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codeinterpreter api

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Basic Information

This repository provides an open source implementation of the ChatGPT Code Interpreter intended for developers building LLM applications. It is a LangChain-based library that enables sandboxed Python code execution using CodeBox as the backend so you can run code safely while keeping the large language model remote. The project supports workflows that combine text and file inputs and produces text plus file outputs. It is designed for local experiments with only the OpenAI API required for the LLM, and it also includes guidance for production scaling via the CodeBox API. The README includes installation instructions, usage examples for synchronous and asynchronous sessions, dataset analysis and plotting examples, and pointers to documentation and contact information for support.

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3855
App URL
https://github.com/shroominic/codeinterpreter-api
Github Repository
https://github.com/shroominic/codeinterpreter-api/blob/main/README.md

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App Details

Features
LangChain implementation of a Code Interpreter that executes sandboxed Python via CodeBox. Support for sending text and files to the session and receiving text and generated files back. Examples for dataset analysis, stock charting and image manipulation are provided. Automatic installation of required Python packages at runtime and internet access for code execution. Conversation memory so responses can use prior context. Works with OpenAI and Azure OpenAI by configuring environment variables. Sync and async session APIs with a context manager for automatic lifecycle management. Helpers for attaching and displaying files and images. Documentation site and example assets included. Notes about production scaling using the CodeBox API.
Use Cases
This project helps developers prototype and integrate code-execution capabilities into LLM applications by packaging a Code Interpreter pattern with ready-made APIs and examples. It reduces infrastructure work by providing a sandboxed execution backend (CodeBox) and utilities to attach files, auto-install packages, and display generated images or files. The conversation memory and file I/O make it straightforward to build data analysis, visualization and image-processing workflows that combine natural language prompts with datasets. Because most components can run locally except the LLM, teams can iterate quickly and later scale with CodeBox for production. The library also documents OpenAI/Azure configuration and includes both sync and async usage patterns to fit different application architectures.

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