraps your local code execution environment (or Docker) and can be configured to never ask for human input (NEVER), always ask (ALWAYS), or wait for termination.

  • Custom Agents – You can create agents by subclassing ConversableAgent and implementing custom reply logic using register_reply().

from autogen import AssistantAgent, UserProxyAgent

assistant = AssistantAgent(
    name="engineer",
    system_message="You are a senior Python engineer. Write clean, efficient code.",
    llm_config={"model": "gpt-4o", "temperature": 0.1}
)

user_proxy = UserProxyAgent(
    name="user",
    human_input_mode="NEVER",
    max_consecutive_auto_reply=10,
    code_execution_config={"work_dir": "coding", "use_docker": False}
)

Two‑Agent Chat​

The simplest conversation pattern is two agents talking. You call initiate_chat() on one agent, pass the other as a recipient, and provide an initial message. The agents reply to each other automatically, using their configured auto‑reply mechanisms.

user_proxy.initiate_chat(
    assistant,
    message="Write a Python function that calculates Fibonacci numbers efficiently."
)

Group Chat​

When you need three or more agents to collaborate, AutoGen provides GroupChat and a GroupChatManager that decides who speaks next. The manager can be:

  • LLM‑based – A separate LLM call chooses the next speaker.
  • Rule‑based – Sequential, random, or custom selector functions.

In a group chat, every message is broadcast to all participants; there is no private agent‑to‑agent messaging. This shapes the conversation style: agents can’t whisper, but they can reference each other by name in the public channel.

from autogen import GroupChat, GroupChatManager

group_chat = GroupChat(
    agents=[researcher, coder, reviewer],
    messages=[],
    max_round=12
)

manager = GroupChatManager(
    groupchat=group_chat,
    llm_config={"model": "gpt-4o"}
)

researcher.initiate_chat(manager, message="Research the latest Python async features.")

Code Execution​

One of AutoGen’s standout features is built‑in code execution. Agents can write Python code, and a UserProxyAgent (or CodeExecutorAgent) can run it and return the output into the conversation. The code executor supports:

  • Local execution – Runs in a specified working directory.
  • Docker execution – Isolated containers for safety.
  • Custom executors – Implement your own execution logic.

Tool Calling​

AutoGen agents can call tools (functions) through the LLM’s native tool‑calling interface. In v0.4, you register tools using decorators, and the LLM decides when to call them. The AssistantAgent handles tool execution transparently.

from autogen import register_function

def search_web(query: str) -> str:
    # your search implementation
    return f"Results for {query}..."

register_function(
    search_web,
    caller=assistant,
    executor=user_proxy,
    description="Search the web for information"
)

Layered Architecture (v0.4)​

AutoGen v0.4 introduced a three‑layer architecture that redefines how the framework is structured:

LayerPackagePurpose
Coreautogen-coreEvent‑driven actor framework (RoutedAgent, pub/sub, checkpointing, OpenTelemetry)
AgentChatautogen-agentchatHigh‑level API for building interactive agents (AssistantAgent, UserProxyAgent, GroupChat)
Extensionsautogen-extPluggable integrations (OpenAI Assistant API, MCP workbench, gRPC distributed agents)

Why this matters: The Core layer is now framework‑agnostic and can be used separately. The AgentChat layer is what most developers use day‑to‑day. Extensions keep the core clean while supporting third‑party additions.

AutoGen Ecosystem​

LLM Providers. AutoGen v0.4+ supports OpenAI, Azure OpenAI, and any OpenAI‑compatible endpoint via OpenAIChatCompletionClient.

MCP Integration. AutoGen provides native MCP support through autogen_ext.tools.mcp. The McpWorkbench can connect to MCP servers using either Stdio (local subprocess) or SSE (remote HTTP) transport. Agents then discover and use MCP tools as ordinary function tools, with the workbench handling the protocol details.

