AutoGen: Microsoft’s Multi-Agent Conversation Framework Engineered for Production
Reviewed by Umar Abbas • Founder & Principal AI Architect
Last reviewed: 14 August 2026
AutoGen is Microsoft's open source framework for building multi-agent LLM applications. Agents converse to solve tasks, delegating work through group chat orchestration and executing generated code in sandboxed environments. The v0.4 rewrite uses an asynchronous, actor-based core with typed messages, supporting both Python and .NET runtimes.
What AutoGen Solves in Production
Single-prompt LLM calls break down when a task needs planning, drafting, criticism, and verification in a loop. Stitching that together by hand means writing bespoke message routing, retry logic, and a way to run the code models produce and feed results back. AutoGen provides those primitives directly, modeling the workflow as agents that exchange typed messages and hand off control through group chat. The framework also standardizes code execution, so generated scripts run in a controlled sandbox rather than ad hoc subprocess calls. This lets teams focus on agent roles and termination logic rather than plumbing.
AutoGen Architecture
Anatomy ExplainerCore Component Component Parts:
AssistantAgent and ConversableAgent
The agent abstractions that hold a system message, model client, and optional tools, and respond to incoming messages.
In v0.4, AssistantAgent from autogen-agentchat wraps a model client and tool set, producing responses and tool calls. ConversableAgent was the central class in v0.2. Agents are addressable participants that consume and emit typed chat messages.
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- Part 1: AssistantAgent and ConversableAgent - The agent abstractions that hold a system message, model client, and optional tools, and respond to incoming messages. [Tech: In v0.4, AssistantAgent from autogen-agentchat wraps a model client and tool set, producing responses and tool calls. ConversableAgent was the central class in v0.2. Agents are addressable participants that consume and emit typed chat messages.]
- Part 2: Model Clients - The provider adapters that translate agent requests into model API calls and normalize responses. [Tech: OpenAIChatCompletionClient and AzureOpenAIChatCompletionClient live in autogen-ext. They handle chat completion, function or tool calling, and streaming. Any OpenAI compatible endpoint can be targeted by setting a custom base URL and model name.]
- Part 3: Teams and Group Chat - The orchestration layer that decides which agent speaks next and when the conversation ends. [Tech: RoundRobinGroupChat cycles participants in order, while SelectorGroupChat uses a model prompt to choose the next speaker. Termination conditions like TextMentionTermination and MaxMessageTermination compose with logical operators to stop the run.]
- Part 4: Code Executors - The components that extract code blocks from messages, run them, and return output into the conversation. [Tech: DockerCommandLineCodeExecutor runs code inside a container image for isolation, and LocalCommandLineCodeExecutor runs on the host. Executors are typically paired with a code executor agent that owns the run and reply loop.]
- Part 5: autogen-core Runtime - The asynchronous message-passing runtime that schedules agent activations and delivers events. [Tech: autogen-core provides an actor style runtime, for example SingleThreadedAgentRuntime, where agents are registered and communicate through typed messages and topics. It underpins autogen-agentchat and enables event-driven, distributed designs.]
Architectural Strengths & Specific Production Limits
- Conversation-native orchestration: Multi-agent coordination is expressed as dialogue with speaker selection, which maps cleanly to critic, coder, and planner style roles.
- First-class code execution: Built-in Docker and local executors run agent-generated code in a controlled loop, removing the need to hand-roll sandboxing and output capture.
- Layered v0.4 architecture: The split across autogen-core, autogen-agentchat, and autogen-ext lets teams use the high level API or drop to the runtime for custom event-driven designs.
- Cross-language and tooling support: Python and .NET runtimes plus AutoGen Studio and MCP tool integration give teams multiple entry points from prototype to service.
- Version churn between v0.2 and v0.4: The v0.4 rewrite changed core APIs, so tutorials, the AG2 fork, and older code target incompatible interfaces, requiring careful version pinning.
