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

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. -->
 
@@ -335,7 +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/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.
 

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
 

website/blog/2024-03-03-AutoGen-Update/index.mdx

@@ -138,7 +138,7 @@ The community has been working hard to address them in several dimensions:
 
 We are working on agent-based evaluation tools and benchmarking tools. For example:
 
-- [AgentEval](/blog/2023/11/20/AgentEval). Our [research](https://arxiv.org/abs/2402.09015) finds that LLM agents built with AutoGen can be used to automatically identify evaluation criteria and assess the performance from task descriptions and execution logs. It is demonstrated as a [notebook example](https://github.com/microsoft/autogen/blob/main/notebook/agenteval_cq_math.ipynb). Feedback and help are welcome for building it into the library.
+- [AgentEval](/blog/2023/11/20/AgentEval). Our [research](https://arxiv.org/abs/2402.09015) finds that LLM agents built with AutoGen can be used to automatically identify evaluation criteria and assess the performance from task descriptions and execution logs. It is demonstrated as a [notebook example](https://github.com/ag2ai/ag2/blob/main/notebook/agenteval_cq_math.ipynb). Feedback and help are welcome for building it into the library.
 - [AutoGenBench](/blog/2024/01/25/AutoGenBench). AutoGenBench is a commandline tool for downloading, configuring, running an agentic benchmark, and reporting results. It is designed to allow repetition, isolation and instrumentation, leveraging the new [runtime logging](/docs/notebooks/agentchat_logging) feature.
 
 These tools have been used for improving the AutoGen library as well as applications. For example, the new state-of-the-art performance achieved by a multi-agent solution to the [GAIA](https://huggingface.co/spaces/gaia-benchmark/leaderboard) benchmark has benefited from these evaluation tools.
@@ -147,7 +147,7 @@ These tools have been used for improving the AutoGen library as well as applicat
 
 We are making rapid progress in further improving the interface to make it even easier to build agent applications. For example:
 
-- [AutoBuild](/blog/2023/11/26/Agent-AutoBuild). AutoBuild is an ongoing area of research to automatically create or select a group of agents for a given task and objective. If successful, it will greatly reduce the effort from users or developers when using the multi-agent technology. It also paves the way for agentic decomposition to handle complex tasks. It is available as an experimental feature and demonstrated in two modes: free-form [creation](https://github.com/microsoft/autogen/blob/main/notebook/autobuild_basic.ipynb) and [selection](https://github.com/microsoft/autogen/blob/main/notebook/autobuild_agent_library.ipynb) from a library.
+- [AutoBuild](/blog/2023/11/26/Agent-AutoBuild). AutoBuild is an ongoing area of research to automatically create or select a group of agents for a given task and objective. If successful, it will greatly reduce the effort from users or developers when using the multi-agent technology. It also paves the way for agentic decomposition to handle complex tasks. It is available as an experimental feature and demonstrated in two modes: free-form [creation](https://github.com/ag2ai/ag2/blob/main/notebook/autobuild_basic.ipynb) and [selection](https://github.com/ag2ai/ag2/blob/main/notebook/autobuild_agent_library.ipynb) from a library.
 - [AutoGen Studio](/blog/2023/12/01/AutoGenStudio). AutoGen Studio is a no-code UI for fast experimentation with the multi-agent conversations. It lowers the barrier of entrance to the AutoGen technology. Models, agents, and workflows can all be configured without writing code. And chatting with multiple agents in a playground is immediately available after the configuration. Although only a subset of `autogen` features are available in this sample app, it demonstrates a promising experience. It has generated tremendous excitement in the community.
 - Conversation Programming+. The [AutoGen paper](https://arxiv.org/abs/2308.08155) introduced a key concept of _Conversation Programming_, which can be used to program diverse conversation patterns such as 1-1 chat, group chat, hierarchical chat, nested chat etc. While we offered dynamic group chat as an example of high-level orchestration, it made other patterns relatively less discoverable. Therefore, we have added more convenient conversation programming features which enables easier definition of other types of complex workflow, such as [finite state machine based group chat](/blog/2024/02/11/FSM-GroupChat), [sequential chats](/docs/notebooks/agentchats_sequential_chats), and [nested chats](/docs/notebooks/agentchat_nestedchat). Many users have found them useful in implementing specific patterns, which have been always possible but more obvious with the added features. I will write another blog post for a deep dive.
 