Code Execution. Built‑in LocalCommandLineCodeExecutor runs Python code in a working directory. Docker support (DockerCommandLineCodeExecutor) provides isolation for untrusted code.

Observability. v0.4+ includes OpenTelemetry tracing natively in the Core layer. You can export traces to any OTel collector (Jaeger, Azure Monitor, etc.) without additional instrumentation.

When to Use AutoGen​

AutoGen is best for open‑ended, conversation‑shaped workflows where agents need to negotiate, debate, brainstorm, or collaborate dynamically without a fixed task graph.

Multi‑Perspective Debate and Analysis​

Example: A market research assistant where a bull‑case agent argues one side, a bear‑case agent argues the other, and a synthesis agent reads both and produces a balanced summary. This is not a task pipeline—agents need to go back and forth until consensus emerges. CrewAI’s role‑and‑task framing fits poorly. LangGraph would work but the graph would be one large conversational node. AutoGen’s GroupChat is the natural fit.

Research and Experimentation​

AutoGen’s conversation‑first model makes it excellent for exploring agent behaviour. Hundreds of published papers have used AutoGen as their implementation platform for multi‑agent reasoning studies.

Coding Assistants​

Agents that write code, run it, observe the output, and improve their code based on execution feedback fit perfectly into AutoGen’s two‑agent (assistant + user proxy) pattern. The assistant writes code; the proxy executes it and returns the result.

Structured Red‑Teaming and Safety Testing​

Set up a team of agents where some try to break a system and others try to defend it. The conversation between attackers and defenders reveals vulnerabilities through dialogue.

Interactive, Human‑in‑the‑Loop Systems​

UserProxyAgent with human_input_mode="ALWAYS" creates a system where humans participate in the conversation, approving actions or providing information when agents ask.

When Not to Use AutoGen​

Simple single‑agent assistants. If you only need one LLM with a few tools, AutoGen’s multi‑agent conversation machinery adds unnecessary overhead. Use OpenAI Agents SDK instead.

Deterministic, linear workflows. If your process is research → write → edit and will always be that sequence, CrewAI is a better fit—it is designed for role handoffs.

Production systems needing enterprise features. AutoGen is in maintenance mode (as of late 2025 / early 2026) and does not receive new features. For new enterprise projects, evaluate the Microsoft Agent Framework, which merges AutoGen’s conversation patterns with Semantic Kernel’s enterprise capabilities.

Systems requiring parallel agent execution. AutoGen’s group chat is sequential—one agent speaks at a time. If you need agents acting in parallel, LangGraph or a custom actor system may be more appropriate.

Teams using .NET or Java. AutoGen is Python‑only. For multi‑language teams, Semantic Kernel or Microsoft Agent Framework provides .NET and Java support.

AutoGen in 2026: Maintenance Mode and the Microsoft Agent Framework​

AutoGen entered maintenance mode in late 2025 / early 2026. The framework is stable and continues to receive bug fixes and security patches, but Microsoft has stopped adding new features. All future feature development is now focused on the Microsoft Agent Framework (MAF), which unifies AutoGen and Semantic Kernel into a single, production‑ready SDK.

What this means for developers:

  • If you have an existing AutoGen codebase: Your application continues to work. The AutoGen repository remains available, and critical patches will be issued for the foreseeable future.

  • If you are starting a new project in 2026: Microsoft recommends the Microsoft Agent Framework (MAF) for greenfield builds. MAF preserves AutoGen’s multi‑agent orchestration patterns while adding enterprise features: thread management, middleware, graph‑based workflows, and Azure AI Foundry integration.

  • If you are moving from v0.2 to v0.4: Complete the migration to v0.4 (the technically mature version) as part of planning your eventual transition to MAF. The MAF migration guide provides a documented path from AutoGen to the unified framework.

  • Community forks: The AutoGen community has produced active forks, notably AG2, which continues development of the original AutoGen codebase independently of Microsoft’s MAF roadmap.

