AutoGen
AutoGen is an open-source multi-agent collaboration framework developed by Microsoft Research, designed to simplify the development of automated applications built on large language models. The framework supports defining multiple AI agents with distinct roles, enabling them to communicate and collaborate on complex tasks. Key features include support for human-in-the-loop workflows, flexible configuration of agent conversation flows, and compatibility with multiple large language model backends. It is widely used in code generation, task planning, and data analysis scenarios.
Core Facts
Multi-agent collaboration is typically implemented through an orchestrator-executor pattern, where a primary control Agent is responsible for task decomposition and scheduling.
Microsoft's AutoGen, LangChain's LangGraph, and OpenAI's Swarm framework are all exploring multi-agent division of labor and collaboration.
Currently mainstream AI Agent frameworks include LangChain, AutoGen, CrewAI, and others.
Timeline (last 90 days)
Compared with LangChain's single-Agent orchestration, AutoGen is better suited to tasks that require role division and repeated negotiation, but it is also more prone to problems such as conversations failing to converge and high token consumption.
The advanced chapter of the tutorial covers hands-on practice with the LangChain and AutoGen frameworks, multi-Agent collaboration, Function Calling tool invocation, hybrid architectures of RAG and Agents, as well as evaluation, debugging, and safety boundaries, with each section accompanied by code courseware.
LangGraph's monthly PyPI installs are about 71.1 million, roughly 65 times AutoGen's (about 1.08 million), and this gap holds under all reasonable qualifying conditions.
AutoGen has entered maintenance mode, the README explicitly states that it no longer accepts new features, and the official recommendation for new users is to move to Microsoft Agent Framework, whose successor officially became GA on April 3, 2026.
AutoGen's license status is inconsistent: GitHub detects it as CC-BY-4.0, the README describes it as dual-licensed under CC-BY-4.0 and MIT, and PyPI reports MIT.
AutoGen has about 60,400 GitHub stars, more than CrewAI's 57,100 and LangGraph's 39,800, but has gone about 320 days without a stable Python release.
OpShield claims to be "zero-dependency" and can be used with OpenAI, Anthropic, LangChain, CrewAI, AutoGen, or any Python function.
guilds.run positions itself as a layer above orchestration frameworks (such as LangChain and AutoGen): an observability and governance layer.
Currently, multi-Agent frameworks such as AutoGen, CrewAI, and LangGraph are all trying to solve the industry-wide challenge of production-grade reliability.
In this benchmark, AutoGen achieved a success rate of 59.8%, an average completion time of 29.4 seconds, and an average token cost of 13,031.
40 more timeline events
All Facts (20)
Multi-agent collaboration is typically implemented through an orchestrator-executor pattern, where a primary control Agent is responsible for task decomposition and scheduling.
90%VerifiedMicrosoft's AutoGen, LangChain's LangGraph, and OpenAI's Swarm framework are all exploring multi-agent division of labor and collaboration.
90%VerifiedCurrently mainstream AI Agent frameworks include LangChain, AutoGen, CrewAI, and others.
90%VerifiedThe ReAct (Reasoning + Acting) framework enables models to alternately perform reasoning and action.
90%VerifiedLangChain, AutoGen, and LangGraph are mainstream frameworks that position agents in the role of process coordination.
80%VerifiedAI application development frameworks such as LangChain and LlamaIndex have built-in memory management modules.
80%VerifiedAutoGen is a multi-agent conversational framework launched by Microsoft.
80%VerifiedAutoGen was launched by Microsoft Research and focuses on multi-Agent collaboration scenarios.
80%VerifiedTool-Augmented AI is an important development direction for current large model applications, with mechanisms such as OpenAI's Function Calling and Anthropic's Tool Use underpinning this paradigm at the foundational level.
80%VerifiedThe ReAct (Reasoning + Acting) framework was proposed by Yao et al. in 2022, alternating Chain-of-Thought reasoning with external tool interactions.
80%VerifiedSince 2023, projects such as Stanford's Generative Agents, Microsoft's AutoGen, and CrewAI have demonstrated the superiority of multi-agent collaboration in complex tasks.
80%VerifiedCrewAI专注于多Agent协作场景，通过角色定义和任务分配实现团队化的Agent编排
80%VerifiedTypical frameworks for Multi-Agent architecture include Microsoft's AutoGen, CrewAI, and LangGraph.
80%VerifiedRepresentative frameworks in the multi-Agent collaboration domain include AutoGen, CrewAI, and LangGraph.
80%VerifiedAI Agent底层通常由大语言模型（LLM）驱动，配合工具调用、记忆模块和规划框架（如ReAct范式）实现复杂任务自动化
80%VerifiedAI Agent具备工具调用（Tool Use）和规划推理（Planning & Reasoning）两大核心能力，能将复杂目标自动分解为可执行的子任务序列
80%VerifiedAutoGen是微软研究院出品的多Agent协作框架，通过可编程的对话模式实现Agent间的灵活协作
80%VerifiedAutoGen是微软出品的Agent框架，侧重多Agent协作场景，支持多个Agent之间的消息传递与角色分工
80%VerifiedAutoGen is an open-source agent framework from Microsoft.
80%VerifiedWhen selecting AI Agent building tools, one should verify whether there is a complete draft-test-publish workflow, out-of-the-box version management and one-click rollback, fine-grained permission controls, and multi-channel deployment with shared configuration.
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