Azure OpenAI: GPT-5-Codex Availability?
Greetings everyone! I just wanted to see if there's any word as to when/if https://openai.com/index/introducing-upgrades-to-codex/ will make it's way to the AI Foundry. It was released on September 15th, 2025, but I have no idea how long Azure tends to follow behind OpenAI's releases. It doesn't really seem like there's any source of information to view whenever new models drop as to what Azure is going to do with them, if any. Any conversation around this would be helpful and appreciated, thanks!The Future of AI: Evaluating and optimizing custom RAG agents using Azure AI Foundry
This blog post explores best practices for evaluating and optimizing Retrieval-Augmented Generation (RAG) agents using Azure AI Foundry. It introduces the RAG triad metrics—Retrieval, Groundedness, and Relevance—and demonstrates how to apply them using Azure AI Search and agentic retrieval for custom agents. Readers will learn how to fine-tune search parameters, use end-to-end evaluation metrics and golden retrieval metrics like XDCG and Max Relevance, and leverage Azure AI Foundry tools to build trustworthy, high-performing AI agents.Building AI Apps with the Foundry Local C# SDK
What Is Foundry Local? Foundry Local is a lightweight runtime designed to run AI models directly on user devices. It supports a wide range of hardware (CPU, GPU, NPU) and provides a consistent developer experience across platforms. The SDKs are available in multiple languages, including Python, JavaScript, Rust, and now C#. Why a C# SDK? The C# SDK brings Foundry Local into the heart of the .NET ecosystem. It allows developers to: Download and manage models locally. Run inference using OpenAI-compatible APIs. Integrate seamlessly with existing .NET applications. This means you can build intelligent apps that run offline, reduce latency, and maintain data privacy—all without sacrificing developer productivity. Bootstrap Process: How the SDK Gets You Started One of the most developer-friendly aspects of the C# SDK is its automatic bootstrap process. Here's what happens under the hood when you initialise the SDK: Service Discovery and Startup The SDK automatically locates the Foundry Local installation on the device and starts the inference service if it's not already running. Model Download and Caching If the specified model isn't already cached locally, the SDK will download the most performant model variant (e.g. GPU, CPU, NPU) for the end user's hardware from the Foundry model catalog. This ensures you're always working with the latest optimised version. Model Loading into Inference Service Once downloaded (or retrieved from cache), the model is loaded into the Foundry Local inference engine, ready to serve requests. This streamlined process means developers can go from zero to inference with just a few lines of code—no manual setup or configuration required. Leverage Your Existing AI Stack One of the most exciting aspects of the Foundry Local C# SDK is its compatibility with popular AI tools such as: OpenAI SDK - Foundry local provides an OpenAI compliant chat completions (and embedding) API meaning. If you’re already using `OpenAI` chat completions API, you can reuse your existing code with minimal changes. Semantic Kernel - Foundry Local also integrates well with Semantic Kernel, Microsoft’s open-source framework for building AI agents. You can use Foundry Local models as plugins or endpoints within Semantic Kernel workflows—enabling advanced capabilities like memory, planning, and tool calling. Quick Start Example Follow these three steps: 1. Create a new project Create a new C# project and navigate to it: dotnet new console -n hello-foundry-local cd hello-foundry-local 2. Install NuGet packages Install the following NuGet packages into your project: dotnet add package Microsoft.AI.Foundry.Local --version 0.1.0 dotnet add package OpenAI --version 2.2.0-beta.4 3. Use the OpenAI SDK with Foundry Local The following example demonstrates how to use the OpenAI SDK with Foundry Local. The code initializes the Foundry Local service, loads a model, and generates a response using the OpenAI SDK. Copy-and-paste the following code into a C# file named Program.cs: using Microsoft.AI.Foundry.Local; using OpenAI; using OpenAI.Chat; using System.ClientModel; using System.Diagnostics.Metrics; var alias = "phi-3.5-mini"; var manager = await FoundryLocalManager.StartModelAsync(aliasOrModelId: alias); var model = await manager.GetModelInfoAsync(aliasOrModelId: alias); ApiKeyCredential key = new ApiKeyCredential(manager.ApiKey); OpenAIClient client = new OpenAIClient(key, new OpenAIClientOptions { Endpoint = manager.Endpoint }); var chatClient = client.GetChatClient(model?.ModelId); var completionUpdates = chatClient.CompleteChatStreaming("Why is the sky blue'"); Console.Write($"[ASSISTANT]: "); foreach (var completionUpdate in completionUpdates) { if (completionUpdate.ContentUpdate.Count > 0) { Console.Write(completionUpdate.ContentUpdate[0].Text); } } Run the code using the following command: dotnet run Final thoughts The Foundry Local C# SDK empowers developers to build intelligent, privacy-preserving applications that run anywhere. Whether you're working on desktop, mobile, or embedded systems, this SDK offers a robust and flexible way to bring AI closer to your users. Ready to get started? Dive into the official documentation: Getting started guide C# Reference documentation You can also make contributions to the C# SDK by creating a PR on GitHub: Foundry Local on GitHub
