Microsoft · Conversational Multi-Agent Framework by Microsoft
AutoGen is Microsoft's open-source framework for building multi-agent AI systems where agents collaborate through structured conversations, in Python and .NET. It is now in maintenance mode — no new features, community managed — and Microsoft recommends its successor, Microsoft Agent Framework, for new projects.
Best for
Existing AutoGen users maintaining projects, and developers experimenting with conversational multi-agent patterns or building iterative workflows (code generation + review, research + verification) where the solution emerges from agent dialogue rather than a predefined execution graph
Not ideal for
Teams building production pipelines that need deterministic execution order, explicit state management, or reliable branching logic — LangGraph is a stronger fit for those requirements
Who it's for
Python and .NET developers building multi-agent systems, particularly for iterative workflows like code generation + review, or research pipelines where conversational agent collaboration is a natural fit
AutoGen's conversational model is genuinely different from graph-based frameworks like LangGraph or role-based frameworks like CrewAI. The mental model is: agents are participants in a conversation, and the task is solved through that conversation. This is natural for code-generation-with-review workflows, debate-style reasoning, or any problem where iterating through critique-and-revision is the right approach. The limitation is that conversational agent systems are harder to make deterministic — you need to think carefully about termination conditions and loop detection. The bigger issue now is maintenance: AutoGen as a whole is in maintenance mode and receives no new features. For new projects, Microsoft points to Microsoft Agent Framework, which carries AutoGen's multi-agent ideas forward with Semantic Kernel's enterprise features and active development.
Who should use it
Python or .NET developers building iterative multi-agent workflows — particularly code generation with review cycles, research pipelines with verification steps, or experimental agent architectures where the conversational collaboration model fits the problem naturally.
Who should skip it
Teams building production pipelines that need deterministic, auditable execution — the conversational model is less predictable than LangGraph's explicit graph approach. Also avoid for new projects: AutoGen is in maintenance mode and receives no new features — Microsoft recommends Microsoft Agent Framework.
Automated code generation and review pipeline
AutoGen's conversational model maps naturally to the code-write / code-review cycle. A coding agent generates a solution, a reviewer agent critiques it, and the loop continues until the reviewer is satisfied or a termination condition is met. This back-and-forth is cumbersome to model as an explicit graph but natural as a conversation.
Multi-agent research with verification
One agent synthesizes an answer from sources; a second agent challenges the claims; a third agent finds supporting or contradicting evidence. The iterative dialogue produces a more vetted result than a single-pass chain. AutoGen's group chat orchestration handles the turn-taking.
Human-in-the-loop collaborative workflows
A human participates as a UserProxyAgent in the conversation — reviewing agent output, providing domain context, or redirecting the task mid-flow. The human's involvement is seamless because AutoGen treats humans and agents as equivalent conversation participants.
Experimentation with multi-agent architectures
AutoGen's flexible agent definition and message-passing model make it well-suited for prototyping different multi-agent designs — testing whether a debate approach, a critic approach, or a specialist crew approach works best for a given problem — before committing to a more rigid framework.
AutoGen vs. LangGraph
LangGraph models agent workflows as explicit directed graphs — you control exactly what runs, when, and in what state. AutoGen models agents as conversational participants — the task is solved through message exchange. LangGraph is stronger for production pipelines where you need deterministic execution, persistent state, and branching logic. AutoGen is stronger for iterative workflows where the solution emerges from agent dialogue rather than a predefined flow. The two frameworks reflect fundamentally different mental models for what 'agent orchestration' means.
AutoGen vs. CrewAI
CrewAI uses a role-based model — agents are crew members with defined roles and tasks, and CrewAI handles their coordination. AutoGen uses a conversational model — agents exchange messages to solve a problem through dialogue. CrewAI is faster to configure for straightforward task pipelines; AutoGen is more natural for iterative, back-and-forth problem-solving. CrewAI is also actively developed, while AutoGen is now in maintenance mode.
