AutoGen (Microsoft): Complete Platform Profile

AutoGen is an open-source framework from Microsoft Research that enables multiple AI agents to collaborate by sending each other messages in a conversational pattern. Released in September 2023 and subsequently rewritten as AutoGen 0.4 in late 2024, it has accumulated more than 33,000 GitHub stars and established itself as the primary alternative to LangChain-based approaches for teams that think about agent coordination in terms of conversations rather than explicit function calls or graphs.

The framework's core insight is that LLMs are trained on human conversations and are therefore highly capable at following conversational instructions. If you structure agent coordination as a multi-party chat — where each agent receives messages, responds, and passes control — you get powerful emergent collaboration with minimal orchestration code.

Microsoft Research continues to drive development, bringing research-grade capabilities around multi-agent reasoning into a production-usable package. AutoGen 0.4 (the current version) was a complete architectural rewrite focused on async execution, modularity, and a more stable API.

This profile examines what makes AutoGen distinctive, when to use it, and how it compares to alternatives.

Explore the AI agent profiles directory for a full overview of the framework landscape.

Overview#

AutoGen's foundational abstraction is the Agent: an entity that can receive messages, process them (optionally by calling an LLM), and send messages in response. Agents participate in Conversations which define who talks to whom and in what order.

The most common AutoGen pattern involves two agent types:

AssistantAgent: An LLM-backed agent that responds to messages, generates code, and calls tools
UserProxyAgent: An agent that represents the human user, can execute code produced by the assistant, and provides feedback

In a typical session, the user sends a request to the AssistantAgent, which may write code. The UserProxyAgent executes the code, sends the result back to the AssistantAgent, which refines its approach based on the outcome. This loop continues until the task is complete or the user intervenes — a direct implementation of the agent loop pattern.

AutoGen's conversational model scales beyond two agents. Group chats with multiple AssistantAgents (each with different expertise) allow complex collaborative problem-solving where agents critique, extend, and correct each other's contributions.

AutoGen 0.4 introduced an async actor model where each agent runs as an independent process, communicating via a message passing interface. This makes it significantly easier to build distributed agent systems.

For a hands-on introduction, see the AutoGen Studio setup guide.

Core Features#

Conversational Agent Coordination#

AutoGen's distinguishing feature is its use of natural language messages as the coordination mechanism between agents. Instead of explicit function calls or graph edges determining what happens next, agents communicate through messages and interpret instructions the same way an LLM interprets any text input. This makes the coordination layer flexible and expressive — you can instruct an agent in plain English rather than code.

The downside of this flexibility is non-determinism. Because agent responses are generated by LLMs, the exact sequence of messages in a conversation can vary between runs with identical inputs.

Code Execution Capabilities#

AutoGen has first-class support for code generation and execution. The UserProxyAgent includes a built-in code execution engine that runs Python and shell commands in a sandboxed environment (Docker container by default, local execution optional). This enables powerful workflows: the AssistantAgent generates code, the UserProxyAgent executes it, results come back as messages, and the AssistantAgent adapts. For technical tasks — data analysis, code debugging, scripting — this code execution loop is highly effective. The function calling glossary covers the underlying model capability that makes this possible.

AutoGen Studio (Low-Code Interface)#

AutoGen Studio is a browser-based GUI built on top of AutoGen that allows users to build and test multi-agent workflows without writing Python code. Users can define agents (with roles, tools, and model configurations), create workflows, and interact with them through a chat interface — all through a drag-and-drop UI.

AutoGen Studio is particularly valuable for non-technical users who need to experiment with agent configurations, or for technical teams that want a rapid prototyping environment before committing to code. It stores configurations as JSON and can export them for use in programmatic AutoGen applications.

Group Chat Orchestration#

AutoGen's GroupChat abstraction manages conversations involving more than two agents. You configure the participating agents, the conversation manager (which selects who speaks next), and the termination condition. The manager can be rule-based (round-robin, always-specific-agent) or LLM-based (the manager LLM reads the conversation and selects the most relevant agent to respond next).

Group chats enable patterns like: critic-generator pairs, debate-style problem solving with opposing agents, and expert panels where different agents contribute domain-specific knowledge.

Tool and Function Calling#

AutoGen agents can be equipped with tools — Python functions registered with the LLM using the function calling mechanism. When an AssistantAgent determines it needs to use a tool, it generates a function call message; the UserProxyAgent executes the corresponding Python function and returns the result as a message. This is the standard tool use pattern, and AutoGen implements it cleanly with minimal boilerplate.

AutoGen 0.4 Architecture (Async Actor Model)#

The 0.4 rewrite introduced a fundamentally different execution model. Agents are now asynchronous actors communicating via a message bus. Each agent runs in its own asyncio task (or separate process), making it straightforward to build distributed multi-agent systems where agents run on different machines. This is a significant architectural advantage over frameworks where all agents run in a single synchronous process.

Pricing and Plans#

AutoGen is fully open source under the MIT license and costs nothing. Microsoft Research develops and maintains it as a research contribution. There is no AutoGen cloud product, no paid tiers, and no commercial license requirement.

