Merge pull request #251 from harishmohanraj/main

Documentation Updates and Fixes
ag2ai · Dec 20, 2024 · 109ee5f · 109ee5f
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diff --git a/.github/workflows/deploy-website.yml b/.github/workflows/deploy-website.yml
@@ -25,12 +25,12 @@ jobs:
           <html>
             <head>
               <meta charset="UTF-8">
-              <meta http-equiv="refresh" content="0; url=https://ag2ai.mintlify.app">
-              <script>window.location.href = "https://ag2ai.mintlify.app";</script>
+              <meta http-equiv="refresh" content="0; url=https://docs.ag2.ai">
+              <script>window.location.href = "https://docs.ag2.ai";</script>
               <title>Page Redirection</title>
             </head>
             <body>
-              If you are not redirected automatically, follow this <a href="https://ag2ai.mintlify.app">link to the new documentation</a>.
+              If you are not redirected automatically, follow this <a href="https://docs.ag2.ai">link to the new documentation</a>.
             </body>
           </html>
           EOF

diff --git a/website/README.md b/website/README.md
@@ -9,7 +9,7 @@ To build and test documentation locally, begin by downloading and installing [No
 ## Installation
 
 ```console
-pip install pydoc-markdown pyyaml colored
+pip install pydoc-markdown pyyaml termcolor nbconvert
 ```
 
 ### Install Quarto
@@ -25,7 +25,7 @@ Install it [here](https://github.com/quarto-dev/quarto-cli/releases).
 Navigate to the `website` folder and run:
 
 ```console
-pydoc-markdown
+python ./process_api_reference.py
 python ./process_notebooks.py render
 npm install
 ```

diff --git a/website/blog/2023-04-21-LLM-tuning-math/index.mdx b/website/blog/2023-04-21-LLM-tuning-math/index.mdx
@@ -32,7 +32,7 @@ We adapt the models using 20 examples in the train set, using the problem statem
 - top_p: The parameter that controls the probability mass of the output tokens. Only tokens with a cumulative probability less than or equal to top-p are considered. A lower top-p means more diversity but less coherence. We search for the optimal top-p in the range of [0, 1].
 - max_tokens: The maximum number of tokens that can be generated for each output. We search for the optimal max length in the range of [50, 1000].
 - n: The number of responses to generate. We search for the optimal n in the range of [1, 100].
-- prompt: We use the template: "{problem} Solve the problem carefully. Simplify your answer as much as possible. Put the final answer in \\boxed{{}}." where {problem} will be replaced by the math problem instance.
+- prompt: We use the template: "\{problem} Solve the problem carefully. Simplify your answer as much as possible. Put the final answer in \\boxed\{{}}." where \{problem} will be replaced by the math problem instance.
 
 In this experiment, when n > 1, we find the answer with highest votes among all the responses and then select it as the final answer to compare with the ground truth. For example, if n = 5 and 3 of the responses contain a final answer 301 while 2 of the responses contain a final answer 159, we choose 301 as the final answer. This can help with resolving potential errors due to randomness. We use the average accuracy and average inference cost as the metric to evaluate the performance over a dataset. The inference cost of a particular instance is measured by the price per 1K tokens and the number of tokens consumed.
 

