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AutoGen

Microsoft's conversational multi-agent framework

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AutoGen is an open-source programming framework from Microsoft Research for building AI agents and facilitating cooperation among multiple agents to solve complex tasks through multi-turn conversations. Pioneered conversable agents that interact, use tools, and involve humans in the loop for multi-agent workflows. v0.4 features a redesigned async event-driven architecture with stronger observability, flexible collaboration patterns, and reusable components.

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AutoGen is an open-source programming framework developed by Microsoft Research for building AI agents and facilitating cooperation among multiple agents to solve complex tasks through multi-turn conversations. It pioneered the concept of conversable agents that can interact with each other, use tools, and involve humans in the loop, enabling sophisticated multi-agent workflows for code generation, task planning, and collaborative problem-solving. AutoGen introduced the idea that complex AI tasks are best solved through agent conversations rather than single-shot prompts, fundamentally changing how developers approach agentic AI development.

AutoGen v0.4 represents a complete redesign with a robust, asynchronous, and event-driven architecture that enables broader agentic scenarios with stronger observability, more flexible collaboration patterns, and reusable components. The framework supports various large language models, tool use, autonomous and human-in-the-loop workflows, and multi-agent conversation patterns including group chat, nested conversations, and teachable agents. AutoGen is now part of the broader Microsoft Agent Framework, which merges AutoGen dynamic multi-agent orchestration with Semantic Kernel production foundations for enterprise deployment.

AutoGen targets AI researchers, enterprise developers, and teams building multi-agent applications for software engineering, data analysis, mathematical reasoning, and automated decision-making workflows. It integrates with the Azure AI ecosystem and supports deployment through the Microsoft Agent Framework, which is available in Python and .NET. While AutoGen continues to receive maintenance and security patches, new projects are encouraged to adopt the Microsoft Agent Framework for production use, combining AutoGen orchestration capabilities with Semantic Kernel stability and enterprise tooling.

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Free, open-source

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Python

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Comparisons

LangChain vs AutoGen — Ecosystem Breadth vs Conversational Multi-Agent

LangChain and AutoGen solve different parts of the agent-framework problem. LangChain is the broader LLM application ecosystem for RAG, tool use, model routing, and production plumbing. AutoGen is more focused on conversational multi-agent workflows, where specialized agents exchange messages, collaborate, and execute code-like tasks through dialogue.

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CrewAI vs AutoGen vs LangGraph — Picking the Right Multi-Agent Framework

CrewAI, AutoGen, and LangGraph are the three leading frameworks for building multi-agent AI systems, each with a distinct philosophy on how agents should collaborate. CrewAI uses a role-based crew metaphor where agents with defined roles work together on sequential or parallel tasks. AutoGen from Microsoft Research focuses on conversational multi-agent patterns with human-in-the-loop support. LangGraph from LangChain provides a graph-based state machine for fine-grained control over agent workflows. This comparison helps developers choose the right foundation for their agent architecture.

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crewAI vs AutoGen — Multi-Agent AI Framework Comparison for Developer Workflows

crewAI and AutoGen (now AG2) are the two most popular open-source multi-agent frameworks in 2026. crewAI uses role-based agent teams with structured collaboration workflows and 100K+ certified developers. AutoGen provides a flexible conversation-driven architecture with 40K+ GitHub stars where agents interact through message passing. Both enable building systems where multiple AI agents collaborate, but their design philosophies lead to fundamentally different development experiences and trade-offs.

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