Serverless MCP Agent with LangChain.js v1 — Burgers, Tools, and Traces 🍔
AI agents that can actually do stuff (not just chat) are the fun part nowadays, but wiring them cleanly into real APIs, keeping things observable, and shipping them to the cloud can get... messy. So we built a fresh end‑to‑end sample to show how to do it right with the brand new LangChain.js v1 and Model Context Protocol (MCP). In case you missed it, MCP is a recent open standard that makes it easy for LLM agents to consume tools and APIs, and LangChain.js, a great framework for building GenAI apps and agents, has first-class support for it. You can quickly get up speed with the MCP for Beginners course and AI Agents for Beginners course. This new sample gives you: A LangChain.js v1 agent that streams its result, along reasoning + tool steps An MCP server exposing real tools (burger menu + ordering) from a business API A web interface with authentication, sessions history, and a debug panel (for developers) A production-ready multi-service architecture Serverless deployment on Azure in one command ( azd up ) Yes, it’s a burger ordering system. Who doesn't like burgers? Grab your favorite beverage ☕, and let’s dive in for a quick tour! TL;DR key takeaways New sample: full-stack Node.js AI agent using LangChain.js v1 + MCP tools Architecture: web app → agent API → MCP server → burger API Runs locally with a single npm start , deploys with azd up Uses streaming (NDJSON) with intermediate tool + LLM steps surfaced to the UI Ready to fork, extend, and plug into your own domain / tools What will you learn here? What this sample is about and its high-level architecture What LangChain.js v1 brings to the table for agents How to deploy and run the sample How MCP tools can expose real-world APIs Reference links for everything we use GitHub repo LangChain.js docs Model Context Protocol Azure Developer CLI MCP Inspector Use case You want an AI assistant that can take a natural language request like “Order two spicy burgers and show me my pending orders” and: Understand intent (query menu, then place order) Call the right MCP tools in sequence, calling in turn the necessary APIs Stream progress (LLM tokens + tool steps) Return a clean final answer Swap “burgers” for “inventory”, “bookings”, “support tickets”, or “IoT devices” and you’ve got a reusable pattern! Sample overview Before we play a bit with the sample, let's have a look at the main services implemented here: Service Role Tech Agent Web App ( agent-webapp ) Chat UI + streaming + session history Azure Static Web Apps, Lit web components Agent API ( agent-api ) LangChain.js v1 agent orchestration + auth + history Azure Functions, Node.js Burger MCP Server ( burger-mcp ) Exposes burger API as tools over MCP (Streamable HTTP + SSE) Azure Functions, Express, MCP SDK Burger API ( burger-api ) Business logic: burgers, toppings, orders lifecycle Azure Functions, Cosmos DB Here's a simplified view of how they interact: There are also other supporting components like databases and storage not shown here for clarity. For this quickstart we'll only interact with the Agent Web App and the Burger MCP Server, as they are the main stars of the show here. LangChain.js v1 agent features The recent release of LangChain.js v1 is a huge milestone for the JavaScript AI community! It marks a significant shift from experimental tools to a production-ready framework. The new version doubles down on what’s needed to build robust AI applications, with a strong focus on agents. This includes first-class support for streaming not just the final output, but also intermediate steps like tool calls and agent reasoning. This makes building transparent and interactive agent experiences (like the one in this sample) much more straightforward. Quickstart Requirements GitHub account Azure account (free signup, or if you're a student, get free credits here) Azure Developer CLI Deploy and run the sample We'll use GitHub Codespaces for a quick zero-install setup here, but if you prefer to run it locally, check the README. Click on the following link or open it in a new tab to launch a Codespace: Create Codespace This will open a VS Code environment in your browser with the repo already cloned and all the tools installed and ready to go. Provision and deploy to Azure Open a terminal and run these commands: # Install dependencies npm install # Login to Azure