Daytona

// Overview

Daytona is a secure and elastic infrastructure runtime for executing AI-generated code and running agent workflows. Its sandboxes are fully isolated, composable computers, each with its own kernel, filesystem, network stack, and allocated vCPU, RAM, and disk.

Sandboxes are the core of the platform. They start in under 90ms, run code in Python, TypeScript, and JavaScript, and are built on OCI/Docker compatibility for predictable environments. Stateful snapshots let agents keep their work across sessions, which makes Daytona a reliable foundation for long-running agent setups.

Agents and developers drive sandboxes programmatically through Daytona's SDKs, REST API, and CLI. You can manage the sandbox lifecycle, work with the filesystem, run processes and code, and configure the runtime with base images, packages, and tooling.

// What it does

• Isolated sandboxes that act as full composable computers, each with a dedicated kernel, filesystem, and network stack
• Fast startup, with sandboxes ready to run code in under 90ms
• Stateful snapshots that persist agent work and let operations continue across sessions
• Agent tools for process and code execution, file system operations, computer use, Git operations, and an MCP server
• Human-facing access through a dashboard, web terminal, SSH, VNC, and live preview URLs
• SDKs for Python, TypeScript, Ruby, Go, and Java, plus a REST API and a CLI

// Getting started

Create an account at app.daytona.io, generate an API key from the dashboard, then install an SDK and create your first sandbox. The Python example below runs a line of code inside a fresh sandbox.

pip install daytona

from daytona import Daytona, DaytonaConfig

config = DaytonaConfig(api_key="YOUR_API_KEY")
daytona = Daytona(config)
sandbox = daytona.create()
response = sandbox.process.code_run('print("Hello World!")')
print(response.result)

daytona create

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

// When to use it

• Running untrusted, AI-generated code from agents inside a fully isolated sandbox
• Giving autonomous agents a persistent workspace that keeps state across sessions via snapshots
• Building agent tooling that needs filesystem access, process execution, and computer use in a safe environment
• Self-hosting an open-source code execution stack with Docker Compose, or running it as a managed or hybrid service

// How Daytona compares

Daytona alongside other open-source gpu & compute clouds tools AI/TLDR tracks, ranked by GitHub stars.