Updated repo links

Author: marklysze

website/docs/Examples.md

@@ -18,7 +18,7 @@ Links to notebook examples:
 - Automated Task Solving by Group Chat (with 3 group member agents and 1 manager agent) - [View Notebook](/docs/notebooks/agentchat_groupchat)
 - Automated Data Visualization by Group Chat (with 3 group member agents and 1 manager agent) - [View Notebook](/docs/notebooks/agentchat_groupchat_vis)
 - Automated Complex Task Solving by Group Chat (with 6 group member agents and 1 manager agent) - [View Notebook](/docs/notebooks/agentchat_groupchat_research)
-- Automated Task Solving with Coding & Planning Agents - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_planning.ipynb)
+- Automated Task Solving with Coding & Planning Agents - [View Notebook](https://github.com/autogen-ai/autogen/blob/main/notebook/agentchat_planning.ipynb)
 - Automated Task Solving with transition paths specified in a graph - [View Notebook](https://autogen-ai.github.io/autogen/docs/notebooks/agentchat_groupchat_finite_state_machine)
 - Running a group chat as an inner-monolgue via the SocietyOfMindAgent - [View Notebook](/docs/notebooks/agentchat_society_of_mind)
 - Running a group chat with custom speaker selection function - [View Notebook](/docs/notebooks/agentchat_groupchat_customized)
@@ -38,87 +38,87 @@ Links to notebook examples:
 
 ### Applications
 
-- Automated Continual Learning from New Data - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_stream.ipynb)
-- [OptiGuide](https://github.com/microsoft/optiguide) - Coding, Tool Using, Safeguarding & Question Answering for Supply Chain Optimization
-- [AutoAnny](https://github.com/microsoft/autogen/tree/main/samples/apps/auto-anny) - A Discord bot built using AutoGen
+- Automated Continual Learning from New Data - [View Notebook](https://github.com/autogen-ai/autogen/blob/main/notebook/agentchat_stream.ipynb)
+<!-- - [OptiGuide](https://github.com/microsoft/optiguide) - Coding, Tool Using, Safeguarding & Question Answering for Supply Chain Optimization -->
+- [AutoAnny](https://github.com/autogen-ai/autogen/tree/main/samples/apps/auto-anny) - A Discord bot built using AutoGen
 
 ### Tool Use
 
-- **Web Search**: Solve Tasks Requiring Web Info - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_web_info.ipynb)
+- **Web Search**: Solve Tasks Requiring Web Info - [View Notebook](https://github.com/autogen-ai/autogen/blob/main/notebook/agentchat_web_info.ipynb)
 - Use Provided Tools as Functions - [View Notebook](/docs/notebooks/agentchat_function_call_currency_calculator)
 - Use Tools via Sync and Async Function Calling - [View Notebook](/docs/notebooks/agentchat_function_call_async)
-- Task Solving with Langchain Provided Tools as Functions - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_langchain.ipynb)
+- Task Solving with Langchain Provided Tools as Functions - [View Notebook](https://github.com/autogen-ai/autogen/blob/main/notebook/agentchat_langchain.ipynb)
 - **RAG**: Group Chat with Retrieval Augmented Generation (with 5 group member agents and 1 manager agent) - [View Notebook](/docs/notebooks/agentchat_groupchat_RAG)
-- Function Inception: Enable AutoGen agents to update/remove functions during conversations. - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_inception_function.ipynb)
+- Function Inception: Enable AutoGen agents to update/remove functions during conversations. - [View Notebook](https://github.com/autogen-ai/autogen/blob/main/notebook/agentchat_inception_function.ipynb)
 - Agent Chat with Whisper - [View Notebook](/docs/notebooks/agentchat_video_transcript_translate_with_whisper)
-- Constrained Responses via Guidance - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_guidance.ipynb)
-- Browse the Web with Agents - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_surfer.ipynb)
-- **SQL**: Natural Language Text to SQL Query using the [Spider](https://yale-lily.github.io/spider) Text-to-SQL Benchmark - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_sql_spider.ipynb)
+- Constrained Responses via Guidance - [View Notebook](https://github.com/autogen-ai/autogen/blob/main/notebook/agentchat_guidance.ipynb)
+- Browse the Web with Agents - [View Notebook](https://github.com/autogen-ai/autogen/blob/main/notebook/agentchat_surfer.ipynb)
+- **SQL**: Natural Language Text to SQL Query using the [Spider](https://yale-lily.github.io/spider) Text-to-SQL Benchmark - [View Notebook](https://github.com/autogen-ai/autogen/blob/main/notebook/agentchat_sql_spider.ipynb)
 - **Web Scraping**: Web Scraping with Apify - [View Notebook](/docs/notebooks/agentchat_webscraping_with_apify)
-- **Write a software app, task by task, with specially designed functions.** - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_function_call_code_writing.ipynb).
+- **Write a software app, task by task, with specially designed functions.** - [View Notebook](https://github.com/autogen-ai/autogen/blob/main/notebook/agentchat_function_call_code_writing.ipynb).
 