Blogsite AI Voice Answer machine
Hi all, I wanted to quickly to write to show how I thought about building a system based on Azure to allow my blogsite to answer questions about a blog post that a reader may suddenly have in their mind while reading through the post to extend learning. The basic flow is: -User loads a blog post -On load, the page populates 3 buttons a third of the way in the page, each with randomly AI generated questions related to the page that a reader might ask about the page content -On clicking a button, the question is answered through voice, with the answer being 'just' enough to answer the question without being over-bearing (at least that's my feeling!) The architecture is constructed as the following: I wrote in full on how I did this for my blog here : https://www.imaginarium.dev/voice-ai-for-blog/ I wanted to perhaps hear on if I was missing anything here on the design, security considerations particularly on the Azure side? Any ways to improve on the AI Voice implementation? I'm using the Azure OpenAI neural voices at the moment. Gemini voices lately are really good too!! I even thought about using a custom neural voice of my own but I ran into issues when trying to do that within Azure due to not having an enterprise subscription readily available to be allowed this capability. Thoughts?Build Enterprise-Ready AI Agents with the New Azure Postgres LangChain + LangGraph Connector
AI agents are only as powerful as the data layer behind them. That’s why we’re excited to announce native LangChain + LangGraph connector for Azure Database for PostgreSQL. With this release, Postgres becomes your single source of truth for AI agents, handling knowledge retrieval, chat history, and long-term memory all in one place. This new connector is packed with everything you need to build secure, scalable and enterprise-ready AI agents on Azure without the complexity. With EntraID authentication, DiskANN acceleration, vector store, and a dedicated agent store, you can go from prototype to production on Azure faster than ever. You can quickly get started with the LangChain + LangGraph connector today pip install langchain-azure-postgresql In this post, we’ll cover: How Azure Postgres connector for LangGraph can serve as the single persistence + retrieval layer for an AI agent New first-class connector for LangChain +LangGraph A practical example to help you get started Azure PostgreSQL as the single persistence + retrieval layer for an AI agent When building AI agents today, developers face a fragmented stack: Vector storage and search require a library, service or separate database. Chat history & short-term memory need yet another data source. Long-term memory often means bolting on yet another system. This sprawl leads to complex integrations, higher costs, and weaker security, making it hard to scale AI agents reliably. The Solution The new Azure Postgres connector for LangChain + LangGraph transforms your Azure Postgres database to the single persistence + retrieval layer for AI agents. Instead of working on a fragmented stack, developers can now: Run embeddings + semantic search with built-in DiskANN acceleration in the same database that powers their application logic. Persist chat history and short-term memory and keep agent conversations grounded via seamless context retrieval from data stored in Postgres. Capture, retrieve, and evolve knowledge over time with a built-in long-term memory without bolting on external systems. All in one database, simplified, secure, and enterprise ready. Postgres becomes the persistent and retrieval data layer for your AI agent. Built for Enterprise Readiness: LangChain + LangGraph Connector This release unlocks several new capabilities that make it easy to build robust, production-ready agents: Auth with EntraID: Enterprise-grade identity to securely connect LangChain + LangGraph workflows to Azure Database for PostgreSQL within a centrally managed security perimeter based on identity. DiskANN & Extensions: First-class support for faster vector search using pgvector combined with DiskANN indexing, enabling support for high-dimensional vectors and cost-efficient