Notebook links update

notebook/agentchat_databricks_dbrx.ipynb

@@ -498,7 +498,7 @@
     "\n",
     "It can be useful to display chat logs to the notebook for debugging, and then persist those logs to a Delta table. The following section demonstrates how to extend the default AutoGen logging libraries.\n",
     "\n",
-    "First, we will implement a Python `class` that extends the capabilities of `autogen.runtime_logging` [docs](https://ag2ai.github.io/ag2/docs/notebooks/agentchat_logging):"
+    "First, we will implement a Python `class` that extends the capabilities of `autogen.runtime_logging` [docs](https://docs.ag2.ai/notebooks/agentchat_logging):"
    ]
   },
   {

notebook/agentchat_swarm_enhanced.ipynb

@@ -10,7 +10,7 @@
     "\n",
     "In this notebook, we look at more advanced features of the swarm orchestration.\n",
     "\n",
-    "If you are new to swarm, check out [this notebook](https://ag2ai.github.io/ag2/docs/notebooks/agentchat_swarm), where we introduce the core features of swarms including global context variables, hand offs, and initiating a swarm chat.\n",
+    "If you are new to swarm, check out [this notebook](https://docs.ag2.ai/notebooks/agentchat_swarm), where we introduce the core features of swarms including global context variables, hand offs, and initiating a swarm chat.\n",
     "\n",
     "In this notebook we're going to demonstrate these features AG2's swarm orchestration:\n",
     "\n",

website/blog/2024-02-11-FSM-GroupChat/index.mdx

@@ -285,4 +285,4 @@ pip install autogen[graph]
     ```
 
 ## Notebook examples
-More examples can be found in the [notebook](https://ag2ai.github.io/ag2/docs/notebooks/agentchat_groupchat_finite_state_machine/). The notebook includes more examples of possible transition paths such as (1) hub and spoke, (2) sequential team operations, and (3) think aloud and debate. It also uses the function `visualize_speaker_transitions_dict` from `autogen.graph_utils` to visualize the various graphs.
+More examples can be found in the [notebook](https://docs.ag2.ai/notebooks/agentchat_groupchat_finite_state_machine/). The notebook includes more examples of possible transition paths such as (1) hub and spoke, (2) sequential team operations, and (3) think aloud and debate. It also uses the function `visualize_speaker_transitions_dict` from `autogen.graph_utils` to visualize the various graphs.

