update

NOTICE.md

@@ -2,9 +2,9 @@
 
 Copyright (c) 2023-2024, Owners of https://github.com/ag2ai
 
-This project is a fork of https://github.com/microsoft/autogen.
+This project is a fork of https://github.com/ag2ai/ag2.
 
-The [original project](https://github.com/microsoft/autogen) is licensed under the MIT License as detailed in [LICENSE_original_MIT](./license_original/LICENSE_original_MIT). The fork was created from version v0.2.35 of the original project.
+The [original project](https://github.com/ag2ai/ag2) is licensed under the MIT License as detailed in [LICENSE_original_MIT](./license_original/LICENSE_original_MIT). The fork was created from version v0.2.35 of the original project.
 
 
 This project, i.e., https://github.com/ag2ai/ag2, is licensed under the Apache License, Version 2.0 as detailed in [LICENSE](./LICENSE)
@@ -13,7 +13,7 @@ This project, i.e., https://github.com/ag2ai/ag2, is licensed under the Apache L
 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
 
 Ongoing MIT-licensed contributions:
-This project regularly incorporates code merged from the [original repository](https://github.com/microsoft/autogen) after the initial fork. This merged code remains under the original MIT license. For specific details on merged commits, please refer to the project's commit history.
-The MIT license applies to portions of code originating from the [original repository](https://github.com/microsoft/autogen) as described above.
+This project regularly incorporates code merged from the [original repository](https://github.com/ag2ai/ag2) after the initial fork. This merged code remains under the original MIT license. For specific details on merged commits, please refer to the project's commit history.
+The MIT license applies to portions of code originating from the [original repository](https://github.com/ag2ai/ag2) as described above.
 
 Last updated: 08/25/2024

README.md

@@ -59,7 +59,7 @@ We adopt the Apache 2.0 license from v0.3. This enhances our commitment to open-
 
 :tada: Dec 31, 2023: [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework](https://arxiv.org/abs/2308.08155) is selected by [TheSequence: My Five Favorite AI Papers of 2023](https://thesequence.substack.com/p/my-five-favorite-ai-papers-of-2023).
 
-<!-- :fire: Nov 24: pyautogen [v0.2](https://github.com/microsoft/autogen/releases/tag/v0.2.0) is released with many updates and new features compared to v0.1.1. It switches to using openai-python v1. Please read the [migration guide](https://ag2ai.github.io/ag2/docs/Installation#python). -->
+<!-- :fire: Nov 24: pyautogen [v0.2](https://github.com/ag2ai/ag2/releases/tag/v0.2.0) is released with many updates and new features compared to v0.1.1. It switches to using openai-python v1. Please read the [migration guide](https://ag2ai.github.io/ag2/docs/Installation#python). -->
 
 <!-- :fire: Nov 11: OpenAI's Assistants are available in AutoGen and interoperatable with other AutoGen agents! Checkout our [blogpost](https://ag2ai.github.io/ag2/blog/2023/11/13/OAI-assistants) for details and examples. -->
 
@@ -335,9 +335,9 @@ 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/microsoft/autogen and contains code under two licenses:
+This project is a spin-off of https://github.com/ag2ai/ag2 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.
+- The original code from https://github.com/ag2ai/ag2 is licensed under the MIT License.  See the [LICENSE_original_MIT](./license_original/LICENSE_original_MIT) file for details.
 
 - Modifications and additions made in this fork are licensed under the Apache License, Version 2.0. See the  [LICENSE](./LICENSE) file for the full license text.
 

TRANSPARENCY_FAQS.md

@@ -31,7 +31,7 @@ While AutoGen automates LLM workflows, decisions about how to use specific LLM o
 -	Current version of AutoGen was evaluated on six applications to illustrate its potential in simplifying the development of high-performance multi-agent applications. These applications are selected based on their real-world relevance,  problem difficulty and problem solving capabilities enabled by AutoGen, and innovative potential.
 -	These applications involve using AutoGen to solve math problems, question answering, decision making in text world environments, supply chain optimization, etc. For each of these domains AutoGen was evaluated on various success based metrics (i.e., how often the AutoGen based implementation solved the task). And, in some cases, AutoGen based approach was also evaluated on implementation efficiency (e.g., to track reductions in developer effort to build). More details can be found at: https://aka.ms/AutoGen/TechReport
 - The team has conducted tests where a “red” agent attempts to get the default AutoGen assistant to break from its alignment and guardrails. The team has observed that out of 70 attempts to break guardrails, only 1 was successful in producing text that would have been flagged as problematic by Azure OpenAI filters. The team has not observed any evidence that AutoGen (or GPT models as hosted by OpenAI or Azure) can produce novel code exploits or jailbreak prompts, since direct prompts to “be a hacker”, “write exploits”, or “produce a phishing email” are refused by existing filters.
-- We also evaluated [a team of AutoGen agents](https://github.com/microsoft/autogen/tree/gaia_multiagent_v01_march_1st/samples/tools/autogenbench/scenarios/GAIA/Templates/Orchestrator) on the [GAIA benchmarks](https://arxiv.org/abs/2311.12983), and got [SOTA results](https://huggingface.co/spaces/gaia-benchmark/leaderboard) as of
+- We also evaluated [a team of AutoGen agents](https://github.com/ag2ai/ag2/tree/gaia_multiagent_v01_march_1st/samples/tools/autogenbench/scenarios/GAIA/Templates/Orchestrator) on the [GAIA benchmarks](https://arxiv.org/abs/2311.12983), and got [SOTA results](https://huggingface.co/spaces/gaia-benchmark/leaderboard) as of
  March 1, 2024.
 
