This article provides the best learning resources for developers who are getting started building AI apps for each programming language. Resources include libraries and samples, documentation, training courses, and more.
Resources for Azure OpenAI Service
Azure OpenAI Service provides REST API access to OpenAI's powerful language models. These models can be easily adapted to your specific task including but not limited to content generation, summarization, image understanding, semantic search, and natural language to code translation. Users can access the service through REST APIs, Azure OpenAI SDK for .NET, or via the Azure AI Foundry portal.
Libraries
| Link |
Description |
| Azure OpenAI SDK for .NET |
The GitHub source version of the Azure OpenAI client library for .NET is an adaptation of OpenAI's REST APIs that provides an idiomatic interface and rich integration with the rest of the Azure SDK ecosystem. It can connect to Azure OpenAI resources or to the non-Azure OpenAI inference endpoint, making it a great choice for even non-Azure OpenAI development. |
| Azure OpenAI SDK Releases |
Links to all Azure OpenAI SDK library packages, including links for .NET, Java, JavaScript and Go. |
| Azure.AI.OpenAI NuGet package |
The NuGet version of the Azure OpenAI client library for .NET. |
Samples
| Link |
Description |
| .NET OpenAI MCP Agent |
This sample is an MCP agent app written in .NET, using Azure OpenAI, with a remote MCP server written in TypeScript. |
| AI Travel Agents |
The AI Travel Agents is a robust enterprise application that leverages multiple AI agents to enhance travel agency operations. The application demonstrates how six AI agents collaborate to assist employees in handling customer queries, providing destination recommendations, and planning itineraries. |
| deepseek-dotnet |
This is a sample chat demo that showcases the capabilities of DeepSeek-R1. |
| Get started using GPT-35-Turbo and GPT-4 |
An article that walks you through creating a chat completion sample. |
| Completions |
A collection of 10 samples that demonstrate how to use the Azure OpenAI client library for .NET to chat, stream replies, use your own data, transcribe/translate audio, generate images, etc. |
| Streaming Chat Completions |
A deep link to the samples demonstrating streaming completions. |
| OpenAI with Microsoft Entra ID Role based access control |
A look at authentication using Microsoft Entra ID. |
| OpenAI with Managed Identities |
An article with more complex security scenarios that require Azure role-based access control (Azure RBAC). This document covers how to authenticate to your OpenAI resource using Microsoft Entra ID. |
| More samples |
A collection of OpenAI samples written in .NET. |
Documentation
Resources for other Azure AI services
In addition to Azure OpenAI Service, there are many other Azure AI services that help developers and organizations rapidly create intelligent, market-ready, and responsible applications with out-of-the-box and prebuilt customizable APIs and models. Example applications include natural language processing for conversations, search, monitoring, translation, speech, vision, and decision-making.
Samples
| Link |
Description |
| Integrate Speech into your apps with Speech SDK Samples |
A repo of samples for the Azure Cognitive Services Speech SDK. Links to samples for speech recognition, translation, speech synthesis, and more. |
| Azure AI Document Intelligence SDK |
Azure AI Document Intelligence (formerly Form Recognizer) is a cloud service that uses machine learning to analyze text and structured data from documents. The Document Intelligence software development kit (SDK) is a set of libraries and tools that enable you to easily integrate Document Intelligence models and capabilities into your applications. |
| Extract structured data from forms, receipts, invoices, and cards using Form Recognizer in .NET |
A repo of samples for the Azure.AI.FormRecognizer client library. |
| Extract, classify, and understand text within documents using Text Analytics in .NET |
The client Library for Text Analytics. This is part of the Azure AI Language service, which provides Natural Language Processing (NLP) features for understanding and analyzing text. |
| Document Translation in .NET |
A quickstart article that details how to use Document Translation to translate a source document into a target language while preserving structure and text formatting. |
| Question Answering in .NET |
A quickstart article to get an answer (and confidence score) from a body of text that you send along with your question. |
| Conversational Language Understanding in .NET |
The client library for Conversational Language Understanding (CLU), a cloud-based conversational AI service, which can extract intents and entities in conversations and acts like an orchestrator to select the best candidate to analyze conversations to get best response from apps like Qna, Luis, and Conversation App. |
| Analyze images |
Sample code and setup documents for the Microsoft Azure AI Image Analysis SDK |
Documentation
Training
| Link |
Description |
| Generative AI for Beginners Workshop |
Learn the fundamentals of building Generative AI apps with our 18-lesson comprehensive course by Microsoft Cloud Advocates. |
| AI Agents for Beginners Workshop |
Learn the fundamentals of building Generative AI agents with our 10-lesson comprehensive course by Microsoft Cloud Advocates. |
| Get started with Azure AI Services |
Azure AI Services is a collection of services that are building blocks of AI functionality you can integrate into your applications. In this learning path, you'll learn how to provision, secure, monitor, and deploy Azure AI Services resources and use them to build intelligent solutions. |
| Microsoft Azure AI Fundamentals: Generative AI |
Training path to help you understand how large language models form the foundation of generative AI: how Azure OpenAI Service provides access to the latest generative AI technology, how prompts and responses can be fine-tuned and how Microsoft's responsible AI principles drive ethical AI advancements. |
| Develop Generative AI solutions with Azure OpenAI Service |
Azure OpenAI Service provides access to OpenAI's powerful large language models such as ChatGPT, GPT, Codex, and Embeddings models. This learning path teaches developers how to generate code, images, and text using the Azure OpenAI SDK and other Azure services. |
AI app templates
AI app templates provide you with well-maintained, easy to deploy reference implementations that provide a high-quality starting point for your AI apps.