azd auth login # Provision and deploy all resources azd up Follow the prompts to select your Azure subscription and region. If you're unsure of which one to pick, choose East US 2 . The deployment will take about 15 minutes the first time, to create all the necessary resources (Functions, Static Web Apps, Cosmos DB, AI Models). If you're curious about what happens under the hood, you can take a look at the main.bicep file in the infra folder, which defines the infrastructure as code for this sample. Test the MCP server While the deployment is running, you can run the MCP server and API locally (even in Codespaces) to see how it works. Open another terminal and run: npm start This will start all services locally, including the Burger API and the MCP server, which will be available at http://localhost:3000/mcp . This may take a few seconds, wait until you see this message in the terminal: 🚀 All services ready 🚀 When these services are running without Azure resources provisioned, they will use in-memory data instead of Cosmos DB so you can experiment freely with the API and MCP server, though the agent won't be functional as it requires a LLM resource. MCP tools The MCP server exposes the following tools, which the agent can use to interact with the burger ordering system: Tool Name Description get_burgers Get a list of all burgers in the menu get_burger_by_id Get a specific burger by its ID get_toppings Get a list of all toppings in the menu get_topping_by_id Get a specific topping by its ID get_topping_categories Get a list of all topping categories get_orders Get a list of all orders in the system get_order_by_id Get a specific order by its ID place_order Place a new order with burgers (requires userId , optional nickname ) delete_order_by_id Cancel an order if it has not yet been started (status must be pending , requires userId ) You can test these tools using the MCP Inspector. Open another terminal and run: npx -y @modelcontextprotocol/inspector Then open the URL printed in the terminal in your browser and connect using these settings: Transport: Streamable HTTP URL: http://localhost:3000/mcp Connection Type: Via Proxy (should be default) Click on Connect, then try listing the tools first, and run get_burgers tool to get the menu info. Test the Agent Web App After the deployment is completed, you can run the command npm run env to print the URLs of the deployed services. Open the Agent Web App URL in your browser (it should look like https://<your-web-app>.azurestaticapps.net ). You'll first be greeted by an authentication page, you can sign in either with your GitHub or Microsoft account and then you should be able to access the chat interface. From there, you can start asking any question or use one of the suggested prompts, for example try asking: Recommend me an extra spicy burger . As the agent processes your request, you'll see the response streaming in real-time, along with the intermediate steps and tool calls. Once the response is complete, you can also unfold the debug panel to see the full reasoning chain and the tools that were invoked: Tip: Our agent service also sends detailed tracing data using OpenTelemetry. You can explore these either in Azure Monitor for the deployed service, or locally using an OpenTelemetry collector. We'll cover this in more detail in a future post. Wrap it up Congratulations, you just finished spinning up a full-stack serverless AI agent using LangChain.js v1, MCP tools, and Azure’s serverless platform. Now it's your turn to dive in the code and extend it for your use cases! 😎 And don't forget to azd down once you're done to avoid any unwanted costs. Going further This was just a quick introduction to this sample, and you can expect more in-depth posts and tutorials soon. Since we're in the era of AI agents, we've also made sure that this sample can be explored and extended easily with code agents like GitHub Copilot. We even built a custom chat mode to help you discover and understand the codebase faster! Check out the Copilot setup guide in the repo to get started. You can quickly get up speed with the MCP for Beginners course and AI Agents for Beginners course. If you like this sample, don't forget to star the repo ⭐️! You can also join us in the Azure AI community Discord to chat and ask any questions. Happy coding and burger ordering! 🍔JS AI Build‑a‑thon: Wrapping Up an Epic June 2025!
