Hack Together: RAG Hack - Building RAG Applications with LangChain.js
In the rapidly evolving landscape of Artificial Intelligence and Natural Language Processing, the use of Retrieval Augmented Generation (RAG) has emerged as a powerful solution to enhance the accuracy and relevance of responses generated by language models. In this article, we will explore the talk given during the Hack Together: RAG Hack event, where Glaucia Lemos, a Cloud Advocate at Microsoft, and Yohan Lasorsa, a Senior Cloud Advocate at Microsoft, demonstrated how LangChain.js is revolutionizing the development of RAG applications, making it easier to create intelligent applications that combine large language models (LLMs) with your own data sources.Microsoft AI Agents Hack April 8-30th 2025
Build, Innovate, and #Hacktogether Learn from 20+ expert-led sessions streamed live on YouTube, covering top frameworks like Semantic Kernel, Autogen, the new Azure AI Agents SDK and the Microsoft 365 Agents SDK. Get hands-on experience, unleash your creativity, and build powerful AI agentsâthen submit your hack for a chance to win amazing prizes! Key Dates Expert sessions: April 8th 2025 â April 30th 2025 Hack submission deadline: April 30th 2025, 11:59 PM PST Don't miss out â join us and start building the future of AI! Registration Register now! That form will register you for the hackathon. Afterwards, browse through the live stream schedule below and register for the sessions you're interested in. Once you're registered, introduce yourself and look for teammates! Project Submission Once your hack is ready, follow the submission process. Prizes and Categories Projects will be evaluated by a panel of judges, including Microsoft engineers, product managers, and developer advocates. Judging criteria will include innovation, impact, technical usability, and alignment with corresponding hackathon category. Each winning team in the categories below will receive a prize. Best Overall Agent - $20,000 Best Agent in Python - $5,000 Best Agent in C# - $5,000 Best Agent in Java - $5,000 Best Agent in JavaScript/TypeScript - $5,000 Best Copilot Agent (using Microsoft Copilot Studio or Microsoft 365 Agents SDK) - $5,000 Best Azure AI Agent Service Usage - $5,000 Each team can only win in one category. All participants who submit a project will receive a digital badge. Stream Schedule The series starts with a kick-off for all developers, and then dives into specific tracks for Python, Java, C#, and JavaScript developers. The Copilots track will focus on building intelligent copilots with Microsoft 365 and Copilot Studio. English Week 1: April 8th-11th Day/Time Topic Track 4/8 09:00 AM PT AI Agents Hackathon Kickoff All 4/9 09:00 AM PT Build your code-first app with Azure AI Agent Service Python 4/9 12:00 PM PT AI Agents for Java using Azure AI Foundry Java 4/9 03:00 PM PT Build your code-first app with Azure AI Agent Service Python 4/10 04:00 AM PT Building Secure and Intelligent Copilots with Microsoft 365 Copilots 4/10 09:00 AM PT Overview of Microsoft 365 Copilot Extensibility Copilots 4/10 12:00 PM PT Transforming business processes with multi-agent AI using Semantic Kernel Python 4/10 03:00 PM PT Build your code-first app with Azure AI Agent Service (.NET) C# Week 2: April 14th-18th Day/Time Topic Track 4/15 07:00 AM PT Your first AI Agent in JS with Azure AI Agent Service JS 4/15 09:00 AM PT Building Agentic Applications with AutoGen v0.4 Python 4/15 12:00 PM PT AI Agents + .NET Aspire C# 4/15 03:00 PM PT Prototyping AI Agents with GitHub Models Python 4/16 04:00 AM PT Multi-agent AI apps with Semantic Kernel and Azure Cosmos DB C# 4/16 06:00 AM PT Building declarative agents with Microsoft Copilot Studio & Teams Toolkit Copilots 4/16 09:00 AM PT Building agents with an army of models from the Azure AI model catalog Python 4/16 12:00 PM PT Multi-Agent API with LangGraph and Azure Cosmos DB Python 4/16 03:00 PM PT Mastering Agentic RAG Python 4/17 06:00 AM PT Build your own agent with OpenAI, .NET, and Copilot Studio C# 4/17 09:00 AM PT Building smarter Python AI agents with code interpreters Python 4/17 12:00 PM PT Building Java AI Agents using LangChain4j and Dynamic Sessions Java 4/17 03:00 PM PT Agentic Voice Mode Unplugged Python Week 3: April 21st-25th Day/Time Topic Track 4/21 12:00 PM PT Knowledge-augmented agents with LlamaIndex.TS JS 4/22 06:00 AM PT Building a AI Agent with Prompty and Azure AI Foundry Python 4/22 09:00 AM PT Real-time Multi-Agent LLM solutions with SignalR, gRPC, and HTTP based on Semantic Kernel Python 4/22 10:30 AM PT Learn Live: Fundamentals of AI agents on Azure - 4/22 12:00 PM PT Demystifying Agents: Building an AI Agent from Scratch on Your Own Data using Azure SQL C# 4/22 03:00 PM PT VoiceRAG: talk to your data Python 4/14 06:00 AM PT Prompting is the New Scripting: Meet GenAIScript JS 4/23 09:00 AM PT Building Multi-Agent Apps on top of Azure PostgreSQL Python 4/23 12:00 PM PT Agentic RAG with reflection Python 4/23 03:00 PM PT Multi-source data patterns for modern RAG apps C# 4/24 09:00 AM PT Extending AI Agents with Azure Functions Python, C# 4/24 12:00 PM PT Build real time voice agents with Azure Communication Services Python 4/24 03:00 PM PT Bringing robots to life: Real-time interactive experiences with Azure OpenAI GPT-4o Python Week 4: April 28th-30th Day/Time Topic Track 4/29, 01:00 PM UTC / 06:00 AM PT Irresponsible AI Agents Java 4/29, 04:00 PM UTC / 09:00 AM PT Securing AI agents on Azure Python Spanish / EspaĂąol See all our Spanish sessions on the Spanish landing page. Consulta todas nuestras sesiones en espaĂąol en la pĂĄgina de inicio en espaĂąol. Portuguese / PortuguĂŞs See our Portuguese sessions on the Portuguese landing page. Veja nossas sessĂľes em portuguĂŞs na pĂĄgina de entrada em portuguĂŞs. Chinese / çŽä˝ĺ See our Chinese sessions on the Chinese landing page. 