AI Memory Leak Detector
Track down memory leaks before they crash apps
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Why does the process need restarting every Sunday night? Does memory climb steadily from Monday, or does it jump after a particular job runs?
Memory leaks in managed languages are rarely about forgetting to free anything. They are about holding on: a cache with no limit, a listener never removed, a closure keeping a whole request alive. The AI Memory Leak Detector reads code for exactly those patterns.
Short answer: The AI Memory Leak Detector is a free AIToolsay tool that finds code holding on to memory it should have released. Paste the code and describe the growth you are seeing, and it identifies the retained references, explains why they survive and suggests the fix.
What is AI Memory Leak Detector?
A memory leak is any reference that outlives its usefulness. In a language with garbage collection nothing is lost, it is simply still reachable, which means it can never be collected.
The prompt box asks you to paste the code and the error or behaviour you are seeing. For leaks the behaviour half is unusually important, because "grows by 40MB an hour under normal load" and "jumps 200MB when the nightly import runs" point at completely different code.
Setting Issue Type to Memory Leak focuses the review on retention: what holds a reference, how long it holds it, and whether anything ever removes it.
Why Use AI Memory Leak Detector?
Leaks are hard to find because the symptom appears far from the cause, sometimes hours later. A profiler tells you what is retained. It rarely tells you which line decided to retain it.
Reading code for retention patterns is a different skill from reading it for logic, and it is one most developers use once a year. That gap is where this helps: it goes looking for the six or seven shapes that account for most leaks rather than reading the file for correctness.
| Pattern | What holds the reference | Typical fix |
|---|---|---|
| Unbounded cache | A dictionary that only ever grows | A size limit and an eviction rule |
| Listener never removed | The event source keeps the handler | Unsubscribe on teardown |
| Closure capturing too much | The callback holds the whole outer scope | Capture only the values needed |
| Growing collection on a long lived object | A list that is appended to and never cleared | Bound it, or move it off the singleton |
| Timer or interval never cancelled | The scheduler holds the callback forever | Cancel on shutdown or on unmount |
Who Should Use It?
- Backend developers running long lived processes, workers and daemons
- Frontend developers building single page applications where components mount and unmount repeatedly
- Anyone with a scheduled restart that exists because nobody found the leak
- Developers writing caches, which are leaks with a good reputation
- People reviewing code before a long running deployment, where retention matters more than usual
Note Describe the growth shape. Steady climb, sawtooth, or a jump tied to an event. That single detail narrows the search more than any amount of extra code, because different patterns produce different curves.
How Does AI Memory Leak Detector Work?
- Prompt box. Open the AI Memory Leak Detector and paste the code plus a description of how memory grows.
- Model selector. Choose the engine, from MSB AI, OpenAI ChatGPT, Google Gemini, Anthropic Claude AI, xAI Grok AI, DeepSeek, Qwen, Meta AI, NVIDIA AI, OpenRouter AI and MiniMax.
- Advanced options accordion. There are ten controls in there: Language, Issue Type, Analysis Depth and Output as dropdowns, four toggles, a Thoroughness slider and a free text field.
- Generate button. Code, description, model and settings run through the prompt engineering layer written for debugging, which is the instruction set that makes it look for retention rather than correctness.
- Output card. The findings appear under the button with a live word count, plus copy, listen, reuse, download and open in full view.
- Export row. DOC, TXT and HTML. DOC when the findings go into a ticket, TXT for the corrected code.
- Activity history. Session generations stay listed, which helps when you check three suspect files one after another.
Key Features
Retention patterns
Looks for the specific shapes that cause leaks rather than reading the code for logic.
Lifetime reasoning
Considers how long each object lives and what still points at it when it should be gone.
Teardown checks
Finds the missing half of a pair: subscribe without unsubscribe, open without close, start without cancel.
Prevention advice
Suggests bounds, weak references and lifecycle hooks that stop the pattern returning.
Best Use Cases
- A worker process whose memory climbs across a week
- Single page applications that slow down after an hour of navigation
- Caches added quickly and never given a size limit
- Code with event listeners, timers or subscriptions in it
- Reviewing a change before it goes into a long running service
| Growth shape you see | What it usually means | Where to look first |
|---|---|---|
| Tracks traffic, flat when idle | Something accumulates per request | Long lived objects touched by the request path |
| Steady climb regardless of traffic | A timer or background loop | Scheduled work and anything never cancelled |
| Step change after one job | A batch holding its whole input | The import or export path |
| Sawtooth that never returns to base | Collection works but something survives each cycle | Caches and registries |
Advanced Options Guide
| Option | What it controls | When to change it | Suggested start |
|---|---|---|---|
| Language | Auto Detect, Python, JavaScript, TypeScript, Java, C#, C++, Go, PHP or Ruby | Set it. Retention rules differ completely between managed and unmanaged languages | Your language |
| Issue Type | Auto Detect, Syntax Error, Runtime Error, Logic Bug, Performance, Memory Leak, Exception or Crash | Memory Leak is what turns this into a retention review | Memory Leak |
| Analysis Depth | Quick, Standard or Deep | Deep, since leaks are usually a relationship between two distant pieces of code | Deep |
| Output | Fix + Explanation, Corrected Code, Root Cause or Step by Step Debug | Step by Step Debug when you want a plan for confirming it with a profiler | Root Cause |
| Explain Root Cause | Explains which reference survives and what holds it | Leave on. Knowing what holds the reference is the entire fix | On |
| Provide Fixed Code | Returns the corrected version with the retention removed | Turn on once you agree with the finding | Off first |
| Suggest Prevention | Suggests bounded caches, weak references and teardown patterns | Leave on. Leaks come back when the pattern is not addressed | On |
| Show Test to Reproduce | Produces a test or loop that makes the growth visible quickly | Turn on. A leak you can trigger in seconds is a leak you can fix | On |
| Thoroughness | Slider from 1 to 100 setting how exhaustively references are traced | Raise it when several long lived objects are involved | 75 |
| Custom Instructions | Free text up to 1000 characters over the settings | Use it for lifetime facts the code cannot show | "This service object is a singleton created once at startup" |
Important Code review narrows the search, it does not prove the leak. Confirm any finding with a profiler or a heap snapshot before and after. A plausible explanation that turns out to be the wrong one costs more than the search did.