diff --git a/...8-RealtimeAgent/img/1_service_running.png → ...0-RealtimeAgent/img/1_service_running.png b/...8-RealtimeAgent/img/1_service_running.png → ...0-RealtimeAgent/img/1_service_running.png
diff --git a/...-18-RealtimeAgent/img/2_incoming_call.png → ...-20-RealtimeAgent/img/2_incoming_call.png b/...-18-RealtimeAgent/img/2_incoming_call.png → ...-20-RealtimeAgent/img/2_incoming_call.png
diff --git a/...img/3_request_for_flight_cancellation.png → ...img/3_request_for_flight_cancellation.png b/...img/3_request_for_flight_cancellation.png → ...img/3_request_for_flight_cancellation.png
diff --git a/...ealtimeAgent/img/4_flight_number_name.png → ...ealtimeAgent/img/4_flight_number_name.png b/...ealtimeAgent/img/4_flight_number_name.png → ...ealtimeAgent/img/4_flight_number_name.png
diff --git a/...-18-RealtimeAgent/img/5_refund_policy.png → ...-20-RealtimeAgent/img/5_refund_policy.png b/...-18-RealtimeAgent/img/5_refund_policy.png → ...-20-RealtimeAgent/img/5_refund_policy.png
diff --git a/...8-RealtimeAgent/img/6_flight_refunded.png → ...0-RealtimeAgent/img/6_flight_refunded.png b/...8-RealtimeAgent/img/6_flight_refunded.png → ...0-RealtimeAgent/img/6_flight_refunded.png
diff --git a/...ealtimeAgent/img/realtime_agent_swarm.png → ...ealtimeAgent/img/realtime_agent_swarm.png b/...ealtimeAgent/img/realtime_agent_swarm.png → ...ealtimeAgent/img/realtime_agent_swarm.png
diff --git a/...e/blog/2024-12-18-RealtimeAgent/index.mdx → ...e/blog/2024-12-20-RealtimeAgent/index.mdx b/...e/blog/2024-12-18-RealtimeAgent/index.mdx → ...e/blog/2024-12-20-RealtimeAgent/index.mdx
diff --git a/...Reasoning-Update/img/reasoningagent_1.png → ...Reasoning-Update/img/reasoningagent_1.png b/...Reasoning-Update/img/reasoningagent_1.png → ...Reasoning-Update/img/reasoningagent_1.png
diff --git a/...log/2024-12-18-Reasoning-Update/index.mdx → ...log/2024-12-20-Reasoning-Update/index.mdx b/...log/2024-12-18-Reasoning-Update/index.mdx → ...log/2024-12-20-Reasoning-Update/index.mdx
diff --git a/...24-12-18-Tools-interoperability/index.mdx → ...24-12-20-Tools-interoperability/index.mdx b/...24-12-18-Tools-interoperability/index.mdx → ...24-12-20-Tools-interoperability/index.mdx
diff --git a/website/docs/Examples.mdx b/website/docs/Examples.mdx
@@ -11,111 +11,111 @@ Links to notebook examples:
 
 ### Code Generation, Execution, and Debugging
 
-- Automated Task Solving with Code Generation, Execution & Debugging - [View Notebook](/docs/notebooks/agentchat_auto_feedback_from_code_execution)
-- Automated Code Generation and Question Answering with Retrieval Augmented Agents - [View Notebook](/docs/notebooks/agentchat_RetrieveChat)
-- Automated Code Generation and Question Answering with [Qdrant](https://qdrant.tech/) based Retrieval Augmented Agents - [View Notebook](/docs/notebooks/agentchat_RetrieveChat_qdrant)
+- Automated Task Solving with Code Generation, Execution & Debugging - [View Notebook](/notebooks/agentchat_auto_feedback_from_code_execution)
+- Automated Code Generation and Question Answering with Retrieval Augmented Agents - [View Notebook](/notebooks/agentchat_RetrieveChat)
+- Automated Code Generation and Question Answering with [Qdrant](https://qdrant.tech/) based Retrieval Augmented Agents - [View Notebook](/notebooks/agentchat_RetrieveChat_qdrant)
 
 ### Multi-Agent Collaboration (>3 Agents)
 
-- 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](/docs/notebooks/agentchat_planning)
-- Automated Task Solving with transition paths specified in a graph - [View Notebook](/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)
+- Automated Task Solving by Group Chat (with 3 group member agents and 1 manager agent) - [View Notebook](/notebooks/agentchat_groupchat)
+- Automated Data Visualization by Group Chat (with 3 group member agents and 1 manager agent) - [View Notebook](/notebooks/agentchat_groupchat_vis)
+- Automated Complex Task Solving by Group Chat (with 6 group member agents and 1 manager agent) - [View Notebook](/notebooks/agentchat_groupchat_research)
+- Automated Task Solving with Coding & Planning Agents - [View Notebook](/notebooks/agentchat_planning)
+- Automated Task Solving with transition paths specified in a graph - [View Notebook](/notebooks/agentchat_groupchat_finite_state_machine)
+- Running a group chat as an inner-monolgue via the SocietyOfMindAgent - [View Notebook](/notebooks/agentchat_society_of_mind)
+- Running a group chat with custom speaker selection function - [View Notebook](/notebooks/agentchat_groupchat_customized)
 