- Non-determinism in model-driven selection: SelectorGroupChat relies on a model to choose the next speaker, which can produce unpredictable routing compared with explicit graph control flow.
- Code execution risk: Running LLM-generated code, especially with the local executor, is a real security surface that demands Docker isolation and human approval gates.
- Token and latency overhead: Long multi-agent conversations accumulate context and turns, driving up token cost and wall-clock latency versus a single tightly scoped prompt.
How We Deploy AutoGen in Production
Our team uses AutoGen where a task genuinely benefits from separate agent roles, such as a generator and a reviewer, rather than forcing it onto every workflow. We pin autogen-agentchat and autogen-ext to exact versions, isolate all code execution inside Docker images we control, and cap conversations with explicit termination conditions to bound cost. Side-effecting steps sit behind human-in-the-loop approval, and we log the full message transcript for every run so behavior stays auditable.
AutoGen Delivery Pipeline
Interactive Flow DiagramWe define each agent's system message, tools, and responsibility, deciding whether the task needs multiple agents at all.
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| Step | Stage Name | Function & Detail | Metrics / SLA |
|---|---|---|---|
| 1 | 1. Role Design | We define each agent's system message, tools, and responsibility, deciding whether the task needs multiple agents at all. | 2 to 5 agents typical |
| 2 | 2. Orchestration | We select RoundRobinGroupChat or SelectorGroupChat and compose termination conditions to bound turns and detect completion. | max_turns capped |
| 3 | 3. Sandboxing | Code executors run generated code in a pinned container image with no host mounts beyond an explicit work directory. | isolated per run |
| 4 | 4. Human Gates | Side-effecting actions pause for review through user input handlers before the conversation continues. | approval before write |
| 5 | 5. Observability | We capture full message transcripts, token usage, and OpenTelemetry traces to monitor cost and behavior over time. | per-run token logs |
# requirements pinned: autogen-agentchat==0.4.0, autogen-ext[openai]==0.4.0
import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_agentchat.conditions import TextMentionTermination, MaxMessageTermination
from autogen_ext.models.openai import OpenAIChatCompletionClient
async def main() -> None:
model_client = OpenAIChatCompletionClient(model="gpt-4o-2024-08-06")
coder = AssistantAgent(
"coder",
model_client=model_client,
system_message="Write clean Python to solve the task.",
)
reviewer = AssistantAgent(
"reviewer",
model_client=model_client,
system_message="Review the code. Reply APPROVE when it is correct.",
)
termination = TextMentionTermination("APPROVE") | MaxMessageTermination(10)
team = RoundRobinGroupChat([coder, reviewer], termination_condition=termination)
result = await team.run(task="Write a function that returns the nth Fibonacci number.")
for message in result.messages:
print(f"{message.source}: {message.content}")
await model_client.close()
asyncio.run(main())Services Engineered with AutoGen
We design, build, and operate AutoGen-based multi-agent systems end to end.
AutoGen vs Alternative Agent Frameworks
How AutoGen compares with LangGraph and CrewAI across the dimensions that matter in production.
Agent Framework Comparison
Benchmark Matrix| Evaluation Metric | AutoGen | LangGraph | CrewAI |
|---|---|---|---|
| Conversational multi-agent patterns | Native group chat and speaker selection Winner | Possible via graph, more manual | Role and crew based, higher level |
| Deterministic control flow | Emergent from conversation | Explicit state graph and edges Winner | Sequential or hierarchical process |
| Built-in code execution | Docker and local executors first class Winner | Bring your own tool or executor | Tool based, less integrated |
| Speed to first prototype | Moderate, v0.4 API learning curve | Steeper graph setup | Fast role oriented setup Winner |
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- Conversational multi-agent patterns: AutoGen: Native group chat and speaker selection vs LangGraph: Possible via graph, more manual vs CrewAI: Role and crew based, higher level (Winning option: AutoGen).
