Getting started with the LocalStack Model Context Protocol (MCP) Server

What if your AI coding assistant could not only write infrastructure code, but also deploy it, test it, and fix issues automatically — all on your local machine? That's exactly what the LocalStack MCP Server makes possible.In this session, we'll introduce the LocalStack Model Context Protocol (MCP) Server, a new tool that lets AI agents manage your entire local cloud development lifecycle through a conversational interface. You'll learn:What MCP is and why it's a game-changer for AI-assisted developmentHow the LocalStack MCP Server turns manual cloud tasks into automated workflowsHow to set up and configure the server with your favorite AI editor (Cursor, VS Code, etc.)Real-world demos: deploying CDK apps, analyzing logs, running chaos tests, managing state with Cloud Pods, and more.Through hands-on examples, we'll walk through a complete workflow where an AI agent deploys a serverless application, verifies resources, troubleshoots issues, and tests resilience, all without leaving the conversation.If you've ever wished your AI assistant could do more than just generate code, this talk will show you what's possible when agents can actually manage your local cloud environment.

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How to save state in LocalStack?

LocalStack is ephemeral, so when you stop and restart it, all data is lost. You can use certain features to save the state & load it back when you restart LocalStack. This includes saving the local state for S3 buckets, DynamoDB tables, RDS databases and more. In this video, we explore three mechanisms that allows you to save state in LocalStack. They are:• Persistence• State Export & Import• Cloud Pods ## Documentation• State management: https://docs.localstack.cloud/user-guide/state-management/ • Cloud Pods: https://docs.localstack.cloud/user-guide/state-management/cloud-pods/ • Persistence: https://docs.localstack.cloud/user-guide/state-management/persistence/ • State Export & Import: https://docs.localstack.cloud/user-guide/state-management/export-import-state/

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LocalStack Neptune development with G.V() — Gremlin IDE

We’re partnering with gdotv to simplify development with our Amazon Neptune cloud emulator component. You can now easily query, visualise and model your graph data either interactively or using the Gremlin querying language with G.V() - Gremlin IDE.With G.V(), you can considerably enhance your graph database development experience whilst gaining access to a powerful reporting and visualisation toolset for your production data. With LocalStack’s core cloud emulator, parity is ensured between a local Neptune instance and AWS’s own, meaning Gremlin queries in your development environment will behave identically on Amazon Neptune. In this video we demonstrate how to use G.V() with LocalStack Neptune.Read the announcement blog here: https://blog.localstack.cloud/2024-06-05-localstack-neptune-development-with-gv-gremlin-ide/

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Towards a LocalStack Snowflake emulator—develop your data pipelines locally!

About a year ago we have released the first version of LocalStack Extensions: Extensions are a powerful mechanism to plug additional functionality into LocalStack - ranging from additional service emulators, to value-add features like Chaos Engineering, request logging, cloud resource replication/proxying, and more.Over the last couple of months we have been experimenting with a LocalStack Snowflake emulator extension, which allows to develop and test your Snowflake data pipelines entirely on your local machine!In this talk, Waldemar discusses and demonstrates how you can develop your Snowflake data pipelines locally with LocalStack.

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