Supercharging IPython with ipyllm

Sun, 11 Oct 2026 23:57:42 +0200

ipyllm is a open source IPython extension that seamlessly integrates Large Language Models (LLMs) into your live IPython environment. Built on top of the versatile llm package, ipyllm extends the IPython REPL with agents that access your current environment to help you solve data analysis tasks, assist you with the exploration of an API or help you debug issues in your code.

What can ipyllm do for you? Link to heading

Effortless Data Exploration: Instead of explaining your dataset in text, ask %llm What columns in df have missing values? or %%llm Plot the rolling average of sales using matplotlib and run the code.. The model inspects the live variables, writes the visualization, and executes it in your session.
Contextual Debugging: When a script stalls or fails, drop ipyllm.embed() into your code. It works just like IPython.embed(), giving the assistant direct access to the function’s local scope and variables. You can ask: “What is holding result in a None state?” and let the model inspect local state on the fly.
Diagnostic Shell Integration: Run commands inside %%llm --run (such as pytest -q) to capture stdout, stderr, and exit codes instantly, letting the model explain failures and suggest fixes without manual log pasting.

Supercharging Data Analysis with Agent Skills Link to heading

Data analysis workflows often require specialized domain knowledge. ipyllm bridges this gap by supporting local Agent Skills.

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Supercharging IPython with ipyllm

What can ipyllm do for you? Link to heading

Supercharging Data Analysis with Agent Skills Link to heading