Memory Leak Detector

Memory leaks are insidious — they do not crash your application immediately, they degrade it slowly until response times collapse or the process is killed by the OS. Finding them requires understanding how the language runtime allocates and releases memory, how to read heap snapshots and profiler output, and how to recognize the code patterns that prevent garbage collection from doing its job. The Memory Leak Detector is an AI assistant that brings all of that expertise to your debugging session.

This assistant works across the major managed and unmanaged language runtimes: JavaScript and Node.js (V8 heap), Python (CPython reference counting and gc module), Java and Kotlin (JVM heap and GC logs), C# and .NET (CLR managed heap), and C/C++ (manual memory management and RAII). For each runtime, it understands the specific mechanisms by which leaks occur — closure-captured references in JavaScript, circular references defeating Python's reference counter, classloader leaks in Java, event handler accumulation in .NET, and buffer overruns in C.

You can share heap snapshots, memory profiler reports, allocation timelines, GC logs, or simply a code sample that you suspect is leaking. The assistant interprets the data, identifies the retaining path that is preventing objects from being collected, explains the root cause in terms of the language's memory model, and provides a corrected implementation.

It also recognizes common leak patterns proactively during code review: uncleaned event listeners, growing caches without eviction policies, static collections accumulating references, unclosed streams and database connections, and detached DOM nodes in browser applications.

Ideal users include backend engineers debugging long-running Node.js or Java services, mobile developers profiling Android or .NET MAUI apps, and any developer whose application's memory footprint grows without bound over time.