Code interpreter (LLM tool)
A code interpreter is a sandboxed execution environment that lets a language model run code (usually Python) it generates, inspect the results, and iterate — turning the model into a data analyst.
OpenAI's Code Interpreter (now Advanced Data Analysis), Anthropic's Claude with Code Execution, and Gemini's Code Execution all let the model write and run code on user-uploaded data. The pattern: model writes Python, sandbox runs it, output goes back to the model, model adjusts or summarises. It is the most reliable way to make models do math, read CSVs, generate charts, and verify their own analytical claims. Production agent stacks in 2026 routinely include a code execution tool alongside web search and retrieval — it's how agents handle anything that text reasoning alone bungles (arithmetic, data shapes, units, dates).
When to use code interpreter (llm tool)
- Data analysis, math, statistical computation.
- Chart generation, data viz.
- Verifying analytical claims before reporting.
Common mistakes
- Letting the interpreter run unrestricted — sandbox it.
- Trusting the model's narrative without checking the code it ran.
FAQ
What is code interpreter (llm tool)?
A code interpreter is a sandboxed execution environment that lets a language model run code (usually Python) it generates, inspect the results, and iterate — turning the model into a data analyst.
When should I use code interpreter (llm tool)?
Data analysis, math, statistical computation. Chart generation, data viz. Verifying analytical claims before reporting.
What are the most common mistakes with code interpreter (llm tool)?
Letting the interpreter run unrestricted — sandbox it. Trusting the model's narrative without checking the code it ran.
Related terms
- AI agent — An AI agent is a system where a language model autonomously plans and executes a sequence of tool calls to accomplish a goal.
- Function calling (tool use) — Function calling lets a language model emit a structured request to invoke a developer-defined tool, enabling reliable JSON output and agent workflows.
- Guardrails — Guardrails are deterministic checks layered around a language model to prevent unsafe, off-topic, or non-compliant outputs from reaching the user.
- Evals (LLM evaluations) — Evals are systematic tests that measure how well a language model or LLM-powered system performs on a defined task using a golden set of inputs and reference outputs.
Last updated: 2026-06-01. Raw markdown: https://promtable.com/glossary/code-interpreter.md.