AutoGen vs. LlamaIndex
LlamaIndex is a data/retrieval framework, not an agent orchestration framework. AutoGen is focused on how agents interact and coordinate through conversations; LlamaIndex is focused on making documents queryable. You can use LlamaIndex inside an AutoGen workflow as a tool — one agent queries a knowledge base. If your primary need is multi-agent collaboration, choose AutoGen. If it is data retrieval and RAG, choose LlamaIndex.
AutoGen vs. Microsoft Agent Framework
Microsoft Agent Framework is the direct successor to both AutoGen and Semantic Kernel, built by the same teams and released as 1.0 in April 2026. It combines AutoGen-style multi-agent patterns with Semantic Kernel's enterprise features and is where Microsoft's new development happens. For any new project in the Microsoft ecosystem, start there; stay on AutoGen only to maintain existing code.
AutoGen vs. Semantic Kernel
Both are Microsoft-backed frameworks with .NET support, and both have been succeeded by Microsoft Agent Framework. Semantic Kernel still receives critical fixes and security patches; AutoGen is in community-managed maintenance mode.
Is AutoGen still maintained?
Only in maintenance mode. AutoGen will not receive new features or enhancements and is community managed going forward; the latest release is python-v0.7.5 (September 2025). Microsoft says new users should start with Microsoft Agent Framework, its successor built by the AutoGen and Semantic Kernel teams, and encourages existing users to migrate using the published migration guide.
AutoGen vs CrewAI: which should I use?
Use AutoGen when your workflow is naturally conversational — iterative code generation and review, multi-step research with critique, or human-agent collaboration where the human participates mid-conversation. Use CrewAI when you want to define a crew of agents with clear roles and tasks and have them execute a pipeline — it is faster to configure and actively developed, while AutoGen is in maintenance mode.
AutoGen vs LangGraph: what is the key difference?
AutoGen is conversational: agents exchange messages, and tasks are solved through dialogue. LangGraph is graph-based: you define an explicit execution graph with nodes and edges, giving you deterministic control over state and flow. LangGraph is stronger for production pipelines that need reliability and auditability; AutoGen is stronger for iterative, exploratory workflows where the right sequence of steps is not fully known in advance.
Does AutoGen support .NET?
Yes — AutoGen is one of the few open-source agent frameworks with a real .NET SDK alongside Python. This is useful for teams with existing .NET infrastructure who want to add multi-agent capabilities without switching to a Python-only stack. For new .NET projects, Microsoft recommends Microsoft Agent Framework, which supports .NET and Python.
What is the difference between AutoGen 0.2 and AutoGen 0.4?
AutoGen 0.4 was a major architectural rewrite (Core, AgentChat, and Extensions APIs), and the current 0.7.x releases build on it. The core concepts (agents, conversations, tool use) are similar to 0.2, but the APIs are significantly different, and much tutorial content online still refers to 0.2. Today the more important choice is between AutoGen (maintenance mode) and Microsoft Agent Framework, which Microsoft recommends for new projects.
Microsoft
Python and .NET multi-agent orchestration, especially with Azure and Microsoft Foundry
FreeLangChain
Developers building production multi-agent systems that need fine-grained control over state, execution flow, and human-in-the-loop checkpoints — and who are willing to trade setup time for that control
FreeCrewAI
Orchestrating autonomous agent teams for enterprise tasks
FreemiumAutoGen's core model is conversational: agents are participants in a structured message exchange, and complex tasks are solved through back-and-forth agent dialogue rather than a predefined execution graph. This makes it a natural fit for iterative problem-solving — code generation with review, research with verification steps, debate-style reasoning — where the solution emerges from the conversation rather than from a predetermined flow. AutoGen supports Python and .NET (unusual dual-language support in this space), works with OpenAI and Azure OpenAI models plus other providers through autogen-ext, supports MCP tools, includes AutoGen Studio as a no-code GUI, and runs entirely in your own environment. Important context: AutoGen is now in maintenance mode. It will not receive new features or enhancements and is community managed going forward; the latest release is python-v0.7.5 (September 2025), built on the 0.4 architecture (Core, AgentChat, and Extensions APIs). Microsoft tells new users to start with Microsoft Agent Framework — the successor built by the AutoGen and Semantic Kernel teams, which reached 1.0 in April 2026 — and encourages existing users to migrate using the published migration guide.
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