After weeks of building, testing, and learning — we’re officially wrapping up the first-ever JS AI Build-a-thon 🎉. This wasn't your average coding challenge. This was a hands-on journey where JavaScript and TypeScript developers dove deep into real-world AI concepts — from local GenAI prototyping to building intelligent agents and deploying production-ready apps. Whether you joined from the start or hopped on midway, you built something that matters — and that’s worth celebrating. Replay the Journey No worries if you joined late or want to revisit any part of the journey. The JS AI Build-a-thon was designed to let you learn at your own pace, so whether you're starting now or polishing up your final project, here’s your complete quest map: Build-a-thon set up guide: https://aka.ms/JSAIBuildathonSetup Quest 1: 🔧 Build your first GenAI app locally with GitHub Models 👉🏽 https://aka.ms/JSAIBuildathonQuest1 Quest 2: ☁️ Move your AI prototype to Azure AI Foundry 👉🏽 https://aka.ms/JSAIBuildathonQuest Quest 3: 🎨 Add a chat UI using Vite + Lit 👉🏽 https://aka.ms/JSAIBuildathonQuest3 Quest 4: 📄 Enhance your app with RAG (Chat with Your Data) 👉🏽 https://aka.ms/JSAIBuildathonQuest4 Quest 5: 🧠 Add memory and context to your AI app 👉🏽 https://aka.ms/JSAIBuildathonQuest5 Quest 6: ⚙️ Build your first AI Agent using AI Foundry 👉🏽 https://aka.ms/JSAIBuildathonQuest6 Quest 7: 🧩 Equip your agent with tools from an MCP server 👉🏽 https://aka.ms/JSAIBuildathonQuest7 Quest 8: 💬 Ground your agent with real-time search using Bing 👉🏽 https://aka.ms/JSAIBuildathonQuest8 Quest 9: 🚀 Build a real-world AI project with full-stack templates 👉🏽 https://aka.ms/JSAIBuildathonQuest9 Link to our space in the AI Discord Community: https://aka.ms/JSAIonDiscord Project Submission Guidelines 📌 Quest 9 is where it all comes together. Participants chose a problem, picked a template, customized it, submitted it, and rallied their community for support! 🏅 Claim Your Badge! Whether you completed select quests or went all the way, we celebrate your learning. If you participated in the June 2025 JS AI Build-a-thon, make sure to Submit the Participation Form to receive your participation badge recognizing your commitment to upskilling in AI with JavaScript/ TypeScript. What’s Next? We’re not done. In fact, we’re just getting started. We’re already cooking up JS AI Build-a-thon v2, which will introduce: Running everything locally with Foundry Local Real-world RAG with vector databases Advanced agent patterns with remote MCPs And much more based on your feedback Want to shape what comes next? Drop your ideas in the participation form and in our Discord. In the meantime, add these resources to your JavaScript + AI Dev Pack: 🔗 Microsoft for JavaScript developers 📚 Generative AI for Beginners with JavaScript Wrap-Up This build-a-thon showed what’s possible when developers are empowered to learn by doing. You didn’t just follow tutorials — you shipped features, connected services, and created working AI experiences. We can’t wait to see what you build next. 👉 Bookmark the repo 👉 Join the community on Join the Azure AI Foundry Discord Server! 👉 Stay building Until next time — keep coding, keep shipping!Quest 5 - I want to add conversation memory to my app
In this quest, you’ll explore how to build GenAI apps using a modern JavaScript AI framework, LangChain.js. LangChain.js helps you orchestrate prompts, manage memory, and build multi-step AI workflows all while staying in your favorite language. Using LangChain.js you will make your GenAI chat app feel truly personal by teaching it to remember. In this quest, you’ll upgrade your AI prototype with conversation memory, allowing it to recall previous interactions making the conversation flow more naturally and human-like. 👉 Want to catch up on the full program or grab more quests? https://aka.ms/JSAIBuildathon 💬 Got questions or want to hang with other builders? Join us on Discord — head to the #js-ai-build-a-thon channel. 🔧 What You’ll Build A smarter, context-aware chat backend that: Remembers user conversations across multiple exchanges (e.g., knowing "Terry" after you introduced yourself as Terry) Maintains session-specific memory so each chat thread feels consistent and coherent Uses LangChain.js abstractions to streamline state management. 