Runtimeuse — AI skill for Claude Code
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.
Alternatives in AI
README
runtimeuse
[](https://twitter.com/getlark)
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())
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