 ### Sequential Multi-Agent Chats
 
-- Solving Multiple Tasks in a Sequence of Chats Initiated by a Single Agent - [View Notebook](/docs/notebooks/agentchat_multi_task_chats)
-- Async-solving Multiple Tasks in a Sequence of Chats Initiated by a Single Agent - [View Notebook](/docs/notebooks/agentchat_multi_task_async_chats)
-- Solving Multiple Tasks in a Sequence of Chats Initiated by Different Agents - [View Notebook](/docs/notebooks/agentchats_sequential_chats)
+- Solving Multiple Tasks in a Sequence of Chats Initiated by a Single Agent - [View Notebook](/notebooks/agentchat_multi_task_chats)
+- Async-solving Multiple Tasks in a Sequence of Chats Initiated by a Single Agent - [View Notebook](/notebooks/agentchat_multi_task_async_chats)
+- Solving Multiple Tasks in a Sequence of Chats Initiated by Different Agents - [View Notebook](/notebooks/agentchats_sequential_chats)
 
 ### Nested Chats
 
-- Solving Complex Tasks with Nested Chats - [View Notebook](/docs/notebooks/agentchat_nestedchat)
-- Solving Complex Tasks with A Sequence of Nested Chats - [View Notebook](/docs/notebooks/agentchat_nested_sequential_chats)
-- OptiGuide for Solving a Supply Chain Optimization Problem with Nested Chats with a Coding Agent and a Safeguard Agent - [View Notebook](/docs/notebooks/agentchat_nestedchat_optiguide)
-- Conversational Chess with Nested Chats and Tool Use - [View Notebook](/docs/notebooks/agentchat_nested_chats_chess)
+- Solving Complex Tasks with Nested Chats - [View Notebook](/notebooks/agentchat_nestedchat)
+- Solving Complex Tasks with A Sequence of Nested Chats - [View Notebook](/notebooks/agentchat_nested_sequential_chats)
+- OptiGuide for Solving a Supply Chain Optimization Problem with Nested Chats with a Coding Agent and a Safeguard Agent - [View Notebook](/notebooks/agentchat_nestedchat_optiguide)
+- Conversational Chess with Nested Chats and Tool Use - [View Notebook](/notebooks/agentchat_nested_chats_chess)
 
 ### Swarms
-- Orchestrating agents in a Swarm - [View Notebook](/docs/notebooks/agentchat_swarm)
-- Orchestrating agents in a Swarm (Enhanced) - [View Notebook](/docs/notebooks/agentchat_swarm_enhanced)
+- Orchestrating agents in a Swarm - [View Notebook](/notebooks/agentchat_swarm)
+- Orchestrating agents in a Swarm (Enhanced) - [View Notebook](/notebooks/agentchat_swarm_enhanced)
 
 ### Applications
 
-- Automated Continual Learning from New Data - [View Notebook](/docs/notebooks/agentchat_stream)
+- Automated Continual Learning from New Data - [View Notebook](/notebooks/agentchat_stream)
 {/*  - [OptiGuide](https://github.com/microsoft/optiguide) - Coding, Tool Using, Safeguarding & Question Answering for Supply Chain Optimization */}
 - [AutoAnny](https://github.com/ag2ai/build-with-ag2/tree/main/samples/apps/auto-anny) - A Discord bot built using AutoGen
 