🚀 What You’ll Need ✅ A GitHub account ✅ Visual Studio Code ✅ Node.js ✅ A working chat app from previous quests (UI + Azure-based chat endpoint) 🛠️ Concepts You’ll Explore Integrating LangChain.js Learn how LangChain.js simplifies building AI-powered web applications by providing a standard interface to connect your backend with Azure’s language models. You’ll see how using this framework decouples your code and unlocks advanced features. Adding Conversation Memory Understand why memory matters in chatbots. Explore how conversation memory lets your app remember previous user messages within each session enabling more context-aware and coherent conversations. Session-based Message History Implement session-specific chat histories using LangChain’s memory modules (ChatMessageHistory and BufferMemory). Each user or session gets its own history, so previous questions and answers inform future responses without manual log management. Seamless State Management Experience how LangChain handles chat logs and memory behind the scenes, freeing you from manually stitching together chat history or juggling context with every prompt. 📖 Bonus Resources to Go Deeper Exploring Generative AI in App Development: LangChain.js and Azure: a video introduction to LangChain.js and how you can build a project with LangChain.js and Azure 🦜️🔗 Langchain: the official LangChain.js documentation. GitHub - Azure-Samples/serverless-chat-langchainjs: Build your own serverless AI Chat with Retrieval-Augmented-Generation using LangChain.js, TypeScript and Azure: A GitHub sample that helps you build your own serverless AI Chat with Retrieval-Augmented-Generation using LangChain.js, TypeScript and Azure GitHub - Azure-Samples/langchainjs-quickstart-demo: Build a generative AI application using LangChain.js, from local to Azure: A GitHub sample that helps you build a generative AI application using LangChain.js, from local to Azure. Microsoft | 🦜️🔗 Langchain Official LangChain documentation on all functionalities related to Microsoft and Microsoft Azure. Quest 4 - I want to connect my AI prototype to external data using RAG | Microsoft Community Hub a link to the previous quest instructions.Quest 4 - I want to connect my AI prototype to external data using RAG
In Quest 4 of the JS AI Build-a-thon, you’ll integrate Retrieval-Augmented Generation (RAG) to give your AI apps access to external data like PDFs. You’ll explore embeddings, vector stores, and how to use the pdf-parse library in JavaScript to build more context-aware apps — with challenges to push you even further.Quest 6 - I want to build an AI Agent
Quest 6 of the JS AI Build-a-thon marks a major milestone — building your first intelligent AI agent using the Azure AI Foundry VS Code extension. In this quest, you’ll design, test, and integrate an agent that can use tools like Bing Search, respond to goals, and adapt in real-time. With updated instructions, real-world workflows, and powerful tooling, this is where your AI app gets truly smart.Quest 7: Create an AI Agent with Tools from an MCP Server
In Quest 7 of the JS AI Build-a-thon, developers explore how to create AI agents that use real tools through the Model Context Protocol (MCP). With the MCP TypeScript SDK and AI Toolkit in VS Code, you’ll connect your agent to a custom MCP server and give it real capabilities, like accessing your system's OS info. This builds on agentic development and introduces tooling practices that reflect how modern AI apps are built.Quest 9: I want to use a ready-made template
Building robust, scalable AI apps is tough, especially when you want to move fast, follow best practices, and avoid being bogged down by endless setup and configuration. In this quest, you’ll discover how to accelerate your journey from prototype to production by leveraging ready-made templates and modern cloud tools. Say goodbye to decision fatigue and hello to streamlined, industry-approved workflows you can make your own. 👉 Want to catch up on the full program or grab more quests? https://aka.ms/JSAIBuildathon 💬 Got questions or want to hang with other builders? Join us on Discord — head to the #js-ai-build-a-thon channel. 🚀 What You’ll Build A fully functional AI application deployed on Azure, customized to solve a real problem that matters to you. A codebase powered by a production-grade template, complete with all the necessary infrastructure-as-code, deployment scripts, and best practices already baked in. Your own proof-of-concept or MVP, ready to scale or show off to the world. 