Long-running agents
Durable execution for long-running agents
For teams with managed agents enabled, deploy a project and Python entrypoint, then send it tasks over time. This requires a qualified managed environment and account admission. Use Sandboxes > New > Run an agent, or the SDK and CLI. Availability does not imply an unlimited runtime or a guaranteed completion time.
Deploy and operate
- Deploy a local project, saved source asset or connected GitHub revision, with dependency setup, network permissions and an explicit spending limit.
- Submit plain-text tasks or structured JSON. Inspect runs, returned results and immutable revisions. A run keeps the code revision it accepted.
- Set worker limits. The default scales from zero to four workers, with a shared deployment budget. Requested warm workers can incur idle charges.
- Use session identifiers for ordered work, optional deadlines, pause/resume, cancellation and explicit retry within the existing authorization.
Wait and continue
Durable agents can wait for named events, receive signals, sleep until a timer and continue into a new segment with recorded input. These waits can release worker compute. Completed journaled steps replay their recorded results when execution resumes. Store larger run-scoped data as verified blobs up to 32 MiB.
Save application progress
With the accepted checkpoint-v1 recovery policy, step completion, durable waits,
continuations and run completion commit verified application files alongside
recorded progress. Your code must write its state into NODUS_CHECKPOINT_DIR
and load those files when restarting. Dependencies belong outside that state.
This restores saved application files and replays steps, not arbitrary memory.
The console exposes useful saved state separately from pending or failed saves. Unknown external effects remain blocked for explicit reconciliation against independent evidence. The step contract distinguishes repeatable work from external actions that cannot safely be repeated after a lost response.