Merge branch 'main' into update-quick-start

Author: ekzhu

CONTRIBUTORS.md

@@ -10,7 +10,7 @@
 # AutoGen
 
 > [!IMPORTANT]
-> AutoGen 0.4 is a from-the-ground-up rewrite of AutoGen. Learn more about the history, goals and future at [this blog post](https://microsoft.github.io/autogen/blog). We’re excited to work with the community to gather feedback, refine, and improve the project before we officially release 0.4. This is a big change, so AutoGen 0.2 is still available, maintained, and developed in the [0.2 branch](https://github.com/microsoft/autogen/tree/0.2).
+> [AutoGen 0.4](https://microsoft.github.io/autogen/dev) is a from-the-ground-up rewrite of AutoGen. Learn more about the history, goals and future at [this blog post](https://microsoft.github.io/autogen/blog). We’re excited to work with the community to gather feedback, refine, and improve the project before we officially release 0.4. This is a big change, so AutoGen 0.2 is still available, maintained, and developed in the [0.2 branch](https://github.com/microsoft/autogen/tree/0.2).
 
 AutoGen is an open-source framework for building AI agent systems.
 It simplifies the creation of event-driven, distributed, scalable, and resilient agentic applications.

README.md

@@ -1 +1,9 @@
-# autogen-agentchat
+# AutoGen AgentChat
+
+- [Documentation](https://microsoft.github.io/autogen/dev/user-guide/agentchat-user-guide/index.html)
+
+## Package structure
+
+- `agents` are the building blocks for creating agents and built-in agents.
+- `teams` are the building blocks for creating teams of agents and built-in teams, such as group chats.
+- `logging` contains logging utilities.

python/packages/autogen-agentchat/README.md

@@ -6,7 +6,7 @@ build-backend = "hatchling.build"
 name = "autogen-agentchat"
 version = "0.4.0dev0"
 license = {file = "LICENSE-CODE"}
-description = "AutoGen agent and group chat library"
+description = "AutoGen agents and teams library"
 readme = "README.md"
 requires-python = ">=3.10"
 classifiers = [

python/packages/autogen-agentchat/pyproject.toml

@@ -1,7 +1,6 @@
 # AutoGen Core
 
-- [Documentation](http://microsoft.github.io/autogen)
-- [Examples](https://github.com/microsoft/autogen/tree/main/python/packages/autogen-core/samples)
+- [Documentation](https://microsoft.github.io/autogen/dev/user-guide/core-user-guide/index.html)
 
 ## Package layering
 

python/packages/autogen-core/README.md

@@ -10,7 +10,7 @@
 import requests
 from autogen_magentic_one.markdown_browser import BingMarkdownSearch, RequestsMarkdownBrowser
 
-BLOG_POST_URL = "https://microsoft.github.io/autogen/blog/2023/04/21/LLM-tuning-math"
+BLOG_POST_URL = "https://microsoft.github.io/autogen/0.2/blog/2023/04/21/LLM-tuning-math"
 BLOG_POST_TITLE = "Does Model and Inference Parameter Matter in LLM Applications? - A Case Study for MATH | AutoGen"
 BLOG_POST_STRING = "Large language models (LLMs) are powerful tools that can generate natural language texts for various applications, such as chatbots, summarization, translation, and more. GPT-4 is currently the state of the art LLM in the world. Is model selection irrelevant? What about inference parameters?"
 BLOG_POST_FIND_ON_PAGE_QUERY = "an example where high * complex"