/*
 * 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
 */


// Load jQuery if not present
if (typeof jQuery === "undefined") {
  const jqueryScript = document.createElement("script");
  jqueryScript.src =
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    // Add Chosen CSS
    const chosenStyles = document.createElement("link");
    chosenStyles.rel = "stylesheet";
    chosenStyles.href =
      "https://cdnjs.cloudflare.com/ajax/libs/chosen/1.8.7/chosen.min.css";
    document.head.appendChild(chosenStyles);

    // Initialize Chosen when everything is loaded
    chosenScript.onload = function () {
      initializeGallerySelect();
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  };
}

function getTagsFromURL() {
  const searchParams = new URLSearchParams(window.location.search);
  const tags = searchParams.get("tags");
  return tags ? tags.split(",") : [];
}

// Function to initialize Chosen on gallery select
function initializeGallerySelect() {
  // Add flag to check if already initialized
  const selectElement = $(".examples-gallery-container .tag-filter");
  if (selectElement.length && !selectElement.data("chosen-initialized")) {
    selectElement
      .chosen({
        width: "100%",
        placeholder_text_multiple: "Filter by tags",
      })
      .data("chosen-initialized", true);

    // Get tags from URL and update chosen
    const urlTags = getTagsFromURL();
    if (urlTags.length > 0) {
      selectElement.val(urlTags);
      selectElement.trigger("chosen:updated");

      // Trigger the gallery:tagChange event with URL tags
      const customEvent = new CustomEvent("gallery:tagChange", {
        detail: urlTags,
      });
      document.dispatchEvent(customEvent);
    }

    // Set up change handler
    selectElement.on("change", function (evt, params) {
      const selectedValues = $(this).val() || [];
      const customEvent = new CustomEvent("gallery:tagChange", {
        detail: selectedValues,
      });
      document.dispatchEvent(customEvent);
    });
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}

// Debounce function
function debounce(func, wait) {
  let timeout;
  return function executedFunction(...args) {
    const later = () => {
      clearTimeout(timeout);
      func(...args);
    };
    clearTimeout(timeout);
    timeout = setTimeout(later, wait);
  };
}

// Debounced version of initialize
const debouncedInitialize = debounce(initializeGallerySelect, 100);

// Watch for URL changes using MutationObserver
const observer = new MutationObserver((mutations) => {
  if (window.location.pathname.includes("/use-cases")) {
    debouncedInitialize();
  }
});

observer.observe(document.body, {
  childList: true,
  subtree: true,
  attributes: false,
  characterData: false,
});

// Initialize on page load
document.addEventListener("DOMContentLoaded", function () {
  if (window.jQuery && window.jQuery.fn.chosen) {
    initializeGallerySelect();
  }
});


Class Attributes

func

global_imports

python_packages

FunctionWithRequirementsStr

autogen.coding.func_with_reqs.FunctionWithRequirementsStr

Key Features

Quick Start

Community Gallery

Contributing to AG2

Setting up Development Environment

Documentation

File A Bug Report

Guidance for Maintainers

Pre-commit

Tests

Research

Frequently Asked Questions

Agent Monitoring and Debugging with AgentOps

Azure Cosmos DB

Composio

Databricks

Llamaindex

Mem0:Long-Term Memory and Personalization for Agents

MemGPT

Microsoft Fabric

Ollama

PGVector

Portkey Integration

Promptflow

Home

User Guide

API References

Use Cases

Contribute

FAQs

Ecosystem

Blog

Community Talks

Installing AG2

LLM configuration

ConversableAgent

Human in the loop

Orchestrating many agents

GroupChat

Swarm

Tools

Structured outputs

Ending a chat

Custom Group Chat

Customized Group Chat flows

Resuming a Group Chat

Swarm Deep-dive

Swarm Enhanced Features

Code Execution

Tools with Secrets

Conversation Patterns Deep-dive

LLM Config Deep-dive

OpenAI

Amazon Bedrock

Anthropic

Cerebras

Cohere

DeepSeek

Google Gemini

Google Vertex AI

Groq

LiteLLM with watsonx

LM Studio

Mistral AI

Together AI

vLLM

Customer Service

Game Design

Travel Planning

Overview

CaptainAgent

ReasoningAgent

WebSurferAgent

All Notebooks

Introduce group chat with customized speaker selection method.

