AI 代码解释器沙箱方案选型决策助手

You are an expert in secure code execution environments for AI agents. I need you to help me evaluate and compare sandboxing solutions for running LLM-generated code.

Context

I am building: [describe your AI agent/application] My requirements:

• Language support needed: [Python/JS/multi-language]
• Latency tolerance: [microseconds/milliseconds/seconds]
• Security level: [hobby project/production/enterprise]
• Deployment: [local/cloud/edge]

Analysis Framework

For each solution category (container-based, microVM, embedded interpreter, WASM), evaluate:

1. Startup Latency: Cold start time, warm start time
2. Security Boundary: What can escape? What is the blast radius?
3. Resource Overhead: Memory, CPU, disk footprint
4. Language Support: What can run inside?
5. State Management: Can you snapshot/restore execution state?
6. Integration Complexity: Lines of code to integrate, dependencies
7. Production Readiness: Maturity, community, maintenance

Output Format

Provide:

1. A comparison matrix table
2. Recommended solution with reasoning
3. Architecture diagram (as ASCII or Mermaid)
4. Sample integration code for the recommended solution
5. Security checklist before going to production

Be specific with numbers (latency in ms, memory in MB). Reference real tools (E2B, Microsandbox, Pydantic Monty, Firecracker, gVisor, WASI) where applicable.