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Daytona: Secure Elastic Infrastructure for AI Code

Run AI-generated code in secure, isolated sandboxes with sub-100ms startup and multi-language SDKs.

View on GitHub ↗ Quick start ↓
Source repo
daytonaio/daytona
Stars
★ 72k
Last updated
3mo ago
FA score
44/100 · Major gaps

At a glance

How it runs
Library / SDKCLIHosted service
Works with
Universal · cross-platform
Cost
Free tier plus a paid hosted plan
Setup effort
Low · running in minutes
You'll need
Python 3Node.js (for TS SDK)Docker (for self-hosted)Shell / CLINetwork accessLocal filesystem
Typical use
AI developer building autonomous agents that need a secure, isolated environment to run AI-generated code.
Not a fit if
  • Teams needing ongoing maintenance or official support; repo unmaintained since June 2026
  • Teams requiring fully offline use or no data leaving their network; managed service relies on Daytona cloud
  • Teams that need to execute languages beyond Python, TypeScript, and JavaScript
Source review
44/100 · Major gaps

What does this agent do, and when should you use it?

Daytona is an open-source platform providing secure and elastic infrastructure for executing AI-generated code and agent workflows. At its core are sandboxes—fully isolated computers with dedicated kernels, filesystems, network stacks, and allocated vCPU, RAM, and disk. Sandboxes spin up in under 90ms and support Python, TypeScript, and JavaScript, built on OCI/Docker compatibility with massive parallelization and unlimited persistence. Developers and agents interact with sandboxes programmatically via SDKs in Python, TypeScript, Ruby, Go, and Java, a REST API, and a CLI. Features include snapshots for persistent state across sessions, file system operations, process execution, MCP server support, SSH, VNC, and web terminal access. The platform also offers organizational controls, audit logs, webhooks, and network limits. Note: As of June 2026, this repository is no longer maintained, but it remains public and free to use under the license.

Daytona enables users to create and manage sandboxes via API, CLI, or SDKs. Sandboxes provide isolated environments where users can run arbitrary code, execute commands, and manage files. The workflow involves: creating an account on app.daytona.io, generating an API key, and then using SDKs (e.g., Python's process.code_run) or curl to create a sandbox and execute code. The platform includes agent tools like process execution, file system operations, LSP, and computer use; human tools like dashboard, web terminal, SSH, VNC, and VPN; and system tools like webhooks and network limits. Client libraries are located in the libs directory, with packages like sdk-python, @daytona/sdk, and sdk-go.

  1. AI developer building autonomous agents that need a secure, isolated environment to run AI-generated code.
  2. Platform team standardizing on a cloud-based code execution infrastructure with elastic scaling.
  3. Educational platform providing consistent programming environments to students without setup overhead.
  4. CI/CD pipeline that requires reproducible build and test environments with quick provisioning.
  5. Security-conscious team executing untrusted code in isolation to prevent host compromise.

How do you install or deploy this agent?

  1. Create an account at app.daytona.io and generate an API key.
  2. Install the SDK for your language: Python via pip install daytona; TypeScript via npm install @daytona/sdk; Ruby via gem install daytona; Go via go get github.com/daytonaio/daytona/libs/sdk-go; Java via Gradle or Maven dependency (io.daytona:sdk:x.y.z).

How do you use this agent?

Example in Python:

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)

Alternatively, call the API:

curl 'https://app.daytona.io/api/sandbox' --request POST --header 'Authorization: Bearer <YOUR_API_KEY>' --header 'Content-Type: application/json' --data '{}'

Or use the CLI: daytona create.

What are this agent's strengths and limitations?

Pros
  • Ultra-fast sandbox startup (under 90ms) enabling low-latency agent workflows.
  • Multi-language SDKs (Python, TypeScript, Ruby, Go, Java) with OpenAPI-generated REST clients.
  • Full isolation with dedicated kernel, filesystem, and network stack, enhancing security.
  • Snapshots enable persistent state across sessions, ideal for long-running agents.
Limitations
  • Repository is no longer maintained (as of June 2026), so no future updates or support.
  • Dependence on Daytona's cloud for managed service; self-hosting requires additional setup.
  • Learning curve for new users due to sandbox concepts, API, and SDK integration.
  • Limited language support for code execution (only Python, TypeScript, JavaScript).

How does this agent compare with similar options?

Key facts side by side with the most closely related agents.

Agent Source review Form / cost Stars Updated Language Full support on
Daytona: Secure Elastic Infrastructure for AI Code This agent 44 · Major gaps Library / SDKFreemium ★ 72k 3mo ago — —
CaveAgent 62 · Some gaps Library / SDKFree + model costs ★ 204 1mo ago Python OpenAI API · Claude API
OSS Autopilot 83 · Good Agent plugin / skillFree ★ 21 1d ago TypeScript Claude Code · Claude.ai
Compartment 76 · Good CLIFree ★ 583 1d ago Python Codex · Claude Code

How does FollowAgents rate this agent?

