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irskep/persistproc

by Various

MCP server to allow LLMs to manage and inspect long-running processes

I

MCP

irskep/persistproc

Added 1 June 2026

Overview

persistproc is an MCP server that lets LLMs manage and inspect long-running processes. It provides a persistent process management layer so models can start, monitor, and interact with background tasks across sessions.

Best for

Best for
Developers building LLM agents that need to run and supervise background shell processes

Use cases

  • Launch and monitor long-running data processing jobs from an LLM
  • Inspect stdout/stderr of background processes during multi-turn conversations
  • Manage process lifecycles (start, stop, status) programmatically via MCP

How to use

Install

pip install persistproc

Tools exposed

  • ctrl
  • list
  • output

Tested with

Cursor, Claude Code, Continue, VS Code

Example client config

http://127.0.0.1:8947

Notes

persistproc is an MCP server that lets LLMs manage and inspect long-running processes. It provides a persistent process management layer so models can start, monitor, and interact with background tasks across sessions.

8 stars on GitHub. Last updated 2025-07-18. Licensed MIT.

Use cases

  • Launch and monitor long-running data processing jobs from an LLM
  • Inspect stdout/stderr of background processes during multi-turn conversations
  • Manage process lifecycles (start, stop, status) programmatically via MCP

Pros

  • Enables LLMs to handle tasks that outlive a single response
  • Lightweight Python implementation with minimal dependencies
  • Open source with a clear MCP interface for integration

Cons

  • Limited to Python environments and MCP-compatible clients
  • No built-in resource or security isolation for spawned processes
  • Small community and low star count may mean slower updates

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Enables LLMs to handle tasks that outlive a single response
  • Lightweight Python implementation with minimal dependencies
  • Open source with a clear MCP interface for integration

Cons

  • Limited to Python environments and MCP-compatible clients
  • No built-in resource or security isolation for spawned processes
  • Small community and low star count may mean slower updates
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