SequentialThinking MCP: Memory Hogging & How To Fix It

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SequentialThinking MCP: Memory Hogging & How to Fix It

Hey guys! Ever run into a situation where your SequentialThinking MCP server just gobbles up all your RAM like it's a buffet? Yeah, me too. This article is all about that memory-hogging issue, digging into why it happens and, more importantly, what you can do about it. We will try to understand the problem, looking at potential causes and, of course, some solutions to get your SequentialThinking MCP back in tip-top shape. This is especially relevant if you're using it in a Docker container, like I was. Let's dive in!

The Memory Monster: Understanding the Problem

So, the problem is pretty straightforward: after running the SequentialThinking MCP (Model Context Protocol) server for a while, the RAM usage just skyrockets. We're talking 10GB, 12GB, or even more. I've personally seen it climb that high because I had the RAM available, but the point is, it shouldn't be doing this. It's not a sustainable way to run the server, and it can bring your entire system to a crawl. The main culprit appears to be the MCP server itself. The planning tasks are the main source of the problem, with the memory usage increasing over time as planning tasks are executed. There's a strong suspicion of a memory leak, meaning that the server isn't properly releasing the memory it's using after tasks are completed. Let's get into the details of the problem. This is a common issue that developers face when working with memory-intensive applications. It's like the server is forgetting to close the door behind it, and memory just keeps piling up. This behavior can quickly lead to performance issues and even crashes, especially in environments with limited resources. I really love the project, and want to support it.

Diving into the details

Now, let's talk about what's likely happening under the hood. The SequentialThinking MCP server, as the name suggests, deals with sequential thinking and planning. This involves handling a lot of data, calculations, and context. Each planning task, especially the longer ones, likely consumes a chunk of memory. When these tasks complete, the memory should be freed up. But, if there's a memory leak, that doesn't happen. The memory gets allocated, used, and then... it just sits there. The server doesn't release it back to the system. The server may be storing the results of the planning tasks. Over time, these planning results can accumulate and occupy a significant amount of memory. As the number of planning tasks increases, the memory footprint of the server also increases. The memory consumption of the server grows gradually over time, eventually leading to high RAM usage. This can lead to issues with the server’s overall performance, including slower response times and increased latency. In addition, the memory leak can eventually lead to the server crashing or becoming unresponsive, as the available memory is exhausted. It’s essential to diagnose and fix memory leaks to ensure the stability and reliability of the server. Let's delve into these aspects to get a better grasp of the situation.

Reproducing the Issue: How It Happens

Reproducing the issue is actually pretty simple. All you need to do is use the SequentialThinking MCP server for a few hours, running frequent and relatively long planning tasks. The more planning, the faster the memory will accumulate. I'll break it down for you step by step to get the same results:

  1. Deploy the MCP: Set up your SequentialThinking MCP server. In my case, I used a standalone Docker container, which is a pretty common setup. The good thing about it is that is isolated, which means if the memory leak goes wild, it does not harm your entire system. That's a plus, but still, we want to solve it. Get your Docker container up and running. Remember, you can deploy it as a distributed server to scale it up. It is important to remember what kind of deployment you want to have when you deploy this MCP, because of the memory leak, it might scale up a lot.
  2. Run Planning Tasks: Start feeding the server with planning requests. The more complex and lengthy the planning tasks, the quicker you'll see the memory usage increase. In the beginning, the memory will be small, but it will grow and grow. Make sure your planning tasks involve different contexts and conditions. The variety of tasks might help you identify some edge cases.
  3. Monitor RAM Usage: Keep an eye on the RAM usage. You can use tools like docker stats (if you're using Docker) or system monitoring tools on your operating system (like top, htop, or the Resource Monitor on Windows). Watch the memory consumption steadily climb over time. You'll see the server's RAM usage creep up, and if you leave it running long enough, you'll hit that 10GB+ mark, or more.

By following these steps, you'll be able to reproduce the memory issue and see the problem firsthand. If you're a developer or just a curious user, try to reproduce this issue. You will get a good insight into the problem, and you might come up with a better solution.

