Comparison with Other Sandbox Tools

Note: The abx commands shown on this page are provided by the Node.js CLI in the cli repository (npm install -g @agent-sandbox/cli). CLI-specific documentation (CLI design, tutorial, reference) lives there too; this repository documents the Python SDK (agent-sandbox) and the container-side runtime (agent-sandbox-server).

This document provides an objective comparison of Agent Sandbox (abx) with other popular sandbox and remote execution platforms: E2B, Modal, and Docker Sandbox.

Positioning and Use Cases

SDK Comparison

Code Examples

Agent Sandbox:

from agent_sandbox.api.sandbox import Sandbox

async with await Sandbox.create(template="python-hello") as sb:
    result = await sb.run_code("print('hello')")
    print(result.text)

E2B:

from e2b import Sandbox

sandbox = Sandbox()
result = sandbox.run_code("print('hello')")
print(result.text)
sandbox.kill()

Modal:

import modal
app = modal.App("example")

@app.function()
def hello():
    print("hello")

Usage Style Comparison

Agent Sandbox supports both imperative (E2B style) and declarative (Modal style) programming paradigms:

E2B Style (Imperative) — fine-grained control:

from agent_sandbox.api.sandbox import Sandbox

async with await Sandbox.create() as sb:
    await sb.commands.run("pip install pandas")
    await sb.files.write("/app/data.csv", csv_content)
    result = await sb.commands.run("python3 /app/analyze.py")
    report = await sb.files.read("/app/report.txt")

Modal Style (Declarative) — call like a local function:

from agent_sandbox.declarative import sandbox

@sandbox(packages=["pandas"])
def analyze(data: list[dict]) -> dict:
    import pandas as pd
    df = pd.DataFrame(data)
    return {"mean": df["value"].mean(), "count": len(df)}

# Called just like a local function — automatically runs in a remote sandbox
result = analyze(my_data)

See examples/compat-demos/comparison.py for a complete example.

CLI Comparison

Infrastructure

Unique Capabilities of Agent Sandbox

E2B-shaped API

Agent Sandbox uses E2B-style method names, so E2B code can often be ported with few edits. It is not a drop-in replacement: configuration, authentication and several behaviours differ.

# Start of a migration: change the import, then follow the guide
from agent_sandbox.compat import Sandbox

See Migrating from E2B for the full guide.

Alibaba Cloud Integration

VPC, OSS volumes and custom domains are platform settings (template runtime configuration and account-level resources). The Python SDK has no API for them.

Registered Commands

Functions registered in the sandbox server (@sandbox.register, implemented by the Server SDK inside the container) are callable through one API:

result = await sandbox.run("analyze", data="input.csv")

This is not available in E2B or Modal.

MCP Server Integration

Agent Sandbox includes a built-in MCP (Model Context Protocol) Server, enabling AI IDEs (Cursor, Claude Desktop, VS Code) to operate sandboxes directly:

abx mcp install --target cursor

Provides 7 tools: create_sandbox, run_code, run_command, read_file, write_file, list_files, kill_sandbox.

Declarative Sandbox Decorator

from agent_sandbox import sandbox

@sandbox(template="python-hello", timeout=60)
async def my_task():
    ...

Other Platforms (Brief)

Summary

Further Reading

• Command and SDK map
• Migrating from E2B
• SDK Usage Guide
• CLI Tutorial
• MCP Integration
• Authentication