/*
 * Purpose of This File:
 * This file is required for Quarto to render both HTML and MDX files in the `website` folder.
 * These rendered files are essential for Mintlify to function correctly.
 *
 * Context:
 * - The `quarto render` command behaves differently when the `docusaurus.config.js` file is
 *   present in the `website` folder.
 * - Without this file, Quarto does not render MDX files, leading to failures
 *   when running the `process_notebook.py` script.
 * - As a result, this file is retained temporarily until a better solution is identified.
 *
 * Options Tried (and Issues Faced):
 * 1. Adding a `_quarto.yml` file in the `website` folder with the following configurations:
 *
 *    **Minimal Configuration:**
 *    ```
 *    project:
 *      type: docusaurus
 *
 *    format:
 *      html: default
 *      docusaurus-md: default
 *    ```
 *
 *    **Full Configuration from Quarto Repository:**
 *    The configuration from [Quarto's docusaurus extension](https://github.com/quarto-dev/quarto-cli/blob/main/src/resources/extensions/quarto/docusaurus/_extension.yml).
 *
 *    - Both configurations instruct Quarto to render HTML and MDX files.
 *    - However, in both cases, Mintlify fails to generate docs, raising the following error:
 *      > Warning: data for page "/[[...slug]]" (path "/docs/Home") is 56.1 MB which exceeds
 *      > the threshold of 21 MB. This amount of data can reduce performance.
 *    - More info: https://nextjs.org/docs/messages/large-page-data
 */


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// Watch for URL changes using MutationObserver
const observer = new MutationObserver((mutations) => {
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observer.observe(document.body, {
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// Initialize on page load
document.addEventListener("DOMContentLoaded", function () {
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visualize_speaker_transitions_dict

autogen.graph_utils.visualize_speaker_transitions_dict

Quick Start

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From Dad Jokes To Sad Jokes: Function Calling with GPTAssistantAgent

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Group Chat with Customized Speaker Selection Method

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Solve complex tasks with one or more sequence chats nested as inner monologue.

Solving Complex Tasks with A Sequence of Nested Chats

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Auto Generated Agent Chat: Teaching AI New Skills via Natural Language Interaction

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Chat with OpenAI Assistant using function call in AG2: OSS Insights for Advanced GitHub Data Analysis

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Solving Multiple Tasks in a Sequence of Async Chats

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Demonstrating the `AgentEval` framework using the task of solving math problems as an example

Adding Browsing Capabilities to AG2

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Auto Generated Agent Chat: GPTAssistant with Code Interpreter

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Using Neo4j's graph database with AG2 agents for Question & Answering

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StateFlow: Build Workflows through State-Oriented Actions

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Using RetrieveChat for Retrieve Augmented Code Generation and Question Answering

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ReasoningAgent - Advanced LLM Reasoning with Multiple Search Strategies

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OpenAI offers a functionality for defining a structure of the messages generated by LLMs, AutoGen enables this functionality by propagating response_format passed to your agents to the underlying client.

Structured output from json configuration

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Agent Observability with OpenLIT

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Use ChromaDBQueryEngine to query Markdown files

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Use conversable language learning model agents to solve tasks and provide automatic feedback through a comprehensive example of writing, executing, and debugging Python code to compare stock price changes.

Task Solving with Code Generation, Execution and Debugging

Use MongoDBQueryEngine to query Markdown files

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Adding Google Search Capability to AG2

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Solving Multiple Tasks in a Sequence of Chats

This notebook demonstrates the usage of QdrantRetrieveUserProxyAgent for RAG.

Using RetrieveChat with Qdrant for Retrieve Augmented Code Generation and Question Answering

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Runtime Logging with AG2

Load the configuration including the response format

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Auto Generated Agent Chat: Task Solving with Provided Tools as Functions

Preprocessing chat history with `TransformMessages`

Preprocessing Chat History with `TransformMessages`

Introducing CaptainAgent, a powerful agent that can manage and orchestrate other agents and tools to solve complex tasks.

Explore the demonstration of the Finite State Machine implementation, which allows the user to input speaker transition constraints.

FSM - User can input speaker transition constraints

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OptiGuide with Nested Chats in AG2

Explore a group chat example using agents such as a coder and visualization agent.

Group Chat with Coder and Visualization Critic

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Mitigating Prompt hacking with JSON Mode in Autogen

Use tools to extract and translate the transcript of a video file.

Translating Video audio using Whisper and GPT-3.5-turbo

Auto Generated Agent Chat: Group Chat with GPTAssistantAgent

Automated continual learning from new data.

Interactive LLM Agent Dealing with Data Stream

Leveraging multimodal models through two different methodologies: MultimodalConversableAgent and VisionCapability.

Name	Description
`speaker_transitions_dict`	A dictionary of keys and list as values. The keys are the names of the agents, and the values are the names of the agents that the key agent can transition to. Type: dict[str, list[Agent]]
`agents`	A list of Agents Type: list[Agent]
`export_path`	The path to export the graph. If None, the graph will be shown. Type: str \| None Default: None

API Reference

​visualize_speaker_transitions_dict

visualize_speaker_transitions_dict