🛠️ What You Need ✅ GitHub account ✅ Visual Studio Code ✅ Node.js ✅ Azure subscription (free trials and student credits available) ✅ Azure Developer CLI (azd) ✅ The curiosity to solve a meaningful problem! 🧩 Concepts You’ll Explore Azure Developer CLI (azd) Learn how azd, the developer-first command-line tool, simplifies authentication, setup, deployment, and teardown for Azure apps. With intuitive commands like azd up and azd deploy, you can go from zero to running in the cloud no deep cloud expertise required. Production-Ready Templates Explore a gallery of customizable templates designed to get your app up and running fast. These templates aren’t just “hello world” they feature scalable architectures, sample code, and reusable infrastructure assets to launch everything from chatbots to RAG apps to full-stack solutions. Infrastructure as Code (IaC) See how every template bundle configuration files and scripts to automatically provision the cloud resources you need. You’ll get a taste of how top teams ship secure, repeatable, and maintainable systems without manually clicking through Azure dashboards. Best Practices by Default Templates incorporate industry best practices for code structure, deployment, and scalability. You’ll spend less time researching how to “do it right” and more time customizing your application to fit your unique use case. Customization for Real-World Problems Pick a template and make it yours! Whether you’re building a copilot, a chat-enabled app, or a serverless API, you’ll learn how to tweak the frontend, swap out backend logic, connect your own data sources, and shape the solution to solve a real-world problem you care about. 🌟 Bonus Resources Here are some additional resources to help you learn more about the Azure Developer CLI (azd) and the templates available: Kickstart JS/TS projects with azd Templates Kickstart your JavaScript projects with azd on YouTube ⏭️ What next? With production-ready templates and the Azure Developer CLI at your side, you’re ready to move from “just an idea” to a deployable, scalable solution without reinventing the wheel. Start with the right foundation, customize with confidence, and ship your next AI app like a pro! Once you have your project done, ensure you submit to GitHub - Azure-Samples/JS-AI-Build-a-thonAI Repo of the Week: Generative AI for Beginners with JavaScript
Introduction Ready to explore the fascinating world of Generative AI using your JavaScript skills? This week’s featured repository, Generative AI for Beginners with JavaScript, is your launchpad into the future of application development. Whether you're just starting out or looking to expand your AI toolbox, this open-source GitHub resource offers a rich, hands-on journey. It includes interactive lessons, quizzes, and even time-travel storytelling featuring historical legends like Leonardo da Vinci and Ada Lovelace. Each chapter combines narrative-driven learning with practical exercises, helping you understand foundational AI concepts and apply them directly in code. It’s immersive, educational, and genuinely fun. What You'll Learn 1. 🧠 Foundations of Generative AI and LLMs Start with the basics: What is generative AI? How do large language models (LLMs) work? This chapter lays the groundwork for how these technologies are transforming JavaScript development. 2. 🚀 Build Your First AI-Powered App Walk through setting up your environment and creating your first AI app. Learn how to configure prompts and unlock the potential of AI in your own projects. 3. 🎯 Prompt Engineering Essentials Get hands-on with prompt engineering techniques that shape how AI models respond. Explore strategies for crafting prompts that are clear, targeted, and effective. 4. 📦 Structured Output with JSON Learn how to guide the model to return structured data formats like JSON—critical for integrating AI into real-world applications. 5. 🔍 Retrieval-Augmented Generation (RAG) Go beyond static prompts by combining LLMs with external data sources. Discover how RAG lets your app pull in live, contextual information for more intelligent results. 6. 🛠️ Function Calling and Tool Use Give your LLM new powers! Learn how to connect your own functions and tools to your app, enabling more dynamic and actionable AI interactions. 7. 