 ### RAG
 
-- GraphRAG agent using FalkorDB (feat. swarms and Google Maps API) - [View Notebook](/docs/notebooks/agentchat_swarm_graphrag_trip_planner)
+- GraphRAG agent using FalkorDB (feat. swarms and Google Maps API) - [View Notebook](/notebooks/agentchat_swarm_graphrag_trip_planner)
 
 ### Tool Use
 
-- **Web Search**: Solve Tasks Requiring Web Info - [View Notebook](/docs/notebooks/agentchat_web_info)
-- 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](/docs/notebooks/agentchat_langchain)
-- **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](/docs/notebooks/agentchat_inception_function)
-- Agent Chat with Whisper - [View Notebook](/docs/notebooks/agentchat_video_transcript_translate_with_whisper)
-- Constrained Responses via Guidance - [View Notebook](/docs/notebooks/agentchat_guidance)
-- Browse the Web with Agents - [View Notebook](/docs/notebooks/agentchat_surfer)
-- **SQL**: Natural Language Text to SQL Query using the [Spider](https://yale-lily.github.io/spider) Text-to-SQL Benchmark - [View Notebook](/docs/notebooks/agentchat_sql_spider)
-- **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](/docs/notebooks/agentchat_function_call_code_writing).
+- **Web Search**: Solve Tasks Requiring Web Info - [View Notebook](/notebooks/agentchat_web_info)
+- Use Provided Tools as Functions - [View Notebook](/notebooks/agentchat_function_call_currency_calculator)
+- Use Tools via Sync and Async Function Calling - [View Notebook](/notebooks/agentchat_function_call_async)
+- Task Solving with Langchain Provided Tools as Functions - [View Notebook](/notebooks/agentchat_langchain)
+- **RAG**: Group Chat with Retrieval Augmented Generation (with 5 group member agents and 1 manager agent) - [View Notebook](/notebooks/agentchat_groupchat_RAG)
+- Function Inception: Enable AutoGen agents to update/remove functions during conversations. - [View Notebook](/notebooks/agentchat_inception_function)
+- Agent Chat with Whisper - [View Notebook](/notebooks/agentchat_video_transcript_translate_with_whisper)
+- Constrained Responses via Guidance - [View Notebook](/notebooks/agentchat_guidance)
+- Browse the Web with Agents - [View Notebook](/notebooks/agentchat_surfer)
+- **SQL**: Natural Language Text to SQL Query using the [Spider](https://yale-lily.github.io/spider) Text-to-SQL Benchmark - [View Notebook](/notebooks/agentchat_sql_spider)
+- **Web Scraping**: Web Scraping with Apify - [View Notebook](/notebooks/agentchat_webscraping_with_apify)
+- **Write a software app, task by task, with specially designed functions.** - [View Notebook](/notebooks/agentchat_function_call_code_writing).
 
 ### Human Involvement
 
 - Simple example in ChatGPT style [View example](https://github.com/ag2ai/build-with-ag2/blob/main/samples/simple_chat.py)
-- Auto Code Generation, Execution, Debugging and **Human Feedback** - [View Notebook](/docs/notebooks/agentchat_human_feedback)
-- Automated Task Solving with GPT-4 + **Multiple Human Users** - [View Notebook](/docs/notebooks/agentchat_two_users)
-- Agent Chat with **Async Human Inputs** - [View Notebook](/docs/notebooks/async_human_input)
+- Auto Code Generation, Execution, Debugging and **Human Feedback** - [View Notebook](/notebooks/agentchat_human_feedback)
+- Automated Task Solving with GPT-4 + **Multiple Human Users** - [View Notebook](/notebooks/agentchat_two_users)
+- Agent Chat with **Async Human Inputs** - [View Notebook](/notebooks/async_human_input)
 
 ### 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](/docs/notebooks/agentchat_teachable_oai_assistants)
-- Agent Optimizer: Train Agents in an Agentic Way - [View Notebook](/docs/notebooks/agentchat_agentoptimizer)
+- Teach Agents New Skills & Reuse via Automated Chat - [View Notebook](/notebooks/agentchat_teaching)
+- Teach Agents New Facts, User Preferences and Skills Beyond Coding - [View Notebook](/notebooks/agentchat_teachability)
+- Teach OpenAI Assistants Through GPTAssistantAgent - [View Notebook](/notebooks/agentchat_teachable_oai_assistants)
+- Agent Optimizer: Train Agents in an Agentic Way - [View Notebook](/notebooks/agentchat_agentoptimizer)
 
