{"id":973047,"date":"2023-10-06T15:16:20","date_gmt":"2023-10-06T22:16:20","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-project&p=973047"},"modified":"2024-09-30T15:27:58","modified_gmt":"2024-09-30T22:27:58","slug":"autogen","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/autogen\/","title":{"rendered":"AutoGen"},"content":{"rendered":"
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AutoGen<\/h1>\n\n\n\n

An Open-Source Programming Framework for Agentic AI
autogen@microsoft.com<\/a><\/p>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n<\/section>\n\n\n\n\n\n

AutoGen provides a multi-agent conversation framework as a high-level abstraction. It is an open-source library for enabling next-generation LLM applications with multi-agent collaborations, teachability and personalization. With this framework, users can build LLM workflows. The agent modularity and conversation-based programming simplifies development and enables reuse for developers. End-users benefit from multiple agents independently learning and collaborating on their behalf, enabling them to accomplish more with less work. Benefits of the multi agent approach with AutoGen include agents that can be backed by various LLM configurations; native support for a generic form of tool usage through code generation and execution; and, a special agent, the Human Proxy Agent that enables easy integration of human feedback and involvement at different levels.<\/p>\n\n\n\n