Anyrun
Description
anyrun is a Docker sandbox execution engine for AI Agents. It provides secure, isolated code execution with full observability — automatically collecting execution traces, discovering error patterns and success paths via statistical rules, extracting reusable experience as…
About
anyrun is a Docker sandbox execution engine for AI Agents. It provides secure, isolated code execution with full observability — automatically collecting execution traces, discovering error patterns and success paths via statistical rules, extracting reusable experience as SKILL.md through LLM, and enabling autonomous…
Details
- Author
- appcom2016
- Downloads
- 326
- Categories
- Other, AI
Jump to
- Zero-config Docker sandbox: one line of Python
- Full observability: traces stored to SQLite + JSON
- Pattern discovery: error clustering and anomaly detection
- LLM auto-generates SKILL.md from execution patterns
- Self-evolution: skills auto-upgrade, decay, and repair
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
AnyrunCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Run any code securely with a single line of Python. All tools (sandbox_run, file operations, toolbox management) are exposed via the MCP protocol.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"anyrun": {
"anyrun": {
"command": "python3",
"args": [
"-m",
"anyrun.mcp_server"
]
}
}
}
}McpServers
{
"anyrun": {
"command": "python3",
"args": [
"-m",
"anyrun.mcp_server"
]
}
}anyrun is a Docker sandbox execution engine for AI Agents. It provides
secure, isolated code execution with full observability —
automatically collecting execution traces, discovering error patterns
and success paths via statistical rules, extracting reusable
experience as SKILL.md through LLM, and enabling autonomous skill
evolution (beta → prod → decayed → retired).
Key features:
- Zero-config Docker sandbox: one line of Python to run any code
securely
- Full observability: every execution is traced to SQLite + JSON
- Pattern discovery: error clustering, success path detection, anomaly
detection
- Experience extraction: LLM auto-generates SKILL.md from execution
patterns
- Self-evolution: skills auto-upgrade, decay, and get repaired
- MCP native: exposes all tools (sandbox_run, file ops, toolbox
management) via MCP protocol
Use cases: AI Agent tool execution, secure code sandboxing, agent
self-improvement pipelines, CI/CD code execution.
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