AutoGen Framework

An AutoGen Framework is an open-source multi-agent conversation framework designed to facilitate automated generation of content.

• Context:
  • It can be designed for specific domains, such as AutoGen Content Creation or AutoGen Code Production.
  • It can incorporate multiple agents or components that work together to produce desired results.
  • It can include predefined templates or examples for various use-cases to guide users.
  • It can integrate with external tools or databases to enrich generated content or functionality.
  • It can be Extensible Framework.
  • It can have a focus on optimization (for efficient use of resources and minimizing computational overhead).
  • …
• Example(s):
  • AutoGen v0.1.11 [1] (~2023-10-16).
  • AutoGen v0.2.0 [2] (~2023-11-24).
  • …
• Counter-Example(s):
  • A Manual Design Tool, which requires users to manually design each element without any automated generation capabilities.
  • A Simple Scripting Tool, which doesn't provide any automatic generation, but simply runs predefined scripts.
  • An OpenAI Swarm Framework.
• See: AutoML Frameworks, Code Generation Tools, Content Creation Platforms, AI-Enhanced Development Environments.

---

---

References

2023

• GBard
  • It provides a multi-agent conversation framework to simplify orchestrating complex LLM workflows.
  • It enables building applications using multiple conversational agents that can interact with each other and humans to solve tasks.
  • It includes prebuilt examples spanning different domains and complexities to demonstrate capabilities.
  • It offers customizable, conversational agents and seamless human participation.
  • It offers drop-in enhanced inference APIs to improve LLM performance, handle errors, optimize cost etc.
  • It is open source and developed through collaboration between Microsoft, Penn State, and University of Washington.
  • It can have features such as: agent conversations, inference optimization, and easy application development.

2023

• (Wu, Bansal et al., 2023) ⇒ Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang. (year2023). “AutoGen: Enabling Next-Gen LLM Applications via Multi-agent Conversation Framework.” In: arXiv preprint arXiv:2308.08155. doi:10.48550/arXiv.2308.08155
  • ABSTRACT: AutoGen is an open-source framework that allows developers to build LLM applications via multiple agents that can converse with each other to accomplish tasks. AutoGen agents are customizable, conversable, and can operate in various modes that employ combinations of LLMs, human inputs, and tools. Using AutoGen, developers can also flexibly define agent interaction behaviors. Both natural language and computer code can be used to program flexible conversation patterns for different applications. AutoGen serves as a generic infrastructure to build diverse applications of various complexities and LLM capacities. Empirical studies demonstrate the effectiveness of the framework in many example applications, with domains ranging from mathematics, coding, question answering, operations research, online decision-making, entertainment, etc.
  • NOTES:
    • The paper highlights how AutoGen can be customized for specific domains like AutoGen Content Creation or AutoGen Code Production, enabling tailored multi-agent solutions.
    • The paper demonstrates the integration of external tools and databases within AutoGen, enriching the generated content and extending the framework's capabilities.
    • The paper discusses the Extensible Framework nature of AutoGen, allowing developers to build on top of existing functionalities and adapt the framework to new use cases.
    • The paper emphasizes the importance of optimization within AutoGen, focusing on efficient resource usage and minimizing computational overhead in multi-agent workflows.
    • The paper compares AutoGen with other frameworks, illustrating its advantages over Manual Design Tools and Simple Scripting Tools by offering automated generation and multi-agent collaboration capabilities.

---