@@ -173,8 +173,8 @@ The extensible design of AutoGen makes it easy to integrate with new technologie
 
 The above only covers a subset of new features and roadmap. There are many other interesting new features, integration examples or sample apps:
 
-- new features like stateful code execution, [tool decorators](/docs/Use-Cases/agent_chat#tool-calling), [long context handling](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_capability_long_context_handling.ipynb), [web agents](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_surfer.ipynb).
-- integration examples like using [guidance](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_guidance.ipynb) to generate structured response.
+- new features like stateful code execution, [tool decorators](/docs/Use-Cases/agent_chat#tool-calling), [long context handling](https://github.com/ag2ai/ag2/blob/main/notebook/agentchat_capability_long_context_handling.ipynb), [web agents](https://github.com/ag2ai/ag2/blob/main/notebook/agentchat_surfer.ipynb).
+- integration examples like using [guidance](https://github.com/ag2ai/ag2/blob/main/notebook/agentchat_guidance.ipynb) to generate structured response.
 - sample apps like [AutoAnny](/blog/2024/02/02/AutoAnny).
 
 ## Call for Help

website/blog/2024-06-21-AgentEval/index.mdx

@@ -76,7 +76,7 @@ def generate_criteria(
 
 Parameters:
 * llm_config (dict or bool): llm inference configuration.
-* task ([Task](https://github.com/microsoft/autogen/tree/main/autogen/agentchat/contrib/agent_eval/task.py)): The task to evaluate.
+* task ([Task](https://github.com/ag2ai/ag2/tree/main/autogen/agentchat/contrib/agent_eval/task.py)): The task to evaluate.
 * additional_instructions (str, optional): Additional instructions for the criteria agent.
 * max_round (int, optional): The maximum number of rounds to run the conversation.
 * use_subcritic (bool, optional): Whether to use the Subcritic agent to generate subcriteria. The Subcritic agent will break down a generated criteria into smaller criteria to be assessed.
@@ -138,8 +138,8 @@ def quantify_criteria(
 
 Parameters:
 * llm_config (dict or bool): llm inference configuration.
-* criteria ([Criterion](https://github.com/microsoft/autogen/tree/main/autogen/agentchat/contrib/agent_eval/criterion.py)): A list of criteria for evaluating the utility of a given task. This can either be generated by the `generate_criteria` function or manually created.
-* task ([Task](https://github.com/microsoft/autogen/tree/main/autogen/agentchat/contrib/agent_eval/task.py)): The task to evaluate. It should match the one used during the `generate_criteria` step.
+* criteria ([Criterion](https://github.com/ag2ai/ag2/tree/main/autogen/agentchat/contrib/agent_eval/criterion.py)): A list of criteria for evaluating the utility of a given task. This can either be generated by the `generate_criteria` function or manually created.
+* task ([Task](https://github.com/ag2ai/ag2/tree/main/autogen/agentchat/contrib/agent_eval/task.py)): The task to evaluate. It should match the one used during the `generate_criteria` step.
 * test_case (str): The execution chain to assess. Typically this is a json list of messages but could be any string representation of a conversation chain.
 * ground_truth (str): The ground truth for the test case.
 
@@ -189,7 +189,7 @@ AgentEval represents a significant advancement in the evaluation of LLM-powered
 
 ## Further reading
 
-Please refer to our [paper](https://arxiv.org/abs/2405.02178) and [codebase](https://github.com/microsoft/autogen/tree/main/autogen/agentchat/contrib/agent_eval) for more details about AgentEval.
+Please refer to our [paper](https://arxiv.org/abs/2405.02178) and [codebase](https://github.com/ag2ai/ag2/tree/main/autogen/agentchat/contrib/agent_eval) for more details about AgentEval.
 
 If you find this blog useful, please consider citing:
 ```bobtex