search. Additionally, helper functions ensure your favorite extensions are installed. Native Vector Store: Store and query embeddings, enabling semantic search and Retrieval-Augmented Generation (RAG) scenarios. Dedicated Agent Store: Persist agent state, memory, and chat history with structured access patterns, perfect for multi-turn conversations and long-term context. Together, these features give developers a turnkey persistence solution for building reliable AI agents without stitching together multiple storage systems. Using LangGraph on Azure Database for PostgreSQL Using LangGraph with Azure Database for PostgreSQL is easy. Enable the vector & pg_diskann Extension: Allowlist the vector and pg_diskann extension within your server configuration. Import LangChain + LangGraph connector pip install langchain-azure-postgresql pip install -qU langchain-openai pip install -qU azure-identity Login to Azure, to your Entra ID Run az login in your terminal, where you will also run the LangGraph code. az login To get started, you need to set up a production-ready vector store for your agent in a few lines of code. # 1. Auth: Securely connect to Azure Postgres connection_pool = AzurePGConnectionPool(azure_conn_info=ConnectionInfo(host=os.environ["PGHOST"])) #2. Create embeddings embeddings = AzureOpenAIEmbeddings(model="text-embedding-3-small") # 3. Initialize a vector store in Postgres with DiskANN vector_store = AzurePGVectorStore(connection=connection, embedding=embeddings) Use LangGraph to build a sample agent. Here’s a practical example that combines vector search and checkpointer inside Postgres: #4 Define the tool for data retrieval. def get_data_from_vector_store(query: str) -> str: """Get data from the vector store.""" results = vector_store.similarity_search(query) return results #5 Define the agent, checkpointer and memory store. with connection_pool.getconn() as conn: agent = create_react_agent( model=model, tools=[get_data_from_vector_store], checkpointer=PostgresSaver(conn) ) #6 Run the agent and print results config = {"configurable": {"thread_id": "1", "user_id": "1"}} response = agent.invoke( {"messages": [{"role": "user", "content": "What does my database say about cats? Make sure you address me with my name"}]}, config ) for msg in response["messages"][-2:]: msg.pretty_print() With just a few lines of code, you can: Uses the vector store backed by Postgres Enable DiskANN for semantic search Use checkpointers for short-term conversation history Learn More This is just the beginning. With native LangChain + LangGraph support in Azure PostgreSQL, developers can now rely on a single, secure, high-performance data layer for building the next generation of AI agents. 👉 Ready to start? All the code are available in the Azure Postgres Agents Demo GitHub repository. See how easy it is to bring your AI agent to life on Azure. 👉 Check out the docs for more details on the LangChain + LangGraph connector.Announcing Live Interpreter API - Now in Public Preview
Today, we’re excited to introduce Live Interpreter –a breakthrough new capability in Azure Speech Translation – that makes real-time, multilingual communication effortless. Live Interpreter continuously identifies the language being spoken without requiring you to set an input language and delivers low latency speech-to-speech translation in a natural voice that preserves the speaker’s style and tone.Model Mondays S2E01 Recap: Advanced Reasoning Session
About Model Mondays Want to know what Reasoning models are and how you can build advanced reasoning scenarios like a Deep Research agent using Azure AI Foundry? Check out this recap from Model Mondays Season 2 Ep 1. Model Mondays is a weekly series to help you build your model IQ in three steps: 1. Catch the 5-min Highlights on Monday, to get up to speed on model news 2. Catch the 15-min Spotlight on Monday, for a deep-dive into a model or tool 3. Catch the 30-min AMA on Friday, for a Q&A session with subject matter experts Want to follow along? Register Here- to watch upcoming livestreams for Season 2 Visit The Forum- to see the full AMA schedule for Season 2 Register Here - to join the AMA on Friday Jun 20 Spotlight On: Advanced Reasoning This week, the Model Mondays spotlight was on Advanced Reasoning with subject matter expert Marlene Mhangami. In this blog post, I'll talk about my five takeaways from this episode: Why Are Reasoning Models Important? What Is an Advanced Reasoning Scenario? How Can I Get Started with Reasoning Models ? Spotlight: My Aha Moment Highlights: What’s New in Azure AI 1. Why Are Reasoning Models Important? In today's fast-evolving AI landscape, it's no longer enough for models to just complete text or summarize content. We need AI that can: Understand multi-step tasks Make decisions based on logic Plan sequences of actions or queries Connect context across turns Reasoning models are large language models (LLMs) trained with reinforcement learning techniques to "think" before they answer. Rather than simply generating a response based on probability, these models follow an internal thought process producing a chain of reasoning before responding. This makes them ideal for complex problem-solving tasks. And they’re the foundation of building intelligent, context-aware agents. They enable next-gen AI workflows in everything from customer support to legal research and healthcare diagnostics. Reason: They allow AI to go beyond surface-level response and deliver solutions that reflect understanding, not just language patterning. 