agent-sandbox
Python SDK for the All-in-One Sandbox API, >=1.7.0
Decision gist · record as of 2026-08-14
Yes, if you need programmatic access to sandboxed execution environments for agent systems or automation. The package is actively maintained, has low install friction, and covers a broad set of execution contexts (shell, Python, Node.js, MCP). Verify whether your use case requires a local sandbox instance or cloud setup, and confirm MCP server availability if you plan to use that feature.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a running sandbox server at the specified base_url (e.g., http://localhost:8091 for local development, or Volcengine credentials for cloud deployment).
- Low friction install with a pure-Python wheel.
- Actively maintained as of March 2026.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and state significant changes.
last release 2026-03-24 (143 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 124,669 downloads/mo, #11,859 on PyPI
Alternatives
Verify before relying
pip install agent-sandbox
from agent_sandbox import Sandbox
client = Sandbox(base_url="http://localhost:8091")
result = client.shell.exec_command(command="ls -la")
print(result)- Whether the Volcengine provider requires cloud credentials to be useful, or if local sandbox mode works standalone
- Performance characteristics and latency for remote sandbox operations
- Whether MCP service integration requires external MCP servers to be running
What it is and what it does
Agent Sandbox is a Python SDK that wraps an All-in-One Sandbox API, giving you programmatic access to isolated execution environments for shell commands, file operations, Jupyter notebooks, Node.js scripts, and Model Context Protocol (MCP) interactions. It's designed for agent systems and automation workflows that need to safely execute untrusted or exploratory code in a sandboxed context.
The SDK provides both synchronous and asynchronous interfaces. You connect to a sandbox instance (either locally at http://localhost:8091 or via a cloud provider like Volcengine), then call methods on the client to execute commands, read/write files, run Python in Jupyter kernels, or execute JavaScript. It depends on httpx for HTTP, pydantic for data validation, and volcengine-python-sdk for cloud provider integration.
Use it for
- Execute shell commands safely in an isolated environment without exposing your host system
- Run untrusted Python code in a Jupyter kernel and capture the output
- Execute JavaScript code in a sandboxed Node.js environment from a Python agent
- Manage files within a sandbox—read, write, and search without direct filesystem access
- Build agent workflows that need to interact with MCP servers in a controlled manner
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need programmatic access to sandboxed execution environments for agent systems or automation.
The package is actively maintained, has low install friction, and covers a broad set of execution contexts (shell, Python, Node.js, MCP). Verify whether your use case requires a local sandbox instance or cloud setup, and confirm MCP server availability if you plan to use that feature.
Install
agent-sandbox on PyPI
Before you install
Low friction install with a pure-Python wheel. Actively maintained as of March 2026. Depends on httpx, pydantic, typing_extensions, and volcengine-python-sdk—all standard, well-maintained packages.
Requires a running sandbox server at the specified base_url (e.g., http://localhost:8091 for local development, or Volcengine credentials for cloud deployment).
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and state significant changes.
Quickstart
pip install agent-sandbox
from agent_sandbox import Sandbox
client = Sandbox(base_url="http://localhost:8091")
result = client.shell.exec_command(command="ls -la")
print(result)Verify before relying
- Whether the Volcengine provider requires cloud credentials to be useful, or if local sandbox mode works standalone
- Performance characteristics and latency for remote sandbox operations
- Whether MCP service integration requires external MCP servers to be running
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packageshttpxpydantictyping_extensionsvolcengine-python-sdk |
| Maintenance | Actively maintained 143 days since the last release |
| First released | |
| Downloads | 124,669 / month, #11,859 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: agent_sandbox-0.0.30-py2.py3-none-any.whl
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See also jupyter-mcp-server · k8s-agent-sandbox · agent-governance-toolkit-cli · prime-sandboxes · jupyter-ai · jupyter-mcp-tools · opensandbox · miniopy-async · agent-framework-hyperlight · openai-agents