The migration path to MAF is documented here: https://aka.ms/autogen-to-af

AutoGen Learning Path​

This handbook provides structured learning paths for AutoGen. Use the following guides to build your expertise—but note that for new projects, you may want to start with the Microsoft Agent Framework instead.

Core Concepts​

/frameworks/autogen/core-concepts/

Learn:

  • Agents: AssistantAgent, UserProxyAgent, and custom agents
  • Conversations: initiate_chat() and the reply mechanism
  • The v0.4 layered architecture (Core, AgentChat, Extensions)
  • Runtime model: event‑driven, asynchronous actor framework

Conversation Patterns​

/frameworks/autogen/conversation-patterns/

Learn:

  • Two‑agent chat patterns (assistant + user proxy)
  • Group chat with LLM‑based or rule‑based managers
  • Termination conditions (max rounds, keyword matching)
  • Nested chats and sequential chats

Tools and Code Execution​

/frameworks/autogen/code-execution/

Learn:

  • Tool registration and function calling
  • MCP integration via McpWorkbench
  • Code execution (local, Docker, custom executors)
  • Safety considerations for untrusted code

Workflows​

/frameworks/autogen/workflows/

Learn:

  • Structured workflows on top of conversations
  • Agent orchestration beyond basic group chat
  • State management across conversation turns

Production​

/frameworks/autogen/production/

Learn:

  • Deployment patterns for AutoGen applications
  • OpenTelemetry tracing and observability
  • Error recovery and conversation checkpointing
  • Azure integration

AutoGen vs Other Frameworks​

FrameworkPrimary ModelBest ForLearning Curve2026 Status
AutoGenConversational multi‑agentDebate, negotiation, open‑ended dialogueModerateMaintenance mode
LangGraphGraph‑based state machinesComplex workflows with branching/persistenceSteepActive
CrewAIRole‑based crewsStructured team collaboration with handoffsModerateActive
OpenAI Agents SDKSingle‑agent loopLightweight assistants, OpenAI‑nativeLowActive
Semantic KernelPlugin‑based kernelEnterprise .NET/Java, Azure integrationSteepMaintenance mode (→ MAF)
Microsoft Agent FrameworkUnified orchestrationEnterprise multi‑agent, graph + conversationModerateActive (successor)

AutoGen vs LangGraph​

LangGraph treats agent workflows as explicit state graphs with typed state, conditional edges, and cycles. AutoGen treats workflows as conversations—agents talk and the conversation determines the next action.

  • Choose AutoGen when your workflow is naturally open‑ended and conversation‑shaped: debate, brainstorming, collaborative reasoning, or any situation where agents need to talk back and forth until they converge.

  • Choose LangGraph when you need precise control over execution order, persistent checkpoints for long‑running workflows, or explicit branching logic that is hard to encode in conversation.

AutoGen vs CrewAI​

CrewAI organises agents into crews with fixed roles and sequential task handoffs (research → write → edit). AutoGen has no fixed task order—agents decide what to do as the conversation unfolds.

  • Choose AutoGen when the optimal sequence of work emerges from the dialogue (e.g., agents negotiating a solution, critiquing each other’s work, revising).

  • Choose CrewAI when the workflow has a stable, known order and each agent has a clear, unchanging responsibility.

AutoGen vs OpenAI Agents SDK​

OpenAI Agents SDK is a single‑agent loop with built‑in handoffs to other agents. AutoGen is a multi‑agent conversation system where every agent participates in the same dialogue space.

  • Choose AutoGen when multiple agents need to interact freely, debate, critique, and collaborate.

  • Choose OpenAI SDK when you need a single primary assistant that can hand off to specialists on request.

AutoGen vs Semantic Kernel / Microsoft Agent Framework​

Semantic Kernel (now part of MAF) emphasises plugin composition, enterprise readiness, and multi‑language support. AutoGen emphasises conversational multi‑agent collaboration.

