md to mdx format

notebook/contributing.md → notebook/contributing.mdx

@@ -1,4 +1,6 @@
-# Contributing
+---
+title: Contributing
+---
 
 ## How to get a notebook displayed on the website
 

website/README.md → website/README.mdx

@@ -1,5 +1,6 @@
-# Website
-
+---
+title: Website
+---
 This website is built using [Docusaurus 3](https://docusaurus.io/), a modern static website generator.
 
 ## Prerequisites

website/docs/Examples.md → website/docs/Examples.mdx

@@ -1,4 +1,6 @@
-# Examples
+---
+title: Examples
+---
 
 ## Automated Multi Agent Chat
 

website/docs/Migration-Guide.md → website/docs/Migration-Guide.mdx

@@ -1,4 +1,6 @@
-# Migration Guide
+---
+title: Migration Guide
+---
 
 ## Migrating to 0.2
 

website/docs/Research.md → website/docs/Research.mdx

@@ -1,4 +1,6 @@
-# Research
+---
+title: Research
+---
 
 For technical details, please check our technical report and research publications.
 

website/docs/Use-Cases/agent_chat.md → website/docs/Use-Cases/agent_chat.mdx

@@ -1,4 +1,6 @@
-# Multi-agent Conversation Framework
+---
+title: Multi-agent Conversation Framework
+---
 
 AutoGen offers a unified multi-agent conversation framework as a high-level abstraction of using foundation models. It features capable, customizable and conversable agents which integrate LLMs, tools, and humans via automated agent chat.
 By automating chat among multiple capable agents, one can easily make them collectively perform tasks autonomously or with human feedback, including tasks that require using tools via code.

website/docs/Use-Cases/enhanced_inference.md → website/docs/Use-Cases/enhanced_inference.mdx

@@ -1,4 +1,6 @@
-# Enhanced Inference
+---
+title: Enhanced Inference
+---
 
 `autogen.OpenAIWrapper` provides enhanced LLM inference for `openai>=1`.
 `autogen.Completion` is a drop-in replacement of `openai.Completion` and `openai.ChatCompletion` for enhanced LLM inference using `openai<1`.

website/docs/autogen-studio/faqs.md → website/docs/autogen-studio/faqs.mdx

@@ -1,5 +1,6 @@
-# AutoGen Studio FAQs
-
+---
+title: AutoGen Studio FAQs
+---
 ## Q: How do I specify the directory where files(e.g. database) are stored?
 
 A: You can specify the directory where files are stored by setting the `--appdir` argument when running the application. For example, `autogenstudio ui --appdir /path/to/folder`. This will store the database (default) and other files in the specified directory e.g. `/path/to/folder/database.sqlite`.

website/docs/autogen-studio/getting-started.md → website/docs/autogen-studio/getting-started.mdx

@@ -1,4 +1,6 @@
-# AutoGen Studio - Getting Started
+---
+title: AutoGen Studio - Getting Started
+---
 
 [![PyPI version](https://badge.fury.io/py/autogenstudio.svg)](https://badge.fury.io/py/autogenstudio)
 [![Downloads](https://static.pepy.tech/badge/autogenstudio/week)](https://pepy.tech/project/autogenstudio)

website/docs/autogen-studio/usage.md → website/docs/autogen-studio/usage.mdx

@@ -1,4 +1,6 @@
-# Using AutoGen Studio
+---
+title: Using AutoGen Studio
+---
 
 AutoGen Studio supports the declarative creation of an agent workflow and tasks can be specified and run in a chat interface for the agents to complete. The expected usage behavior is that developers can create skills and models, _attach_ them to agents, and compose agents into workflows that can be tested interactively in the chat interface.
 

website/docs/contributor-guide/contributing.md → website/docs/contributor-guide/contributing.mdx

@@ -1,4 +1,6 @@
-# Contributing to AG2
+---
+title: Contributing to AG2
+---
 
 The project welcomes contributions from developers and organizations worldwide. Our goal is to foster a collaborative and inclusive community where diverse perspectives and expertise can drive innovation and enhance the project's capabilities. Whether you are an individual contributor or represent an organization, we invite you to join us in shaping the future of this project. Together, we can build something truly remarkable. Possible contributions include but not limited to:
 

website/docs/contributor-guide/docker.md → website/docs/contributor-guide/docker.mdx

@@ -1,4 +1,6 @@
-# Docker for Development
+---
+title: Docker for Development
+---
 
 For developers contributing to the AG2 project, we offer a specialized Docker environment. This setup is designed to streamline the development process, ensuring that all contributors work within a consistent and well-equipped environment.
 