Group Chat with Customized Speaker Selection Method

OpenAI Assistant with retrieval augmentation.

RAG OpenAI Assistants in AutoGen

This notebook demonstrates the usage of QdrantRetrieveUserProxyAgent for RAG.

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

Function Inception: Enable AutoGen agents to update/remove functions during conversations.

Auto Generated Agent Chat: Function Inception

Learn how to implement both synchronous and asynchronous function calls using AssistantAgent and UserProxyAgent in AutoGen, with examples of their application in individual and group chat settings for task execution with language models.

Task Solving with Provided Tools as Functions (Asynchronous Function Calls)

Using Guidance with AutoGen

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

Solving Complex Tasks with A Sequence of Nested Chats

Explore the utilization of large language models in automated group chat scenarios, where agents perform tasks collectively, demonstrating how they can be configured, interact with each other, and retrieve specific information from external resources.

Group Chat

Use conversational agents to solve a set of tasks with a sequence of async chats.

Solving Multiple Tasks in a Sequence of Async Chats

Register function calls using AssistantAgent and UserProxyAgent to execute python or shell code in customized ways. Demonstrating two ways of registering functions.

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

LLM-backed agents playing chess with each other using nested chats.

Conversational Chess using non-OpenAI clients

RealtimeAgent with local websocket connection

Scrapping web pages and summarizing the content using agents with tools.

Web Scraping using Apify Tools

DeepSeek: Adding Browsing Capabilities to AG2

Automated continual learning from new data.

Interactive LLM Agent Dealing with Data Stream

Generate Dalle Images With Conversable Agents

Supercharging Web Crawling with Crawl4AI

RealtimeAgent in a Swarm Orchestration

Perform research using a group chat with a number of specialized agents.

Perform Research with Multi-Agent Group Chat

Use AgentOps to simplify the development process and monitor your agents in production.

Agent Tracking with AgentOps

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

Translating Video audio using Whisper and GPT-3.5-turbo

Automatically build multi-agent system from agent library

Automatically Build Multi-agent System from Agent Library

Involve multiple human users via function calls and nested chat.

Auto Generated Agent Chat: Collaborative Task Solving with Multiple Agents and Human Users

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

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

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

Group Chat with Coder and Visualization Critic

Cross-Framework LLM Tool Integration with AG2

Using FalkorGraphRagCapability with agents for GraphRAG Question & Answering

AgentEval: a multi-agent system for assessing utility of LLM-powered applications

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

RealtimeAgent in a Swarm Orchestration using WebRTC

A Uniform interface to call different LLMs

Use tools in a GPTAssistantAgent Multi-Agent System by utilizing functions such as calling an API and writing to a file.

From Dad Jokes To Sad Jokes: Function Calling with GPTAssistantAgent

Solve complex tasks with a chat nested as inner monologue.

Solving Complex Tasks with Nested Chats

Usage tracking with AutoGen

Agent with memory using Mem0

Explore the use of AutoGen's RetrieveChat for tasks like code generation from docstrings, answering complex questions with human feedback, and exploiting features like Update Context, custom prompts, and few-shot learning.

Using RetrieveChat Powered by PGVector for Retrieve Augmented Code Generation and Question Answering

Tools with Dependency Injection

Use AutoGen to solve a set of tasks with a sequence of chats.