 ### Multi-Agent Chat with OpenAI Assistants in the loop
 
-- Hello-World Chat with OpenAi Assistant in AutoGen - [View Notebook](/docs/notebooks/agentchat_oai_assistant_twoagents_basic)
-- Chat with OpenAI Assistant using Function Call - [View Notebook](/docs/notebooks/agentchat_oai_assistant_function_call)
-- Chat with OpenAI Assistant with Code Interpreter - [View Notebook](/docs/notebooks/agentchat_oai_code_interpreter)
-- Chat with OpenAI Assistant with Retrieval Augmentation - [View Notebook](/docs/notebooks/agentchat_oai_assistant_retrieval)
-- OpenAI Assistant in a Group Chat - [View Notebook](/docs/notebooks/agentchat_oai_assistant_groupchat)
-- GPTAssistantAgent based Multi-Agent Tool Use - [View Notebook](/docs/notebooks/gpt_assistant_agent_function_call)
+- Hello-World Chat with OpenAi Assistant in AutoGen - [View Notebook](/notebooks/agentchat_oai_assistant_twoagents_basic)
+- Chat with OpenAI Assistant using Function Call - [View Notebook](/notebooks/agentchat_oai_assistant_function_call)
+- Chat with OpenAI Assistant with Code Interpreter - [View Notebook](/notebooks/agentchat_oai_code_interpreter)
+- Chat with OpenAI Assistant with Retrieval Augmentation - [View Notebook](/notebooks/agentchat_oai_assistant_retrieval)
+- OpenAI Assistant in a Group Chat - [View Notebook](/notebooks/agentchat_oai_assistant_groupchat)
+- GPTAssistantAgent based Multi-Agent Tool Use - [View Notebook](/notebooks/gpt_assistant_agent_function_call)
 
 ### Non-OpenAI Models
-- Conversational Chess using non-OpenAI Models - [View Notebook](/docs/notebooks/agentchat_nested_chats_chess_altmodels)
+- Conversational Chess using non-OpenAI Models - [View Notebook](/notebooks/agentchat_nested_chats_chess_altmodels)
 
 ### Multimodal Agent
 
-- Multimodal Agent Chat with DALLE and GPT-4V - [View Notebook](/docs/notebooks/agentchat_dalle_and_gpt4v)
-- Multimodal Agent Chat with Llava - [View Notebook](/docs/notebooks/agentchat_lmm_llava)
-- Multimodal Agent Chat with GPT-4V - [View Notebook](/docs/notebooks/agentchat_lmm_gpt-4v)
+- Multimodal Agent Chat with DALLE and GPT-4V - [View Notebook](/notebooks/agentchat_dalle_and_gpt4v)
+- Multimodal Agent Chat with Llava - [View Notebook](/notebooks/agentchat_lmm_llava)
+- Multimodal Agent Chat with GPT-4V - [View Notebook](/notebooks/agentchat_lmm_gpt-4v)
 
 ### Long Context Handling
 
 {/* - Conversations with Chat History Compression Enabled - [View Notebook](https://github.com/ag2ai/ag2/blob/main/notebook/agentchat_compression.ipynb) */}
-- Long Context Handling as A Capability - [View Notebook](/docs/notebooks/agentchat_transform_messages)
+- Long Context Handling as A Capability - [View Notebook](/notebooks/agentchat_transform_messages)
 
 ### Evaluation and Assessment
 
-- AgentEval: A Multi-Agent System for Assess Utility of LLM-powered Applications - [View Notebook](/docs/notebooks/agenteval_cq_math)
+- AgentEval: A Multi-Agent System for Assess Utility of LLM-powered Applications - [View Notebook](/notebooks/agenteval_cq_math)
 
