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AutoGen is an open-source programming framework for building AI agents and facilitating cooperation among multiple agents to solve tasks. AutoGen aims to provide an easy-to-use and flexible framework for accelerating development and research on agentic AI, like PyTorch for Deep Learning. It offers features such as agents that can converse with other agents, LLM and tool use support, autonomous and human-in-the-loop workflows, and multi-agent conversation patterns. AutoGen provides multi-agent conversation framework as a high-level abstraction. With this framework, one can conveniently build LLM workflows. AutoGen offers a collection of working systems spanning a wide range of applications from various domains and complexities. AutoGen supports enhanced LLM inference APIs, which can be used to improve inference performance and reduce cost.
Features
- AutoGen enables building next-gen LLM applications based on multi-agent conversations with minimal effort
- It supports diverse conversation patterns for complex workflows
- It provides a collection of working systems with different complexities
- AutoGen is powered by collaborative research studies from Microsoft, Penn State University, and University of Washington
- It simplifies the orchestration, automation, and optimization of a complex LLM workflow
- It maximizes the performance of LLM models and overcomes their weaknesses
Project Samples