2. What does Advanced Reasoning involve? An advanced reasoning scenario is one where a model: Breaks a complex prompt into smaller steps Retrieves relevant external data Uses logic to connect dots Outputs a structured, reasoned answer Example: A user asks: What are the financial and operational risks of expanding a startup to Southeast Asia in 2025? This is the kind of question that requires extensive research and analysis. A reasoning model might tackle this by: Retrieving reports on Southeast Asia market conditions Breaking down risks into financial, political, and operational buckets Cross-referencing data with recent trends Returning a reasoned, multi-part answer 3. How Can I Get Started with Reasoning Models? To get started, you need to visit a catalog that has examples of these models. Try the GitHub Models Marketplace and look for the reasoning category in the filter. Try the Azure AI Foundry model catalog and look for reasoning models by name. Example: The o-series of models from Azure Open AI The DeepSeek-R1 models The Grok 3 models The Phi-4 reasoning models Next, you can use SDKs or Playground for exploring the model capabiliies. 1. Try Lab 331 - for a beginner-friendly guide. 2. Try Lab 333 - for an advanced project. 3. Try the GitHub Model Playground - to compare reasoning and GPT models. 4. Try the Deep Research Agent using LangChain - sample as a great starting project. Have questions or comments? Join the Friday AMA on Azure AI Foundry Discord: 4. Spotlight: My Aha Moment Before this session, I thought reasoning meant longer or more detailed responses. But this session helped me realize that reasoning means structured thinking — models now plan, retrieve, and respond with logic. This inspired me to think about building AI agents that go beyond chat and actually assist users like a teammate. It also made me want to dive deeper into LangChain + Azure AI workflows to build mini-agents for real-world use. 5. Highlights: What’s New in Azure AI Here’s what’s new in the Azure AI Foundry: Direct From Azure Models - Try hosted models like OpenAI GPT on PTU plans SORA Video Playground - Generate video from prompts via SORA models Grok 3 Models - Now available for secure, scalable LLM experiences DeepSeek R1-0528 - A reasoning-optimized, Microsoft-tuned open-source model These are all available in the Azure Model Catalog and can be tried with your Azure account. Did You Know? Your first step is to find the right model for your task. But what if you could have the model automatically selected for you_ based on the prompt you provide? That's the magic of Model Router a deployable AI chat model that dynamically selects the best LLM based on your prompt. Instead of choosing one model manually, the Router makes that choice in real time. Currently, this works with a fixed set of Azure OpenAI models, including a reasoning model option. Keep an eye on the documentation for more updates. Why it’s powerful: Saves cost by switching between models based on complexity Optimizes performance by selecting the right model for the task Lets you test and compare model outputs quickly Try it out in Azure AI Foundry or read more in the Model Catalog Coming Up Next Next week, we dive into Model Context Protocol, an open protocol that empowers agentic AI applications by making it easier to discover and integrate knowledge and action tools with your model choices. Register Here to get reminded - and join us live on Monday! Join The Community Great devs don't build alone! In a fast-pased developer ecosystem, there's no time to hunt for help. That's why we have the Azure AI Developer Community. Join us today and let's journey together! Join the Discord - for real-time chats, events & learning Explore the Forum - for AMA recaps, Q&A, and help! About Me. I'm Sharda, a Gold Microsoft Learn Student Ambassador interested in cloud and AI. Find me on Github, Dev.to,, Tech Community and Linkedin. In this blog series I have summarizef my takeaways from this week's Model Mondays livestream .Model Mondays S2:E4 Understanding AI Developer Experiences with Leo Yao