There are two categories of AI app templates, building blocks and end-to-end solutions. Building blocks are smaller-scale samples that focus on specific scenarios and tasks. End-to-end solutions are comprehensive reference samples including documentation, source code, and deployment to allow you to take and extend for your own purposes.
To review a list of key templates available for each programming language, see AI app templates. To browse all available templates, see the AI app templates on the AI App Template gallery.
Resources for Azure OpenAI Service
Azure OpenAI Service provides REST API access to the powerful language models available in OpenAI. Azure OpenAI helps you adapt these models to accomplish specific tasks, such as content generation, summarization, image understanding, semantic search, and natural language to code translation. Access Azure OpenAI by using the REST APIs, the Azure OpenAI SDK for .NET, or the web-based interface in Azure OpenAI Studio.
SDKs and libraries
Documentation
Resources for other Azure AI services
In addition to Azure OpenAI Service, there are many other Azure AI services. Developers and organizations can rapidly create intelligent, market-ready, and responsible applications with out-of-the-box and prebuilt customizable APIs and models. Example applications include natural language processing for conversations, search, monitoring, translation, speech, vision, and decision-making.
Samples
| Link |
Description |
| Integrate speech into your apps with Azure AI Speech SDK Samples |
Samples for the Azure Cognitive Services Speech SDK. Links to samples for speech recognition, translation, speech synthesis, and more. |
| Azure AI Document Intelligence SDK |
Azure AI Document Intelligence (formerly Form Recognizer) is a cloud service that uses machine learning to analyze text and structured data from documents. The Document Intelligence software development kit (SDK) is a set of libraries and tools that enable you to easily integrate Document Intelligence models and capabilities into your applications. |
| Extract structured data from forms, receipts, invoices, and cards using Form Recognizer in Python |
Samples for the Azure.AI.FormRecognizer client library. |
| Extract, classify, and understand text within documents using Text Analytics in Python |
The client Library for Text Analytics. These APIs are part of the Azure AI Language service, which provides Natural Language Processing (NLP) features for understanding and analyzing text. |
| Document Translation in Python |
A quickstart article that uses Document Translation to translate a source document into a target language while preserving structure and text formatting. |
| Question answering in Python |
A quickstart article with steps to get an answer (and confidence score) from a body of text that you send along with your question. |
| Conversational Language Understanding in Python |
The client library for Conversational Language Understanding (CLU). CLU is a cloud-based conversational AI service that can extract intents and entities in conversations. CLU acts like an orchestrator to select the best candidate to analyze conversations to get the best response from apps like QnA, Luis, and Conversation App. |
| Analyze images |
Sample code and setup documents for the Microsoft Azure AI Image Analysis SDK. |
| Azure AI Content Safety SDK for Python |
The SDK can help detect harmful user-generated and AI-generated content in applications and services. Content Safety includes text and image APIs that allow you to detect material that is harmful. |
Documentation
Training
| Link |
Description |
| Generative AI for beginners workshop |
Learn the fundamentals of building Generative AI apps with our 18-lesson comprehensive course by Microsoft Cloud Advocates. |
| Get started with Azure AI services |
Azure AI services are building blocks of AI functionality you can integrate into your applications. Complete this learning path to explore how to provision, secure, monitor, and deploy Azure AI services resources and use them to build intelligent solutions. |
| Microsoft Azure AI Fundamentals: Generative AI |
Complete this learning path to understand how large language models form the foundation of generative AI. Explore how Azure OpenAI Service provides access to the latest generative AI technology. Learn how Azure OpenAI prompts and responses can be fine-tuned and how Microsoft's responsible AI principles drive ethical AI advancements. |
| Develop Generative AI solutions with Azure OpenAI Service |
Azure OpenAI Service provides access to OpenAI's powerful large language models such as ChatGPT, GPT, Codex, and Embeddings models. Complete this learning path for developers and explore how to generate code, images, and text by using the Azure OpenAI SDK and other Azure services. |
| Build AI apps with Azure Database for PostgreSQL |
Complete this learning path to explore Azure AI and Azure Machine Learning Services integrations provided by the Azure AI extension for Azure Database for PostgreSQL - Flexible Server. Learn how these services can enable you to build AI-powered apps. |
AI application templates
AI application templates supply you with well-maintained, easy to deploy reference implementations that provide a high-quality starting point for your AI apps.
