"A sandbox environment for running REPL code" matching MCP servers:

An MCP server to create secure code sandbox environment for executing code within Docker containers.

MCP server for E2B code sandbox, enabling creation, management, and execution of code in secure sandboxes using Python and shell commands.

Creates short-lived, offline JavaScript sandbox containers for safe code execution via MCP, with no network access and strict resource limits.

Provides agents with real Linux VMs to run commands, manage files, expose ports, and track costs, all through an ephemeral sandbox service.

Enables AI agents to run untrusted, model-generated Python code in a locked-down throwaway Docker container with enforced isolation and resource limits. Supports starting, checking, and stopping asynchronous runs while returning structured status reports without exposing the host, network, or Docker controls.

Execute Python, JavaScript, or SQL code in a sandboxed environment and retrieve stdout, execution time, and errors with pay-per-call via x402 (USDC on Base L2).

Enables coding agents and users to share live tmux-based REPL sessions for many languages, allowing agents to execute code in the same interactive workspace via MCP.

Enables creation and management of persistent REPL sessions for various languages and shells, with web-based monitoring and session recovery capabilities.

This server gives AI clients a persistent, isolated Linux container in which the model can run any bash command as root with internet access, install packages and start long-running processes without command filtering. Files produced inside it can be handed back to the user as signed, expiring public download links suitable for PDFs, images, audio and video.

An extensible Message Communication Protocol server that provides secure code execution capabilities in isolated sandbox environments, compatible with Claude for Desktop and other MCP clients.

A persistent Python REPL MCP server for AI agents with stateful execution, real timeouts, crash isolation, and an MCP bridge to call other tools in the project.

A production-grade MCP server providing a persistent Python REPL with multi-session support, sandboxing, and timeout protection, enabling LLM agents to execute Python code across multiple turns with variables that persist between calls.

A secure Node.js execution environment that allows coding agents and LLMs to run JavaScript dynamically, install NPM packages, and retrieve results while adhering to the Model Control Protocol.

Async Python REPL + Shell execution for MCP. Provides persistent state, background jobs, interactive input() bridging, and crash isolation in a single server.

Enables AI agents to run persistent Node.js REPL sessions that preserve state across evaluations, supporting top-level await, module loading, and per-session npm package installation.

Provides a local, isolated Linux VM sandbox for AI agents using Apple's Virtualization.framework, enabling fast command execution (~60ms) and package management without cloud costs.

Enables running arbitrary JavaScript code in isolated Docker containers with on-demand npm dependency installation, allowing for ephemeral script execution and long-running services with controlled resource limits.

This MCP server enables MCP clients such as Claude Desktop to run LLM-generated code in a secure, isolated sandbox across multiple container backends and programming languages.

Provides a persistent JavaScript kernel that treats any configured MCP server as a capability catalog, exposing all its tools to the model through just two tools (js and js_reset) while keeping the model-visible schema small and injecting host parameters like projectPath.

Production-ready MCP server for secure Python code execution with artifact capture, virtual environment support, and LM Studio integration.