website/docs/contributor-guide/documentation.md → website/docs/contributor-guide/documentation.mdx

@@ -1,4 +1,6 @@
-# Documentation
+---
+title: Documentation
+---
 
 ## How to get a notebook rendered on the website
 

website/docs/contributor-guide/file-bug-report.md → website/docs/contributor-guide/file-bug-report.mdx

@@ -1,4 +1,6 @@
-# File A Bug Report
+---
+title: File A Bug Report
+---
 
 When you submit an issue to [GitHub](https://github.com/ag2ai/ag2/issues), please do your best to
 follow these guidelines! This will make it a lot easier to provide you with good

website/docs/contributor-guide/maintainer.md → website/docs/contributor-guide/maintainer.mdx

@@ -1,4 +1,6 @@
-# Guidance for Maintainers
+---
+title: Guidance for Maintainers
+---
 
 ## General
 

website/docs/contributor-guide/pre-commit.md → website/docs/contributor-guide/pre-commit.mdx

@@ -1,4 +1,6 @@
-# Pre-commit
+---
+title: Pre-commit
+---
 
 Run `pre-commit install` to install pre-commit into your git hooks. Before you commit, run
 `pre-commit run` to check if you meet the pre-commit requirements. If you use Windows (without WSL) and can't commit after installing pre-commit, you can run `pre-commit uninstall` to uninstall the hook. In WSL or Linux this is supposed to work.

website/docs/contributor-guide/tests.md → website/docs/contributor-guide/tests.mdx

@@ -1,4 +1,6 @@
-# Tests
+---
+title: Tests
+---
 
 Tests are automatically run via GitHub actions. There are two workflows:
 

website/docs/ecosystem/agentops.md → website/docs/ecosystem/agentops.mdx

@@ -1,4 +1,6 @@
-# Agent Monitoring and Debugging with AgentOps
+---
+title: Agent Monitoring and Debugging with AgentOps
+---
 
 <img src="https://github.com/AgentOps-AI/agentops/blob/main/docs/images/external/logo/banner-badge.png?raw=true" style="width: 40%;" alt="AgentOps logo"/>
 

website/docs/ecosystem/azure_cosmos_db.md → website/docs/ecosystem/azure_cosmos_db.mdx

@@ -1,4 +1,6 @@
-# Azure Cosmos DB
+---
+title: Azure Cosmos DB
+---
 
 > "OpenAI relies on Cosmos DB to dynamically scale their ChatGPT service – one of the fastest-growing consumer apps ever – enabling high reliability and low maintenance."
 > – Satya Nadella, Microsoft chairman and chief executive officer

website/docs/ecosystem/composio.md → website/docs/ecosystem/composio.mdx

@@ -1,4 +1,6 @@
-# Composio
+---
+title: Composio
+---
 
 ![Composio Example](img/ecosystem-composio.png)
 

website/docs/ecosystem/databricks.md → website/docs/ecosystem/databricks.mdx

@@ -1,5 +1,6 @@
-# Databricks
-
+---
+title: Databricks
+---
 ![Databricks Data Intelligence Platform](img/ecosystem-databricks.png)
 
 The [Databricks Data Intelligence Platform ](https://www.databricks.com/product/data-intelligence-platform) allows your entire organization to use data and AI. It’s built on a lakehouse to provide an open, unified foundation for all data and governance, and is powered by a Data Intelligence Engine that understands the uniqueness of your data.

website/docs/ecosystem/llamaindex.md → website/docs/ecosystem/llamaindex.mdx

@@ -1,4 +1,6 @@
-# Llamaindex
+---
+title: Llamaindex
+---
 
 ![Llamaindex Example](img/ecosystem-llamaindex.png)
 

website/docs/ecosystem/mem0.md → website/docs/ecosystem/mem0.mdx

@@ -1,4 +1,6 @@
-# Mem0: Long-Term Memory and Personalization for Agents
+---
+title: Mem0: Long-Term Memory and Personalization for Agents
+---
 
 <img src="https://github.com/mem0ai/mem0/blob/main/docs/images/mem0-bg.png?raw=true" alt="Mem0 logo" style="width: 40%;" />
 

website/docs/ecosystem/memgpt.md → website/docs/ecosystem/memgpt.mdx

@@ -1,4 +1,6 @@
-# MemGPT
+---
+title: MemGPT (Letta)
+---
 
 ![MemGPT Example](img/ecosystem-memgpt.png)
 

website/docs/ecosystem/microsoft-fabric.md → website/docs/ecosystem/microsoft-fabric.mdx

