yujunjun/autogen
1
0
Fork 0
mirror of https://github.com/microsoft/autogen.git synced 2025-10-17 19:09:36 +00:00
Code Issues Packages Projects Releases Wiki Activity

wording

Browse Source
This commit is contained in:
Qingyun Wu 2023-08-30 17:48:31 -04:00
parent f687a6c57d
commit 8b74d7a698
3 changed files with 5 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

10
website/docs/Getting-Started.md
View File

@ -11,9 +11,7 @@ AutoGen is a framework that enables development of LLM applications using multip
* 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 augments their weakness.
* 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. They demonstrate how
AutoGen can easily support different conversation patterns.
* It provides a collection of working systems with different complexities. These systems span a wide range of applications from various domains and complexities. They demonstrate how AutoGen can easily support different conversation patterns.
AutoGen is powered by collaborative [research studies](/docs/Research) from Microsoft, Penn State University, and University of Washington.
@ -22,8 +20,7 @@ AutoGen is powered by collaborative [research studies](/docs/Research) from Micr
Install AutoGen from pip: `pip install pyautogen`. Find more options in [Installation](/docs/Installation).
Autogen enables the next-gen GPT-X applications with a generic multi-agent conversation framework.
It offers customizable and conversable agents which integrate LLMs, tools and human.
Autogen enables the next-gen LLM applications with a generic multi-agent conversation framework. It offers customizable and conversable agents which integrate LLMs, tools and human.
By automating chat among multiple capable agents, one can easily make them collectively perform tasks autonomously or with human feedback, including tasks that require using tools via code. For example,
```python
from autogen import AssistantAgent, UserProxyAgent
@ -51,7 +48,8 @@ response = autogen.Completion.create(context=test_instance, **config)
### Where to Go Next?
* Understand the use cases for [Autogen](/docs/Use-Cases/Autogen).
* Understand the use cases for [multi-agent conversation](/docs/Use-Cases/multiagent_conversation).
* Understand the use cases for [enhanced LLM inference](/docs/Use-Cases/enhanced_inference).
* Find code examples from [Examples](/docs/Examples).
* Learn about [research](/docs/Research) around AutoGen and check [blogposts](/blog).
* Chat on [Discord](TBD).

2
website/docs/Research.md
View File

@ -1,6 +1,6 @@
# Research
For technical details, please check our research publications.
For technical details, please check our technical report.
* [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework](https://arxiv.org/abs/2308.08155) Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang and Chi Wang. ArXiv 2023.

1
website/docs/Use-Cases/multiagent_conversation.md
View File

@ -1,6 +1,5 @@
# Multi-agent conversation Framework
AutoGen offers a unified multi-agent conversation framework as a high-level abstraction of using foundation models. It features capable, customizable and conversable agents which integrate LLM, tool and human via automated agent chat.
By automating chat among multiple capable agents, one can easily make them collectively perform tasks autonomously or with human feedback, including tasks that require using tools via code.