This week in Model Mondays, we put the spotlight on the AI Toolkit for Visual Studio Code - and explore the tools and workflows that make building generative AI apps and agents easier for developers. Read on for my recap. This post was generated with AI help and human revision & review. To learn more about our motivation and workflows, please refer to this document in our website. About Model Mondays Model Mondays is a weekly series designed to help you grow your Azure AI Foundry Model IQ step by step. Each week includes: 5-Minute Highlights – Quick news and updates about Azure AI models and tools on Monday 15-Minute Spotlight – Deep dive into a key model, protocol, or feature on Monday 30-Minute AMA on Friday – Live Q&A with subject matter experts from the Monday livestream If you're looking to grow your skills with the latest in AI model development, this series is a great place to begin. Useful links: Register for upcoming livestreams Watch past episodes Join the AMA on AI Developer Experiences Visit the Model Mondays forum Spotlight On: AI Developer Experiences 1. What is this topic and why is it important? AI Developer Experiences focus on making the process of building, testing, and deploying AI models as efficient as possible. With the right tools—such as the AI Toolkit and Azure AI Foundry extensions for Visual Studio Code—developers can eliminate unnecessary friction and focus on innovation. This is essential for accelerating the real-world impact of generative AI. 2. What is one key takeaway from the episode? The integration of Azure AI Foundry with Visual Studio Code allows developers to manage models, run experiments, and deploy applications directly from their preferred development environment. This unified workflow enhances productivity and simplifies the AI development lifecycle. 3. How can I get started? Here are a few resources to explore: Install the AI Toolkit for VS Code Explore Azure AI Foundry Documentation Join the Microsoft Tech Community to follow and contribute to discussions 4. What’s New in Azure AI Foundry? Azure AI Foundry continues to evolve to meet developer needs with more power, flexibility, and productivity. Here are some of the latest updates highlighted in this week’s episode: AI Toolkit for Visual Studio Code Now with deeper integration, allowing developers to manage models, run experiments, and deploy applications directly within their editor—streamlining the entire workflow. Prompt Shields Enhanced security capabilities designed to protect generative AI applications from prompt injection and unsafe content, improving reliability in production environments. Model Router A new intelligent routing system that dynamically directs model requests to the most suitable model available—enhancing performance and efficiency at scale. Expanded Model Catalog The catalog now includes more open-source and proprietary models, featuring the latest from Hugging Face, OpenAI, and other leading providers. Improved Documentation and Sample Projects Newly added guides and ready-to-use examples to help developers get started faster, understand workflows, and build confidently. My A-Ha Moment Before watching this episode, setting up an AI development environment always felt like a challenge. There were so many moving parts—configurations, integrations, and dependencies—that it was hard to know where to begin. Seeing the AI Toolkit in action inside Visual Studio Code changed everything for me. It was a realization moment: “That’s it? I can explore models, test prompts, and deploy apps—without ever leaving my editor?” This episode made it clear that building with AI doesn’t have to be complex or intimidating. With the right tools, experimentation becomes faster and far more enjoyable. Now, I’m genuinely excited to build, test, and explore new generative AI solutions because the process finally feels accessible. Coming Up Next Week In the next episode, we’ll be exploring Fine-Tuning and Distillation with Dave Voutila. This session will focus on how to adapt Azure OpenAI models to your unique use cases and apply best practices for efficient knowledge transfer. Register here to reserve your spot and be part of the conversation. Join the Community Building in AI is better when we do it together. That’s why the Azure AI Developer Community exists—to support your journey and provide resources every step of the way. Join the Discord for real-time discussions, events, and peer learning Explore the Forum to catch up on AMAs, ask questions, and connect with other developers About Me I'm Sharda, a Gold Microsoft Learn Student Ambassador passionate about cloud technologies and artificial intelligence. I enjoy learning, building, and helping others grow in tech. Connect with me: LinkedIn GitHub Dev.to Microsoft Tech Community
Agent in Azure AI Foundry not able to access SharePoint data via C# (but works in Foundry portal)