Example Inputs
Behaviour: node process grows about 60MB per hour under
steady traffic. Restarting fixes it. No growth overnight
when traffic stops.
Code pasted: the request middleware, the metrics collector
it calls, and the module level cache the collector uses.
Extra context: the metrics collector is created once at
startup. Requests are around 40 per second.The three facts underneath the code are what make this findable. Growth proportional to traffic, no growth when idle, and one long lived object in the middle. That combination points at something accumulating per request on a singleton before any code has been read.
Example Outputs
FINDING
The metrics collector keys its internal map by request id.
Entries are written on every request and only removed on a
successful flush. Failed flushes leave their entries behind.
WHY IT MATCHES THE CURVE
Growth tracks request volume and stops when traffic stops,
which fits a per request entry rather than a per hour timer.
...The second half is the part worth reading carefully. A finding that explains the shape of the curve as well as the mechanism is far more likely to be the real one, and that is the check to apply to any leak hypothesis before you act on it.
Once the leak is fixed, the AI Monitoring Configuration Generator is the way to make sure the next one is noticed early, and the AI Code Optimizer helps if the fix has a performance cost you want to recover.
Where it helps
- Narrowing a large codebase down to a few suspect lines
- Spotting missing teardown for listeners, timers and subscriptions
- Explaining why growth follows the shape you observed
- Suggesting bounds so the same pattern cannot return
Where it stops
- It cannot measure anything, so a profiler still confirms the finding
- Leaks inside third party libraries need the calling pattern described
- Native memory in unmanaged languages is a different problem
- ✅ The growth shape described, not just the fact of growth
- ✅ Long lived objects identified in the prompt
- ✅ Both halves of every pair pasted, such as subscribe and unsubscribe
- ✅ Issue Type set to Memory Leak and depth Deep
- ✅ The finding confirmed with a heap snapshot before the fix ships
Pro tip Ask for a reproduction loop rather than a fix on the first pass. A script that makes the leak visible in thirty seconds turns a week long guessing game into a normal debugging session, and it also proves the fix worked afterwards.
AIToolsay is a free AI tools platform where every tool is a dedicated workspace with its own prompt engineering and its own options panel, rather than one general chat box under many labels. All tools are free to run and none need an account. The engine is yours to pick, from MSB AI, OpenAI ChatGPT, Google Gemini, Anthropic Claude AI, xAI Grok AI, DeepSeek, Qwen, Meta AI, NVIDIA AI, OpenRouter AI and MiniMax, and on a difficult leak two engines reading the same files often suggest different retention paths, which is exactly what you want at that stage. Around the tools sit an AI directory, an AI models directory, courses, prompts, guides and news, all reachable from the AIToolsay homepage.
Frequently Asked Questions
Is the AI Memory Leak Detector free?
Yes. It is free to use, nothing is installed, and no account is needed to run a review.
Does it profile my running application?
No. It reads code and reasons about retention. Use it to narrow the search, then confirm the finding with a profiler or heap snapshot.
What should I include besides the code?
The growth shape, whether it tracks traffic, whether restarting fixes it, and which objects live for the whole process. Those facts do more work than extra code does.
Does it work for frontend applications?
Yes, and it is a good fit. Detached DOM nodes, listeners surviving unmount and intervals that were never cleared are among the most common findings in single page applications.
Is a cache a memory leak?
An unbounded one is. A cache with a size limit and an eviction rule is a cache. If nothing ever removes entries, it is a leak with a friendly name.
What about languages that manage memory manually?
It reasons about allocation and ownership in C and C++ too, but the failure modes are different. Say which language you are using, because the patterns do not transfer.
How do I know the fix worked?
Take a heap snapshot before and after the same workload. Turn Show Test to Reproduce on first, so you have a repeatable workload to compare rather than waiting for production to tell you.
A weekly restart is a note to yourself that nobody had time to look. Describe the growth, paste the long lived objects and the code that feeds them, and let the AI Memory Leak Detector narrow a week of guessing down to a handful of lines you can actually check.
Thanks for reading, and may your process survive the weekend. If this becomes part of how you investigate, join the AIToolsay community, follow along on social media, turn on push notifications for new tools, and subscribe to the newsletter for the highlights.
Let AI Speak.
Sabir
Founder of AIToolsay and a passionate AI enthusiast dedicated to building practical, user-friendly AI tools that simplify everyday tasks.
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