📚 Model Context Protocol (MCP) Dive into MCP, a new standard for organizing prompts, tools, and resources. Learn how it simplifies AI app development and fosters consistency across projects. 8. ⚙️ Enhancing MCP Clients with LLMs Build on what you’ve learned by integrating LLMs directly into your MCP clients. See how to make them smarter, faster, and more helpful. ✨ More chapters coming soon—watch the repo for updates! Companion App: Interact with History Experience the power of generative AI in action through the companion web app—where you can chat with historical figures and witness how JavaScript brings AI to life in real time. Conclusion Generative AI for Beginners with JavaScript is more than a course—it’s an adventure into how storytelling, coding, and AI can come together to create something fun and educational. Whether you're here to upskill, experiment, or build the next big thing, this repository is your all-in-one resource to get started with confidence. 🔗 Jump into the future of development—check out the repo and start building with AI today!Introducing Azure AI Travel Agents: A Flagship MCP-Powered Sample for AI Travel Solutions
We are excited to introduce AI Travel Agents, a sample application with enterprise functionality that demonstrates how developers can coordinate multiple AI agents (written in multiple languages) to explore travel planning scenarios. It's built with LlamaIndex.TS for agent orchestration, Model Context Protocol (MCP) for structured tool interactions, and Azure Container Apps for scalable deployment. TL;DR: Experience the power of MCP and Azure Container Apps with The AI Travel Agents! Try out live demo locally on your computer for free to see real-time agent collaboration in action. Share your feedback on our community forum. We’re already planning enhancements, like new MCP-integrated agents, enabling secure communication between the AI agents and MCP servers and more. NOTE: This example uses mock data and is intended for demonstration purposes rather than production use. The Challenge: Scaling Personalized Travel Planning Travel agencies grapple with complex tasks: analyzing diverse customer needs, recommending destinations, and crafting itineraries, all while integrating real-time data like trending spots or logistics. Traditional systems falter with latency, scalability, and coordination, leading to delays and frustrated clients. The AI Travel Agents tackles these issues with a technical trifecta: LlamaIndex.TS orchestrates six AI agents for efficient task handling. MCP equips agents with travel-specific data and tools. Azure Container Apps ensures scalable, serverless deployment. This architecture delivers operational efficiency and personalized service at scale, transforming chaos into opportunity. LlamaIndex.TS: Orchestrating AI Agents The heart of The AI Travel Agents is LlamaIndex.TS, a powerful agentic framework that orchestrates multiple AI agents to handle travel planning tasks. Built on a Node.js backend, LlamaIndex.TS manages agent interactions in a seamless and intelligent manner: Task Delegation: The Triage Agent analyzes queries and routes them to specialized agents, like the Itinerary Planning Agent, ensuring efficient workflows. Agent Coordination: LlamaIndex.TS maintains context across interactions, enabling coherent responses for complex queries, such as multi-city trip plans. LLM Integration: Connects to Azure OpenAI, GitHub Models or any local LLM using Foundy Local for advanced AI capabilities. LlamaIndex.TS’s modular design supports extensibility, allowing new agents to be added with ease. LlamaIndex.TS is the conductor, ensuring agents work in sync to deliver accurate, timely results. Its lightweight orchestration minimizes latency, making it ideal for real-time applications. MCP: Fueling Agents with Data and Tools The Model Context Protocol (MCP) empowers AI agents by providing travel-specific data and tools, enhancing their functionality. MCP acts as a data and tool hub: Real-Time Data: Supplies up-to-date travel information, such as trending destinations or seasonal events, via the Web Search Agent using Bing Search. Tool Access: Connects agents to external tools, like the .NET-based customer queries analyzer for sentiment analysis, the Python-based itinerary planning for trip schedules or destination recommendation tools written in Java. For example, when the Destination Recommendation Agent needs current travel trends, MCP delivers via the