 ## What are the limitations of AutoGen? How can users minimize the impact of AutoGen’s limitations when using the system?

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

@@ -204,7 +204,7 @@ We are using chromadb as the default vector database, you can also use mongodb,
 by simply set `vector_db` to `mongodb`, `pgvector` and `qdrant` in `retrieve_config`, respectively.
 
 To plugin any other dbs, you can also extend class `agentchat.contrib.vectordb.base`,
-check out the code [here](https://github.com/microsoft/autogen/blob/main/autogen/agentchat/contrib/vectordb/base.py).
+check out the code [here](https://github.com/ag2ai/ag2/blob/main/autogen/agentchat/contrib/vectordb/base.py).
 
 
 ## Advanced Usage of RAG Agents
@@ -399,8 +399,8 @@ The online app and the source code are hosted in [HuggingFace](https://huggingfa
 
 ## Read More
 You can check out more example notebooks for RAG use cases:
-- [Automated Code Generation and Question Answering with Retrieval Augmented Agents](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_RetrieveChat.ipynb)
-- [Group Chat with Retrieval Augmented Generation (with 5 group member agents and 1 manager agent)](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat_RAG.ipynb)
-- [Using RetrieveChat with Qdrant for Retrieve Augmented Code Generation and Question Answering](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_RetrieveChat_qdrant.ipynb)
-- [Using RetrieveChat Powered by PGVector for Retrieve Augmented Code Generation and Question Answering](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_RetrieveChat_pgvector.ipynb)
-- [Using RetrieveChat Powered by MongoDB Atlas for Retrieve Augmented Code Generation and Question Answering](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_RetrieveChat_mongodb.ipynb)
+- [Automated Code Generation and Question Answering with Retrieval Augmented Agents](https://github.com/ag2ai/ag2/blob/main/notebook/agentchat_RetrieveChat.ipynb)
+- [Group Chat with Retrieval Augmented Generation (with 5 group member agents and 1 manager agent)](https://github.com/ag2ai/ag2/blob/main/notebook/agentchat_groupchat_RAG.ipynb)
+- [Using RetrieveChat with Qdrant for Retrieve Augmented Code Generation and Question Answering](https://github.com/ag2ai/ag2/blob/main/notebook/agentchat_RetrieveChat_qdrant.ipynb)
+- [Using RetrieveChat Powered by PGVector for Retrieve Augmented Code Generation and Question Answering](https://github.com/ag2ai/ag2/blob/main/notebook/agentchat_RetrieveChat_pgvector.ipynb)
+- [Using RetrieveChat Powered by MongoDB Atlas for Retrieve Augmented Code Generation and Question Answering](https://github.com/ag2ai/ag2/blob/main/notebook/agentchat_RetrieveChat_mongodb.ipynb)

website/blog/2023-10-26-TeachableAgent/index.mdx

@@ -24,13 +24,13 @@ In order to make effective decisions about memo storage and retrieval, the `Teac
 
 AutoGen contains four code examples that use `Teachability`.
 
-1. Run [chat_with_teachable_agent.py](https://github.com/microsoft/autogen/blob/main/test/agentchat/contrib/capabilities/chat_with_teachable_agent.py) to converse with a teachable agent.
+1. Run [chat_with_teachable_agent.py](https://github.com/ag2ai/ag2/blob/main/test/agentchat/contrib/capabilities/chat_with_teachable_agent.py) to converse with a teachable agent.
 
-2. Run [test_teachable_agent.py](https://github.com/microsoft/autogen/blob/main/test/agentchat/contrib/capabilities/test_teachable_agent.py) for quick unit testing of a teachable agent.
+2. Run [test_teachable_agent.py](https://github.com/ag2ai/ag2/blob/main/test/agentchat/contrib/capabilities/test_teachable_agent.py) for quick unit testing of a teachable agent.
 
-3. Use the Jupyter notebook [agentchat_teachability.ipynb](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_teachability.ipynb) to step through examples discussed below.
+3. Use the Jupyter notebook [agentchat_teachability.ipynb](https://github.com/ag2ai/ag2/blob/main/notebook/agentchat_teachability.ipynb) to step through examples discussed below.
 
