What is the Microsoft Graph API replacement for deprecated Recent, SharedWithMe, and Insights APIs?
Hi Microsoft Graph community, I'm currently working on a Microsoft Graph integration that needs to retrieve: Files recently accessed/used by the current user Files shared with the current user Files from OneDrive and SharePoint I'm currently using the following APIs: GET /me/drive/recent GET /me/drive/sharedWithMe GET /me/insights/used However, these APIs are deprecated, and some of them will stop returning data after November 2026. (https://learn.microsoft.com/en-us/graph/api/drive-sharedwithme?view=graph-rest-1.0&utm_source=chatgpt.com) I'm stuck in development because I haven't been able to identify a Microsoft-supported replacement that provides the same functionality. Could someone please clarify: What is the official replacement for /me/drive/recent? What is the official replacement for /me/drive/sharedWithMe? What is the official replacement for /me/insights/used? Is there a single recommended Microsoft Graph API that can provide recently used and shared files across both OneDrive and SharePoint? I'm specifically looking for the officially supported API/recommended approach, rather than a workaround that may also become deprecated. Any guidance from Microsoft Graph experts or Microsoft employees would be greatly appreciated. Thanks!Integrating Microsoft Foundry with OpenClaw: Step by Step Model Configuration
Step 1: Deploying Models on Microsoft Foundry Let us kick things off in the Azure portal. To get our OpenClaw agent thinking like a genius, we need to deploy our models in Microsoft Foundry. For this guide, we are going to focus on deploying gpt-5.2-codex on Microsoft Foundry with OpenClaw. Navigate to your AI Hub, head over to the model catalog, choose the model you wish to use with OpenClaw and hit deploy. Once your deployment is successful, head to the endpoints section. Important: Grab your Endpoint URL and your API Keys right now and save them in a secure note. We will need these exact values to connect OpenClaw in a few minutes. Step 2: Installing and Initializing OpenClaw Next up, we need to get OpenClaw running on your machine. Open up your terminal and run the official installation script: curl -fsSL https://openclaw.ai/install.sh | bash The wizard will walk you through a few prompts. Here is exactly how to answer them to link up with our Azure setup: First Page (Model Selection): Choose "Skip for now". Second Page (Provider): Select azure-openai-responses. Model Selection: Select gpt-5.2-codex , For now only the models listed (hosted on Microsoft Foundry) in the picture below are available to be used with OpenClaw. Follow the rest of the standard prompts to finish the initial setup. Step 3: Editing the OpenClaw Configuration File Now for the fun part. We need to manually configure OpenClaw to talk to Microsoft Foundry. Open your configuration file located at ~/.openclaw/openclaw.json in your favorite text editor. Replace the contents of the models and agents sections with the following code block: { "models": { "providers": { "azure-openai-responses": { "baseUrl": "https://<YOUR_RESOURCE_NAME>.openai.azure.com/openai/v1", "apiKey": "<YOUR_AZURE_OPENAI_API_KEY>", "api": "openai-responses", "authHeader": false, "headers": { "api-key": "<YOUR_AZURE_OPENAI_API_KEY>" }, "models": [ { "id": "gpt-5.2-codex", "name": "GPT-5.2-Codex (Azure)", "reasoning": true, "input": ["text", "image"], "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 }, "contextWindow": 400000, "maxTokens": 16384, "compat": { "supportsStore": false } }, { "id": "gpt-5.2", "name": "GPT-5.2 (Azure)", "reasoning": false, "input": ["text", "image"], "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 }, "contextWindow": 272000, "maxTokens": 16384, "compat": { "supportsStore": false } } ] } } }, "agents": { "defaults": { "model": { "primary": "azure-openai-responses/gpt-5.2-codex" }, "models": { "azure-openai-responses/gpt-5.2-codex": {} }, "workspace": "/home/<USERNAME>/.openclaw/workspace", "compaction": { "mode": "safeguard" }, "maxConcurrent": 4, "subagents": { "maxConcurrent": 8 } } } } You will notice a few placeholders in that JSON. Here is exactly what you need to swap out: Placeholder Variable What It Is Where to Find It <YOUR_RESOURCE_NAME> The unique name of your Azure OpenAI resource. Found in your Azure Portal under the Azure OpenAI resource overview. <YOUR_AZURE_OPENAI_API_KEY> The secret key required to authenticate your requests. Found in Microsoft Foundry under your project endpoints or Azure Portal keys section. <USERNAME> Your local computer's user profile name. Open your terminal and type whoami to find this. Step 4: Restart the Gateway After saving the configuration file, you must restart the OpenClaw gateway for the new Foundry settings to take effect. Run this simple command: openclaw gateway restart Configuration Notes & Deep Dive If you are curious about why we configured the JSON that way, here is a quick breakdown of the technical details. Authentication Differences Azure OpenAI uses the api-key HTTP header for authentication. This is entirely different from the standard OpenAI Authorization: Bearer header. Our configuration file addresses this in two ways: