DOCUMENTATION / USING THE WORKSPACE

State & continuity.

What stays between steps, what is saved separately, and what a restart can clear.

Development guide · Public beta in preparation. Release status ↗
On this pageWorking data stays in live PythonFive kinds of working or saved stateOptional checkpointsApproved memory is separateBefore relying on earlier work
V—1 / GUIDE 05

Working data stays in live Python

Each chat has a live Python runtime. Variables, imports, and helper functions remain available across calls and follow-up messages while that runtime stays alive.

A reset, restart, resource eviction, or process failure can end it. A new runtime does not automatically contain the old runtime’s objects.

Five kinds of working or saved state

KindWhat it holdsBoundary to remember
Chat recordsDurable conversation and associated records.Chat history does not keep Python objects alive.
Live PythonVariables, imports, helpers, and live objects.Available while that runtime is alive.
Output filesArtifacts written to a destination.Check that the file was actually saved and remains present.
Portable checkpointSupported values selected for persistence.Optional; excludes unsupported live objects and resources.
Approved memoryExplicit user memories and approved inferred facts.Stored separately, with controls to review it.

Optional checkpoints

Checkpointing saves supported Python values so they can be restored later. Both checkpointing and automatic restoration are off by default.

A checkpoint is not a complete copy of a running environment. Open files, browser connections, threads, and other live objects may need to be created again. A saved helper can also depend on data that is no longer available.

Approved memory is separate

Approved memory stores explicit user memories and approved inferred facts. It is not an automatic recording of everything a model encounters or learns.

A useful runtime variable and a durable user preference serve different purposes. Keep working data in the appropriate task context; use the memory workflow for facts you intend to retain as memory.

Before relying on earlier work

  1. Check whether the runtime restarted.
  2. Inspect the values or resources needed for the next step.
  3. Check any persisted output or restored value independently of the transcript's description.
  4. If something is absent, identify the gap before deciding how to reconstruct it.

Check what is present before repeating earlier steps.

Updated 7 September 2026 · VARIANT-1 documentation