 ### Human Involvement
 
-- Simple example in ChatGPT style [View example](https://github.com/microsoft/autogen/blob/main/samples/simple_chat.py)
-- Auto Code Generation, Execution, Debugging and **Human Feedback** - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_human_feedback.ipynb)
-- Automated Task Solving with GPT-4 + **Multiple Human Users** - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_two_users.ipynb)
-- Agent Chat with **Async Human Inputs** - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/Async_human_input.ipynb)
+- Simple example in ChatGPT style [View example](https://github.com/autogen-ai/autogen/blob/main/samples/simple_chat.py)
+- Auto Code Generation, Execution, Debugging and **Human Feedback** - [View Notebook](https://github.com/autogen-ai/autogen/blob/main/notebook/agentchat_human_feedback.ipynb)
+- Automated Task Solving with GPT-4 + **Multiple Human Users** - [View Notebook](https://github.com/autogen-ai/autogen/blob/main/notebook/agentchat_two_users.ipynb)
+- Agent Chat with **Async Human Inputs** - [View Notebook](https://github.com/autogen-ai/autogen/blob/main/notebook/Async_human_input.ipynb)
 
 ### Agent Teaching and Learning
 
 - Teach Agents New Skills & Reuse via Automated Chat - [View Notebook](/docs/notebooks/agentchat_teaching)
 - Teach Agents New Facts, User Preferences and Skills Beyond Coding - [View Notebook](/docs/notebooks/agentchat_teachability)
-- Teach OpenAI Assistants Through GPTAssistantAgent - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_teachable_oai_assistants.ipynb)
-- Agent Optimizer: Train Agents in an Agentic Way - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_agentoptimizer.ipynb)
+- Teach OpenAI Assistants Through GPTAssistantAgent - [View Notebook](https://github.com/autogen-ai/autogen/blob/main/notebook/agentchat_teachable_oai_assistants.ipynb)
+- Agent Optimizer: Train Agents in an Agentic Way - [View Notebook](https://github.com/autogen-ai/autogen/blob/main/notebook/agentchat_agentoptimizer.ipynb)
 
 ### Multi-Agent Chat with OpenAI Assistants in the loop
 
-- Hello-World Chat with OpenAi Assistant in AutoGen - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_oai_assistant_twoagents_basic.ipynb)
-- Chat with OpenAI Assistant using Function Call - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_oai_assistant_function_call.ipynb)
-- Chat with OpenAI Assistant with Code Interpreter - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_oai_code_interpreter.ipynb)
-- Chat with OpenAI Assistant with Retrieval Augmentation - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_oai_assistant_retrieval.ipynb)
-- OpenAI Assistant in a Group Chat - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_oai_assistant_groupchat.ipynb)
-- GPTAssistantAgent based Multi-Agent Tool Use - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/gpt_assistant_agent_function_call.ipynb)
+- Hello-World Chat with OpenAi Assistant in AutoGen - [View Notebook](https://github.com/autogen-ai/autogen/blob/main/notebook/agentchat_oai_assistant_twoagents_basic.ipynb)
+- Chat with OpenAI Assistant using Function Call - [View Notebook](https://github.com/autogen-ai/autogen/blob/main/notebook/agentchat_oai_assistant_function_call.ipynb)
+- Chat with OpenAI Assistant with Code Interpreter - [View Notebook](https://github.com/autogen-ai/autogen/blob/main/notebook/agentchat_oai_code_interpreter.ipynb)
+- Chat with OpenAI Assistant with Retrieval Augmentation - [View Notebook](https://github.com/autogen-ai/autogen/blob/main/notebook/agentchat_oai_assistant_retrieval.ipynb)
+- OpenAI Assistant in a Group Chat - [View Notebook](https://github.com/autogen-ai/autogen/blob/main/notebook/agentchat_oai_assistant_groupchat.ipynb)
+- GPTAssistantAgent based Multi-Agent Tool Use - [View Notebook](https://github.com/autogen-ai/autogen/blob/main/notebook/gpt_assistant_agent_function_call.ipynb)
 
 ### Non-OpenAI Models
 - Conversational Chess using non-OpenAI Models - [View Notebook](/docs/notebooks/agentchat_nested_chats_chess_altmodels)
 
