daytonaio/daytona
Daytona is a Secure and Elastic Infrastructure for Running AI-Generated Code
Daytona provides secure, isolated sandboxes where AI coding agents execute code without touching the host system. Each workspace gets its own file system, shell environment, and network configuration -- the execution runtime that autonomous AI coding tools rely on in 2026.
Our Review
When AI coding agents crossed the threshold from suggestion to execution in 2024-2025, a new infrastructure problem appeared: where should the code actually run? Executing AI-generated code directly on a developer's machine is a security risk by design -- the agent cannot be trusted to write only safe operations. Daytona was built to solve this at the infrastructure layer: a sandboxed, elastic runtime where agents can write files, execute shell commands, install dependencies, and run tests in complete isolation from the host. Ivan Burazin brought his Codeanywhere background to this problem, and the project's 72,000 GitHub stars signal that the market recognized the gap.
Key capabilities
- •Isolated workspaces: each agent run gets its own file system, shell environment, and network config -- writes and commands stay inside the container, not on the host
- •Elastic scaling: workspaces spin up in under 2 seconds and scale horizontally for parallel agent runs without shared-state conflicts
- •Language and runtime agnostic: executes Python, TypeScript, Go, Rust, and any language supported by standard Docker images
- •REST API and SDK: first-class Python and TypeScript SDKs let orchestration layers create, use, and destroy workspaces programmatically
- •Persistent file systems: workspaces can persist between agent sessions, letting an agent pick up where it left off without re-bootstrapping
- •Native AI agent integrations: adopted by OpenHands, Codex, and other autonomous coding tools as their default execution backend
Getting started
Install via npm install @daytona/sdk or pip install daytona-sdk. Authenticate with a Daytona API key, create a workspace with sdk.create(), then execute code via the sandbox's shell or file system API. A full isolated workspace spins up in seconds.
Limitation
AGPL-3.0 license -- teams embedding Daytona in proprietary AI products need to evaluate copyleft obligations before shipping. The project releases frequently (v0.177 as of May 2026) and SDK APIs may evolve between minor versions. Self-hosting requires Docker infrastructure. Parallel workspaces at scale incur compute costs that grow linearly with concurrent agent runs.
Our Verdict
Daytona addresses the infrastructure gap that becomes visible when AI coding agents mature from suggestion to execution: the tools that write code need a safe place to run it. The sandboxed workspace model -- isolated file system, POSIX shell, network config -- is architecturally correct for this use case, and the active release cadence (v0.177 in May 2026) shows real usage driving real iteration.
The strongest signal is adoption: OpenHands and other serious autonomous coding tools use Daytona's execution environment as their default runtime because building and securing a sandboxed runtime from scratch is engineering cost that does not differentiate the agent itself. That adoption pattern typically precedes broad awareness by 12-18 months.
The AGPL-3.0 license is the practical gate for commercial teams. For open-source projects and research environments it is a non-issue. For teams building proprietary AI coding tools on Daytona, legal review of the copyleft obligations is the right first step -- the rest of the evaluation is favorable in 2026.
Frequently Asked Questions
What is Daytona and why do AI coding agents need it?
Daytona is a sandboxed runtime that gives AI coding agents a safe place to execute code. When autonomous agents like OpenHands or Codex write and run code, they need an isolated environment -- one where file writes and shell commands cannot affect the host machine or production systems. Daytona provides that: each workspace gets its own file system, shell environment, and network config, fully isolated from the host in 2026.
How does Daytona differ from running code in a plain Docker container?
Docker containers give you isolation, but Daytona adds the orchestration layer that AI agent workflows need: on-demand workspace creation via SDK (create a workspace in one line of code), automatic cleanup, persistent file systems across agent sessions, and native integrations with popular AI coding tools. Daytona handles the lifecycle management that you would otherwise have to build on top of raw Docker in 2026.
What does the AGPL-3.0 license mean for commercial users?
AGPL-3.0 is a strong copyleft license. If you use Daytona as a library or as part of a networked service, the copyleft obligations may apply to your code. For internal tooling, research, and open-source projects it is generally a non-issue. For teams building proprietary AI coding products that embed or wrap Daytona, legal review of AGPL obligations is the right first step before shipping in 2026.
Which AI coding tools use Daytona as their execution backend?
OpenHands is the most prominent public adopter -- it uses Daytona workspaces as the sandboxed environment where its AI agents write code, run terminal commands, and execute tests. Other autonomous coding tools and agent frameworks have integrated Daytona's SDK because the alternative is building and maintaining their own sandboxing infrastructure. The adoption list is growing as of 2026.
How do I get started with Daytona in my AI agent project?
Install the Daytona SDK with npm install @daytona/sdk (TypeScript) or pip install daytona-sdk (Python). Sign up for a Daytona API key at daytona.io, then create a workspace with sdk.create() and start executing code via the shell or file system API. A full isolated workspace is available in under 2 seconds. The Daytona GitHub repo and documentation at daytona.io cover language-specific examples and configuration options in 2026.
What is daytona?
Daytona provides secure, isolated sandboxes where AI coding agents execute code without touching the host system. Each workspace gets its own file system, shell environment, and network configuration -- the execution runtime that autonomous AI coding tools rely on in 2026.
What license does daytona use?
daytona uses the AGPL-3.0 license.
What are alternatives to daytona?
Explore related tools and alternatives on My AI Guide.
Open source & community-verified
AGPL-3.0 licensed: free to use in any project, no strings attached. 72,456 developers have starred this, meaning the community has reviewed and trusted it.
Reviewed by My AI Guide for relevance, quality, and active maintenance before listing.
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