Web Search Agent. This modularity allows new tools to be integrated seamlessly, future-proofing the platform. MCP’s role is to enrich agent capabilities, leaving orchestration to LlamaIndex.TS. Azure Container Apps: Scalability and Resilience Azure Container Apps powers The AI Travel Agents sample application with a serverless, scalable platform for deploying microservices. It ensures the application handles varying workloads with ease: Dynamic Scaling: Automatically adjusts container instances based on demand, managing booking surges without downtime. Polyglot Microservices: Supports .NET (Customer Query), Python (Itinerary Planning), Java (Destination Recommandation) and Node.js services in isolated containers. Observability: Integrates tracing, metrics, and logging enabling real-time monitoring. Serverless Efficiency: Abstracts infrastructure, reducing costs and accelerating deployment. Azure Container Apps' global infrastructure delivers low-latency performance, critical for travel agencies serving clients worldwide. The AI Agents: A Quick Look While MCP and Azure Container Apps are the stars, they support a team of multiple AI agents that drive the application’s functionality. Built and orchestrated with Llamaindex.TS via MCP, these agents collaborate to handle travel planning tasks: Triage Agent: Directs queries to the right agent, leveraging MCP for task delegation. Customer Query Agent: Analyzes customer needs (emotions, intents), using .NET tools. Destination Recommendation Agent: Suggests tailored destinations, using Java. Itinerary Planning Agent: Crafts efficient itineraries, powered by Python. Web Search Agent: Fetches real-time data via Bing Search. These agents rely on MCP’s real-time communication and Azure Container Apps’ scalability to deliver responsive, accurate results. It's worth noting though this sample application uses mock data for demonstration purpose. In real worl scenario, the application would communicate with an MCP server that is plugged in a real production travel API. Key Features and Benefits The AI Travel Agents offers features that showcase the power of MCP and Azure Container Apps: Real-Time Chat: A responsive Angular UI streams agent responses via MCP’s SSE, ensuring fluid interactions. Modular Tools: MCP enables tools like analyze_customer_query to integrate seamlessly, supporting future additions. Scalable Performance: Azure Container Apps ensures the UI, backend and the MCP servers handle high traffic effortlessly. Transparent Debugging: An accordion UI displays agent reasoning providing backend insights. Benefits: Efficiency: LlamaIndex.TS streamlines operations. Personalization: MCP’s data drives tailored recommendations. Scalability: Azure ensures reliability at scale. Thank You to Our Contributors! The AI Travel Agents wouldn’t exist without the incredible work of our contributors. Their expertise in MCP development, Azure deployment, and AI orchestration brought this project to life. A special shoutout to: Pamela Fox – Leading the developement of the Python MCP server. Aaron Powell and Justin Yoo – Leading the developement of the .NET MCP server. Rory Preddy – Leading the developement of the Java MCP server. Lee Stott and Kinfey Lo – Leading the developement of the Local AI Foundry Anthony Chu and Vyom Nagrani – Leading Azure Container Apps roadmap Matt Soucoup and Julien Dubois – Leading the ACA DevRel strategy Wassim Chegham – Architected MCP and backend orchestration. And many more! See the GitHub repository for all contributors. Thank you for your dedication to pushing the boundaries of AI and cloud technology! Try It Out Experience the power of MCP and Azure Container Apps with The AI Travel Agents! Try out live demo locally on your computer for free to see real-time agent collaboration in action. Conclusion Developers can explore today the open-source project on GitHub, with setup and deployment instructions. Share your feedback on our community forum. We’re already planning enhancements, like new MCP-integrated agents, enabling secure communication between the AI agents and MCP servers and more. This is still a work in progress and we also welcome all kind of contributions. Please fork and star the repo to stay tuned for updates! ◾️We would love your feedback and continue the discussion in the Azure AI Foundry Discord aka.ms/foundry/discord On behalf of Microsoft DevRel Team.