website/blog/2024-06-24-AltModels-Classes/index.mdx

@@ -150,7 +150,7 @@ user_proxy.intiate_chat(assistant, message="Write python code to print Hello Wor
 
 ```
 
-**NOTE: To integrate this setup into GroupChat, follow the [tutorial](https://ag2ai.github.io/ag2/docs/notebooks/agentchat_groupchat) with the same config as above.**
+**NOTE: To integrate this setup into GroupChat, follow the [tutorial](https://docs.ag2.ai/notebooks/agentchat_groupchat) with the same config as above.**
 
 
 ## Function Calls
@@ -390,4 +390,4 @@ So we can see how Anthropic's Sonnet is able to suggest multiple tools in a sing
 
 ## More tips and tricks
 
-For an interesting chess game between Anthropic's Sonnet and Mistral's Mixtral, we've put together a sample notebook that highlights some of the tips and tricks for working with non-OpenAI LLMs. [See the notebook here](https://ag2ai.github.io/ag2/docs/notebooks/agentchat_nested_chats_chess_altmodels).
+For an interesting chess game between Anthropic's Sonnet and Mistral's Mixtral, we've put together a sample notebook that highlights some of the tips and tricks for working with non-OpenAI LLMs. [See the notebook here](https://docs.ag2.ai/notebooks/agentchat_nested_chats_chess_altmodels).

website/blog/2024-07-25-AgentOps/index.mdx

@@ -28,7 +28,7 @@ Agent observability, in its most basic form, allows you to monitor, troubleshoot
 
 ## Why AgentOps?
 
-AutoGen has simplified the process of building agents, yet we recognized the need for an easy-to-use, native tool for observability. We've previously discussed AgentOps, and now we're excited to partner with AgentOps as our official agent observability tool. Integrating AgentOps with AutoGen simplifies your workflow and boosts your agents' performance through clear observability, ensuring they operate optimally. For more details, check out our [AgentOps documentation](https://ag2ai.github.io/ag2/docs/notebooks/agentchat_agentops/).
+AutoGen has simplified the process of building agents, yet we recognized the need for an easy-to-use, native tool for observability. We've previously discussed AgentOps, and now we're excited to partner with AgentOps as our official agent observability tool. Integrating AgentOps with AutoGen simplifies your workflow and boosts your agents' performance through clear observability, ensuring they operate optimally. For more details, check out our [AgentOps documentation](https://docs.ag2.ai/notebooks/agentchat_agentops/).
 
 <img src="https://github.com/AgentOps-AI/agentops/blob/main/docs/images/external/autogen/session-replay.png?raw=true" alt="Agent Session Replay" style={{ maxWidth: "100%" }} />
 

website/blog/2024-11-15-CaptainAgent/index.mdx

@@ -93,7 +93,7 @@ result = user_proxy.initiate_chat(captain_agent, message=query)
 # Further Reading
 For a detailed description of how to configure the CaptainAgent, please refer to
 - [document](https://ag2ai.github.io/ag2/docs/topics/captainagent/agent_library).
-- [Notebook example](https://ag2ai.github.io/ag2/docs/notebooks/agentchat_captainagent/)
+- [Notebook example](https://docs.ag2.ai/notebooks/agentchat_captainagent/)
 
 Please also refer to our [paper](https://arxiv.org/pdf/2405.19425) for more details about CaptainAgent and the proposed new team-building paradigm: adaptive build.
 

website/blog/2024-11-27-Prompt-Leakage-Probing/index.mdx

@@ -80,7 +80,7 @@ The **Prompt Leakage Probing Framework** leverages key agentic design patterns f
 1. **Group Chat Pattern**
    - The chat between agents in this project is modeled on the [Group Chat Pattern](https://ag2ai.github.io/ag2/docs/tutorial/conversation-patterns#group-chat), where multiple agents collaboratively interact to perform tasks.
    - This structure allows for seamless coordination between agents like the prompt generator, classifier, and user proxy agent.
-   - The chat has a [custom speaker selection](https://ag2ai.github.io/ag2/docs/notebooks/agentchat_groupchat_customized) method implemented so that it guarantees the prompt->response->classification chat flow.
+   - The chat has a [custom speaker selection](https://docs.ag2.ai/notebooks/agentchat_groupchat_customized) method implemented so that it guarantees the prompt->response->classification chat flow.
 
 2. **ConversableAgents**
    - The system includes two [ConversableAgents](https://ag2ai.github.io/ag2/docs/reference/agentchat/conversable_agent#conversableagent):

website/blog/2024-12-02-ReasoningAgent2/index.mdx

@@ -258,7 +258,7 @@ The implementation is flexible and can be customized for different types of prob
 ## For Further Reading
 
 * [Documentation about ReasoningAgent](/docs/reference/agentchat/contrib/reasoning_agent)
-* [Example notebook](https://ag2ai.github.io/ag2/docs/notebooks/agentchat_reasoning_agent/)
+* [Example notebook](https://docs.ag2.ai/notebooks/agentchat_reasoning_agent/)
 * [The Original research paper about Tree of Thoughts](https://arxiv.org/abs/2305.10601) from Google DeepMind and Princeton University.
 
 *Do you have interesting use cases for ReasoningAgent? Would you like to see more features or improvements? Please join our [Discord](https://discord.com/invite/pAbnFJrkgZ) server for discussion.*

website/blog/2024-12-06-FalkorDB-Structured/index.mdx

@@ -121,7 +121,7 @@ Based on the provided information, there is no additional data about other actor
 --------------------------------------------------------------------------------
 ```
 
