getlark

Runtimeuse — AI skill for Claude Code

AI community

Run AI agents inside sandboxes over WebSockets - from the engineering team at https://getlark.ai.

How to install Runtimeuse

This entry records only its repository, not the path inside it, so there is no exact command to give. Open getlark/runtimeuse and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What Runtimeuse does

Run AI agents inside sandboxes over WebSockets - from the engineering team at https://getlark.ai.

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README

runtimeuse

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Run AI agents inside sandboxes and communicate with them over WebSocket.

Package Language Role Install
`runtimeuse` TypeScript Agent runtime (runs inside the sandbox) npm install runtimeuse
`runtimeuse-client` Python Client (connects from outside the sandbox) pip install runtimeuse-client

Quick Start

1. Start the runtime (inside a sandbox)

export OPENAI_API_KEY=your_openai_api_key
npx -y runtimeuse@latest

This starts a WebSocket server on port 8080 using the default OpenAI handler. For fuller Claude-based sandbox examples, see [`examples/`](./examples).

2. Connect from Python

import asyncio
from runtimeuse_client import (
    QueryOptions,
    RuntimeEnvironmentDownloadableInterface,
    RuntimeUseClient,
    TextResult,
)

WORKDIR = "/runtimeuse"

async def main():
    client = RuntimeUseClient(ws_url="ws://localhost:8080")

    result = await client.query(
        prompt="Summarize the contents of the codex repository.",
        options=QueryOptions(
            system_prompt="You are a helpful assistant.",
            model="gpt-5.4",
            pre_agent_downloadables=[
                RuntimeEnvironmentDownloadableInterface(
                    download_url="https://github.com/openai/codex/archive/refs/heads/main.zip",
                    working_dir=WORKDIR,
                )
            ],
        ),
    )

    assert isinstance(result.data, TextResult)
    print(result.data.text)

asyncio.run(main())

See the