Cline has added native Jupyter Notebook support that treats notebooks as structured JSON documents rather than flat text files. The update brings AI-assisted, cell-aware workflows to VS Code notebooks, letting developers generate, explain, and improve cells while preserving cell boundaries, metadata, execution counts, and outputs.
Why notebooks need different handling
Jupyter notebooks are JSON documents made of an array of cells. Each cell carries a type (code, markdown, raw), source lines, metadata, an execution count, and outputs such as HTML tables or error traces. Tools that operate on .ipynb files as plain text often break this structure, losing outputs or corrupting metadata. Cline’s integration extracts the full cell context and packages it as structured JSON before sending it to the AI, enabling reasoning about notebooks at the same granularity as developers work: cell by cell, with awareness of execution state and actual outputs.
Three VS Code commands for notebook workflows
Cline introduces three VS Code commands accessible from the Command Palette (search “Jupyter”) and bindable to shortcuts:
cline.jupyterGenerateCell— Creates new, properly structured notebook cells from a natural-language prompt. The AI receives context from surrounding cells (imports, variable names, output schemas) and inserts a JSON-structured cell ready to execute.cline.jupyterExplainCell— Produces detailed explanations of a selected cell’s logic. Explanations reference the cell’s outputs when available, breaking down chained operations and making it easier to understand complex transformations.cline.jupyterImproveCell— Suggests optimizations or refactors for existing cells while preserving position and metadata. Typical improvements include vectorizing slow loop-based pandas code, adding error handling, or clarifying transformations.
How it works under the hood
When a command runs, VS Code’s notebook API extracts the complete cell JSON for the current cell and relevant surrounding context. The handler sends this structured context plus the user prompt to the AI with system instructions tailored for working with notebook JSON. The AI returns content that preserves notebook structure; Cline applies edits directly to the notebook JSON rather than performing diff-based edits on plain text. This approach ensures execution counts, cell metadata, and outputs remain associated with their source cells.
Built for data science workflows
The cell-level granularity aligns with exploratory analysis patterns: run, inspect, iterate. The integration supports common tasks such as generating data-loading boilerplate, producing publication-quality visualizations, explaining colleague analyses, and refactoring exploratory code into cleaner steps. The release notes an engineering contribution from Amazon toward making comprehensive notebook support possible.
Getting started
Enable the feature by updating Cline, enabling Enhanced Notebook Interaction in settings, and opening a .ipynb in VS Code. Full setup and command reference are available in the Jupyter Notebooks documentation.
Original source: https://cline.bot/blog/cline-now-speaks-jupyter