Merge remote-tracking branch 'origin/main' into swarmag2

.github/PULL_REQUEST_TEMPLATE.md

@@ -12,6 +12,6 @@
 
 ## Checks
 
-- [ ] I've included any doc changes needed for https://ag2ai.github.io/autogen/. See https://ag2ai.github.io/ag2/docs/Contribute#documentation to build and test documentation locally.
+- [ ] I've included any doc changes needed for https://ag2ai.github.io/ag2/. See https://ag2ai.github.io/ag2/docs/Contribute#documentation to build and test documentation locally.
 - [ ] I've added tests (if relevant) corresponding to the changes introduced in this PR.
 - [ ] I've made sure all auto checks have passed.

README.md

@@ -84,7 +84,7 @@ We adopt the Apache 2.0 license from v0.3. This enhances our commitment to open-
 
 ## What is AG2
 
-AG2 (formally AutoGen) is an open-source programming framework for building AI agents and facilitating cooperation among multiple agents to solve tasks. AG2 aims to streamline the development and research of agentic AI, much like PyTorch does for Deep Learning. It offers features such as agents capable of interacting with each other, facilitates the use of various large language models (LLMs) and tool use support, autonomous and human-in-the-loop workflows, and multi-agent conversation patterns.
+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 streamline the development and research of agentic AI, much like PyTorch does for Deep Learning. It offers features such as agents capable of interacting with each other, facilitates the use of various large language models (LLMs) and tool use support, autonomous and human-in-the-loop workflows, and multi-agent conversation patterns.
 
 **Open Source Statement**: The project welcomes contributions from developers and organizations worldwide. Our goal is to foster a collaborative and inclusive community where diverse perspectives and expertise can drive innovation and enhance the project's capabilities. Whether you are an individual contributor or represent an organization, we invite you to join us in shaping the future of this project. Together, we can build something truly remarkable.
 
@@ -335,7 +335,7 @@ Explore detailed implementations with sample code and applications to help you g
 ## License
 This project is licensed under the [Apache License, Version 2.0 (Apache-2.0)](./LICENSE).
 
-This project is a spin-off of https://github.com/ag2ai/ag2 and contains code under two licenses:
+This project is a spin-off of [AutoGen](https://github.com/microsoft/autogen) and contains code under two licenses:
 
 - The original code from https://github.com/microsoft/autogen is licensed under the MIT License.  See the [LICENSE_original_MIT](./license_original/LICENSE_original_MIT) file for details.
 

autogen/agentchat/contrib/agent_eval/README.md

@@ -1,9 +1,7 @@
-Agents for running the [AgentEval](https://ag2ai.github.io/autogen/blog/2023/11/20/AgentEval/) pipeline.
+Agents for running the [AgentEval](https://ag2ai.github.io/ag2/blog/2023/11/20/AgentEval/) pipeline.
 
 AgentEval is a process for evaluating a LLM-based system's performance on a given task.
 
 When given a task to evaluate and a few example runs, the critic and subcritic agents create evaluation criteria for evaluating a system's solution. Once the criteria has been created, the quantifier agent can evaluate subsequent task solutions based on the generated criteria.
 
-For more information see: [AgentEval Integration Roadmap](https://github.com/microsoft/autogen/issues/2162)
-
-See our [blog post](https://ag2ai.github.io/autogen/blog/2024/06/21/AgentEval) for usage examples and general explanations.
+See our [blog post](https://ag2ai.github.io/ag2/blog/2024/06/21/AgentEval) for usage examples and general explanations.

notebook/JSON_mode_example.ipynb

@@ -19,7 +19,7 @@
     "\n",
     "\n",
     "Please find documentation about this feature in OpenAI  [here](https://platform.openai.com/docs/guides/text-generation/json-mode).\n",
-    "More information about Agent Descriptions is located [here](https://ag2ai.github.io/autogen/blog/2023/12/29/AgentDescriptions/)\n",
+    "More information about Agent Descriptions is located [here](https://ag2ai.github.io/ag2/blog/2023/12/29/AgentDescriptions/)\n",
     "\n",
     "Benefits\n",
     "- This contribution provides a method to implement precise speaker transitions based on content of the input message. The example can prevent Prompt hacks that use coersive language.\n",

notebook/agentchat_MathChat.ipynb

@@ -9,7 +9,7 @@
     "\n",
     "AutoGen offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framework allows tool use and human participation through multi-agent conversation. Please find documentation about this feature [here](https://ag2ai.github.io/ag2/docs/Use-Cases/agent_chat).\n",
     "\n",
-    "MathChat is an experimental conversational framework for math problem solving. In this notebook, we demonstrate how to use MathChat to solve math problems. MathChat uses the `AssistantAgent` and `MathUserProxyAgent`, which is similar to the usage of `AssistantAgent` and `UserProxyAgent` in other notebooks (e.g., [Automated Task Solving with Code Generation, Execution & Debugging](https://github.com/ag2ai/ag2/blob/main/notebook/agentchat_auto_feedback_from_code_execution.ipynb)). Essentially, `MathUserProxyAgent` implements a different auto reply mechanism corresponding to the MathChat prompts. You can find more details in the paper [An Empirical Study on Challenging Math Problem Solving with GPT-4](https://arxiv.org/abs/2306.01337) or the [blogpost](https://ag2ai.github.io/autogen/blog/2023/06/28/MathChat).\n",
+    "MathChat is an experimental conversational framework for math problem solving. In this notebook, we demonstrate how to use MathChat to solve math problems. MathChat uses the `AssistantAgent` and `MathUserProxyAgent`, which is similar to the usage of `AssistantAgent` and `UserProxyAgent` in other notebooks (e.g., [Automated Task Solving with Code Generation, Execution & Debugging](https://github.com/ag2ai/ag2/blob/main/notebook/agentchat_auto_feedback_from_code_execution.ipynb)). Essentially, `MathUserProxyAgent` implements a different auto reply mechanism corresponding to the MathChat prompts. You can find more details in the paper [An Empirical Study on Challenging Math Problem Solving with GPT-4](https://arxiv.org/abs/2306.01337) or the [blogpost](https://ag2ai.github.io/ag2/blog/2023/06/28/MathChat).\n",
     "\n",
     "````{=mdx}\n",
     ":::info Requirements\n",