Turn your Jupyter Lab into an AI IDE
You don't need to change to new tools, just with runcell, your jupyter can be transformed into a morden AI IDE with code agent, data agent, code completions, etc features.
🎓 Interactive Learning Mode
AI teacher that explains concepts with live examples - perfect for learning differences between algorithms like K-means vs DBSCAN.
Ready to run your own notebook?
Put Data Science AI to Work in Your Notebook
Modern research teams crave tools that keep pace with the scale of their ideas. runcell introduces a data science AI companion that lives directly in JupyterLab, watching every cell and waiting to assist. When you highlight a question or describe a transformation in plain language, the agent proposes code that matches your style and references the variables already in memory. This tight feedback loop helps scientists stay in the flow and keeps experiments moving instead of stalling on boilerplate.
The data science AI model inside runcell constantly scans the surrounding notebook context. It reads markdown notes, interprets charts, and follows the evolution of your datasets. Rather than offering generic snippets, the agent tailors suggestions to the structure of your project and the libraries you rely on most. You can ask the model to plot a distribution, clean a column, or engineer features for a machine learning experiment, and it will write the code and run the cells so you can immediately inspect the results.
Building a rich analysis often requires combining many small tasks, and the runcell agent excels at stringing those tasks together. The data science AI can refactor earlier cells, add missing imports, and suggest reusable functions that make notebooks cleaner. It also explains the reasoning behind each edit, creating a narrative that teammates can follow. Beginners get interactive guidance, while veterans enjoy faster execution for routine chores.
Security and privacy are baked into the design. The core data science AI runs in the browser, and heavy computations are delegated to secure APIs that never retain your data. This architecture lets enterprises benefit from cutting-edge models without compromising control. Whether you are exploring sensitive health records or proprietary financial metrics, runcell keeps the workflow compliant and confined to your environment.
Collaboration becomes easier when everyone can see how the analysis unfolded. runcell’s data science AI annotates key cells, summarizes outputs, and encourages consistent documentation. Share a notebook with colleagues and they’ll immediately understand the steps the agent took, making peer review and onboarding smoother. Teams can reproduce results, compare branches, and maintain a living record of decisions.
The more you work with runcell, the more capable the data science AI becomes. It learns from each interaction, adapting to project conventions and personal preferences. Over time the agent anticipates the next query or chart you’re likely to need, turning JupyterLab into a proactive environment rather than a passive editor. Analysts free up time for interpretation and storytelling, while researchers push models further with less manual setup.
Ready to experience this data science AI firsthand? Install the runcell extension and invite the agent into your notebooks. Within minutes you'll have an intelligent partner that transforms experiments into polished, reproducible workflows and keeps your focus on discovery.
Frequently Asked Questions About Data Science AI
What is a data science AI agent?
A data science AI agent is an intelligent assistant that automates common data science tasks — writing Python code, running cells, cleaning datasets, building visualizations, and training ML models. runcell is a data science AI agent that works directly inside JupyterLab, understanding your notebook context and generating production-ready code from natural-language prompts.
How does AI change data science workflows?
AI eliminates the repetitive parts of data science — writing boilerplate code, formatting DataFrames, and creating standard charts. With a data science AI agent like runcell, you describe what you want in plain English and the agent generates, executes, and iterates on the code. This lets data scientists focus on interpreting results and making decisions rather than debugging syntax.
Can I use a data science AI agent with my existing JupyterLab setup?
Yes. runcell installs as a JupyterLab extension and works alongside your existing notebooks, kernels, and libraries. There is nothing to migrate — install the extension, sign in, and the AI agent is ready to assist in any open notebook.
What data science tasks can the AI automate?
runcell automates exploratory data analysis, data cleaning and wrangling, feature engineering, statistical testing, visualization, model training and evaluation, and notebook documentation. You can also ask the agent to refactor code, add missing imports, or explain complex outputs.
Is a data science AI agent suitable for beginners?
Absolutely. The AI agent explains each code snippet it generates, so beginners learn best practices while getting work done. More experienced data scientists benefit from faster execution of routine tasks and less time spent on boilerplate code.
How is my data protected when using the AI?
runcell executes code locally in your Jupyter environment. Your datasets remain on your machine and are never uploaded to external servers without explicit permission. The architecture is designed for enterprise use, including work with sensitive or proprietary data.