-For a more in-depth example, [see this notebook](https://ag2ai.github.io/ag2/docs/notebooks/agentchat_swarm_graphrag_trip_planner/) where we create this Trip Planner workflow.
+For a more in-depth example, [see this notebook](https://docs.ag2.ai/notebooks/agentchat_swarm_graphrag_trip_planner/) where we create this Trip Planner workflow.
 ![Trip Planner](img/tripplanner.png)
 
 ## Structured Outputs
@@ -181,7 +181,7 @@ A sample response to `how can I solve 8x + 7 = -23` would be:
 }
 ```
 
-See the [Trip Planner](https://ag2ai.github.io/ag2/docs/notebooks/agentchat_swarm_graphrag_trip_planner/) and [Structured Output](https://ag2ai.github.io/ag2/docs/notebooks/agentchat_structured_outputs/) notebooks to start using Structured Outputs.
+See the [Trip Planner](https://docs.ag2.ai/notebooks/agentchat_swarm_graphrag_trip_planner/) and [Structured Output](https://docs.ag2.ai/notebooks/agentchat_structured_outputs/) notebooks to start using Structured Outputs.
 
 
 ## Nested Chats in Swarms
@@ -194,9 +194,9 @@ See the [Swarm documentation](https://ag2ai.github.io/ag2/docs/topics/swarm#regi
 
 ## For Further Reading
 
-* [Trip Planner Notebook Example Using GraphRag, Structured Output & Swarm](https://ag2ai.github.io/ag2/docs/notebooks/agentchat_swarm_graphrag_trip_planner/)
+* [Trip Planner Notebook Example Using GraphRag, Structured Output & Swarm](https://docs.ag2.ai/notebooks/agentchat_swarm_graphrag_trip_planner/)
 * [Documentation about FalkorDB](https://docs.falkordb.com/)
 * [OpenAI's Structured Outputs](https://platform.openai.com/docs/guides/structured-outputs)
-* [Structured Output Notebook Example](https://ag2ai.github.io/ag2/docs/notebooks/agentchat_structured_outputs/)
+* [Structured Output Notebook Example](https://docs.ag2.ai/notebooks/agentchat_structured_outputs/)
 
 *Do you have interesting use cases for FalkorDB / RAG? Would you like to see more features or improvements? Please join our [Discord](https://discord.com/invite/pAbnFJrkgZ) server for discussion.*

website/blog/2024-12-18-Reasoning-Update/index.mdx

@@ -278,7 +278,7 @@ The new ReasoningAgent offers a flexible toolkit for systematic reasoning with L
 * [Original ReasoningAgent with Beam Search](https://docs.ag2.ai/blog/2024-12-02-ReasoningAgent2/)
 * [Documentation about ReasoningAgent](/docs/reference/agentchat/contrib/reasoning_agent)
 * [MCTS in Wikipedia](https://en.wikipedia.org/wiki/Monte_Carlo_tree_search)
-* [Example Notebook](https://ag2ai.github.io/ag2/docs/notebooks/agentchat_reasoning_agent/)
+* [Example Notebook](https://docs.ag2.ai/notebooks/agentchat_reasoning_agent/)
 
 
 *Join our [Discord](https://discord.com/invite/pAbnFJrkgZ) server to discuss your experiences with these approaches and suggest improvements.*

website/docs/Use-Cases/agent_chat.mdx

@@ -84,7 +84,7 @@ 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/ag2ai/optiguide).
 - [Dynamic Group Chat](https://github.com/ag2ai/ag2/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://ag2ai.github.io/ag2/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.
+- [Finite State Machine graphs to set speaker transition constraints](https://docs.ag2.ai/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/ag2ai/ag2/blob/main/notebook/agentchat_nested_chats_chess.ipynb).
 
 2. LLM-Based Function Call