wording of the docs

.devcontainer/README.md

@@ -1,10 +1,10 @@
-# Devcontainer Configurations for AutoGen
+# Devcontainer Configurations for AG2
 
-Welcome to the `.devcontainer` directory! Here you'll find Dockerfiles and devcontainer configurations that are essential for setting up your AutoGen development environment. Below is a brief overview and how you can utilize them effectively.
+Welcome to the `.devcontainer` directory! Here you'll find Dockerfiles and devcontainer configurations that are essential for setting up your AG2 development environment. Below is a brief overview and how you can utilize them effectively.
 
 These configurations can be used with Codespaces and locally.
 
-## Developing AutoGen with Devcontainers
+## Developing AG2 with Devcontainers
 
 ### Prerequisites
 
@@ -17,7 +17,7 @@ These configurations can be used with Codespaces and locally.
 1. Open the project in Visual Studio Code.
 2. Press `Ctrl+Shift+P` and select `Dev Containers: Reopen in Container`.
 3. Select the desired python environment and wait for the container to build.
-4. Once the container is built, you can start developing AutoGen.
+4. Once the container is built, you can start developing AG2.
 
 ### Troubleshooting Common Issues
 

.devcontainer/dev/Dockerfile

@@ -18,11 +18,11 @@ USER autogen
 # Set environment variable
 # ENV OPENAI_API_KEY="{OpenAI-API-Key}"
 
-# Clone the AutoGen repository
+# Clone the AG2 repository
 RUN git clone https://github.com/ag2ai/ag2.git /home/autogen/ag2
 WORKDIR /home/autogen/ag2
 
-# Install AutoGen in editable mode with extra components
+# Install AG2 in editable mode with extra components
 RUN sudo pip install --upgrade pip && \
     sudo pip install -e .[test,teachable,lmm,retrievechat,mathchat,blendsearch] && \
     pip install pytest-xdist pytest-cov

.github/ISSUE_TEMPLATE.md

@@ -2,8 +2,8 @@
 <!-- A clear and concise description of the issue or feature request. -->
 
 ### Environment
-- AutoGen version: <!-- Specify the AutoGen version (e.g., v0.2.0) -->
-- Python version: <!-- Specify the Python version (e.g., 3.9) -->
+- Package name & version: <!-- Specify the ag2 package name and version (e.g., autogen v0.7.2) -->
+- Python version: <!-- Specify the Python version (e.g., 3.12) -->
 - Operating System: <!-- Specify the OS (e.g., Windows 10, Ubuntu 20.04) -->
 
 ### Steps to Reproduce (for bugs)

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -45,7 +45,7 @@ body:
     attributes:
       label: Additional Information
       description: |
-        - AutoGen Version: <!-- Specify the AutoGen version (e.g., v0.2.0) -->
+        - Package Name & Version: <!-- Specify the package name and version (e.g., autogen v0.7.2) -->
         - Operating System: <!-- Specify the OS (e.g., Windows 10, Ubuntu 20.04) -->
         - Python Version: <!-- Specify the Python version (e.g., 3.9) -->
         - Related Issues: <!-- Link to any related issues here (e.g., #1) -->

.github/ISSUE_TEMPLATE/general_issue.yml

@@ -33,7 +33,7 @@ body:
     attributes:
       label: Additional Information
       description: |
-        - AutoGen Version: <!-- Specify the AutoGen version (e.g., v0.2.0) -->
+        - Package Name & Version: <!-- Specify the package name and version (e.g., autogen v0.7.2) -->
         - Operating System: <!-- Specify the OS (e.g., Windows 10, Ubuntu 20.04) -->
         - Python Version: <!-- Specify the Python version (e.g., 3.9) -->
         - Related Issues: <!-- Link to any related issues here (e.g., #1) -->

README.md

@@ -20,7 +20,7 @@
 > **:tada: IMPORTANT**
 >
 > :fire: :tada: **Nov 11, 2024:** We are evolving AutoGen into **AG2**!
-> A new organization [AG2ai](https://github.com/ag2ai) is created to host the development of AG2 and related projects with open governance. Check [AG2's new look](https://ag2.ai/).
+> A new organization [AG2AI](https://github.com/ag2ai) is created to host the development of AG2 and related projects with open governance. Check [AG2's new look](https://ag2.ai/).
 >
 > We invite collaborators from all organizations and individuals to join the development.
 