-4. Use the Jupyter notebook [agentchat_teachable_oai_assistants.ipynb](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_teachable_oai_assistants.ipynb) to make arbitrary OpenAI Assistants teachable through `GPTAssistantAgent`.
+4. Use the Jupyter notebook [agentchat_teachable_oai_assistants.ipynb](https://github.com/ag2ai/ag2/blob/main/notebook/agentchat_teachable_oai_assistants.ipynb) to make arbitrary OpenAI Assistants teachable through `GPTAssistantAgent`.
 
 ## Basic Usage of Teachability
 

website/blog/2023-11-20-AgentEval/index.mdx

@@ -14,7 +14,7 @@ tags: [LLM, GPT, evaluation, task utility]
 **TL;DR:**
 * As a developer of an LLM-powered application, how can you assess the utility it brings to end users while helping them with their tasks?
 * To shed light on the question above, we introduce `AgentEval` — the first version of the framework to assess the utility of any LLM-powered application crafted to assist users in specific tasks.  AgentEval aims to simplify the evaluation process by automatically proposing a set of criteria tailored to the unique purpose of your application. This allows for a comprehensive assessment, quantifying the utility of your application against the suggested criteria.
-* We demonstrate how `AgentEval` work using [math problems dataset](https://ag2ai.github.io/autogen/blog/2023/06/28/MathChat) as an example in the [following notebook](https://github.com/microsoft/autogen/blob/main/notebook/agenteval_cq_math.ipynb). Any feedback would be useful for future development. Please contact us on our [Discord](http://aka.ms/autogen-dc).
+* We demonstrate how `AgentEval` work using [math problems dataset](https://ag2ai.github.io/autogen/blog/2023/06/28/MathChat) as an example in the [following notebook](https://github.com/ag2ai/ag2/blob/main/notebook/agenteval_cq_math.ipynb). Any feedback would be useful for future development. Please contact us on our [Discord](http://aka.ms/autogen-dc).
 
 
 ## Introduction
@@ -54,7 +54,7 @@ critic = autogen.AssistantAgent(
 )
 ```
 
-Next, the critic is given successful and failed examples of the task execution; then, it is able to return a list of criteria (Fig. 1). For reference, use the [following notebook](https://github.com/microsoft/autogen/blob/main/notebook/agenteval_cq_math.ipynb).
+Next, the critic is given successful and failed examples of the task execution; then, it is able to return a list of criteria (Fig. 1). For reference, use the [following notebook](https://github.com/ag2ai/ag2/blob/main/notebook/agenteval_cq_math.ipynb).
 
 * The goal of `QuantifierAgent` is to quantify each of the suggested criteria (Fig. 1), providing us with an idea of the utility of this system for the given task. Here is an example of how it can be defined:
 

website/blog/2023-11-26-Agent-AutoBuild/index.mdx

@@ -14,8 +14,8 @@ user prompt required, powered by a new designed class **AgentBuilder**. AgentBui
 leveraging [vLLM](https://docs.vllm.ai/en/latest/index.html) and [FastChat](https://github.com/lm-sys/FastChat).
 Checkout example notebooks and source code for reference:
 
-- [AutoBuild Examples](https://github.com/microsoft/autogen/blob/main/notebook/autobuild_basic.ipynb)
-- [AgentBuilder](https://github.com/microsoft/autogen/blob/main/autogen/agentchat/contrib/agent_builder.py)
+- [AutoBuild Examples](https://github.com/ag2ai/ag2/blob/main/notebook/autobuild_basic.ipynb)
+- [AgentBuilder](https://github.com/ag2ai/ag2/blob/main/autogen/agentchat/contrib/agent_builder.py)
 
 ## Introduction
 In this blog, we introduce **AutoBuild**, a pipeline that can automatically build multi-agent systems for complex tasks.

website/blog/2024-01-25-AutoGenBench/index.mdx

@@ -141,7 +141,7 @@ While we are announcing AutoGenBench, we note that it is very much an evolving p
 - Introduce new core metrics including total costs, task completion time, conversation turns, etc.
 - Provide tighter integration with AgentEval and AutoGen Studio
 
-For an up to date tracking of our work items on this project, please see [AutoGenBench Work Items](https://github.com/microsoft/autogen/issues/973)
+For an up to date tracking of our work items on this project, please see [AutoGenBench Work Items](https://github.com/ag2ai/ag2/issues/973)
 
 ## Call for Participation
 

website/blog/2024-01-26-Custom-Models/index.mdx

@@ -13,7 +13,7 @@ AutoGen now supports custom models! This feature empowers users to define and lo
 
 ## Quickstart
 
-An interactive and easy way to get started is by following the notebook [here](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_custom_model.ipynb) which loads a local model from HuggingFace into AutoGen and uses it for inference, and making changes to the class provided.
+An interactive and easy way to get started is by following the notebook [here](https://github.com/ag2ai/ag2/blob/main/notebook/agentchat_custom_model.ipynb) which loads a local model from HuggingFace into AutoGen and uses it for inference, and making changes to the class provided.
 
 ### Step 1: Create the custom model client class