Agent Framework

35K+ StarsMicrosoft ResearchMIT License

AutoGen

by Microsoft Research · microsoft.github.io/autogen

AutoGen is Microsoft Research's open-source framework for building multi-agent AI systems through conversations. Instead of defining rigid workflows, you create agents that solve problems by talking to each other — debating approaches, delegating subtasks, writing and executing code, and converging on solutions through structured dialogue.

With 35,000+ GitHub stars and backing from Microsoft Research, AutoGen represents the conversation-first approach to multi-agent systems. It supports human-in-the-loop participation (you can join the agent conversation), sandboxed code execution, and flexible agent topologies from simple two-agent chats to complex group conversations.

35K+

GitHub Stars

Microsoft backed

Conversation

Paradigm

agents chat to solve

Sandboxed

Code Exec

Docker / local

MIT

License

fully open-source

Quick Install

pip install autogen-agentchat

Key Features

Conversation-Based Agents

Agents solve problems by talking to each other. Define conversation patterns — two-agent, group chat, hierarchical — and let agents collaborate through structured dialogue.

Human-in-the-Loop

Join agent conversations as a human participant. Approve decisions, provide guidance, or take over when the agents need help. Seamless integration between AI and human judgment.

Sandboxed Code Execution

Agents can write and execute Python code in Docker containers or isolated processes. Essential for data analysis, math, and software development tasks.

Flexible Agent Topologies

Two-agent conversations, round-robin group chats, hierarchical delegation, and custom routing. Match the conversation structure to your problem.

Multi-Model Support

Use OpenAI, Anthropic, Google, Azure, or any OpenAI-compatible endpoint. Assign different models to different agents for cost and capability optimization.

AutoGen Studio

Visual interface for building, testing, and debugging multi-agent workflows without code. Drag-and-drop agent creation with conversation visualization.

Execution Brief

Use this page as a rollout checklist, not just reference text.

Suggest update

Tool Mapping Lens

Organize Tools by Workflow Phase

Catalog-oriented pages work best when users can map discovery, evaluation, and rollout in a clear path instead of reading an undifferentiated list.

Define the job-to-be-done first
Group tools by stage
Prioritize by adoption friction

Actionable Utility Module

Skill Implementation Board

Use this board for AutoGen before rollout. Capture inputs, apply one decision rule, execute the checklist, and log outcome.

Input: Objective

Deliver one measurable improvement with autogen microsoft multi agent conversation framework review

Input: Baseline Window

20-30 minutes

Input: Fallback Window

8-12 minutes

Decision Trigger	Action	Expected Output
Input: one workflow objective and release owner are defined	Run preview execution with fixed acceptance criteria.	Go or hold decision backed by repeatable evidence.
Input: output quality below baseline or retries increase	Limit scope, isolate root issue, and rerun controlled test.	One confirmed correction path before wider rollout.
Input: checks pass for two consecutive replay windows	Promote to broader traffic with fallback path active.	Stable rollout with low operational surprise.

Execution Steps

Record objective, owner, and stop condition.
Execute one controlled preview run.
Measure quality, latency, and correction burden.
Promote only when pass criteria are stable.

Output Template

tool=autogen microsoft multi agent conversation framework review
objective=
preview_result=pass|fail
primary_metric=
next_step=rollout|patch|hold

Share execution feedback

What Is AutoGen?

AutoGen is a multi-agent framework created by Microsoft Research that takes a fundamentally different approach from role-based frameworks like CrewAI. In AutoGen, agents solve problems through conversations. You define agents with different capabilities and personalities, put them in a conversation, and they collaborate through dialogue to reach a solution.

The conversation-based paradigm is powerful for problems that benefit from debate and iteration. A coding agent writes code, a reviewer agent critiques it, the coder revises, and the cycle continues until both are satisfied. A research agent gathers data, an analyst interprets it, and they discuss discrepancies until they converge on a conclusion. This mimics how human teams actually work.

AutoGen 0.4 (released as AgentChat) simplified the framework significantly. Earlier versions required complex configuration dictionaries. The new API uses clean Python classes: AssistantAgent, UserProxyAgent, and GroupChat. Creating a multi-agent system takes about 20 lines of code, making it accessible to developers who are not AI specialists.

The competitive positioning is: LangChain provides agent building blocks, CrewAI provides role-based multi-agent orchestration, and AutoGen provides conversation-based multi-agent collaboration. AutoGen's unique strength is the human-in-the-loop design — you can participate in agent conversations as naturally as joining a group chat, making it ideal for tasks that need human judgment at critical decision points.

