Toolofficial publisher

Daytona

Tool : A precise action the AI can call · requires daytona

Fast-starting sandboxes for agents.

Alternative to E2B, same pricing.

Docs

Overall score

6.1 /10

Security: one caveat, see the card

Utility

7.0

Reliability

6.0

Security

One caveat

Ease

Technical · Technical

Security reflects where data goes, requested permissions and incidents on record at the verification date. It is not a guarantee of zero risk: check the deployment mode and the connections you enable.

What it does, concretely

When an AI agent generates code, running it directly on a host machine carries significant security risks. Daytona addresses this by providing isolated execution environments called sandboxes, which start in under 90 milliseconds and shut down cleanly after use.

Each sandbox has a dedicated kernel, filesystem, network stack, and allocated vCPU, RAM, and disk resources. Developers and agents interact with these environments through SDKs (Python, TypeScript, Ruby, Go, Java), a REST API, or a command-line interface, covering lifecycle management, code execution, and filesystem operations.

Daytona is designed for developers building AI agents, reinforcement learning pipelines, or any system that needs to run automatically generated code in a safe and reproducible environment. OCI/Docker compatibility and massive parallelization allow many sandboxes to run simultaneously.

However, Daytona is not suited for non-technical end users: the tool is entirely programmatic and does not offer a graphical interface for those without development skills.

What it offers

·Sandbox startup in under 90 milliseconds, from code to execution.
·Full isolation with a dedicated kernel, filesystem, network stack, and vCPU/RAM/disk resources per sandbox.
·OCI/Docker compatibility for consistent and portable environments.
·Stateful environment snapshots, allowing agent sessions to resume where they left off.
·Shared volumes for file access across multiple sandboxes.
·SDKs available in Python, TypeScript, Ruby, Go, and Java, plus a REST API and CLI.
·Multi-region deployment to choose where sandboxes run geographically.
·Massive parallelization to launch a large number of sandboxes simultaneously.

Our tips

Use snapshots to save sandbox state between agent sessions, avoiding the need to reconfigure the environment on every restart.
Prefer shared volumes when multiple sandboxes need access to the same files, rather than duplicating data in each environment.
Choose the region closest to your users or services to reduce latency during code execution.
Refer to the llms.txt and llms-full.txt files provided by Daytona to inject documentation directly into your LLM agent's context.
Use the CLI to quickly test a sandbox locally before integrating SDK calls into your production pipeline.

Watch out

The tool is entirely programmatic: without development skills, it cannot be used directly.
Usage-based pricing (0.05 USD per vCPU-hour) can be hard to predict when many sandboxes run in parallel over extended periods.

In practice

Price: Freemium
Commercial use: Commercial use: yes
Available on: sdk-python, sdk-ts
Host: daytona
FR interface: No

Security and data

Rating: Security: one caveat, see the card
Capabilities: readswritesexecutesnetwork
Your data: Data with the vendor
Maintenance: Active

Links and repository checked on 25 October 2026 · added on 1 January 2024

How to install Daytona

1. Create an account at app.daytona.io. 2. Generate an API key from the dashboard (app.daytona.io/dashboard/keys) and store it securely, never hard-coded in your source code. 3. Install the SDK for your language (for example: pip install daytona for Python, or npm install @daytona/sdk for TypeScript). 4. Initialize the Daytona client in your code by passing the API key through an environment variable rather than in plain text. 5. Create a sandbox with daytona.create() and run your code via sandbox.process.code_run(). 6. Delete or stop the sandbox after use to avoid unnecessary charges.

pip install daytona

Official documentation ↗

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