Hi Team, I created an agent in Azure AI Foundry and added a knowledge source using the SharePoint tool. When I test the agent inside the Foundry portal, it works correctly; it can read from the SharePoint site and return file names/data. However, when I call the same agent using C# code, it answers normal questions fine, but whenever I ask about the SharePoint data, I get the error: Sorry, something went wrong. Run status: failed I also referred to the official documentation and sample here: https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/tools/sharepoint-samples?pivots=rest I tried the cURL samples as well, and while the agent is created successfully, the run status always comes back as failed. Has anyone faced this issue? Do I need to configure something extra for SharePoint when calling the agent programmatically (like additional permissions or connection binding)? Any help on this would be greatly appreciated. Thanks!Power Up Your Open WebUI with Azure AI Speech: Quick STT & TTS Integration
Introduction Ever found yourself wishing your web interface could really talk and listen back to you? With a few clicks (and a bit of code), you can turn your plain Open WebUI into a full-on voice assistant. In this post, you’ll see how to spin up an Azure Speech resource, hook it into your frontend, and watch as user speech transforms into text and your app’s responses leap off the screen in a human-like voice. By the end of this guide, you’ll have a voice-enabled web UI that actually converses with users, opening the door to hands-free controls, better accessibility, and a genuinely richer user experience. Ready to make your web app speak? Let’s dive in. Why Azure AI Speech? We use Azure AI Speech service in Open Web UI to enable voice interactions directly within web applications. This allows users to: Speak commands or input instead of typing, making the interface more accessible and user-friendly. Hear responses or information read aloud, which improves usability for people with visual impairments or those who prefer audio. Provide a more natural and hands-free experience especially on devices like smartphones or tablets. In short, integrating Azure AI Speech service into Open Web UI helps make web apps smarter, more interactive, and easier to use by adding speech recognition and voice output features. If you haven’t hosted Open WebUI already, follow my other step-by-step guide to host Ollama WebUI on Azure. Proceed to the next step if you have Open WebUI deployed already. Learn More about OpenWeb UI here. Deploy Azure AI Speech service in Azure. Navigate to the Azure Portal and search for Azure AI Speech on the Azure portal search bar. Create a new Speech Service by filling up the fields in the resource creation page. Click on “Create” to finalize the setup. After the resource has been deployed, click on “View resource” button and you should be redirected to the Azure AI Speech service page. The page should display the API Keys and Endpoints for Azure AI Speech services, which you can use in Open Web UI. Settings things up in Open Web UI Speech to Text settings (STT) Head to the Open Web UI Admin page > Settings > Audio. Paste the API Key obtained from the Azure AI Speech service page into the API key field below. Unless you use different Azure Region, or want to change the default configurations for the STT settings, leave all settings to blank. Text to Speech settings (TTS) Now, let's proceed with configuring the TTS Settings on OpenWeb UI by toggling the TTS Engine to Azure AI Speech option. Again, paste the API Key obtained from Azure AI Speech service page and leave all settings to blank. You can change the TTS Voice from the dropdown selection in the TTS settings as depicted in the image below: Click Save to reflect the change. Expected Result Now, let’s test if everything works well. Open a new chat / temporary chat on Open Web UI and click on the Call / Record button. The STT Engine (Azure AI Speech) should identify your voice and provide a response based on the voice input. To test the TTS feature, click on the Read Aloud (Speaker Icon) under any response from Open Web UI. The TTS Engine should reflect Azure AI Speech service! Conclusion And that’s a wrap! You’ve just given your Open WebUI the gift of capturing user speech, turning it into text, and then talking right back with Azure’s neural voices. Along the way you saw how easy it is to spin up a Speech resource in the Azure portal, wire up real-time transcription in the browser, and pipe responses through the TTS engine. From here, it’s all about experimentation. Try swapping in different neural voices or dialing in new languages. Tweak how you start and stop listening, play with silence detection, or add custom pronunciation tweaks for those tricky product names. Before you know it, your interface will feel less like a web page and more like a conversation partner.