诡ćĽçć䝏çä¸ć诞ç¨ĺ¨ä¸ćçťĺ˝éĄľé˘. Office Hours For additional help with your hacks, you can drop by Office Hours in our AI Discord channel. Here are the Office Hours scheduled so far: Day/Time Topic/Hosts Every Thursday, 12:30 PM PT Python + AI (English) Every Monday, 03:00 PM PT Python + AI (Spanish) Learning Resources Access resources here! Join TheSource EHub to explore top picks including trainings, livestreams, repositories, technical guides, blogs, downloads, certifications, and more, all updated monthly. The AI Agent section offers essential resources for creating AI Agents, while other sections provide insights into AI, development tools, and programming languages. You can also post questions in our discussions forum, or chat with attendees in the Discord channel.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. Weâre 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.TSâs 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. MCPâs 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 applicationâs 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 MCPâs real-time communication and Azure Container Appsâ 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 MCPâs 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: MCPâs data drives tailored recommendations. Scalability: Azure ensures reliability at scale. Thank You to Our Contributors! The AI Travel Agents wouldnât 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 â Leading the developement of the Python MCP server. Aaron Powell and Justin Yoo â Leading the developement of the .NET MCP server. Rory Preddy â Leading the developement of the Java MCP server. Lee Stott and Kinfey Lo â Leading the developement of the Local AI Foundry Anthony Chu and Vyom Nagrani â Leading Azure Container Apps roadmap Matt Soucoup and Julien Dubois â Leading the ACA DevRel strategy Wassim Chegham â 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. Weâre 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! âžď¸We would love your feedback and continue the discussion in the Azure AI Foundry Discord aka.ms/foundry/discordâ On behalf of Microsoft DevRel Team.Playwright in Action: From Setup to Best Practices | Azure Developers JavaScript Day 2024
Another excellent session was held during the Azure Developers JavaScript Day 2024 event! This time, the focus was on Playwright. But what is Playwright? And what are the best practices for using it? Let's find out with the talk "Playwright in Action: From Setup to Best Practices"! Presented by Max Schmitt, Software Engineer on the Playwright team at Microsoft, and Stefan Judis, Playwright Ambassador! During this session, Max and Stefan provided a comprehensive overview of Playwright, a browser automation library, and demonstrated how to leverage it for creating robust, end-to-end tests for modern web applications.Serverless MCP Agent with LangChain.js v1 â Burgers, Tools, and Traces đ
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 endâtoâend 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, itâs a burger ordering system. Who doesn't like burgers? Grab your favorite beverage â, and letâs 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 â agent API â MCP server â 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 âOrder two spicy burgers and show me my pending ordersâ 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 âburgersâ for âinventoryâ, âbookingsâ, âsupport ticketsâ, or âIoT devicesâ and youâve 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 whatâs 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: đ All services ready đ 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 Azureâs serverless platform. Now it's your turn to dive in the code and extend it for your use cases! đ 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 âď¸! You can also join us in the Azure AI community Discord to chat and ask any questions. Happy coding and burger ordering! đUse AI for Free with GitHub Models and TypeScript! đ¸đ¸đ¸
Learn how to use AI for free with GitHub Models! Test models like GPT-4o without paying for APIs or setting up infrastructure. This step-by-step guide shows how to integrate GitHub Models with TypeScript in the Microblog AI Remix project. Start exploring AI for free today!Quest 1 â I Want to Build a Local Gen AI Prototype
In this quest, youâll build a local Gen AI app prototype using JavaScript or TypeScript. Youâll explore open-source models via GitHub, test them in a visual playground, and use them in real code â all from the comfort of VS Code with the AI Toolkit. Itâs fast, hands-on, and sets you up to build real AI apps, starting with a sketch.