How to Calculate Better Results with autogen microsoft multi agent conversation framework review

Install AutoGen: pip install autogen-agentchat. Set your model API key (OPENAI_API_KEY or equivalent).

Create agents: AssistantAgent for AI agents with model access, UserProxyAgent for agents that can execute code or represent humans.

Start a two-agent conversation: coder = AssistantAgent("coder"), executor = UserProxyAgent("executor", code_execution_config={"use_docker": True}). Then executor.initiate_chat(coder, message="your task").

For group conversations: create a GroupChat with multiple agents and a GroupChatManager. Agents take turns based on the conversation flow, or you can define custom speaker selection logic.

Treat this page as a decision map. Build a shortlist fast, then run a focused second pass for security, ownership, and operational fit.

When a team keeps one shared selection rubric, tool adoption speeds up because evaluators stop debating criteria every time a new option appears.

Worked Examples

Code review through agent conversation

Create a Coder agent (AssistantAgent with coding persona)
Create a Reviewer agent (AssistantAgent with code review persona)
Create a UserProxy agent for code execution
Start chat: "Write a Python function to merge two sorted arrays efficiently"
Coder writes the initial implementation
Reviewer critiques: edge cases, time complexity, naming conventions
Coder revises based on feedback
UserProxy executes the code with test cases to verify correctness
Reviewer approves the final version after 2 revision rounds

Outcome: A well-tested, reviewed function produced through agent conversation. The debate between Coder and Reviewer caught edge cases that a single agent would miss. Total: 3 conversation rounds, 2 code executions.

Data analysis with human oversight

Create Analyst agent (data analysis specialist)
Create Statistician agent (statistical methodology specialist)
Create UserProxy with human-in-the-loop enabled
Task: "Analyze sales data in sales.csv and identify growth opportunities"
Analyst writes pandas code to load and explore the data
UserProxy executes the code in Docker, returns results
Statistician reviews methodology: suggests controlling for seasonality
Analyst revises analysis with seasonal adjustment
Human (you) joins the conversation to ask about a specific product category
Agents incorporate your question and produce a focused sub-analysis
Final output: comprehensive report with your specific question answered

Outcome: A data analysis workflow where AI agents handled the heavy lifting, debated methodology, and a human provided domain expertise at the right moment. The conversation format made it natural to contribute without disrupting the workflow.

Frequently Asked Questions

What is AutoGen?

AutoGen is an open-source framework by Microsoft Research for building multi-agent AI systems. Its core concept is that agents solve problems through conversations — they talk to each other, debate approaches, delegate subtasks, and converge on solutions. It supports human-in-the-loop participation, code execution in sandboxed environments, and complex multi-agent topologies.

How does AutoGen differ from CrewAI?

AutoGen uses a conversation-based paradigm: agents chat with each other to solve tasks, with flexible conversation patterns (two-agent, group chat, hierarchical). CrewAI uses a role-based paradigm: agents have defined roles and goals, executing tasks in sequential or hierarchical processes. AutoGen is more flexible for complex interactions. CrewAI is simpler to set up for standard workflows.

What is AutoGen 0.4 (AgentChat)?

AutoGen 0.4 is a major rewrite that introduced AgentChat — a simplified API for building multi-agent systems. It replaces the complex configuration of earlier versions with a cleaner interface. Key improvements: better type safety, simplified agent creation, improved group chat management, and native async support. If you are starting new, use 0.4.

Does AutoGen support code execution?

Yes. AutoGen agents can write and execute code in sandboxed environments (Docker containers or local processes). This is especially powerful for data analysis, math problems, and software development tasks. The code execution is isolated by default, preventing agents from affecting your system.

What AI models work with AutoGen?

AutoGen supports all major model providers through a unified interface: OpenAI (GPT-4o, o1), Anthropic (Claude), Google (Gemini), Azure OpenAI, and any OpenAI-compatible endpoint (Ollama, vLLM, LiteLLM). You can assign different models to different agents for cost optimization.

Is AutoGen production-ready?

AutoGen 0.4 is considered production-ready for structured use cases. Microsoft uses it internally for several products. However, complex multi-agent conversations can be unpredictable — production deployments should include conversation limits, error handling, and human oversight for critical decisions.

Missing a better tool match?

Send the exact workflow you are solving and we will prioritize a new comparison or rollout guide.

Submit feedback