AutoGen is Microsoft's open-source framework for building multi-agent AI systems through conversational collaboration. Originally released by Microsoft Research in 2023, the project has since been forked and rebranded as AG2 by the broader community, though both names remain in active use. The framework is backed by research into how multiple AI agents can solve complex problems more effectively through dialogue and iterative refinement than any single agent could alone.
The central abstraction in AutoGen is the conversable agent — an entity that can send and receive messages, generate responses using an LLM, and execute code or use tools. Multi-agent workflows emerge from the patterns of how these agents converse: one agent proposes, another critiques, a third executes, and so on.
Key Features#
Conversation-First Multi-Agent Architecture AutoGen's unique contribution is its conversation-centric model. Agents interact through natural language messages in a back-and-forth pattern until a task is complete or a termination condition triggers. This design enables emergent problem-solving behavior that is difficult to achieve with rigid task-delegation frameworks.
AssistantAgent and UserProxyAgent Pattern The classic AutoGen pattern pairs an AssistantAgent (powered by an LLM) with a UserProxyAgent (which can execute code and tools on behalf of a human). The assistant proposes solutions, the proxy executes them, reports results, and the loop continues. This pattern is especially effective for code generation and debugging workflows.
GroupChat Orchestration AutoGen's GroupChat allows multiple agents to participate in a shared conversation with a configurable GroupChatManager directing who speaks next. This supports complex workflows with specialist agents (e.g., a planner, a coder, a tester, a reviewer) all collaborating in one thread.
Code Execution Sandbox AutoGen includes a Docker-based code execution environment for safely running agent-generated code. The user proxy agent can execute Python, shell commands, and other scripts in isolation, with results fed back into the conversation.
Flexible Termination Conditions Conversations can terminate based on message count limits, keyword detection (like "TERMINATE"), human interruption, or custom logic. This makes it possible to build both automated pipelines and human-in-the-loop review workflows with the same framework.
Pricing#
AutoGen is completely free and open-source (Apache 2.0). There is no paid version, cloud service, or enterprise offering from Microsoft. Costs are limited to:
- LLM API usage (OpenAI, Azure OpenAI, Anthropic, etc.)
- Compute for running agent code
- Azure-hosted models if using Microsoft's cloud (standard Azure pricing)
The AG2 community fork adds additional features and may introduce managed services in the future; check ag2.ai for current information.
Who It's For#
AutoGen is the right choice for:
- AI researchers exploring multi-agent systems, emergent collaboration, and agent communication patterns
- Software developers building coding assistants, automated code review, or test generation pipelines
- Data scientists automating complex analytical workflows requiring iterative hypothesis testing
- Engineers building autonomous systems where open-ended problem decomposition is needed
It is less suitable for business users without coding experience, for applications requiring predictable structured outputs, or for high-volume production deployments where conversation loops may produce inconsistent results.
Strengths#
Research pedigree. AutoGen comes from Microsoft Research with peer-reviewed papers backing its design. The framework is grounded in formal analysis of multi-agent interaction patterns, not just engineering intuition.
Exceptional for code generation workflows. The AssistantAgent + UserProxyAgent + code execution sandbox combination is one of the most effective patterns for automated software development tasks available in any open-source tool.
Highly configurable conversation patterns. AutoGen gives you more control over agent interaction dynamics than most frameworks. You can implement custom speaker selection logic, message filtering, and termination conditions.
Strong Azure integration. Teams operating in Microsoft's Azure ecosystem get first-class support for Azure OpenAI, Azure Functions, and Azure Container Instances for code execution.
Limitations#
Unpredictability in production. Conversation-based workflows are inherently less deterministic than task-delegation frameworks. The same input can produce different execution paths, making it harder to guarantee consistent outputs in production.
Termination reliability. Designing reliable termination conditions is non-trivial. Agents can get stuck in loops, fail to reach consensus, or terminate prematurely. This requires careful prompt engineering and defensive configuration.
Higher LLM cost per task. Because agents engage in multi-turn dialogue, a task that takes one LLM call in a simple chain might take 5–15 calls in an AutoGen workflow. This increases both cost and latency significantly.
Related Resources#
Explore the full AI Agent Tools Directory for a comprehensive view of available frameworks.
For direct comparisons, see CrewAI vs AutoGen: Multi-Agent Framework Comparison and AutoGen vs LangGraph: Choosing the Right Agent Architecture.
Related directory profiles: CrewAI and LangGraph represent alternative approaches to multi-agent orchestration.
For implementation examples, see AI Agents for Software Development: Automated Code Review Examples and Building Multi-Agent Systems: Architecture Patterns.