Solving Multiple Tasks in a Sequence of Chats with Different Conversable Agent Pairs

Using RetrieveChat Powered by MongoDB Atlas for Retrieve Augmented Code Generation and Question Answering

This notebook demonstrates the use of Assistant Agents in conjunction with Azure Cognitive Search and Azure Identity

Assistants with Azure Cognitive Search and Azure Identity

Use ReasoningAgent for o1 style reasoning in Agentic workflows with LLMs using AG2

ReasoningAgent - Advanced LLM Reasoning with Multiple Search Strategies

Agentic RAG workflow on tabular data from a PDF file

Making OpenAI Assistants Teachable

Run a standalone AssistantAgent, browsing the web using the BrowserUseTool

Run a standalone AssistantAgent

Automatically build multi-agent system with AgentBuilder

AutoBuild

Use conversational agents to solve a set of tasks with a sequence of chats.

Solving Multiple Tasks in a Sequence of Chats

Learn how to register function calls using AssistantAgent and UserProxyAgent.

Currency Calculator: Task Solving with Provided Tools as Functions

Swarm Orchestration with AG2

Use Databricks DBRX and Foundation Model APIs to build AutoGen applications backed by open-source LLMs.

Use AutoGen in Databricks with DBRX

FalkorDB GraphRAG utilises a knowledge graph and can be added as a capability to agents. Together with a swarm orchestration of agents is highly effective at providing a RAG capability.

Using a local Telemetry server to monitor a GraphRAG agent

Auto Generated Agent Chat: Solving Tasks Requiring Web Info

StateFlow: Build workflows through state-oriented actions.

StateFlow: Build Workflows through State-Oriented Actions

Integrate llamaindex agents with Autogen.

Groupchat with Llamaindex agents

Neo4j Native GraphRAG utilizes a knowledge graph and can be added as a capability to agents.

Using Neo4j's native GraphRAG SDK with AG2 agents for Question & Answering

Agent Chat with Multimodal Models: LLaVA

Implement and manage a multi-agent chat system using AutoGen, where AI assistants retrieve information, generate code, and interact collaboratively to solve complex tasks, especially in areas not covered by their training data.

Group Chat with Retrieval Augmented Generation

Provide capabilities of runtime logging for debugging and performance analysis.

Runtime Logging with AutoGen

Explore the demonstration of the SocietyOfMindAgent in the AutoGen library, which runs a group chat as an internal monologue, but appears to the external world as a single agent, offering a structured way to manage complex interactions among multiple agents and handle issues such as extracting responses from complex dialogues and dealing with context window constraints.

SocietyOfMindAgent

Multimodal agent chat with DALL-E and GPT-4v.

Agent Chat with Multimodal Models: DALLE  and GPT-4V

Use OpenLIT to easily monitor AI agents in production with OpenTelemetry.

Agent Observability with OpenLIT

Use JSON mode and Agent Descriptions to mitigate prompt manipulation and control speaker transition.

Mitigating Prompt hacking with JSON Mode in Autogen

Trip planning with a FalkorDB GraphRAG agent using a Swarm

Language Agent Tree Search

Auto Generated Agent Chat: Collaborative Task Solving with Coding and Planning Agent

This is a nested chat re-implementation of OptiGuide which is an LLM-based supply chain optimization framework.

OptiGuide with Nested Chats in AutoGen

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

Equip your agent with functions that can efficiently implement features into your software application.

Writing a software application using function calls

This Jupyter Notebook showcases the integration of the Code Interpreter tool which executes Python code dynamically within applications.

Auto Generated Agent Chat: GPTAssistant with Code Interpreter

Adding Browsing Capabilities to AG2

Learn how to persist memories across chat sessions using the Teachability capability

Chatting with a teachable agent

RealtimeAgent with gemini client using websockets

RealtimeAgent with gemini client

Preprocessing chat history with `TransformMessages`

Name	Description
`func`	Type: `str`
`python_packages`	Type: `list[str]` Default: []
`global_imports`	Type: `list[str \| autogen.coding.func_with_reqs.ImportFromModule \| autogen.coding.func_with_reqs.Alias]` Default: []

API Reference

​FunctionWithRequirementsStr

​Class Attributes

​func

​global_imports

​python_packages

FunctionWithRequirementsStr

Class Attributes

func

global_imports

python_packages