Setting "authHeader": false completely disables the default Bearer header. Adding "headers": { "api-key": "<key>" } forces OpenClaw to send the API key via Azure's native header format. Important Note: Your API key must appear in both the apiKey field AND the headers.api-key field within the JSON for this to work correctly. The Base URL Azure OpenAI's v1-compatible endpoint follows this specific format: https://<your_resource_name>.openai.azure.com/openai/v1 The beautiful thing about this v1 endpoint is that it is largely compatible with the standard OpenAI API and does not require you to manually pass an api-version query parameter. Model Compatibility Settings "compat": { "supportsStore": false } disables the store parameter since Azure OpenAI does not currently support it. "reasoning": true enables the thinking mode for GPT-5.2-Codex. This supports low, medium, high, and xhigh levels. "reasoning": false is set for GPT-5.2 because it is a standard, non-reasoning model. Model Specifications & Cost Tracking If you want OpenClaw to accurately track your token usage costs, you can update the cost fields from 0 to the current Azure pricing. Here are the specs and costs for the models we just deployed: Model Specifications Model Context Window Max Output Tokens Image Input Reasoning gpt-5.2-codex 400,000 tokens 16,384 tokens Yes Yes gpt-5.2 272,000 tokens 16,384 tokens Yes No Current Cost (Adjust in JSON) Model Input (per 1M tokens) Output (per 1M tokens) Cached Input (per 1M tokens) gpt-5.2-codex $1.75 $14.00 $0.175 gpt-5.2 $2.00 $8.00 $0.50 Conclusion: And there you have it! You have successfully bridged the gap between the enterprise-grade infrastructure of Microsoft Foundry and the local autonomy of OpenClaw. By following these steps, you are not just running a chatbot; you are running a sophisticated agent capable of reasoning, coding, and executing tasks with the full power of GPT-5.2-codex behind it. The combination of Azure's reliability and OpenClaw's flexibility opens up a world of possibilities. Whether you are building an automated devops assistant, a research agent, or just exploring the bleeding edge of AI, you now have a robust foundation to build upon. Now it is time to let your agent loose on some real tasks. Go forth, experiment with different system prompts, and see what you can build. If you run into any interesting edge cases or come up with a unique configuration, let me know in the comments below. Happy coding!Configure Embedding Models on Azure AI Foundry with Open Web UI
Introduction Let’s take a closer look at an exciting development in the AI space. Embedding models are the key to transforming complex data into usable insights, driving innovations like smarter chatbots and tailored recommendations. With Azure AI Foundry, Microsoft’s powerful platform, you’ve got the tools to build and scale these models effortlessly. Add in Open Web UI, a intuitive interface for engaging with AI systems, and you’ve got a winning combo that’s hard to beat. In this article, we’ll explore how embedding models on Azure AI Foundry, paired with Open Web UI, are paving the way for accessible and impactful AI solutions for developers and businesses. Let’s dive in! To proceed with configuring the embedding model from Azure AI Foundry on Open Web UI, please firstly configure the requirements below. Requirements: Setup Azure AI Foundry Hub/Projects Deploy Open Web UI – refer to my previous article on how you can deploy Open Web UI on Azure VM. Optional: Deploy LiteLLM with Azure AI Foundry models to work on Open Web UI - refer to my previous article on how you can do this as well. Deploying Embedding Models on Azure AI Foundry Navigate to the Azure AI Foundry site and deploy an embedding model from the “Model + Endpoint” section. For the purpose of this demonstration, we will deploy the “text-embedding-3-large” model by OpenAI. You should be receiving a URL endpoint and API Key to the embedding model deployed just now. Take note of that credential because we will be using it in Open Web UI. Configuring the embedding models on Open Web UI Now head to the Open Web UI Admin Setting Page > Documents and Select Azure Open AI as the Embedding Model Engine. Copy and Paste the Base URL, API Key, the Embedding Model deployed on Azure AI Foundry and the API version (not the model version) into the fields below: Click “Save” to reflect the changes. Expected Output Now let us look into the scenario for when the embedding model configured on Open Web UI and when it is not. Without Embedding Models configured. With Azure Open AI Embedding models configured. Conclusion And there you have it! Embedding models on Azure AI Foundry, combined with the seamless interaction offered by Open Web UI, are truly revolutionizing how we approach AI solutions. This powerful duo not only simplifies the process of building and deploying intelligent systems but also makes cutting-edge technology more accessible to developers and businesses of all sizes. As we move forward, it’s clear that such integrations will continue to drive innovation, breaking down barriers and unlocking new possibilities in the AI landscape. So, whether you’re a seasoned developer or just stepping into this exciting field, now’s the time to explore what Azure AI Foundry and Open Web UI can do for you. Let’s keep pushing the boundaries of what’s possible!Announcing: Azure API Center Hands-on Workshop 🚀
