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together-code-interpreter

Execute Python code in sandboxed sessions with persistent state.

2|1|Updated Feb 20, 2026
Search Tags:#api#python#session-management#sandbox#data-visualization#code-execution
One-click install
npx skills add https://github.com/zainhas/togetherai-skills --skill together-code-interpreter
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: together-code-interpreter
Source: https://github.com/zainhas/togetherai-skills/tree/main/skills/together-code-interpreter
Command: npx skills add https://github.com/zainhas/togetherai-skills --skill together-code-interpreter

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires together, together-ai, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Secure, scalable execution of Python code without local compute, enabling experiments, data analysis, and automation in a sandboxed environment.

Core Features & Use Cases

  • Stateful sandboxed code execution with a 60-minute lifespan and pre-installed data science packages.
  • Session reuse via session_id to maintain state across executions and enable iterative workflows.
  • File uploads and multi-language client support (Python and TypeScript) through a simple API.
  • Use cases include data analysis, chart generation, RL experimentation, and agentic code execution workflows.

Quick Start

Run a simple Python snippet in a sandboxed session to verify code execution and persistent state.

Frequently Asked Questions about together-code-interpreter

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run Python code securely in a sandbox without local compute?▼

You can run Python code securely in a sandbox by sending execution requests to a remote REST API endpoint. This enables data analysis and algorithm prototyping without relying on local compute resources.

Can I maintain state across multiple Python code executions in a remote sandbox?▼

Yes, you can maintain state across remote Python code executions by reusing a session ID. This allows iterative workflows and persistent experimentation within a single 60-minute session lifespan.

What data science packages are available for sandboxed code execution?▼

Sandboxed code execution environments come with pre-installed data science packages. This allows you to perform data analysis, generate charts, and run machine learning experiments immediately without manual setup.

Does the Together SDK support TypeScript clients for remote code execution?▼

Yes, the Together SDK supports both Python and TypeScript clients for remote code execution. You can use these multi-language clients to interact with the sandboxed environment via a simple API.

What are the limitations of using a stateful sandbox for long-running tasks?▼

The primary limitation of a stateful sandbox is a 60-minute session lifespan. You must manage your session ID and complete long-running tasks or data analysis workflows within this fixed timeframe.

Can I upload files to a sandboxed session for data visualization?▼

Yes, you can upload files to a sandboxed session for data visualization and analysis. The API supports file uploads to facilitate iterative workflows within the active 60-minute session.

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