The only costs are:

LLM API usage (OpenAI, Azure OpenAI, Anthropic, or local models)
Infrastructure for running the AutoGen application
Optionally, Azure cloud services if deploying to Azure

This makes AutoGen one of the most cost-effective frameworks in the ecosystem from a licensing perspective. Organizations using Azure OpenAI can also benefit from Microsoft's native integration support for that combination.

Strengths#

Conversational coordination. For problem domains where the solution emerges from multi-turn dialogue — brainstorming, peer review, iterative refinement — AutoGen's message-passing model is more natural than explicit task assignment or graph routing.

Code execution ecosystem. AutoGen's built-in code execution loop is among the best in any agent framework. The combination of LLM-generated code and automatic execution with results fed back to the LLM is powerful for technical tasks. See the agent evaluation glossary for how to measure performance on such tasks.

Microsoft ecosystem integration. For organizations on Azure, AutoGen integrates naturally with Azure OpenAI, Azure Container Apps (for Docker-based code execution), and other Azure services. Microsoft's research backing means the framework receives continued investment and attention to production concerns.

AutoGen Studio accessibility. The low-code UI lowers the barrier for non-developers to experiment with agent configurations, making AutoGen valuable in enterprise environments where not everyone can write Python.

Research-grade capabilities. Microsoft Research publishes papers alongside AutoGen covering multi-agent reasoning, self-improvement, and evaluation. The framework incorporates findings from active research rather than just engineering pragmatics.

Limitations#

Non-determinism. AutoGen's conversational coordination is powerful but inherently unpredictable. For production systems requiring deterministic behavior or auditability, the lack of explicit control flow is a meaningful limitation.

Debugging complexity. Tracing why a group chat took a particular path — why one agent was selected over another, why a particular code block was generated — requires careful log inspection. There is no native observability dashboard comparable to LangSmith.

0.4 migration burden. The AutoGen 0.4 rewrite introduced breaking changes. Teams that built applications on 0.2 or 0.3 face non-trivial migration work. The API instability is a real concern for teams building production systems.

Learning curve for group chats. Designing a group chat that reliably solves a problem requires careful thought about agent roles, conversation termination conditions, and manager selection strategy. The flexibility is also the source of difficulty — it is easy to create a group chat that meanders or loops without converging.

Ideal Use Cases#

AutoGen excels in:

Code debugging and generation: The code-execute-feedback loop is AutoGen's strongest capability. Data analysis, script generation, and automated debugging are natural fits.
Research assistance: Multi-agent debates where different agents take opposing views to explore a topic comprehensively
Technical problem-solving: Engineering challenges that benefit from a generator-critic pattern where one agent proposes solutions and another identifies flaws
Azure-native deployments: Organizations that want to deploy agents on Azure infrastructure with minimal integration complexity
Prototyping with AutoGen Studio: Teams that want a quick UI-based environment to test agent configurations before writing application code

See the how to build a research AI agent tutorial for a walkthrough of the types of workflows where AutoGen's conversational model shines.

Getting Started#

With AutoGen 0.4 (current version):

Install: pip install autogen-agentchat autogen-ext[openai]
Configure your LLM: set OPENAI_API_KEY or Azure credentials
Create an AssistantAgent with a system message defining its role
Create a UserProxyAgent with a code executor configured
Start a conversation using asyncio.run(user_proxy.initiate_chat(assistant, message="your task"))

For AutoGen Studio:

Install: pip install autogenstudio
Run: autogenstudio ui --port 8081
Open browser to localhost:8081 and use the visual interface

The AutoGen Studio setup guide on this site covers the full installation and configuration process.

How It Compares#

AutoGen vs LangGraph: LangGraph provides explicit, deterministic control flow through a state graph. AutoGen's conversational coordination is more emergent and flexible but harder to control. Use LangGraph when you need predictable execution paths; use AutoGen when you want agents to reason collaboratively about how to proceed. See the LangChain vs AutoGen comparison for a structured analysis.

AutoGen vs CrewAI: CrewAI assigns explicit roles and tasks; AutoGen coordinates through conversation. CrewAI is more predictable and easier to reason about; AutoGen is more flexible for open-ended collaborative problem-solving. For content and research workflows, CrewAI is often faster to production. For technical problem-solving with code execution, AutoGen frequently outperforms.

AutoGen vs Flowise: Flowise is a visual no-code builder; AutoGen is a code-first framework. They serve different audiences and skill levels. See the Flowise vs LangFlow comparison for context on the visual builder space.

Bottom Line#

AutoGen is the most research-sophisticated multi-agent framework available and the most natural choice for technical problem-solving tasks where code generation and execution are central. Microsoft Research's continued investment means it stays at the frontier of multi-agent AI research. The framework's conversational coordination model is genuinely powerful for the right use cases — particularly collaborative reasoning, code debugging, and iterative refinement workflows. Its limitations — non-determinism, debugging difficulty, API instability across versions — are real and should be weighted carefully for production deployments. Teams using Azure infrastructure or building on top of Microsoft's AI stack will find AutoGen the most natural and well-supported choice.

Best for: Technical teams building code generation, debugging, or collaborative reasoning agents, especially in Azure environments.