Snapshot & fork

Goal: Use snapshots to checkpoint a sandbox at a stable state, fork into N independent branches to run experiments in parallel, then restore from the checkpoint if any branch produces a bad outcome.

What you’ll use: Sandboxes, Snapshots

What you’ll build

A reusable fork-and-compare pattern:

sandbox (baseline built) → snapshot_A
      ├── restore → branch-1 (try approach X) → compare
      ├── restore → branch-2 (try approach Y) → compare
      └── restore → branch-3 (try approach Z) → compare
                                                     └── pick winner, destroy losers

The same pattern applies to: A/B testing AI agent strategies, running parallel database migrations, comparing LLM prompt variants, and safe-guarding long operations behind a checkpoint.

Prerequisites

• A MIOSA workspace API key (msk_live_*) — see API Keys
• Node 22+ or Python 3.11+

Step 1 — Install and configure

Step 2 — Build a baseline and checkpoint it

Run all the expensive setup work once — dependency installs, model downloads, dataset loading — then snapshot the sandbox so every branch starts from the same state without repeating the cost.

Step 3 — Fork into branches

Restore the snapshot once per branch. Each restore creates an independent sandbox with the same filesystem and process state as the checkpoint — no shared memory, no interference.

Step 4 — Run each branch and collect results

Execute the experiment on each branch independently and capture the outcome metric.

Step 5 — Pick the winner and clean up

Compare outcomes, keep the best branch, and destroy the rest. If every branch failed, restore from the checkpoint and try a different strategy.

Restore to a known-good state on failure

If production goes wrong during a risky operation, restore from a pre-operation snapshot:

Snapshot lifecycle

Snapshots persist until you explicitly delete them. They do not expire.

What you learned

• sbx.snapshot() captures in-memory state without stopping the sandbox. The sandbox keeps running while the snapshot uploads.
• client.snapshots.restore(sandbox_id, snap_id) creates an independent sandbox from the checkpoint. N restores = N independent sandboxes.
• Parallel restores let you run experiments without the per-experiment setup cost.
• Snapshots are the rollback primitive: checkpoint before risk, restore on failure.

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