@@ -1,4 +1,6 @@
-# Microsoft Fabric
+---
+title: Microsoft Fabric
+---
 
 ![Fabric Example](img/ecosystem-fabric.png)
 

website/docs/ecosystem/ollama.md → website/docs/ecosystem/ollama.mdx

@@ -1,4 +1,6 @@
-# Ollama
+---
+title: Ollama
+---
 
 ![Ollama Example](img/ecosystem-ollama.png)
 

website/docs/ecosystem/pgvector.md → website/docs/ecosystem/pgvector.mdx

@@ -1,4 +1,6 @@
-# PGVector
+---
+title: PGVector
+---
 
 [PGVector](https://github.com/pgvector/pgvector) is an open-source vector similarity search for Postgres.
 

website/docs/ecosystem/portkey.md → website/docs/ecosystem/portkey.mdx

@@ -1,4 +1,7 @@
-# Portkey Integration with AutoGen
+---
+title: Portkey Integration with AutoGen
+---
+
  <img src="https://github.com/siddharthsambharia-portkey/Portkey-Product-Images/blob/main/Portkey-Autogen.png?raw=true" alt="Portkey Metrics Visualization" width=70% />
 
 [Portkey](https://portkey.ai) is a 2-line upgrade to make your AutoGen agents reliable, cost-efficient, and fast.

website/docs/ecosystem/promptflow.md → website/docs/ecosystem/promptflow.mdx

@@ -1,4 +1,6 @@
-# Promptflow
+---
+title: Promptflow
+---
 
 Promptflow is a comprehensive suite of tools that simplifies the development, testing, evaluation, and deployment of LLM based AI applications. It also supports integration with Azure AI for cloud-based operations and is designed to streamline end-to-end development.
 

website/docs/installation/Docker.md → website/docs/installation/Docker.mdx

@@ -1,5 +1,6 @@
-# Docker
-
+---
+title: Docker
+---
 Docker, an indispensable tool in modern software development, offers a compelling solution for AG2's setup. Docker allows you to create consistent environments that are portable and isolated from the host OS. With Docker, everything AG2 needs to run, from the operating system to specific libraries, is encapsulated in a container, ensuring uniform functionality across different systems. The Dockerfiles necessary for AG2 are conveniently located in the project's GitHub repository at [https://github.com/ag2ai/ag2/tree/main/.devcontainer](https://github.com/ag2ai/ag2/tree/main/.devcontainer).
 
 **Pre-configured DockerFiles**: The AG2 Project offers pre-configured Dockerfiles for your use. These Dockerfiles will run as is, however they can be modified to suit your development needs. Please see the README.md file in autogen/.devcontainer

website/docs/installation/Optional-Dependencies.md → website/docs/installation/Optional-Dependencies.mdx

@@ -1,4 +1,6 @@
-# Optional Dependencies
+---
+title: Optional Dependencies
+---
 
 ## Different LLMs
 

website/docs/topics/handling_long_contexts/compressing_text_w_llmligua.md → website/docs/topics/handling_long_contexts/compressing_text_w_llmligua.mdx

@@ -1,4 +1,6 @@
-# Compressing Text with LLMLingua
+---
+title: Compressing Text with LLMLingua
+---
 
 Text compression is crucial for optimizing interactions with LLMs, especially when dealing with long prompts that can lead to higher costs and slower response times. LLMLingua is a tool designed to compress prompts effectively, enhancing the efficiency and cost-effectiveness of LLM operations.
 

website/docs/topics/handling_long_contexts/intro_to_transform_messages.md → website/docs/topics/handling_long_contexts/intro_to_transform_messages.mdx

@@ -1,4 +1,7 @@
-# Introduction to Transform Messages
+---
+title: Introduction to Transform Messages
+---
+
 
 Why do we need to handle long contexts? The problem arises from several constraints and requirements:
 

website/docs/topics/llm-caching.md → website/docs/topics/llm-caching.mdx

@@ -1,4 +1,7 @@
-# LLM Caching
+---
+title: LLM Caching
+---
+
 
 AutoGen supports caching API requests so that they can be reused when the same request is issued. This is useful when repeating or continuing experiments for reproducibility and cost saving.
 

website/docs/topics/llm-observability.md → website/docs/topics/llm-observability.mdx

@@ -1,4 +1,6 @@
-# Agent Observability
+---
+title: Agent Observability
+---
 
 AutoGen supports advanced LLM agent observability and monitoring through built-in logging and partner providers.