 ### Multimodal Agent
 
-- Multimodal Agent Chat with DALLE and GPT-4V   - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_dalle_and_gpt4v.ipynb)
-- Multimodal Agent Chat with Llava  - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_lmm_llava.ipynb)
-- Multimodal Agent Chat with GPT-4V - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_lmm_gpt-4v.ipynb)
+- Multimodal Agent Chat with DALLE and GPT-4V   - [View Notebook](https://github.com/autogen-ai/autogen/blob/main/notebook/agentchat_dalle_and_gpt4v.ipynb)
+- Multimodal Agent Chat with Llava  - [View Notebook](https://github.com/autogen-ai/autogen/blob/main/notebook/agentchat_lmm_llava.ipynb)
+- Multimodal Agent Chat with GPT-4V - [View Notebook](https://github.com/autogen-ai/autogen/blob/main/notebook/agentchat_lmm_gpt-4v.ipynb)
 
 ### Long Context Handling
 
-<!-- - Conversations with Chat History Compression Enabled - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_compression.ipynb) -->
+<!-- - Conversations with Chat History Compression Enabled - [View Notebook](https://github.com/autogen-ai/autogen/blob/main/notebook/agentchat_compression.ipynb) -->
 - Long Context Handling as A Capability - [View Notebook](/docs/notebooks/agentchat_transform_messages)
 
 ### Evaluation and Assessment
 
-- AgentEval: A Multi-Agent System for Assess Utility of LLM-powered Applications - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agenteval_cq_math.ipynb)
+- AgentEval: A Multi-Agent System for Assess Utility of LLM-powered Applications - [View Notebook](https://github.com/autogen-ai/autogen/blob/main/notebook/agenteval_cq_math.ipynb)
 
 ### Automatic Agent Building
 
-- Automatically Build Multi-agent System with AgentBuilder - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/autobuild_basic.ipynb)
-- Automatically Build Multi-agent System from Agent Library - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/autobuild_agent_library.ipynb)
+- Automatically Build Multi-agent System with AgentBuilder - [View Notebook](https://github.com/autogen-ai/autogen/blob/main/notebook/autobuild_basic.ipynb)
+- Automatically Build Multi-agent System from Agent Library - [View Notebook](https://github.com/autogen-ai/autogen/blob/main/notebook/autobuild_agent_library.ipynb)
 
 ### Observability
-- Track LLM calls, tool usage, actions and errors using AgentOps - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_agentops.ipynb)
+- Track LLM calls, tool usage, actions and errors using AgentOps - [View Notebook](https://github.com/autogen-ai/autogen/blob/main/notebook/agentchat_agentops.ipynb)
 
 ## Enhanced Inferences
 
 ### Utilities
 
 - API Unification  - [View Documentation with Code Example](https://autogen-ai.github.io/autogen/docs/Use-Cases/enhanced_inference/#api-unification)
 - Utility Functions to Help Managing API configurations effectively - [View Notebook](/docs/topics/llm_configuration)
-- Cost Calculation - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_cost_token_tracking.ipynb)
+- Cost Calculation - [View Notebook](https://github.com/autogen-ai/autogen/blob/main/notebook/agentchat_cost_token_tracking.ipynb)
 
 ### Inference Hyperparameters Tuning
 
 AutoGen offers a cost-effective hyperparameter optimization technique [EcoOptiGen](https://arxiv.org/abs/2303.04673) for tuning Large Language Models. The research study finds that tuning hyperparameters can significantly improve the utility of them.
 Please find documentation about this feature [here](/docs/Use-Cases/enhanced_inference).
 
 Links to notebook examples:
-* [Optimize for Code Generation](https://github.com/microsoft/autogen/blob/main/notebook/oai_completion.ipynb) | [Open in colab](https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/oai_completion.ipynb)
-* [Optimize for Math](https://github.com/microsoft/autogen/blob/main/notebook/oai_chatgpt_gpt4.ipynb) | [Open in colab](https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/oai_chatgpt_gpt4.ipynb)
+* [Optimize for Code Generation](https://github.com/autogen-ai/autogen/blob/main/notebook/oai_completion.ipynb) | [Open in colab](https://colab.research.google.com/github/autogen-ai/autogen/blob/main/notebook/oai_completion.ipynb)
+* [Optimize for Math](https://github.com/autogen-ai/autogen/blob/main/notebook/oai_chatgpt_gpt4.ipynb) | [Open in colab](https://colab.research.google.com/github/autogen-ai/autogen/blob/main/notebook/oai_chatgpt_gpt4.ipynb)

website/docs/FAQ.mdx

@@ -106,7 +106,7 @@ for each code-execution agent, or set `AUTOGEN_USE_DOCKER` to `False` as an
 environment variable.
 