  • Choose MAF when you need enterprise features (Azure compliance, telemetry, .NET/Java support) and want a framework that will receive ongoing feature development.

  • Choose AutoGen (only) when you are committed to staying on the existing AutoGen codebase for a specific use case and do not need new features or enterprise support.

Common AutoGen Use Cases​

AI Coding Assistant​

Agents: Assistant (writes code) + User Proxy (executes code) Pattern: Two‑agent chat with code execution

The assistant writes Python code. The user proxy runs it (in Docker for safety) and returns the output. The assistant sees the output and can improve the code in the next turn. This loop continues until the code works correctly.

Multi‑Perspective Research Analysis​

Agents: Bull‑case, bear‑case, synthesis Pattern: Group chat with LLM‑based manager

Three agents argue different sides of a market research question, with a manager deciding the speaking order. The synthesis agent reads the debate and produces a balanced summary. No fixed task order—agents talk until the synthesis agent has enough information.

Automated Software Debugging​

Agents: Developer, Tester, Reviewer Pattern: Group chat with nested debugging cycles

A developer agent writes code. A tester agent runs tests and reports failures. A reviewer agent analyses the failures. The conversation cycles through improving the code, retesting, and reviewing until all tests pass.

Structured Red‑Teaming​

Agents: Attacker, Defender, Evaluator Pattern: Group chat with max rounds

The attacker tries to exploit the system, the defender attempts to block it, and the evaluator scores each attempt. The conversation continues until a safety threshold is reached.

Interactive Data Analysis​

Agents: Analyst (writes analysis code) + User (provides feedback) Pattern: Two‑agent with human input

The analyst writes code to analyse data and returns results and visualisations. The human user provides feedback, asks follow‑up questions, or approves the analysis. UserProxyAgent with human_input_mode="ALWAYS" keeps the human in the loop.

Common Beginner Mistakes​

Infinite Conversation Loops​

Without termination conditions, agents can talk forever. A poorly designed group chat can generate thousands of turns and significant API costs before you notice.

Recommendation: Always set termination conditions. Use max_round in GroupChat, check for keywords (TERMINATE, FINAL ANSWER) in messages, or implement guardrails that stop the conversation when the goal is met.

Unsafe Code Execution​

Allowing untrusted agents to run arbitrary code without isolation is a security risk. User‑provided code could delete files, access sensitive data, or consume system resources.

Recommendation: Use DockerCommandLineCodeExecutor with a restrictive Docker configuration for any production deployment that executes untrusted code. Disable Docker execution for internal‑only use cases where the code is trusted.

Too Many Agents in Group Chat​

Group chat with 5+ agents (especially LLM‑based managers) can become chaotic. Agents interrupt each other, repeat points, or lose track of the conversation.

Recommendation: Start with 2–3 agents. Add agents only when you can articulate a distinct perspective they bring that cannot be merged into an existing agent.

Ignoring Conversation Cost​

AutoGen sits slightly above single‑agent frameworks in baseline latency and token usage because two agents exchange messages even for simple tasks. In benchmark runs (2,000 runs across 5 tasks), AutoGen occasionally adds an extra verification step during tool calling, increasing both latency and tokens compared to pure single‑agent loops.

Recommendation: For simple tasks, consider whether a multi‑agent conversation is necessary. Use single‑agent tools for basic queries and reserve AutoGen for problems that genuinely need collaboration.

Hard‑Coding Model Configurations​

AutoGen v0.4 expects a specific model configuration, not a fallback list like v0.2. Hard‑coding API keys or model names in code leads to brittle systems.

Recommendation: Use environment variables for API keys and component configuration for model clients. AutoGen v0.4 has a generic component configuration system that handles model clients cleanly.

Not Upgrading from v0.2​

AutoGen v0.2 is deprecated and no longer maintained by Microsoft. Code written in the v0.2 style will continue to run but will not receive security updates or compatibility fixes.

