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website/docs/Getting-Started.mdx

@@ -3,33 +3,33 @@ import TabItem from "@theme/TabItem";
 
 # Getting Started
 
-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
+AG2 (formerly AutoGen) is an open-source programming framework for building AI agents and facilitating
+cooperation among multiple agents to solve tasks. AG2 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 Overview](/img/autogen_agentchat.png)
+![AG2 Overview](/img/autogen_agentchat.png)
 
 ### Main Features
 
-- AutoGen enables building next-gen LLM applications based on [multi-agent
+- AG2 enables building next-gen LLM applications based on [multi-agent
   conversations](/docs/Use-Cases/agent_chat) 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](/docs/Use-Cases/agent_chat#supporting-diverse-conversation-patterns)
   for complex workflows. With customizable and conversable agents, developers can
-  use AutoGen to build a wide range of conversation patterns concerning
+  use AG2 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](/docs/Use-Cases/agent_chat#diverse-applications-implemented-with-autogen)
-  from various domains and complexities. This demonstrates how AutoGen can
+  from various domains and complexities. This demonstrates how AG2 can
   easily support diverse conversation patterns.
 
-AutoGen is powered by collaborative [research studies](/docs/Research) from
+AG2 is powered by collaborative [research studies](/docs/Research) from
 Microsoft, Penn State University, and University of Washington.
 
 ### Quickstart
@@ -120,20 +120,20 @@ Learn more about configuring LLMs for agents [here](/docs/topics/llm_configurati
 
 #### Multi-Agent Conversation Framework
 
-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 humans.
+AG2 enables the next-gen LLM applications with a generic multi-agent conversation framework. It offers customizable and conversable agents which integrate LLMs, tools, and humans.
 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](https://github.com/ag2ai/ag2/blob/main/test/twoagent.py),
 
-The figure below shows an example conversation flow with AutoGen.
+The figure below shows an example conversation flow with AG2.
 
 ![Agent Chat Example](/img/chat_example.png)
 
 ### Where to Go Next?
 
-- Go through the [tutorial](/docs/tutorial/introduction) to learn more about the core concepts in AutoGen
+- Go through the [tutorial](/docs/tutorial/introduction) to learn more about the core concepts in AG2
 - Read the examples and guides in the [notebooks section](/docs/notebooks)
 - Understand the use cases for [multi-agent conversation](/docs/Use-Cases/agent_chat) and [enhanced LLM inference](/docs/Use-Cases/enhanced_inference)
 - Read the [API](/docs/reference/agentchat/conversable_agent/) docs
-- Learn about [research](/docs/Research) around AutoGen
+- Learn about [research](/docs/Research) around AG2
 - Chat on [Discord](https://discord.gg/pAbnFJrkgZ)
 - Follow on [Twitter](https://twitter.com/Chi_Wang_)
 - See our [roadmaps](https://github.com/ag2ai/ag2/issues?q=is%3Aissue+is%3Aopen+label%3Aroadmap)