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!How to build Tool-calling Agents with Azure OpenAI and Lang Graph
Introducing MyTreat Our demo is a fictional website that shows customers their total bill in dollars, but they have the option of getting the total bill in their local currencies. The button sends a request to the Node.js service and a response is simply returned from our Agent given the tool it chooses. Let’s dive in and understand how this works from a broader perspective. Prerequisites An active Azure subscription. You can sign up for a free trial here or get $100 worth of credits on Azure every year if you are a student. A GitHub account (not necessarily) Node.js LTS 18 + VS Code installed (or your favorite IDE) Basic knowledge of HTML, CSS, JS Creating an Azure OpenAI Resource Go over to your browser and key in portal.azure.com to access the Microsoft Azure Portal. Over there navigate to the search bar and type Azure OpenAI. Go ahead and click on + Create. Fill in the input boxes with appropriate, for example, as shown below then press on next until you reach review and submit then finally click on Create. After the deployment is done, go to the deployment and access Azure AI Foundry portal using the button as show below. You can also use the link as demonstrated below. In the Azure AI Foundry portal, we have to create our model instance so we have to go over to Model Catalog on the left panel beneath Get Started. Select a desired model, in this case I used gpt-35-turbo for chat completion (in your case use gpt-4o). Below is a way of doing this. Choose a model (gpt-4o) Click on deploy Give the deployment a new name e.g. myTreatmodel, then click deploy and wait for it to finish On the left panel go over to deployments and you will see the model you have created. Access your Azure OpenAI Resource Key Go back to Azure portal and specifically to the deployment instance that we have and select on the left panel, Resource Management. Click on Keys and Endpoints. Copy any of the keys as shown below and keep it very safe as we will use it in our .env file. Configuring your project Create a new project folder on your local machine and add these variables to the .env file in the root folder. AZURE_OPENAI_API_INSTANCE_NAME= AZURE_OPENAI_API_DEPLOYMENT_NAME= AZURE_OPENAI_API_KEY= AZURE_OPENAI_API_VERSION="2024-08-01-preview" LANGCHAIN_TRACING_V2="false" LANGCHAIN_CALLBACKS_BACKGROUND = "false" PORT=4556 Starting a new project Go over to https://github.com/tiprock-network/mytreat.git and follow the instructions to setup the new project, if you do not have git installed, go over to the Code button and press Download ZIP. This will enable you get the project folder and follow the same procedure for setting up. Creating a custom tool In the utils folder the math tool was created, this code show below uses tool from Langchain to build a tool and the schema of the tool is created using zod.js, a library that helps in validating an object’s property value. The price function takes in an array of prices and the exchange rate, adds the prices up and converts them using the exchange rate as shown below. import { tool } from '@langchain/core/tools' import { z } from 'zod' const priceConv = tool((input) =>{ //get the prices and add them up after turning each into let sum = 0 input.prices.forEach((price) => { let price_check = parseFloat(price) sum += price_check }) //now change the price using exchange rate let final_price = parseFloat(input.exchange_rate) * sum //return return final_price },{ name: 'add_prices_and_convert', description: 'Add prices and convert based on exchange rate.', schema: z.object({ prices: z.number({ required_error: 'Price should not be empty.', invalid_type_error: 'Price must be a number.' }).array().nonempty().describe('Prices of items listed.'), exchange_rate: z.string().describe('Current currency exchange rate.') }) }) export { priceConv } Utilizing the tool In the controller’s folder we then bring the tool in by importing it. After that we pass it in to our array of tools. Notice that we have the Tavily Search Tool, you can learn how to implement in the Additional Reads Section or just remove it. Agent Model and the Call Process This code defines an AI agent using LangGraph and LangChain.js, powered by GPT-4o from Azure OpenAI. It initializes a ToolNode to manage tools like priceConv and binds them to the agent model. The StateGraph handles decision-making, determining whether the agent should call a tool or return a direct response. If a tool is needed, the workflow routes the request accordingly; otherwise, the agent responds to the user. The callModel function invokes the agent, processing messages and ensuring seamless tool integration. The