The Expected Behavior: What Should Happen

Ideally, here’s what we'd want to see from the SequentialThinking MCP server. It boils down to efficient memory management. Here are the core expectations:

  1. Memory Release After Tasks: After each planning task completes, the memory used by that task should be released back to the system. This means that the server frees up the memory that was allocated during the planning process. This is the most crucial part. The goal is to prevent memory accumulation, so after the task is done, it releases everything related to that task.
  2. Offloading to Disk: For long-term planning, or if you want to save the task, the server should be able to offload the planning data to disk space. This could be done after a certain period or when the memory usage hits a certain threshold. In this way, the server is able to manage the memory usage and to allow you to do long-term planning. The offloading mechanism might be able to help with persistent planning.
  3. Memory Limits: The server might need to have a system to limit the memory usage, to prevent it to grow infinitely. When the memory reaches the limit, the server could either pause the planning tasks, or shut down some of them. In this way, we can be sure that the server does not consume too much of your precious memory.
  4. No Leaks: There should be no memory leaks. The server should be designed in a way that it can automatically release the memory. This could be achieved by proper code management, code reviews, and memory usage profiling.

If the server behaves this way, the memory issue will not even happen. The server should be able to efficiently manage the memory usage, without hogging the resources.

Additional Context: My Setup

I was running the SequentialThinking MCP as a distributed, standalone Docker container. This is a great way to deploy the MCP, as it's isolated and easily scalable. I'm using the Docker image mekayelanik/sequential-thinking-mcp. This setup is pretty standard, but the memory issue still popped up. This highlights that the problem is not necessarily in my specific environment but is likely a core issue within the MCP itself. If you also use a Docker container, it's pretty easy to check this behavior using the docker stats command. You can keep an eye on the memory and CPU usage.

Potential Solutions and Workarounds

Alright, so what can we do to fix this memory-hogging problem? Here are a few potential solutions and workarounds:

1. Identify and Fix the Memory Leak

This is the most direct solution. Developers need to dive into the code and find the root cause of the memory leak. This involves:

  • Code Review: Thoroughly review the code, especially the parts dealing with memory allocation and deallocation. Look for any instances where memory might be allocated but not properly released. Check any potential memory leaks, such as unused variables and allocated resources.
  • Profiling: Use memory profiling tools (like Valgrind for C/C++ or similar tools for other languages) to identify where the memory is being allocated and if it's being freed. These tools can help pinpoint the exact lines of code causing the issue.
  • Testing: Write tests to specifically check for memory leaks. Run the server with these tests to ensure that the memory is released after tasks are completed. Try a variety of test cases.

2. Implement Memory Management Techniques

Even before fixing the leak, there are techniques that can help mitigate the problem:

  • Garbage Collection: Ensure the server uses efficient garbage collection, if the programming language supports it. Configure the garbage collector to run frequently and aggressively.
  • Resource Pooling: Implement resource pooling, where frequently used resources (like memory blocks) are pre-allocated and reused. This can reduce the overhead of constant allocation and deallocation. This may help to improve performance.
  • Memory Caching: Use a memory caching mechanism to store frequently used data. The use of caching may reduce the need to allocate memory for the same data repeatedly.

3. Implement Memory Limits and Monitoring

Set hard memory limits for the server. This prevents the server from consuming all available RAM. Implement monitoring to track memory usage and alert you if it exceeds a certain threshold. Docker has built-in features to do this, and you can also use external monitoring tools.

4. Optimize Planning Tasks

If the planning tasks themselves are memory-intensive, there might be ways to optimize them. This includes:

  • Data Structures: Choose memory-efficient data structures for storing planning data. This can help to reduce the amount of memory consumed by each task.
  • Algorithms: Optimize the algorithms used in the planning tasks. Faster algorithms can consume less memory.
  • Data Compression: Consider compressing the data used in planning tasks to reduce their memory footprint.

5. Disk Offloading and Persistent Planning

As suggested in the