Autogen Studio
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A Autogen Studio is a LLM-based automation framework for developing LLM-based applications using multiple agents that can converse with each other to solve tasks.
- Context:
- It can (typically) provide a framework for building next-generation LLM applications based on multi-agent conversations.
- It can (typically) involve collaboration with research institutions such as Microsoft, Penn State University, and the University of Washington.
- It can (often) be installed using Python (Python 3.10 or newer) and Node.js (version above 14.15.0), and accessed via a web UI.
- It can (often) enable users to create customizable and conversable agents, integrating human participation seamlessly.
- It can ) support diverse conversation patterns for complex workflows.
- It can ) offer enhanced LLM inference capabilities, such as API unification, caching, and advanced usage patterns like error handling and context programming.
- It can ) be used for various purposes like automating tasks, analyzing data, generating images, and retrieving data, catering to both beginners and advanced users.
- ...
- Example(s):
- AutoGen, v0.2.6 (~2024-01-12) [1].
- ...
- Counter-Example(s):
- A traditional software development framework that does not focus on LLM applications.
- A single-agent language model application.
- See: Software Framework, Large Language Model, Agent-Based Model, Artificial Intelligence.
References
2024
- (GitHub, 2024) ⇒ https://github.com/microsoft/autogen
- AutoGen is a framework that enables the development of LLM applications using multiple agents that can converse with each other to solve tasks. AutoGen agents are customizable, conversable, and seamlessly allow human participation. They can operate in various modes that employ combinations of LLMs, human inputs, and tools.
- AutoGen Overview
- AutoGen enables building next-gen LLM applications based on multi-agent conversations with minimal effort. It simplifies the orchestration, automation, and optimization of a complex LLM workflow. It maximizes the performance of LLM models and overcomes their weaknesses.
- It supports diverse conversation patterns for complex workflows. With customizable and conversable agents, developers can use AutoGen to build a wide range of conversation patterns concerning conversation autonomy, the number of agents, and agent conversation topology.
- It provides a collection of working systems with different complexities. These systems span a wide range of applications from various domains and complexities. This demonstrates how AutoGen can easily support diverse conversation patterns.
- AutoGen provides enhanced LLM inference. It offers utilities like API unification and caching, and advanced usage patterns, such as error handling, multi-config inference, context programming, etc.
2024
- (Wu et al., 2024) ⇒ Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Jiale Liu, Ahmed Hassan Awadallah, Ryen W White, Doug Burger, and Chi Wang. (2024). "AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework." In: arXiv:2308.08155.
- NOTE: AutoGen is a framework that enables the development of LLM applications using multiple agents that can converse with each other to solve tasks. It offers customizable and conversable agents, integrates human participation seamlessly, and supports diverse conversation patterns for complex workflows.
2024
- (Wu et al., 2024) ⇒ Yiran Wu, Feiran Jia, Shaokun Zhang, Hangyu Li, Erkang Zhu, Yue Wang, Yin Tat Lee, Richard Peng, Qingyun Wu, and Chi Wang. (2024). "An Empirical Study on Challenging Math Problem Solving with GPT-4." In: ArXiv preprint arXiv:2306.01337.
2023
- (Wang et al., 2023) ⇒ Chi Wang, Susan Xueqing Liu, and Ahmed H. Awadallah. (2023). "Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference." In: AutoML'23.