@@ -87,7 +87,7 @@ AG2 (formerly AutoGen) is an open-source programming framework for building AI a
 The project is currently maintained by a [dynamic group of volunteers](MAINTAINERS.md) from several organizations. Contact project administrators Chi Wang and Qingyun Wu via [[email protected]](mailto:[email protected]) if you are interested in becoming a maintainer.
 
 
-![AutoGen Overview](https://media.githubusercontent.com/media/ag2ai/ag2/refs/heads/main/website/static/img/autogen_agentchat.png)
+![AG2 Overview](https://media.githubusercontent.com/media/ag2ai/ag2/refs/heads/main/website/static/img/autogen_agentchat.png)
 
 
 <p align="right" style="font-size: 14px; color: #555; margin-top: 20px;">

TRANSPARENCY_FAQS.md

@@ -1,7 +1,7 @@
 # AG2: Responsible AI FAQs
 
 ## What is AG2?
-AG2 is a framework for simplifying the orchestration, optimization, and automation of LLM workflows. It offers customizable and conversable agents that leverage the strongest capabilities of the most advanced LLMs, like GPT-4, while addressing their limitations by integrating with humans and tools and having conversations between multiple agents via automated chat. AG2 is a spin-off of [AutoGen](https://github.com/microsoft/autogen) created by [a team consisting of AutoGen’s founders and contributors ](https://github.com/ag2ai/ag2/blob/main/MAINTAINERS.md) of AutoGen.
+AG2 is a framework for simplifying the orchestration, optimization, and automation of LLM workflows. It offers customizable and conversable agents that leverage the strongest capabilities of the most advanced LLMs, like GPT-4, while addressing their limitations by integrating with humans and tools and having conversations between multiple agents via automated chat. AG2 is a spin-off of [AutoGen](https://github.com/microsoft/autogen) created by [a team consisting of AutoGen’s founders and contributors](https://github.com/ag2ai/ag2/blob/main/MAINTAINERS.md) of AutoGen.
 
 ## What can AG2 do?
 AG2 is a framework for building a complex multi-agent conversation system by:

test/website/test_process_api_reference.py

@@ -31,14 +31,14 @@ def template_content():
                 ],
             },
             {"group": "API Reference", "pages": ["PLACEHOLDER"]},
-            {
-                "group": "AutoGen Studio",
-                "pages": [
-                    "docs/autogen-studio/getting-started",
-                    "docs/autogen-studio/usage",
-                    "docs/autogen-studio/faqs",
-                ],
-            },
+            # {
+            #     "group": "AutoGen Studio",
+            #     "pages": [
+            #         "docs/autogen-studio/getting-started",
+            #         "docs/autogen-studio/usage",
+            #         "docs/autogen-studio/faqs",
+            #     ],
+            # },
         ],
     }
 

test/website/test_process_notebooks.py

@@ -151,14 +151,14 @@ def setup(self, temp_dir: Path) -> None:
                     ],
                 },
                 {"group": "API Reference", "pages": ["PLACEHOLDER"]},
-                {
-                    "group": "AutoGen Studio",
-                    "pages": [
-                        "docs/autogen-studio/getting-started",
-                        "docs/autogen-studio/usage",
-                        "docs/autogen-studio/faqs",
-                    ],
-                },
+                # {
+                #     "group": "AutoGen Studio",
+                #     "pages": [
+                #         "docs/autogen-studio/getting-started",
+                #         "docs/autogen-studio/usage",
+                #         "docs/autogen-studio/faqs",
+                #     ],
+                # },
             ],
         }
 
@@ -172,7 +172,7 @@ def setup(self, temp_dir: Path) -> None:
         {
             "title": "Using RetrieveChat Powered by MongoDB Atlas for Retrieve Augmented Code Generation and Question Answering",
             "link": "/notebooks/agentchat_RetrieveChat_mongodb",
-            "description": "Explore the use of AutoGen's RetrieveChat for tasks like code generation from docstrings, answering complex questions with human feedback, and exploiting features like Update Context, custom prompts, and few-shot learning.",
+            "description": "Explore the use of RetrieveChat for tasks like code generation from docstrings, answering complex questions with human feedback, and exploiting features like Update Context, custom prompts, and few-shot learning.",
             "image": null,
             "tags": [
                 "MongoDB",
@@ -182,7 +182,7 @@ def setup(self, temp_dir: Path) -> None:
             "source": "/notebook/agentchat_RetrieveChat_mongodb.ipynb"
         },
         {
-            "title": "Mitigating Prompt hacking with JSON Mode in Autogen",
+            "title": "Mitigating Prompt hacking with JSON Mode",
             "link": "/notebooks/JSON_mode_example",
             "description": "Use JSON mode and Agent Descriptions to mitigate prompt manipulation and control speaker transition.",
             "image": null,

