Code Interpreter API

👾 Open source implementation of the ChatGPT Code Interpreter

No commits in 23 months — may not be actively maintained. See maintained alternatives →

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Overview

codeinterpreter-api 是 ChatGPT Code Interpreter 的开源实现，基于 LangChain 框架构建，使用 CodeBox 作为沙盒化 Python 代码执行环境。该工具允许开发者在本地部署类似 ChatGPT 代码解释器的功能，支持数据分析、图表绘制、图像处理等多种场景。核心特性包括自动 Python 包安装、互联网访问、文件输入输出处理以及对话记忆功能。与闭源解决方案不同，该工具提供了完全可控的代码执行环境，除了大语言模型服务外，所有组件都可以在本地运行。它特别适合需要将 AI 代码生成和执行能力集成到自有应用中的场景，同时保持对执行环境的完全控制权。

Deep Analysis

Key Differentiator

vs raw LangChain code execution: sandboxed Code Interpreter replica with file I/O and conversation memory — the closest open-source implementation of ChatGPT's Code Interpreter feature

⚡ Capabilities

  • • LangChain implementation of ChatGPT Code Interpreter
  • • Sandboxed Python code execution via CodeBox
  • • Dataset analysis, stock charting, image manipulation
  • • File input/output (text + files → text + files)
  • • Internet access and auto package installation
  • • Conversation memory for context-aware responses
  • • Async and sync interfaces

🔗 Integrations

LangChainOpenAICodeBox (sandbox)Azure OpenAI

✓ Best For

  • ✓ Developers wanting open-source ChatGPT Code Interpreter functionality
  • ✓ Data analysis automation with file input/output
  • ✓ Building code execution agents with sandboxed safety

✗ Not Ideal For

  • ✗ Teams needing local/offline code execution with local LLMs
  • ✗ Production systems without CodeBox API access
  • ✗ Non-Python code execution tasks

Languages

Python

Deployment

pip install codeinterpreterapiCodeBox API for production

⚠ Known Limitations

  • ⚠ Requires OpenAI API key
  • ⚠ CodeBox API needed for production scaling
  • ⚠ Local LLM support not yet available
  • ⚠ Code execution in sandbox may have library limitations

Pros

  • + 开源架构提供完全的透明度和可定制性，不受第三方服务限制
  • + 支持文件处理和对话记忆，可以处理复杂的多轮交互场景
  • + 本地部署能力强，除 LLM API 外所有组件都可在本地运行，保障数据安全

Cons

  • - 依赖 OpenAI API Key，仍需要外部 LLM 服务支持
  • - 需要配置 CodeBox 后端环境，增加了部署和维护的复杂性
  • - 文档和生态相对较小，相比官方 ChatGPT Code Interpreter 功能可能有限

Use Cases

  • • 企业内部数据分析和可视化，需要在受控环境中执行代码
  • • 教育平台集成代码解释器功能，为学习者提供交互式编程体验
  • • 产品原型开发，快速验证数据处理和图表生成功能的可行性

Getting Started

1. 安装包：pip install "codeinterpreterapi[all]" 2. 配置环境变量：export OPENAI_API_KEY=your_key 3. 创建会话：使用 CodeInterpreterSession() 创建对话会话并调用 generate_response() 生成响应

Alternatives

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Works with Code Interpreter API

Tools that integrate with Code Interpreter API, often used together in the same stack.

L
LangChain
Reference implementations of several LangChain agents as Streamlit apps

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