What is the Azure API Center Workshop? The Azure API Center flash workshop is a resource designed to expand your understanding of how organizations can enhance and streamline their API management and governance strategies using Azure API Center. With this practical knowledge and insights, you will be able to streamline secure API integration and enforce security and compliance with tools that evolve to meet your growing business needs. Azure API Center is a centralized inventory designed to track all your APIs, regardless of their type, lifecycle stage, or deployment location. It enhances discoverability, development, and reuse of APIs. While Azure API Management focuses on API deployment and runtime governance, Azure API Center complements it by centralizing APIs and streamlining the registration of new APIs and design-time governance. Who can go through the workshop? The Azure API Center workshop benefits anyone who is interested in improving their API development, at-scale governance, discovery, and consumption workflow experience. Throughout the workshop, we reference 3 key personas heavily involved in the API ecosystem. API Producers - Individual developers or teams who consolidate API specifications and requirements and design API architectures to fit defined goals. They also develop, secure, publish, test APIs to ensure they meet functional and performance requirements and document APIs. API Platform Engineers/ API Admins - Establish and enforce API best practices and design standards across teams and the entire organization. They also enforce monitoring and analysis of definitions for adherence to organizational style rules, generating both individual and summary reports. This ensures timely correction of common errors and inconsistencies in your API definitions. API consumers - Consumers of APIs to build systems/ applications that use services provided by the organization, or direct consumers using APIs to satisfy business needs. To put these personas into perspective, we use a fictitious company, Contoso Airlines, to demonstrate how API Center integrates with existing API development, deployment, governance and discovery workflows. ines If you are passionate about teaching and skilling others on API best practices and concepts, visit the workshop's GitHub repository for session delivery resources you can use for a step-by-step presentation on API Center using this workshop. Don't forget to like and star the repo ⭐ What will you learn after going through the workshop? The workshop is divided into three key pillars as follows: API Inventory Under the API Inventory pillar, you will go through guided steps to install the Azure API Center extension on VS Code, connect to your Azure account and create an API Center resource with custom API metadata/ properties defined. You will then use GitHub Copilot to register current APIs into your API Center resource, configure environments and deployments for your APIs and set up automatic synchronization as you import more sample APIs from an API Management service. API Governance You will then go through the API Governance experience first from an API producer's perspective, as you define and apply a custom API style ruleset on VS Code, and from an API Admin perspective to deploy the ruleset to Azure, and view API Analysis reports on Azure to determine the health and safety of all APIs across the organization. API Discovery & Consumption Here, you will learn how you can discover all APIs on VS Code and on the Azure portal, a key step before creating any new APIs to ensure no duplication and promote reusable APIs. You will also quickly load API Documentation and test your APIs. Where do I go to get started with the workshop? To get started with the workshop, you can directly go to https://aka.ms/APICenter/Workshop or go through our GitHub Repository where you can open issues, leave feedback, and leave a star 😉Configuring OAuth 2.0 Authentication for Microsoft Power Platform Custom Connectors
If you enjoy working with APIs and you have not yet read about the opportunity to build custom connectors for the Power Platform, read along to learn more about how you can integrate your API to Low Code platforms and authenticate using OAuth 2.0.Microsoft Student Summit March 2023 - Start and Accelerate Your Career in Tech
Student Summit - Start and Accelerate Your Career in Tech In partnership with Microsoft Reactor this exciting 90-minute event will help build your confidence and motivation to skill on the Microsoft Cloud, and coach you on the next steps to continue your learning on topics including - Application Development and Developer Tools, Low Code/ No-Code / Fusion Development, and AI, Data and Machine Learning.How to Build a Custom Connector from Scratch
A Connector is a formal definition of a REST API that allows the REST Service (Not only Microsoft services but also external services) to talk to Microsoft Power Automate, Power Apps and Logic Apps. This blog provides an introduction and resources for you to learn how to build your own custom connector.
