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. It supports Python and .NET, and is designed for developers building agentic pipelines where agents exchange messages, use tools, and work alongside humans to complete complex tasks.
Best for
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 versioning situation is also worth noting: a lot of AutoGen tutorials and examples online refer to 0.2.x, which is now in maintenance mode. If you are starting a new project, check the current GitHub docs to understand which API is recommended. For teams that value Microsoft support and .NET compatibility, Semantic Kernel is the more actively maintained option in the same family.
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 if you are looking for a stable, fully-maintained framework without versioning complexity: check the current GitHub docs before committing.
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 more actively maintained and better documented than AutoGen's current state.
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. Semantic Kernel
Both are Microsoft-backed frameworks with .NET support, but Semantic Kernel is more actively maintained and has stronger enterprise features (observability, security, multi-language support). For new projects in the Microsoft ecosystem, Semantic Kernel is the more stable choice. AutoGen's advantage is its conversational multi-agent model, which Semantic Kernel does not replicate directly.
Is AutoGen still maintained?
AutoGen 0.2.x is in maintenance mode with limited updates from Microsoft. AutoGen 0.4 is a significant rewrite with a different architecture and is more actively developed. Before starting a new project, check the current GitHub repository (github.com/microsoft/autogen) for the recommended version and migration guidance. Microsoft also maintains Semantic Kernel as an alternative for production enterprise use cases.
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 better documented for production use.
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. Semantic Kernel also supports .NET and is more actively maintained for enterprise .NET use cases.
What is the difference between AutoGen 0.2 and AutoGen 0.4?
AutoGen 0.4 is a major architectural rewrite, not an incremental update. The core concepts (agents, conversations, tool use) are similar, but the APIs are significantly different. AutoGen 0.2.x is in maintenance mode and much of the tutorial content online refers to it. If you are starting a new project, check the current GitHub docs to determine which version to use and whether migration to 0.4 is recommended for your use case.
LangChain
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
FreemiumMicrosoft
Adding AI agents to .NET, Python, and Java apps
FreeLlamaIndex
Developers building RAG systems and document-grounded agents who need intelligent data parsing, indexing, and retrieval — especially teams with large or complex document sets
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, and runs entirely in your own environment. Important context: the AutoGen codebase has gone through significant versioning changes. The earlier 0.2.x releases are in maintenance mode; AutoGen 0.4 is a substantial rewrite with a different architecture. Before starting a new project, verify which version of AutoGen is recommended in the current GitHub docs. Existing 0.2.x users can continue with community support. New projects should review both AutoGen 0.4 and Semantic Kernel as the current Microsoft-backed options.
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