Onlyboxes, developed by Coooolfan, is a self-hosted sandbox for running and isolating code execution for AI agents and developers. The tool exposes REST APIs and a Model Context Protocol interface so models and clients can execute scripts, interact with a persistent filesystem, and manage stateful terminal sessions. It supports Python and Node.js execution, model-readable images, an account system with resource isolation, and horizontal scaling via worker nodes. The target audience is AI developers and teams needing a private on‑premise alternative to cloud interpreters.

What tasks can you actually use it for?

Onlyboxes targets agent-driven workflows that require controlled execution and persistent state, such as automated testing, iterative script runs, and multi-agent orchestration. The architecture separates management from execution, which helps route jobs to dedicated worker nodes while keeping a distinct control layer for scheduling and access control. For teams building LLM-assisted tooling, the sandbox functions as a place to run code, capture artifacts, and keep terminal state over longer sessions.

How consistent are execution outputs compared to manual runs?

The system runs code inside isolated runtimes, so outputs reflect the actual sandbox environment rather than simulated responses. The package includes a pythonExec utility for executing Python code, therefore reproducibility depends on installed runtimes and third-party libraries on the host. Multimedia and image results are written to the sandbox file system and returned as files, which preserves binary output but requires environment parity to match local development results.

What inputs and deployment requirements shape results?

Server components are designed for Linux distributions with a one-click installer and Docker deployment options, so hosts should meet those platform prerequisites for smooth setup. Once deployed, clients can interact remotely through APIs or MCP-compatible connectors. Operators should plan for TLS on external endpoints and for filesystem quotas, since account-level isolation enforces per-user resource boundaries that affect long-running jobs and large outputs.

Is it practical to deploy and scale for team use?

The architecture supports horizontal scaling by adding worker nodes and an account system that separates resources between users, which maps directly to team and multi-tenant environments. An active repository and a focus on MCP compatibility help integration with existing LLM clients. Recommended operational tasks include:

• centralized monitoring of worker health
• consistent package management across nodes
• routine integration tests to validate agent outputs

A practical choice for teams that accept operational responsibility

Onlyboxes fits teams that prioritize keeping execution in-house and who can maintain runtime consistency across hosts. The tool delivers predictable, file-backed outputs when operators enforce library parity and monitoring. Treat agent-generated results the same way as any automated interpreter output, with validation and integration tests. Onlyboxes is a solid option for developers seeking local control of agent execution and willing to run the required operational processes.