> ## Documentation Index
> Fetch the complete documentation index at: https://docs.fetchhive.com/llms.txt
> Use this file to discover all available pages before exploring further.

# OpenAI Code Interpreter

> Add OpenAI Code Interpreter so an agent can run Python for exact computation and data analysis

OpenAI Code Interpreter gives an OpenAI agent a sandboxed Python environment for exact math, data slicing, and analysis.

## What it does

When enabled, the agent can call OpenAI's hosted **python tool** to write and run Python in an ephemeral container. Use it when you need exact results that language models alone cannot guarantee — for example splitting spreadsheet rows into precise batches, computing statistics, or transforming tabular data.

**Important:** Code Interpreter is for computation only. Downloadable files (PDF, DOCX, XLSX, CSV) are still created through **File Tools** (`write_file`). The agent should print results as CSV/JSON text, then call `write_file` so Fetch Hive stores a real asset in your workspace.

## Requirements

* Agent provider must be **OpenAI**.
* The tool is **not** enabled by default — add it from the tool picker.

## Adding the tool to an agent

1. Open an agent in the editor.
2. Click the button with the tooltip **Add MCP Tool or Sub Agent**.
3. In **MCP Tools**, click **OpenAI Code Interpreter**.

There are no per-tool settings in v1. Containers use OpenAI's default **1 GB** memory tier.

## How it works with File Tools

Typical flow for spreadsheet slicing:

1. The agent (or File Tools `read_file`) loads the source data.
2. Code Interpreter runs Python to slice or transform the data and **prints** CSV/JSON.
3. The agent calls `write_file` with that text to create downloadable XLSX/CSV assets.

Files created inside OpenAI's container are ephemeral and are **not** surfaced as Fetch Hive downloads. Sandbox images and ephemeral OpenAI file URLs are redacted from tool activity — they are not shown as downloads. Always prefer `write_file` for user-facing files.

## Limitations (v1)

* **OpenAI only** — Anthropic and xAI code-execution tools are not wired yet.
* **No multi-turn Python state** — each agent turn starts a fresh container; variables from a previous turn do not persist.
* **Fixed 1 GB memory** — higher OpenAI memory tiers are not exposed yet.
* Each Code Interpreter call counts as **1 task credit** (same pattern as GPT Search). Container-minute spend is tracked only as estimated operator metadata (`estimated: true`) on the completion — not a separate user-facing USD line item. On hosted keys, OpenAI container minutes are absorbed by Fetch Hive.

## Use cases

* Exact row slicing (rows 1–50, 51–100, …) before exporting XLSX batches.
* Numerical / statistical calculations that must be precise.
* Parsing or reshaping tabular data before writing a file.

## Notes

* Use [Testing with Chat](../testing-with-chat) to watch Code Interpreter and File Tools calls during a conversation.
* To add or remove tools from an agent, see [Creating and Configuring](../creating-and-configuring).
* See also [File Tools](./file-tools) for creating downloadable assets.