website/_blogs/2023-10-18-RetrieveChat/index.mdx

@@ -96,7 +96,7 @@ ragproxyagent = RetrieveUserProxyAgent(
     name="ragproxyagent",
     retrieve_config={
         "task": "qa",
-        "docs_path": "https://raw.githubusercontent.com/microsoft/autogen/main/README.md",
+        "docs_path": "https://raw.githubusercontent.com/ag2ai/ag2/main/README.md",
     },
 )
 ```
@@ -297,7 +297,7 @@ boss.initiate_chat(
 ### Build a Chat application with Gradio
 Now, let's wrap it up and make a Chat application with AutoGen and Gradio.
 
-![RAG ChatBot with AutoGen](img/autogen-rag.gif)
+![RAG ChatBot](img/autogen-rag.gif)
 
 ```python
 # Initialize Agents

website/docs/Notebooks.mdx

@@ -8,7 +8,7 @@ import { ClientSideComponent } from "/snippets/components/ClientSideComponent.md
 import { notebooksMetadata } from "/snippets/data/NotebooksMetadata.mdx";
 
 This page contains a collection of notebooks that demonstrate how to use
-AutoGen. The notebooks are tagged with the topics they cover.
+AG2. The notebooks are tagged with the topics they cover.
 For example, a notebook that demonstrates how to use function calling will
 be tagged with `tool/function`.
 

website/docs/contributor-guide/docker.mdx

@@ -11,18 +11,18 @@ For developers contributing to the AG2 project, we offer a specialized devcontai
 - **Forking the Project**: It's advisable to fork the AG2 GitHub project to your own repository. This allows you to make changes in a separate environment without affecting the main project.
 - **Submitting Pull Requests**: Once your changes are ready, submit a pull request from your branch to the upstream AG2 GitHub project for review and integration. For more details on contributing, see the [AG2 Contributing](/docs/contributor-guide/contributing) page.
 
-## Developing AutoGen with Devcontainers
+## Developing with Devcontainers
 
 1. Open the project in Visual Studio Code.
 2. Press `Ctrl+Shift+P` and select `Dev Containers: Reopen in Container`.
 3. Select the desired python environment and wait for the container to build.
-4. Once the container is built, you can start developing AutoGen.
+4. Once the container is built, you can start developing AG2.
 
-## Developing Autogen with Codespaces
+## Developing with Codespaces
 
 Provided devcontainer files can be used with GitHub Codespaces. To use the devcontainer with GitHub Codespaces, follow the steps below:
 
 1. Open the AG2 repository in GitHub.
 2. Click on the `Code` button and select `Open with Codespaces`.
 3. Select the desired python environment and wait for the container to build.
-4. Once the container is built, you can start developing AutoGen.
+4. Once the container is built, you can start developing AG2.

website/docs/ecosystem/agentops.mdx

@@ -14,7 +14,7 @@ At a high level, AgentOps gives you the ability to monitor LLM calls, costs, lat
 | 💸 **LLM Cost Management**            | Track spend with LLM foundation model providers               |
 | 🧪 **Agent Benchmarking**             | Test your agents against 1,000+ evals                         |
 | 🔐 **Compliance and Security**        | Detect common prompt injection and data exfiltration exploits |
-| 🤝 **Framework Integrations**         | Native Integrations with CrewAI, AutoGen, & LangChain         |
+| 🤝 **Framework Integrations**         | Native Integrations with CrewAI, AG2, & LangChain         |
 
 <AccordionGroup>
   <Accordion title="Agent Dashboard" defaultOpen>
@@ -34,7 +34,7 @@ At a high level, AgentOps gives you the ability to monitor LLM calls, costs, lat
 
 ## Installation
 
-AgentOps works seamlessly with applications built using Autogen.
+AgentOps works seamlessly with applications built using AG2.
 