There are two categories of AI app templates, building blocks and end-to-end solutions. Building blocks are smaller-scale samples that focus on specific scenarios and tasks. End-to-end solutions are comprehensive reference samples that include documentation, source code, and deployment features. You can build on the solutions and extend them for your own purposes.
Resources for Azure OpenAI Service
Azure OpenAI Service provides REST API access to OpenAI's powerful language models. These models can be easily adapted to your specific task including but not limited to content generation, summarization, image understanding, semantic search, and natural language to code translation. Users can access the service through REST APIs, the OpenAI Node API Library, or via the Azure AI Foundry portal.
Libraries
Samples
| Link |
Description |
| AI Travel Agents |
The AI Travel Agents is a robust enterprise application that leverages multiple AI agents to enhance travel agency operations. The application demonstrates how six AI agents collaborate to assist employees in handling customer queries, providing destination recommendations, and planning itineraries. |
| Getting Started with Remote MCP Servers using Azure Container Apps (Node.js/TypeScript) |
This quick start guide provides the basic building blocks to set up a remote Model Context Protocol (MCP) server using Azure Container Apps. The MCP server is built using Node.js and TypeScript, and it can be used to run various tools and services in a serverless environment. |
| DeepSeek on Azure |
Demonstrates how to use DeepSeek with JavaScript/TypeScript via the OpenAI Node.js client library or LangChain.js. DeepSeek excels at tasks requiring deep context understanding and complex reasoning. You may experience longer response times compared to other models, because it simulates a thought process (under the <think> tag) before providing an actual answer. |
| Microblog AI |
Microblog AI is an application that showcases the power of Azure Static Web Apps combined with Azure Functions and Server-Side Rendering (SSR) using Remix. The application leverages Azure OpenAI's GPT-4o artificial intelligence to enable the creation of microblogs in a simple and intuitive way. |
| Completions |
A simple example demonstrating how to get completions for the provided prompt. |
| Streaming Chat Completions |
A simple example demonstrating how to use streaming chat completions. |
| Switch from OpenAI to Azure OpenAI |
Article with guidance on the small changes you need to make to your code in order to swap back and forth between OpenAI and the Azure OpenAI Service. |
| OpenAI with Microsoft Entra ID Role based access control |
A look at authentication using Microsoft Entra ID. |
| OpenAI with Managed Identities |
An article detailing more complex security scenarios require Azure role-based access control (Azure RBAC). This document covers how to authenticate to your OpenAI resource using Microsoft Entra ID. |
| LangChain.js with Azure OpenAI and Azure AI Search |
Build an intelligent RAG agent with LangChain.js that evaluates whether questions can be answered using unstructured data in Azure AI Search, and delivers contextually relevant responses from that knowledge base when possible. |
| More samples |
OpenAI samples covering a range of scenarios. |
Documentation
Resources for other Azure AI services
In addition to Azure OpenAI Service, there are many other Azure AI services that help developers and organizations rapidly create intelligent, market-ready, and responsible applications with out-of-the-box and prebuilt customizable APIs and models. Example applications include natural language processing for conversations, search, monitoring, translation, speech, vision, and decision-making.