Recommendation: Migrate existing v0.2 codebases to v0.4 using the official migration guide. The v0.4 API is more consistent, better documented, and forms the basis for migrating to Microsoft Agent Framework.

Best Practices Overview​

Always set termination conditions. Without explicit termination logic, conversations can continue indefinitely. Use max_round in GroupChat and keyword matching ("TERMINATE") in messages.

Use Docker for untrusted code execution. The built‑in DockerCommandLineCodeExecutor provides container isolation. Never run untrusted agent code on your local machine without isolation.

Start with two agents and iterate. Do not begin with a five‑agent group chat. Prove the conversation works with two agents, then add complexity incrementally.

Instrument with OpenTelemetry. AutoGen v0.4+ includes native OTel tracing. Instrumenting your application from day one makes debugging conversation flows much easier.

Define clear termination messages. Train agents to output specific termination markers (e.g., "FINAL ANSWER: ...") by including this instruction in their system messages.

Use guardrails for validation. Validate tool call parameters and code execution outputs before feeding them back into the conversation. Malformed outputs can derail the entire dialogue.

For new projects, evaluate MAF first. If you are starting a fresh project in 2026 and enterprise support or ongoing feature development matters, start with Microsoft Agent Framework rather than AutoGen.

FAQ​

1. What is AutoGen?​

AutoGen is an open‑source Python framework, originally from Microsoft Research, for building multi‑agent systems as conversations. Agents exchange messages, call tools, execute code, and collaborate like a human team.

2. Is AutoGen developed by Microsoft?​

Yes. AutoGen originated at Microsoft Research and was developed by Microsoft until late 2025. It is now in maintenance mode, with ongoing feature development moved to the Microsoft Agent Framework.

3. Is AutoGen production‑ready?​

AutoGen is stable and has been used in production by many teams. However, it entered maintenance mode in late 2025 / early 2026, so it does not receive new features. For new enterprise projects, Microsoft recommends the Microsoft Agent Framework (MAF) instead.

4. Is AutoGen still maintained in 2026?​

Yes, but only for bug fixes and security patches. No new feature development is happening on AutoGen. Microsoft’s active development is now on the Microsoft Agent Framework, which unifies AutoGen and Semantic Kernel.

5. Does AutoGen support MCP (Model Context Protocol)?​

Yes. AutoGen v0.4+ provides native MCP support through autogen_ext.tools.mcp. The McpWorkbench connects to MCP servers via Stdio (local subprocess) or SSE (remote HTTP) transport and exposes their tools to agents.

6. Can AutoGen execute code?​

Yes. AutoGen includes built‑in code execution through UserProxyAgent and CodeExecutorAgent. Code can run locally or inside Docker containers for isolation.

7. Is AutoGen suitable for beginners?​

Yes for learning multi‑agent conversation concepts. The two‑agent pattern (assistant + user proxy) is simple to understand. However, AutoGen’s v0.4 architecture introduces concepts (actor model, async runtime) that raise the learning curve compared to CrewAI or OpenAI Agents SDK.

8. When should I choose AutoGen over CrewAI?​

Choose AutoGen when your workflow is conversation‑shaped (agents need to debate, negotiate, brainstorm, or critique each other). Choose CrewAI when your workflow has stable, sequential role handoffs (research → write → edit).

9. When should I choose AutoGen over LangGraph?​

Choose AutoGen when the optimal sequence emerges from dialogue rather than being predetermined. Choose LangGraph when you need precise control over execution flow, cycles, checkpoints, or conditional edges.

10. Does AutoGen support .NET or Java?​

No. AutoGen is Python‑only. For .NET or Java, use Microsoft Agent Framework (successor) or Semantic Kernel.