 1. **Install AgentOps**
 ```bash
@@ -53,14 +53,14 @@ AGENTOPS_API_KEY=<YOUR_AGENTOPS_API_KEY>
 
 4. **Initialize AgentOps**
 
-To start tracking all available data on Autogen runs, simply add two lines of code before implementing Autogen.
+To start tracking all available data on AG2 runs, simply add two lines of code before using AG2.
 
 ```python
 import agentops
 agentops.init() # Or: agentops.init(api_key="your-api-key-here")
 ```
 
-After initializing AgentOps, Autogen will now start automatically tracking your agent runs.
+After initializing AgentOps, AG2 will now start automatically tracking your agent runs.
 
 ## Features
 
@@ -75,7 +75,7 @@ After initializing AgentOps, Autogen will now start automatically tracking your
 - **Compliance and Security**: Create audit logs and detect potential threats such as profanity and PII leaks
 - **Prompt Injection Detection**: Identify potential code injection and secret leaks
 
-## Autogen + AgentOps examples
+## AG2 + AgentOps examples
 * [AgentChat with AgentOps Notebook](/notebooks/agentchat_agentops)
 * [More AgentOps Examples](https://docs.agentops.ai/v1/quickstart)
 

website/docs/ecosystem/composio.mdx

@@ -5,6 +5,6 @@ sidebarTitle: Composio
 
 <img src="img/ecosystem-composio.png" style={{ width: '40%' }} alt="Composio Logo"/>
 
-Composio empowers AI agents to seamlessly connect with external tools, Apps, and APIs to perform actions and receive triggers. With built-in support for AutoGen, Composio enables the creation of highly capable and adaptable AI agents that can autonomously execute complex tasks and deliver personalized experiences.
+Composio empowers AI agents to seamlessly connect with external tools, Apps, and APIs to perform actions and receive triggers. With built-in support for AG2, Composio enables the creation of highly capable and adaptable AI agents that can autonomously execute complex tasks and deliver personalized experiences.
 
-- [Composio + AutoGen Documentation with Code Examples](https://docs.composio.dev/framework/autogen)
+- [Composio + AG2 Documentation with Code Examples](https://docs.composio.dev/framework/autogen)

website/docs/ecosystem/databricks.mdx

@@ -7,6 +7,6 @@ sidebarTitle: Databricks
 
 The [Databricks Data Intelligence Platform ](https://www.databricks.com/product/data-intelligence-platform) allows your entire organization to use data and AI. It’s built on a lakehouse to provide an open, unified foundation for all data and governance, and is powered by a Data Intelligence Engine that understands the uniqueness of your data.
 
-This example demonstrates how to use AutoGen alongside Databricks Foundation Model APIs and open-source LLM DBRX.
+This example demonstrates how to use AG2 alongside Databricks Foundation Model APIs and open-source LLM DBRX.
 
-- [Databricks + AutoGen Code Examples](/notebooks/agentchat_databricks_dbrx)
+- [Databricks + AG2 Code Examples](/notebooks/agentchat_databricks_dbrx)

website/docs/ecosystem/llamaindex.mdx

@@ -5,6 +5,6 @@ sidebarTitle: Llamaindex
 
 ![Llamaindex Example](img/ecosystem-llamaindex.png)
 
-[Llamaindex](https://www.llamaindex.ai/) allows the users to create Llamaindex agents and integrate them in autogen conversation patterns.
+[Llamaindex](https://www.llamaindex.ai/) allows the users to create Llamaindex agents and integrate them in AG2 conversation patterns.
 
-- [Llamaindex + AutoGen Code Examples](https://github.com/ag2ai/ag2/blob/main/notebook/agentchat_group_chat_with_llamaindex_agents.ipynb)
+- [Llamaindex + AG2 Code Examples](https://github.com/ag2ai/ag2/blob/main/notebook/agentchat_group_chat_with_llamaindex_agents.ipynb)

website/docs/ecosystem/mem0.mdx

@@ -69,9 +69,9 @@ After initializing Mem0, you can start using its memory management features in y
 
 ## Mem0 Platform Examples
 
-### AutoGen with Mem0 Example
+### AG2 with Mem0 Example
 
-This example demonstrates how to use Mem0 with AutoGen to create a conversational AI system with memory capabilities.
+This example demonstrates how to use Mem0 with AG2 to create a conversational AI system with memory capabilities.
 