searchAgentController is a GET endpoint that accepts user queries (text_message). It processes input through the compiled LangGraph workflow, invoking the agent to generate a response. If a tool is required, the agent calls it before finalizing the output. The response is then sent back to the user, ensuring dynamic and efficient tool-assisted reasoning. //create tools the agent will use //const agentTools = [new TavilySearchResults({maxResults:5}), priceConv] const agentTools = [ priceConv] const toolNode = new ToolNode(agentTools) const agentModel = new AzureChatOpenAI({ model:'gpt-4o', temperature:0, azureOpenAIApiKey: AZURE_OPENAI_API_KEY, azureOpenAIApiInstanceName:AZURE_OPENAI_API_INSTANCE_NAME, azureOpenAIApiDeploymentName:AZURE_OPENAI_API_DEPLOYMENT_NAME, azureOpenAIApiVersion:AZURE_OPENAI_API_VERSION }).bindTools(agentTools) //make a decision to continue or not const shouldContinue = ( state ) => { const { messages } = state const lastMessage = messages[messages.length -1] //upon tool call we go to tools if("tool_calls" in lastMessage && Array.isArray(lastMessage.tool_calls) && lastMessage.tool_calls?.length) return "tools"; //if no tool call is made we stop and return back to the user return END } const callModel = async (state) => { const response = await agentModel.invoke(state.messages) return { messages: [response] } } //define a new graph const workflow = new StateGraph(MessagesAnnotation) .addNode("agent", callModel) .addNode("tools", toolNode) .addEdge(START, "agent") .addConditionalEdges("agent", shouldContinue, ["tools", END]) .addEdge("tools", "agent") const appAgent = workflow.compile() The above is implemented with the following code: Frontend The frontend is a simple HTML+CSS+JS stack that demonstrated how you can use an API to integrate this AI Agent to your website. It sends a GET request and uses the response to get back the right answer. Below is an illustration of how fetch API has been used. const searchAgentController = async ( req, res ) => { //get human text const { text_message } = req.query if(!text_message) return res.status(400).json({ message:'No text sent.' }) //invoke the agent const agentFinalState = await appAgent.invoke( { messages: [new HumanMessage(text_message)] }, {streamMode: 'values'} ) //const agentFinalState_b = await agentModel.invoke(text_message) /*return res.status(200).json({ answer:agentFinalState.messages[agentFinalState.messages.length - 1].content })*/ //console.log(agentFinalState_b.tool_calls) res.status(200).json({ text: agentFinalState.messages[agentFinalState.messages.length - 1].content }) } There you go! We have created a basic tool-calling agent using Azure and Langchain successfully, go ahead and expand the code base to your liking. If you have questions you can comment below or reach out on my socials. Additional Reads Azure Open AI Service Models Generative AI for Beginners AI Agents for Beginners Course Lang Graph Tutorial Develop Generative AI Apps in Azure AI Foundry PortalUnderstanding Azure OpenAI Service Quotas and Limits: A Beginner-Friendly Guide
Azure OpenAI Service allows developers, researchers, and students to integrate powerful AI models like GPT-4, GPT-3.5, and DALL·E into their applications. But with great power comes great responsibility and limits. Before you dive into building your next AI-powered solution, it's crucial to understand how quotas and limits work in the Azure OpenAI ecosystem. This guide is designed to help students and beginners easily understand the concept of quotas, limits, and how to manage them effectively. What Are Quotas and Limits? Think of Azure's quotas as your "AI data pack." It defines how much you can use the service. Meanwhile, limits are hard boundaries set by Azure to ensure fair use and system stability. Quota The maximum number of resources (e.g., tokens, requests) allocated to your Azure subscription. Limit The technical cap imposed by Azure on specific resources (e.g., number of files, deployments). Key Metrics: TPM & RPM Tokens Per Minute (TPM) TPM refers to how many tokens you can use per minute across all your requests in each region. A token is a chunk of text. For example, the word "Hello" is 1 token, but "Understanding" might be 2 tokens. Each model has its own default TPM. Example: GPT-4 might allow 240,000 tokens per minute. You can split this quota across multiple deployments. Requests Per Minute (RPM) RPM defines how many API requests you can make every minute. For instance, GPT-3.5-turbo might allow 350 RPM. DALL·E image generation models might allow 6 RPM. Deployment, File, and Training Limits Here are some standard limits imposed on your OpenAI resource: Resource Type Limit Standard model deployments 32 Fine-tuned model deployments 5 Training