/*
 * 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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update_conditional_functions

autogen.agentchat.group.group_utils.update_conditional_functions

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

Mitigating Prompt hacking with JSON Mode in Autogen

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Structured output from json configuration

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

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Group Chat with Coder and Visualization Critic

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Auto Generated Agent Chat: Task Solving with Code Generation, Execution, Debugging & Human Feedback

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

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

Writing a software application using function calls

DeepSeek: Adding Browsing Capabilities to AG2

This Jupyter Notebook demonstrates how to leverage OSS Insight (Open Source Software Insight) for advanced GitHub data analysis by defining `Function calls` in AG2 for the OpenAI Assistant.

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

Agent with memory using Mem0

Adding Google Search Capability to AG2

OpenAI Assistant with retrieval augmentation.

RAG OpenAI Assistants in AG2

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

Translating Video audio using Whisper and GPT-3.5-turbo

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Use AG2 in Databricks with DBRX

Use MongoDBQueryEngine to query Markdown files

Google Drive Tools

Usage tracking with AG2

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

Nested Chats for Tool Use in Conversational Chess

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

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

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FSM - User can input speaker transition constraints

Tavily Search Tool

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Trip planning with a FalkorDB GraphRAG agent using a Swarm

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RAG with DocAgent

Wikipedia Agent

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Automatically Build Multi-agent System from Agent Library

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From Dad Jokes To Sad Jokes: Function Calling with GPTAssistantAgent

Agentic RAG workflow on tabular data from a PDF file

Auto Generated Agent Chat: Solving Tasks Requiring Web Info

Integrate llamaindex agents with Autogen.

Groupchat with Llamaindex agents

Using OpenAI’s Web Search Tool with AG2

A Uniform interface to call different LLMs

ChromaDB Query Engine for document queries

Use ChromaDBQueryEngine to query Markdown files

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

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

Using FalkorGraphRagCapability with agents for GraphRAG Question & Answering

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

OptiGuide with Nested Chats in AG2

Use AG2 to Tune ChatGPT

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AgentOptimizer: An Agentic Way to Train Your LLM Agent

DuckDuckGo Search Tool

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Auto Generated Agent Chat: GPTAssistant with Code Interpreter

RealtimeAgent in a Swarm Orchestration

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Solving Multiple Tasks in a Sequence of Chats

Explore the demonstration of the SocietyOfMindAgent in the AG2 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

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Interactive LLM Agent Dealing with Data Stream

RealtimeAgent with local websocket connection

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Engaging with Multimodal Models: GPT-4V in AG2

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

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Group Chat with Customized Speaker Selection Method

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Preprocessing Chat History with `TransformMessages`

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Load the configuration including the response format

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

Use AG2 to Tune OpenAI Models

StateFlow: Build workflows through state-oriented actions.

StateFlow: Build Workflows through State-Oriented Actions

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

Solving Complex Tasks with A Sequence of Nested Chats

LLM providers offer functionality for defining a structure of the messages generated by LLMs, AG2 enables this functionality by propagating a `response_format`, in the LLM configuration for your agents, to the underlying client.

Structured output

Involve multiple human users via function calls and nested chat.

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

Tools with Dependency Injection

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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

Auto Generated Agent Chat: Group Chat with GPTAssistantAgent

Use AG2 in Microsoft Fabric

(Legacy) Implement Swarm-style orchestration with GroupChat

Name	Description
`agent`	Type: ConversableAgent
`messages`	Type: list[dict[str, typing.Any]]

API Reference

​update_conditional_functions

update_conditional_functions