runcell 0.2.1

AI Agent for Jupyter - Turn Jupyter into an AI IDE for data science

runcell

RunCell is an AI-powered JupyterLab extension for working with notebooks, code, and data more efficiently inside JupyterLab.

It allow you to use LLM like Claude/GPT/Gemini, etc to analysis your notebooks and work in your jupyter lab env.

Requirements

• JupyterLab 4.4 or newer

Installation

Install RunCell from PyPI:

pip install runcell

Then start JupyterLab as usual:

jupyter lab

Local Agent Development

From packages/d5m-ai, use the dev agent process manager to run the local development stack:

yarn dev:agent:start

This starts four long-running processes:

• server: venv/bin/python run_d5m_server.py from the repo root
• jupyter: venv/bin/jupyter lab --LabApp.extension_manager=readonly
• watch: yarn watch
• tailwind: npx @tailwindcss/cli -i ./style/tailwind.css -o ./style/tailwindDist.css --watch=always

The manager writes logs and PID files under .dev-agent/, which is ignored by git:

.dev-agent/logs/server.log
.dev-agent/logs/jupyter.log
.dev-agent/logs/watch.log
.dev-agent/logs/tailwind.log
.dev-agent/pids/*.pid

Useful commands:

yarn dev:agent:status
yarn dev:agent:logs
yarn dev:agent:logs:follow
yarn dev:agent:restart:server
yarn dev:agent:restart:jupyter
yarn dev:agent:stop

To send a single Agent V2 task through the Jupyter proxy without driving the UI:

yarn dev:agent:send --message "Create a new notebook with a simple scatter plot" --allow-tools

This calls the local-only POST /api/d5m_ai/v2/agent/dev/send endpoint. The response is JSON with request_id, session/job metadata, streamed events, assistant text, tool calls, tool outputs, and any notebooks created via create_notebook. Pass --notebook path/to/file.ipynb to make an existing notebook the active target. The same request id is forwarded to the remote Agent V2 server so Jupyter and server logs can be correlated.

Learn More

For documentation, product updates, and the latest setup information, visit www.runcell.dev.