 You can also develop your AutoGen application in a docker container.
-For example, when developing in [GitHub codespace](https://codespaces.new/microsoft/autogen?quickstart=1),
+For example, when developing in [GitHub codespace](https://codespaces.new/autogen-ai/autogen?quickstart=1),
 AutoGen runs in a docker container.
 If you are not developing in GitHub Codespaces,
 follow instructions [here](installation/Docker.md#option-1-install-and-run-autogen-in-docker)

website/docs/Getting-Started.mdx

@@ -118,7 +118,7 @@ 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.
-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/microsoft/autogen/blob/main/test/twoagent.py),
+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/autogen-ai/autogen/blob/main/test/twoagent.py),
 
 The figure below shows an example conversation flow with AutoGen.
 
@@ -135,10 +135,10 @@ The figure below shows an example conversation flow with AutoGen.
 - Follow on [Twitter](https://twitter.com/pyautogen)
 - See our [roadmaps](https://aka.ms/autogen-roadmap)
 
-If you like our project, please give it a [star](https://github.com/microsoft/autogen/stargazers) on GitHub. If you are interested in contributing, please read [Contributor's Guide](/docs/contributor-guide/contributing).
+If you like our project, please give it a [star](https://github.com/autogen-ai/autogen/stargazers) on GitHub. If you are interested in contributing, please read [Contributor's Guide](/docs/contributor-guide/contributing).
 
 <iframe
-  src="https://ghbtns.com/github-btn.html?user=microsoft&amp;repo=autogen&amp;type=star&amp;count=true&amp;size=large"
+  src="https://ghbtns.com/github-btn.html?user=autogen-ai&amp;repo=autogen&amp;type=star&amp;count=true&amp;size=large"
   frameborder="0"
   scrolling="0"
   width="170"

website/docs/Migration-Guide.md

@@ -26,7 +26,7 @@ autogen.runtime_logging.start()
 # Stop logging
 autogen.runtime_logging.stop()
 ```
-Checkout [Logging documentation](https://autogen-ai.github.io/autogen/docs/Use-Cases/enhanced_inference#logging) and [Logging example notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_logging.ipynb) to learn more.
+Checkout [Logging documentation](https://autogen-ai.github.io/autogen/docs/Use-Cases/enhanced_inference#logging) and [Logging example notebook](https://github.com/autogen-ai/autogen/blob/main/notebook/agentchat_logging.ipynb) to learn more.
 
 Inference parameter tuning can be done via [`flaml.tune`](https://autogen-ai.github.io/FLAML/docs/Use-Cases/Tune-User-Defined-Function).
 - `seed` in autogen is renamed into `cache_seed` to accommodate the newly added `seed` param in openai chat completion api. `use_cache` is removed as a kwarg in `OpenAIWrapper.create()` for being automatically decided by `cache_seed`: int | None. The difference between autogen's `cache_seed` and openai's `seed` is that:

website/docs/Use-Cases/agent_chat.md

@@ -81,13 +81,13 @@ AutoGen, by integrating conversation-driven control utilizing both programming a
 
 With the pluggable auto-reply function, one can choose to invoke conversations with other agents depending on the content of the current message and context. For example:
 - Hierarchical chat like in [OptiGuide](https://github.com/microsoft/optiguide).
-- [Dynamic Group Chat](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat.ipynb) which is a special form of hierarchical chat. In the system, we register a reply function in the group chat manager, which broadcasts messages and decides who the next speaker will be in a group chat setting.
+- [Dynamic Group Chat](https://github.com/autogen-ai/autogen/blob/main/notebook/agentchat_groupchat.ipynb) which is a special form of hierarchical chat. In the system, we register a reply function in the group chat manager, which broadcasts messages and decides who the next speaker will be in a group chat setting.
 - [Finite State Machine graphs to set speaker transition constraints](https://autogen-ai.github.io/autogen/docs/notebooks/agentchat_groupchat_finite_state_machine) which is a special form of dynamic group chat. In this approach, a directed transition matrix is fed into group chat. Users can specify legal transitions or specify disallowed transitions.
-- Nested chat like in [conversational chess](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_nested_chats_chess.ipynb).
+- Nested chat like in [conversational chess](https://github.com/autogen-ai/autogen/blob/main/notebook/agentchat_nested_chats_chess.ipynb).
 
 2. LLM-Based Function Call
 
-Another approach involves LLM-based function calls, where LLM decides if a specific function should be invoked based on the conversation's status during each inference. This approach enables dynamic multi-agent conversations, as seen in scenarios like [multi-user math problem solving scenario](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_two_users.ipynb), where a student assistant automatically seeks expertise via function calls.
+Another approach involves LLM-based function calls, where LLM decides if a specific function should be invoked based on the conversation's status during each inference. This approach enables dynamic multi-agent conversations, as seen in scenarios like [multi-user math problem solving scenario](https://github.com/autogen-ai/autogen/blob/main/notebook/agentchat_two_users.ipynb), where a student assistant automatically seeks expertise via function calls.
 
 ### Diverse Applications Implemented with AutoGen