Samples
Documentation
Training
| Link |
Description |
| Generative AI for Beginners Workshop |
Learn the fundamentals of building Generative AI apps with our 18-lesson comprehensive course by Microsoft Cloud Advocates. |
| Generative AI for JavaScript developers |
This covers the basics of generative AI and how to build AI applications using JavaScript, from local development to deployment on Azure, up to running and scaling your AI models. The YouTube playlist of videos includes a series of videos around 10 minutes long, each focusing on a specific topic. |
| AI Agents for Beginners Workshop |
Learn the fundamentals of building Generative AI agents with our 10-lesson comprehensive course by Microsoft Cloud Advocates. |
| Get started with Azure AI Services |
Azure AI Services is a collection of services that are building blocks of AI functionality you can integrate into your applications. In this learning path, you learn how to provision, secure, monitor, and deploy Azure AI Services resources and use them to build intelligent solutions. |
| Microsoft Azure AI Fundamentals: Generative AI |
Training path to help you understand how large language models form the foundation of generative AI: how Azure OpenAI Service provides access to the latest generative AI technology, how prompts and responses can be fine-tuned and how Microsoft's responsible AI principles drive ethical AI advancements. |
| Develop Generative AI solutions with Azure OpenAI Service |
Azure OpenAI Service provides access to OpenAI's powerful large language models such as ChatGPT, GPT, Codex, and Embeddings models. This learning path teaches developers how to generate code, images, and text using the Azure OpenAI SDK and other Azure services. |
| Build AI apps with Azure Database for PostgreSQL |
This learning path explores how the Azure AI and Azure Machine Learning Services integrations provided by the Azure AI extension for Azure Database for PostgreSQL - Flexible Server can enable you to build AI-powered apps. |
| Secure Azure OpenAI authentication and authorization |
Learn why you should use managed identity for Azure OpenAI authentication and how you can configure role based access controls to minimize necessary permissions. |
| Introduction to Azure OpenAI managed identity authentication with JavaScript |
This module helps you understand how to use a managed identity in JavaScript apps that interact with Azure OpenAI Service. |
AI app templates
AI app templates provide you with well-maintained, easy to deploy reference implementations that provide a high-quality starting point for your AI apps.
There are two categories of AI app templates, building blocks and end-to-end solutions. Building blocks are smaller-scale samples that focus on specific scenarios and tasks. End-to-end solutions are comprehensive reference samples including documentation, source code, and deployment to allow you to take and extend for your own purposes.
To review a list of key templates available for each programming language, see AI app templates. To browse all available templates, see the AI app templates on the AI App Template gallery.
Resources for Azure OpenAI Service
Azure OpenAI Service provides REST API access to OpenAI's powerful language models. These models can be easily adapted to your specific task including but not limited to content generation, summarization, image understanding, semantic search, and natural language to code translation. Users can access the service through REST APIs, the langchain4j-azure-open-ai package, or via the Azure AI Foundry portal.
Libraries and samples
Documentation
Resources for other Azure AI services
In addition to Azure OpenAI Service, there are many other Azure AI services that help developers and organizations rapidly create intelligent, market-ready, and responsible applications with out-of-the-box and prebuilt customizable APIs and models. Example applications include natural language processing for conversations, search, monitoring, translation, speech, vision, and decision-making.
Samples
Documentation
Training
| Link |
Description |
| Generative AI for Beginners Workshop |
Learn the fundamentals of building Generative AI apps with our 18-lesson comprehensive course by Microsoft Cloud Advocates. |
| AI Agents for Beginners Workshop |
Learn the fundamentals of building Generative AI agents with our 10-lesson comprehensive course by Microsoft Cloud Advocates. |
| Get started with Azure AI Services |
Azure AI Services is a collection of services that are building blocks of AI functionality you can integrate into your applications. In this learning path, you learn how to provision, secure, monitor, and deploy Azure AI Services resources and use them to build intelligent solutions. |
| Microsoft Azure AI Fundamentals: Generative AI |
Training path to help you understand how large language models form the foundation of generative AI: how Azure OpenAI Service provides access to the latest generative AI technology, how prompts and responses can be fine-tuned and how Microsoft's responsible AI principles drive ethical AI advancements. |
| Develop Generative AI solutions with Azure OpenAI Service |
Azure OpenAI Service provides access to OpenAI's powerful large language models such as ChatGPT, GPT, Codex, and Embeddings models. This learning path teaches developers how to generate code, images, and text using the Azure OpenAI SDK and other Azure services. |
AI app templates
AI app templates provide you with well-maintained, easy to deploy reference implementations that provide a high-quality starting point for your AI apps.
There are two categories of AI app templates, building blocks and end-to-end solutions. Building blocks are smaller-scale samples that focus on specific scenarios and tasks. End-to-end solutions are comprehensive reference samples including documentation, source code, and deployment to allow you to take and extend for your own purposes.
To review a list of key templates available for each programming language, see AI app templates. To browse all available templates, see the AI app templates on the AI App Template gallery.
OpenAI for Go developers
Azure OpenAI Service provides REST API access to OpenAI's powerful language models. These models can be easily adapted to your specific task including but not limited to content generation, summarization, image understanding, semantic search, and natural language to code translation. Users can access the service through REST APIs, Azure OpenAI SDK for Go, or via the Azure AI Foundry portal.
Secure your Azure AI resources
Speech/Vision
Language