sandbox

AIO Sandbox is an all-in-one Docker-based sandbox for AI agents that exposes browser automation (VNC/CDP plus MCP tools), shell execution, file read/write/list/search operations, Jupyter code execution, and an MCP hub. It also provides a web-based VSCode Server and integrates pre-configured MCP servers (browser, file, shell, markitdown) running within the same container with a shared filesystem.

Evaluated Mar 30, 2026 (21d ago)
Homepage ↗ Repo ↗ Infrastructure ai-ml sandbox agents browser-automation mcp filesystem shell jupyter vscode docker api
⚙ Agent Friendliness
52
/ 100
Can an agent use this?
🔒 Security
32
/ 100
Is it safe for agents?
⚡ Reliability
25
/ 100
Does it work consistently?

Score Breakdown

⚙ Agent Friendliness

MCP Quality
75
Documentation
70
Error Messages
0
Auth Simplicity
40
Rate Limits
5

🔒 Security

TLS Enforcement
30
Auth Strength
35
Scope Granularity
10
Dep. Hygiene
40
Secret Handling
45

Security posture cannot be fully verified from the provided README. Quick start uses seccomp=unconfined, which weakens syscall filtering. Auth requirements for the REST API are not clearly documented; JWT_PUBLIC_KEY is mentioned but enforcement/scoping is unclear. Since the service enables shell/file/browser actions, network and access controls (e.g., binding to localhost, firewall rules, auth) are critical. TLS is not discussed in the README.

⚡ Reliability

Uptime/SLA
0
Version Stability
40
Breaking Changes
40
Error Recovery
20
AF Security Reliability

Best When

You need a unified, agent-friendly execution environment to coordinate browser actions, code execution, and filesystem changes across interfaces (MCP + REST + SDK).

Avoid When

You cannot restrict container access/networking or you need strong, verifiable assurances of sandbox isolation, auditability, and operational SLOs.

Use Cases

  • Letting LLM agents browse and interact with websites (CDP/VNC and MCP browser tools)
  • Running shell commands safely inside a controlled environment
  • Programmatic file manipulation for multi-step agent workflows
  • Executing Python in Jupyter for data processing or transformations
  • Using an MCP-compatible tool layer to connect agents to browser/file/shell capabilities
  • Remote development in a browser via code-server

Not For

  • Direct production use without careful threat modeling and network isolation
  • Handling highly sensitive data without additional isolation controls
  • Scenarios requiring strict, documented guarantees about sandbox security boundaries
  • Workloads needing enterprise-grade uptime/SLA guarantees

Interface

REST API
Yes
GraphQL
No
gRPC
No
MCP Server
Yes
SDK
Yes
Webhooks
No

Authentication

OAuth: No Scopes: No

The README examples show localhost usage and environment variables (JWT_PUBLIC_KEY) but do not document required auth mechanisms, enforcement, or scopes for the REST endpoints.

Pricing

Free tier: No
Requires CC: No

No pricing information is provided; appears designed for self-hosted/container deployment.

Agent Metadata

Pagination
none
Idempotent
False
Retry Guidance
Not documented

Known Gotchas

  • Container access should be restricted; REST endpoints can execute commands and read/write files.
  • Use of seccomp=unconfined in quick start suggests the security boundary is not strictly hardened by default.
  • No explicit guidance found on retries, idempotency, or handling partial failures across multi-step agent workflows.

Alternatives

Full Evaluation Report

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Score Monitoring

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Scores are editorial opinions as of 2026-03-30.

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