Sanbox vs Skillscript
Detailed side-by-side comparison to help you choose the right tool
Sanbox
🔴Developeragent-infrastructure
Sanbox by Sanlabs runs AI agents in isolated, resumable MicroVM sandboxes with persistent filesystems, live run events, network ACLs, and self-hosting options.
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Not publicly listedSkillscript
🔴Developeragent-infrastructure
A declarative, sandboxed, non-Turing-complete language and runtime that lets AI agents crystallize learned procedures into auditable, reusable skills.
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$0 (self-hosted)Feature Comparison
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Sanbox - Pros & Cons
Pros
- ✓Kernel-level MicroVM isolation is a real boundary, not shared-kernel containerization
- ✓Snapshots make agent runs pausable and resumable from the exact state — inspectable mid-run
- ✓EU data residency, on-prem, and VPC deployment options cover compliance-sensitive teams
- ✓Live structured event stream removes the need to poll for run status
- ✓Reusable run templates pin the approved image, model, limits, and egress policy in one place
Cons
- ✗Pricing is not published — access requires a signup and likely sales conversation
- ✗No MCP support currently — integration is CLI, template, and run-based only
- ✗Newer entrant; smaller ecosystem than E2B, Modal, or Daytona in adapters and community examples
- ✗Documentation on runner adapter contracts is thin outside the OpenCode reference
- ✗CLI-first workflow may feel heavier than one-line SDK calls for quick prototyping
Skillscript - Pros & Cons
Pros
- ✓Non-Turing-complete language is the safety story — an unsafe skill literally cannot compile
- ✓Ed25519 signature model gives you human-in-the-loop approval without holding signing keys server-side
- ✓Two-way MCP surface: runtime is both server (agent-driven) and client (skills call MCP tools)
- ✓Local-model delegation reduces frontier-model spend on classify/extract/summarize subtasks
- ✓MIT license and self-hosted — no vendor dependency for a control-plane feature
Cons
- ✗Pre-1.0 project; API and skill format are still evolving
- ✗Declarative-only means procedural workflows must be expressed as DAGs, which is a learning curve
- ✗Connectors are your capability surface — poor connector design silently caps what agents can do
- ✗Documentation is thin compared to more mature agent-infra like LangGraph
- ✗Signature and approval workflow adds operational overhead for teams without existing key management
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