 ### Automatic Agent Building
 
-- Automatically Build Multi-agent System with AgentBuilder - [View Notebook](/docs/notebooks/autobuild_basic)
-- Automatically Build Multi-agent System from Agent Library - [View Notebook](/docs/notebooks/autobuild_agent_library)
+- Automatically Build Multi-agent System with AgentBuilder - [View Notebook](/notebooks/autobuild_basic)
+- Automatically Build Multi-agent System from Agent Library - [View Notebook](/notebooks/autobuild_agent_library)
 
 ### Observability
-- Track LLM calls, tool usage, actions and errors using AgentOps - [View Notebook](/docs/notebooks/agentchat_agentops)
-- Cost Calculation - [View Notebook](/docs/notebooks/agentchat_cost_token_tracking)
+- Track LLM calls, tool usage, actions and errors using AgentOps - [View Notebook](/notebooks/agentchat_agentops)
+- Cost Calculation - [View Notebook](/notebooks/agentchat_cost_token_tracking)
 
 ## Enhanced Inferences
 

diff --git a/website/docs/Use-Cases/enhanced_inference.mdx b/website/docs/Use-Cases/enhanced_inference.mdx
@@ -241,7 +241,7 @@ response = client.create(
 
 The example above will try to use text-ada-001, gpt-3.5-turbo-instruct, and text-davinci-003 iteratively, until a valid json string is returned or the last config is used. One can also repeat the same model in the list for multiple times (with different seeds) to try one model multiple times for increasing the robustness of the final response.
 
-*Advanced use case: Check this [blogpost](/blog/2023/05/18/GPT-adaptive-humaneval) to find how to improve GPT-4's coding performance from 68% to 90% while reducing the inference cost.*
+*Advanced use case: Check this [blogpost](/blog/2023-05-18-GPT-adaptive-humaneval/index) to find how to improve GPT-4's coding performance from 68% to 90% while reducing the inference cost.*
 
 ## Templating
 

diff --git a/website/docs/installation/Optional-Dependencies.mdx b/website/docs/installation/Optional-Dependencies.mdx
@@ -6,8 +6,8 @@ AG2 installs OpenAI package by default. To use LLMs by other providers, you can
 pip install autogen[gemini,anthropic,mistral,together,groq,cohere]
 ```
 
-Check out the [notebook](/docs/notebooks/autogen_uniformed_api_calling) and
-[blogpost](/blog/2024/06/24/AltModels-Classes) for more details.
+Check out the [notebook](/notebooks/autogen_uniformed_api_calling) and
+[blogpost](/blog/2024-06-24-AltModels-Classes/index) for more details.
 
 ## LLM Caching
 
@@ -90,7 +90,7 @@ To use Teachability, please install AG2 with the [teachable] option.
 pip install "autogen[teachable]"
 ```
 
-Example notebook: [Chatting with a teachable agent](/docs/notebooks/agentchat_teachability)
+Example notebook: [Chatting with a teachable agent](/notebooks/agentchat_teachability)
 
 ## Large Multimodal Model (LMM) Agents
 
@@ -100,7 +100,7 @@ We offered Multimodal Conversable Agent and LLaVA Agent. Please install with the
 pip install "autogen[lmm]"
 ```
 
-Example notebook: [LLaVA Agent](/docs/notebooks/agentchat_lmm_llava)
+Example notebook: [LLaVA Agent](/notebooks/agentchat_lmm_llava)
 
 ## mathchat
 
@@ -120,7 +120,7 @@ To use a graph in `GroupChat`, particularly for graph visualization, please inst
 pip install "autogen[graph]"
 ```
 
-Example notebook: [Finite State Machine graphs to set speaker transition constraints](/docs/notebooks/agentchat_groupchat_finite_state_machine)
+Example notebook: [Finite State Machine graphs to set speaker transition constraints](/notebooks/agentchat_groupchat_finite_state_machine)
 
 ## Long Context Handling