jobs 100 total per resource (1 active at a time) Fine-tuning files 50 files (total size: 1 GB) Max prompt tokens per request Varies by model (e.g., 4096 tokens for GPT-3.5) How to View and Manage Your Quota Step-by-Step: Go to the Azure Portal. Navigate to your Azure OpenAI resource. Click on "Usage + quotas" in the left-hand menu. You will see TPM, RPM, and current usage status. To Request More Quota: In the same "Usage + quotas" panel, click on "Request quota increase". Fill in the form: Select the region. Choose the model family (e.g., GPT-4, GPT-3.5). Enter the desired TPM and RPM values. Submit and wait for Azure to review and approve. What is Dynamic Quota? Sometimes, Azure gives you extra quota based on demand and availability. “Dynamic quota” is not guaranteed and may increase or decrease. Useful for short-term spikes but should not be relied on for production apps. Example: During weekends, your GPT-3.5 TPM may temporarily increase if there's less traffic in your region. Best Practices for Students Monitor Regularly: Use the Azure Portal to keep an eye on your usage. Batch Requests: Combine multiple tasks in one API call to save tokens. Start Small: Begin with GPT-3.5 before requesting GPT-4 access. Plan Ahead: If you're preparing a demo or a project, request quota in advance. Handle Limits Gracefully: Code should manage 429 Too Many Requests errors. Quick Resources Azure OpenAI Quotas and Limits How to Request Quota in Azure Join the Conversation on Azure AI Foundry Discussions! Have ideas, questions, or insights about AI? Don't keep them to yourself! Share your thoughts, engage with experts, and connect with a community that’s shaping the future of artificial intelligence. 🧠✨ 👉 Click here to join the discussion!Create your own QA RAG Chatbot with LangChain.js + Azure OpenAI Service
Demo: Mpesa for Business Setup QA RAG Application In this tutorial we are going to build a Question-Answering RAG Chat Web App. We utilize Node.js and HTML, CSS, JS. We also incorporate Langchain.js + Azure OpenAI + MongoDB Vector Store (MongoDB Search Index). Get a quick look below. Note: Documents and illustrations shared here are for demo purposes only and Microsoft or its products are not part of Mpesa. The content demonstrated here should be used for educational purposes only. Additionally, all views shared here are solely mine. What you will need: An active Azure subscription, get Azure for Student for free or get started with Azure for 12 months free. VS Code Basic knowledge in JavaScript (not a must) Access to Azure OpenAI, click here if you don't have access. Create a MongoDB account (You can also use Azure Cosmos DB vector store) Setting Up the Project In order to build this project, you will have to fork this repository and clone it. GitHub Repository link: https://github.com/tiprock-network/azure-qa-rag-mpesa . Follow the steps highlighted in the README.md to setup the project under Setting Up the Node.js Application. Create Resources that you Need In order to do this, you will need to have Azure CLI or Azure Developer CLI installed in your computer. Go ahead and follow the steps indicated in the README.md to create Azure resources under Azure Resources Set Up with Azure CLI. You might want to use Azure CLI to login in differently use a code. Here's how you can do this. Instead of using az login. You can do az login --use-code-device OR you would prefer using Azure Developer CLI and execute this command instead azd auth login --use-device-code Remember to update the .env file with the values you have used to name Azure OpenAI instance, Azure models and even the API Keys you have obtained while creating your resources. Setting Up MongoDB After accessing you MongoDB account get the URI link to your database and add it to the .env file along with your database name and vector store collection name you specified while creating your indexes for a vector search. Running the Project In order to run this Node.js project you will need to start the project using the following command. npm run dev The Vector Store The vector store used in this project is MongoDB store where the word embeddings were stored in MongoDB. From the embeddings model instance we created on Azure AI Foundry we are able to create embeddings that can be stored in a vector store. The following code below shows our embeddings model instance. //create new embedding model instance const azOpenEmbedding = new AzureOpenAIEmbeddings({ azureADTokenProvider, azureOpenAIApiInstanceName: process.env.AZURE_OPENAI_API_INSTANCE_NAME, azureOpenAIApiEmbeddingsDeploymentName: process.env.AZURE_OPENAI_API_DEPLOYMENT_EMBEDDING_NAME, azureOpenAIApiVersion: process.env.AZURE_OPENAI_API_VERSION, azureOpenAIBasePath: "https://eastus2.api.cognitive.microsoft.com/openai/deployments" }); The code in uploadDoc.js offers a simple way to do embeddings and store them to MongoDB. In this approach the text from the documents is loaded using the PDFLoader from Langchain community. The following code demonstrates how the embeddings are stored in the vector store. // Call the function and handle the result with await const storeToCosmosVectorStore = async () => { try { const documents = await returnSplittedContent() //create store instance const store = await MongoDBAtlasVectorSearch.fromDocuments( documents, azOpenEmbedding, { collection: vectorCollection, indexName: "myrag_index", textKey: "text", embeddingKey: "embedding", } ) if(!store){ console.log('Something wrong happened while creating store or getting store!') return false } console.log('Done creating/getting and uploading to store.') return true } catch (e) { console.log(`This error occurred: ${e}`) return false } } In this setup, Question Answering (QA) is achieved by integrating Azure OpenAI’s GPT-4o with MongoDB Vector Search through LangChain.js. The system processes user queries via an LLM (Large Language Model), which retrieves relevant information from a vectorized database, ensuring contextual and accurate responses. Azure OpenAI Embeddings convert text into dense vector representations, enabling semantic search within MongoDB. The LangChain RunnableSequence structures the retrieval and response generation workflow, while the StringOutputParser ensures proper text formatting. The most relevant code snippets to include are: AzureChatOpenAI instantiation, MongoDB connection setup, and the API endpoint handling QA queries using vector search and embeddings. There are some code snippets below to explain major parts of the code. Azure AI Chat Completion Model This is the model used in this implementation of RAG, where we use it as the model for chat completion. Below is a code snippet for it. const llm = new AzureChatOpenAI({ azTokenProvider, azureOpenAIApiInstanceName: process.env.AZURE_OPENAI_API_INSTANCE_NAME, azureOpenAIApiDeploymentName: process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME, azureOpenAIApiVersion: process.env.AZURE_OPENAI_API_VERSION }) Using a Runnable Sequence to give out Chat Output This shows how a runnable sequence can be used to give out a response given the particular output format/ output parser added on to the chain. //Stream response app.post(`${process.env.BASE_URL}/az-openai/runnable-sequence/stream/chat`, async (req,res) => { //check for human message const { chatMsg } = req.body if(!chatMsg) return res.status(201).json({ message:'Hey, you didn\'t send anything.' }) //put the code in an error-handler try{ //create a prompt template format template const prompt = ChatPromptTemplate.fromMessages( [ ["system", `You are a French-to-English translator that detects if a message isn't in French. If it's not, you respond, "This is not French." Otherwise, you translate it to English.`], ["human", `${chatMsg}`] ] ) //runnable chain const chain = RunnableSequence.from([prompt, llm, outPutParser]) //chain result let result_stream = await chain.stream() //set response headers res.setHeader('Content-Type','application/json') res.setHeader('Transfer-Encoding','chunked') //create readable stream const readable = Readable.from(result_stream) res.status(201).write(`{"message": "Successful translation.", "response": "`); readable.on('data', (chunk) => { // Convert chunk to string and write it res.write(`${chunk}`); }); readable.on('end', () => { // Close the JSON response properly res.write('" }'); res.end(); }); readable.on('error', (err) => { console.error("Stream error:", err); res.status(500).json({ message: "Translation failed.", error: err.message }); }); }catch(e){ //deliver a 500 error response return res.status(500).json( { message:'Failed to send request.', error:e } ) } }) To run the front end of the code, go to your BASE_URL with the port given. This enables you to run the chatbot above and achieve similar results. The chatbot is basically HTML+CSS+JS. Where JavaScript is mainly used with fetch API to get a response. Thanks for reading. I hope you play around with the code and learn some new things. Additional Reads Introduction to LangChain.js Create an FAQ Bot on Azure Build a basic chat app in Python using Azure AI Foundry SDKAutogen: Microsoft’s Open-Source Tool for Streamlining Development
Are you a technical student looking for a tool that can help you generate high-quality code, documentation, and tests for your projects? If so, you might want to check out AutoGen a framework that enables development of large language model (LLM) applications using multiple agents that can converse with each other to solve tasks.
