Memalot

Memalot is a tool that analyzes memory leaks in Python programs and exposes its findings through an MCP server for convenient integration with AI tools. This MCP server lets you run Memalot's leak-analysis workflow from your AI environment, enabling you to scan leak reports, filter results, and generate insights without leaving your tooling ecosystem.

How to use

You connect your MCP-enabled AI tool to Memalot’s local MCP server using the stdio transport. Start the MCP server with the terminal command and then configure your AI tool to load the server as a local microservice. Once connected, you can instruct the tool to list available leak reports, inspect a specific report, filter results by object type, and request targeted analyses or diagrams of leaking object graphs.

How to install

Prerequisites: you must have UV installed on your machine to run the MCP server.

Install the Memalot MCP server runtimes and prepare your environment with the following steps.

1. Install Memalot into your Python environment.

2. Ensure UVX is available on your system for the MCP transport.

3. Run the MCP server using the command shown in the configuration example below.

MCP Server configuration

"Memalot": {
  "command": "uvx",
  "args": [
    "--python", ">=3.10", "--from", "memalot[mcp]", "memalot-mcp"
  ]
}

Notes and usage tips

• The MCP server uses stdio transport, so the AI tool and the Memalot process must run on the same machine. - You can customize the MCP server invocation with environment variables if needed. - When starting out, generate a few reports to familiarize yourself with the responses and available iterations.

Security and troubleshooting

• If the MCP server does not start, verify that UVX is installed and accessible in your PATH. - If the AI tool cannot spawn the MCP server, check that you used the exact command and arguments shown in the configuration example.

Examples of useful prompts for the MCP server

• List memalot leak reports
• Analyze the most recent memalot report <report-id>
• Filter to include MyObject objects only
• Create a diagram of the references to leaking objects in memalot report <report-id>

End-to-end usage scenario

First generate some leak reports with Memalot in your Python environment. Then start the Memalot MCP server locally. From your AI tool, load Memalot as an MCP server, and begin querying reports, filtering by type, and requesting detailed analyses until you identify and understand the leak paths.

Developer tooling and capabilities

Memalot provides several tools and options you can leverage through the MCP server: starting and configuring leak discovery, wrapping code blocks with a leak-monitoring context, filtering by object types, and exporting or inspecting reports. Use the available functions to tailor leak analysis to your workflow and performance considerations.

Additional resources

You can access Memalot’s capabilities through its CLI for reports, its time-based and function-based leak discovery modes, and the MCP server for AI-assisted analysis and report navigation.

Available tools

start_leak_monitoring

Starts memory leak monitoring in a Python program, enabling time-based leak discovery and periodic reporting of suspected leaks.

leak_monitor decorator

Wraps a function to monitor memory allocations that occur during its execution, identifying leaks created within that function.

create_leak_monitor

Creates a context manager to monitor a block of code for memory leaks, usable with the with statement.

check_referrers

Option to enable or disable searching for referrers of leaked objects to understand why they are kept in memory.

included_type_names

Filter to include only objects whose fully-qualified type names match specified substrings.

excluded_type_names

Filter to exclude objects by their fully-qualified type names.

list

CLI subcommand to list stored leak reports.

print

CLI subcommand to print details of a specific leak report by ID.