- Deterministic control flow: AutoGen: Emergent from conversation vs LangGraph: Explicit state graph and edges vs CrewAI: Sequential or hierarchical process (Winning option: LangGraph).
- Built-in code execution: AutoGen: Docker and local executors first class vs LangGraph: Bring your own tool or executor vs CrewAI: Tool based, less integrated (Winning option: AutoGen).
- Speed to first prototype: AutoGen: Moderate, v0.4 API learning curve vs LangGraph: Steeper graph setup vs CrewAI: Fast role oriented setup (Winning option: CrewAI).
AutoGen in a Reference Architecture
For a fintech document automation workflow, we used an AutoGen agent team to separate extraction from validation, with one agent drafting structured output and another checking it against required fields before release. Generated parsing code ran inside a Docker executor, and low-confidence records were routed to human reviewers through an approval gate. The conversational structure made the review logic explicit and easy to audit.
Read Reference Architecture →Frequently Asked Questions
What is AutoGen used for?↓
AutoGen is used to build applications where multiple LLM agents converse to complete a task, such as a coder agent writing a script and a critic agent reviewing it. It handles the message passing, turn taking, and code execution loop between agents. It is common in code generation, data analysis, and research automation workflows.
Who develops AutoGen?↓
AutoGen is developed by Microsoft, originating from Microsoft Research and now maintained across the AI Frontiers and developer teams. It is released as open source under the MIT license. A separate community fork called AG2 was created by some original contributors and continues the v0.2 line independently.
What is the difference between AutoGen v0.2 and v0.4?↓
AutoGen v0.2 was a synchronous framework built around ConversableAgent and GroupChat. The v0.4 rewrite introduced an asynchronous, event-driven, actor-based core with typed messages and clearer layering across autogen-core, autogen-agentchat, and autogen-ext. The high level agent API is exposed through autogen-agentchat, while autogen-core provides the runtime.
How does AutoGen execute code?↓
AutoGen runs code that agents generate through code executors. The DockerCommandLineCodeExecutor runs code inside a Docker container for isolation, while LocalCommandLineCodeExecutor runs it on the host. Executors extract code blocks from messages, run them, and return the output back into the conversation for the next agent turn.
Is AutoGen free and open source?↓
Yes, AutoGen is open source and released under the MIT license, so it can be used commercially without licensing fees. You still pay for the underlying model provider, such as OpenAI or Azure OpenAI, and any compute you run. The source is hosted in the microsoft/autogen repository on GitHub.
What is the difference between AutoGen and LangGraph?↓
AutoGen models coordination as a conversation between agents, where orchestration emerges from group chat and speaker selection. LangGraph models it as an explicit state graph with nodes and edges, giving deterministic control flow and clear branching. AutoGen tends to be faster for conversational prototypes, while LangGraph gives tighter control for auditable production pipelines.
What group chat patterns does AutoGen support?↓
In v0.4 the team abstractions include RoundRobinGroupChat, which cycles agents in fixed order, and SelectorGroupChat, which uses a model to pick the next speaker. Swarm style handoffs are also supported. Termination conditions such as text mention or max turns control when a conversation stops.
Does AutoGen support human in the loop?↓
Yes, AutoGen supports human input through user proxy style agents and UserInputFunc handlers that pause the conversation for approval or additional input. This lets a person review generated code before it executes or steer the agents mid task. It is a common safeguard when code execution has side effects.
What models does AutoGen work with?↓
AutoGen works with any model exposed through its model client interface, including OpenAI, Azure OpenAI, and providers reachable through the OpenAI compatible client. Local models served through Ollama or similar OpenAI compatible endpoints are supported by pointing the client at a custom base URL. Tool calling behavior depends on the underlying model's capabilities.
What is AutoGen Studio?↓
AutoGen Studio is a low code interface for prototyping multi-agent workflows without writing all the orchestration by hand. It lets you define agents, teams, and tools visually and test conversations interactively. It is a companion tool for experimentation, while production systems are usually built directly against the Python or .NET libraries.