Skillscript vs Sanbox

Detailed side-by-side comparison to help you choose the right tool

Skillscript

🔴Developer

agent-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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Starting Price

$0 (self-hosted)
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Sanbox

🔴Developer

agent-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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Starting Price

Not publicly listed
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Feature Comparison

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FeatureSkillscriptSanbox
Categoryagent-infrastructureagent-infrastructure
Pricing Plans1 listed plan1 listed plan
Starting Price$0 (self-hosted)Not publicly listed
Key Features
  • • Declarative, orchestration-only language: skills are DAGs of typed dispatches, not procedural code
  • • Not Turing complete by design — no eval, no subprocess, no arbitrary imports; sandboxing enforced at the language level
  • • Connector-mediated capability: skills invoke gated connectors with curated tool surfaces instead of importing packages
  • • One isolated MicroVM per run with a dedicated guest kernel and explicit CPU/memory/timeout limits
  • • Resumable filesystem snapshots preserving artifacts, agent state, and conversation history
  • • Fan-out CLI (npm i -g @sanlabs/sanbox-cli) for parallel runs from terminals, CI, Codex, or Claude Code

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

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

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Review SkillscriptReview Sanbox