FollowAgents source review · FARS-2.1
Major gaps
44/ 100 5-point scale 2.2 / 5
Trust 10/29
Reliability 6/14
Adaptability 10/18
Convention 8/18
Effectiveness 7/13
Verifiability 3/8
Why each dimension lost points
Trust10 / 29 · 1.7/5

Evidence shows: README describes sandbox isolation, API keys, audit logs, network limits, but lacks implementation details or least-privilege evidence. Deductions: no code-level evidence to confirm actual permission controls.

Reliability6 / 14 · 2.1/5

Evidence shows: README describes architecture and features, but lacks error handling or failure message details. Deductions: no error handling documentation or test evidence.

Adaptability10 / 18 · 2.8/5

Evidence shows: README describes multiple use cases (AI agents, developers) and multiple SDK languages, but lacks clear capability boundaries or trigger conditions. Deductions: capability boundaries and trigger precision are insufficiently described.

Convention8 / 18 · 2.2/5

Evidence shows: README provides installation instructions, examples, and documentation links, but lacks version changelog and known limitations. Deductions: version changelog and known limitations are missing.

Effectiveness7 / 13 · 2.7/5

Evidence shows: README provides quick start examples and multiple SDKs, but lacks performance or cost data. Deductions: no cost-benefit analysis.

Verifiability3 / 8 · 1.9/5

Evidence shows: claims in README lack verifiable tests or independent sources. Deductions: no traceable verification evidence.

Risks and how to mitigate them
  • Repository is marked as no longer maintained; assess risks before use.
  • No AGENTS.md manifest provided, may not meet some agent framework requirements.
  • Security feature descriptions lack code-level verification; rely with caution.
Evidence confidence: Low Reviewed Aug 09, 2026 Reviewed revision ec4c21b2d597
Review evidence README.md
See the full review method →

Recent releases

Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.

  1. v0.190.0
    Release notes excerpt
    0.190.0 (2026-06-23) 🚀 Features - Pi extension and docs guide ([#4938](https://github.com/daytonaio/daytona/pull/4938)) - api,runner,sdk,cli: add pause action ([#4714](https://github.com/daytonaio/daytona/pull/4714)) - api,sdk: sandbox domain allow list ([#5117](https://github.com/daytonaio/daytona/pull/5117)) - dashboard: otel config notice & alignment tweaks ([#5107](https://github.com/daytonaio/daytona/pull/5107)) - dashboard: regions & runners react query & design touchup ([#5106](https://github.com/daytonaio/daytona/pull/5106)) …
    View on GitHub ↗
  2. v0.189.0
    Release notes excerpt
    0.189.0 (2026-06-19) 🚀 Features - dashboard: tailwind v4 ([8be4772fb](https://github.com/daytonaio/daytona/commit/8be4772fb)) - dashboard: table col resize and config menu ([e114af4c2](https://github.com/daytonaio/daytona/commit/e114af4c2)) - docs: add async python sdk variable ([#5083](https://github.com/daytonaio/daytona/pull/5083)) - guides,docs: add SGLang GPU model-serving guide ([#5085](https://github.com/daytonaio/daytona/pull/5085)) - proxy: proxy connection handling hardening ([#5080](https://github.com/daytonaio/daytona/pull/5080)) 🩹 Fixes …
    View on GitHub ↗
  3. v0.188.0
    Release notes excerpt
    0.188.0 (2026-06-16) 🚀 Features - api: add regionType to sandbox usage periods ([#5064](https://github.com/daytonaio/daytona/pull/5064)) - dashboard: sandbox create sheet snapshot select update ([#5024](https://github.com/daytonaio/daytona/pull/5024)) - docs: windows snapshot and fork ([#4989](https://github.com/daytonaio/daytona/pull/4989)) - docs: add default snapshots ([#5042](https://github.com/daytonaio/daytona/pull/5042)) - guides, docs: add vLLM model serving guide for GPU sandboxes ([#4980](https://github.com/daytonaio/daytona/pull/4980)) 🩹 Fixes …
    View on GitHub ↗

FAQ

How does Daytona differ from Docker containers?
Daytona sandboxes are full composable computers with dedicated kernels and persistent snapshots, whereas Docker containers typically share the host kernel and lack built-in state persistence. Daytona is optimized for AI agent workflows with fast startup.
Can I self-host Daytona?
Yes, the platform is open-source. However, detailed deployment instructions are not provided in this repository; you would need to configure the control plane and compute plane yourself based on architecture docs.
What programming languages can I run in sandboxes?
Sandboxes support running Python, TypeScript, and JavaScript code. SDKs are available for Python, TypeScript, Ruby, Go, and Java for client integration.
How secure are the sandboxes?
Each sandbox has its own kernel, filesystem, and network stack, providing strong isolation. Additional features like network limits, audit logs, and VPN connections enhance security.
View on GitHub ↗ Install ↓

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