11. How does AutoGen v0.4 differ from v0.2?​

v0.4 is a complete rewrite using an event‑driven, asynchronous actor model. Key differences: asynchronous architecture vs synchronous, layered packages (core/agentchat/ext) vs monolithic, native OpenTelemetry vs custom logging, and modular components vs monolithic classes. v0.2 code will not run on v0.4 without migration.

12. What is the Microsoft Agent Framework (MAF)?​

MAF is Microsoft’s unified agent SDK, released in October 2025, that merges AutoGen (conversational multi‑agent) and Semantic Kernel (enterprise plugins, .NET/Java) into one framework. MAF is the recommended starting point for new agent projects in 2026.

13. Should I start a new project with AutoGen in 2026?​

No for most projects. Microsoft recommends the Microsoft Agent Framework for greenfield builds. Only start with AutoGen if you are specifically maintaining an existing AutoGen codebase or need conversation‑only patterns and cannot migrate to MAF.

14. Is there a migration path from AutoGen to MAF?​

Yes. Microsoft provides a documented migration guide at https://aka.ms/autogen-to-af. The MAF single‑agent interface is nearly identical to AutoGen’s, with added capabilities for thread management, middleware, and graph‑based workflows.

15. What community forks of AutoGen exist?​

AG2 is the primary community‑maintained fork that continues active development of the original AutoGen codebase. The AG2 project maintains the 0.7.x release line and adds features not included in Microsoft’s MAF roadmap.

16. Which AutoGen version should I use?​

Use the latest 0.7.x release for existing projects. If you are on v0.2, migrate to v0.4 as soon as practical—v0.2 is no longer maintained. For new projects, use Microsoft Agent Framework instead.

17. Can AutoGen agents make parallel tool calls?​

Agents can call multiple tools in sequence within a single turn if the LLM generates multiple tool calls. However, the group chat remains sequential—only one agent speaks at a time.

18. How do I debug an AutoGen conversation?​

Enable OpenTelemetry tracing in v0.4+ to export spans to a collector (Jaeger, Azure Monitor). You can also print the complete ChatResult.messages after a conversation ends. For interactive debugging, set human_input_mode="ALWAYS" on a UserProxyAgent to step through the conversation manually.

19. Does AutoGen support streaming responses?​

Yes. In v0.4+, the AssistantAgent and UserProxyAgent support streaming via the model client’s streaming interface.

20. What is AutoGen Studio?​

AutoGen Studio is a separate, no‑code UI for prototyping AutoGen agents. It provides a visual interface for building agents, configuring tools, and running conversations without writing Python code. It is maintained separately from the core AutoGen framework.

Conclusion​

AutoGen pioneered conversational multi‑agent systems, introducing the idea that teams of LLM experts can solve problems through dialogue. With approximately 50,000 GitHub stars, hundreds of research papers, and production deployments across industry, it has been one of the most influential multi‑agent frameworks since its release in 2023.

In 2026, AutoGen enters a new phase. Microsoft has moved active development to the Microsoft Agent Framework, which merges AutoGen’s conversation patterns with Semantic Kernel’s enterprise capabilities. AutoGen remains stable, secure, and usable for existing workloads, but new projects should start with MAF.

Core strengths to remember:

  • Conversation‑first mental model unmatched for debate, negotiation, and open‑ended collaboration.
  • Built‑in code execution (local or Docker) for agents that write and run code.
  • Native MCP support for dynamic tool discovery.
  • OpenTelemetry tracing and event‑driven runtime (v0.4+).
  • Large community and proven research heritage.

If you are an existing AutoGen user, complete the migration from v0.2 to v0.4, then evaluate moving to the Microsoft Agent Framework for enterprise features and ongoing support.

If you are starting fresh, begin with the Microsoft Agent Framework guide rather than AutoGen. The conversational patterns you learn there will preserve what made AutoGen great while adding production‑grade capabilities.

Continue your learning:

Compare AutoGen with other frameworks:

Additional resources:

Last updated: June 2026