Description#
Java memory analysis is often slow, manual, and difficult to scale. Whether you’re investigating an OutOfMemoryError or proactively analyzing memory utilization, engineers still rely on tools and workflows that have changed little in the past 15 years. But what if AI could help explore and reason about heap dump data?
In this talk, we’ll start with a quick tour of traditional open-source Java memory analysis tools and how they expose information from heap dumps. Then we’ll take things further by unlocking that data through the Model Context Protocol (MCP) and enabling an AI model to interact with it.
You’ll see how to build an MCP server that exposes key insights from a Java heap dump, allowing an LLM to query object graphs, identify memory hotspots, and surface potential optimization opportunities. Through examples, we’ll explore how AI can assist with diagnosing memory issues, navigating complex object relationships, and suggesting areas for improvement in your application.
By the end of this session, you’ll understand how to extend existing Java performance tooling with MCP and apply AI-assisted analysis to scale memory diagnostics across your organization. If you’re tired of manually digging through heap dumps through traditional UIs, this talk is for you.