 ```python
 import os
@@ -153,10 +153,10 @@ Question: {data}
 result = manager.send(prompt, customer_bot, request_reply=True)
 ```
 
-Access the complete code from this notebook: [Mem0 with AutoGen](https://colab.research.google.com/drive/1NZEwC9w6V2S6hYmK7l2SQ9jhQrG1uKk8?usp=sharing)
+Access the complete code from this notebook: [Mem0 with AG2](https://colab.research.google.com/drive/1NZEwC9w6V2S6hYmK7l2SQ9jhQrG1uKk8?usp=sharing)
 
 This example showcases:
-1. Setting up AutoGen agents and Mem0 memory
+1. Setting up AG2 agents and Mem0 memory
 2. Adding a conversation to Mem0 memory
 3. Using Mem0 to retrieve relevant memories for agent inference
 4. Implementing a multi-agent conversation with memory-augmented context

website/docs/ecosystem/memgpt.mdx

@@ -5,6 +5,6 @@ sidebarTitle: MemGPT
 
 ![MemGPT Example](img/ecosystem-memgpt.png)
 
-MemGPT enables LLMs to manage their own memory and overcome limited context windows. You can use MemGPT to create perpetual chatbots that learn about you and modify their own personalities over time. You can connect MemGPT to your own local filesystems and databases, as well as connect MemGPT to your own tools and APIs. The MemGPT + AutoGen integration allows you to equip any AutoGen agent with MemGPT capabilities.
+MemGPT enables LLMs to manage their own memory and overcome limited context windows. You can use MemGPT to create perpetual chatbots that learn about you and modify their own personalities over time. You can connect MemGPT to your own local filesystems and databases, as well as connect MemGPT to your own tools and APIs. The MemGPT + AG2 integration allows you to equip any AG2 agent with MemGPT capabilities.
 
-- [MemGPT + AutoGen Documentation with Code Examples](https://memgpt.readme.io/docs/autogen)
+- [MemGPT + AG2 Documentation with Code Examples](https://memgpt.readme.io/docs/autogen)

website/docs/ecosystem/microsoft-fabric.mdx

@@ -5,6 +5,6 @@ sidebarTitle: Microsoft Fabric
 
 ![Fabric Example](img/ecosystem-fabric.png)
 
-[Microsoft Fabric](https://learn.microsoft.com/en-us/fabric/get-started/microsoft-fabric-overview) is an all-in-one analytics solution for enterprises that covers everything from data movement to data science, Real-Time Analytics, and business intelligence. It offers a comprehensive suite of services, including data lake, data engineering, and data integration, all in one place. In this notenook, we give a simple example for using AutoGen in Microsoft Fabric.
+[Microsoft Fabric](https://learn.microsoft.com/en-us/fabric/get-started/microsoft-fabric-overview) is an all-in-one analytics solution for enterprises that covers everything from data movement to data science, Real-Time Analytics, and business intelligence. It offers a comprehensive suite of services, including data lake, data engineering, and data integration, all in one place. In this notenook, we give a simple example for using AG2 in Microsoft Fabric.
 
-- [Microsoft Fabric + AutoGen Code Examples](https://github.com/ag2ai/ag2/blob/main/notebook/agentchat_microsoft_fabric.ipynb)
+- [Microsoft Fabric + AG2 Code Examples](https://github.com/ag2ai/ag2/blob/main/notebook/agentchat_microsoft_fabric.ipynb)

website/docs/ecosystem/ollama.mdx

@@ -7,4 +7,4 @@ sidebarTitle: Ollama
 
 [Ollama](https://ollama.com/) allows the users to run open-source large language models, such as Llama 2, locally. Ollama bundles model weights, configuration, and data into a single package, defined by a Modelfile. It optimizes setup and configuration details, including GPU usage.
 
-- [Ollama + AutoGen instruction](https://ollama.ai/blog/openai-compatibility)
+- [Ollama + AG2 instruction](https://ollama.ai/blog/openai-compatibility)

website/docs/ecosystem/pgvector.mdx

@@ -5,4 +5,4 @@ sidebarTitle: PGVector
 
 [PGVector](https://github.com/pgvector/pgvector) is an open-source vector similarity search for Postgres.
 
-- [PGVector + AutoGen Code Examples](https://github.com/ag2ai/ag2/blob/main/notebook/agentchat_RetrieveChat_pgvector.ipynb)
+- [PGVector + AG2 Code Examples](https://github.com/ag2ai/ag2/blob/main/notebook/agentchat_RetrieveChat_pgvector.ipynb)