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A logic function run is capped by its timeoutSeconds (900 seconds maximum). Anything that can’t finish in that window — a full re-sync, a per-record fan-out, a third-party API that rate-limits you — has to be split into smaller runs. enqueueJob does exactly that: it asks the Twenty workers to run one of your app’s logic functions later, in its own process, with its own timeout budget. The caller returns immediately.

Enqueue a run

Import enqueueJob from twenty-sdk/logic-function and point it at the universalIdentifier of the logic function you want to run.
src/logic-functions/sync-all-contacts.ts
The target function receives payload as its handler argument, exactly like any other trigger. It must belong to the same application as the caller — enqueuing another app’s function is rejected with Logic function not found.
enqueueJob returns as soon as the job is accepted, not when it has run. It does not return the target’s result — have the target write what it produces to the key-value store or to a workspace record if you need to read it back.

Job options

Priority is not configurable yet. Enqueued jobs always run at the lowest priority, so platform work is never delayed behind application jobs. Control over priority is coming soon.
The queued run inherits the acting user of the function that enqueued it, so it acts with the same permissions.

Use it: page through a long sync

The classic shape is a function that enqueues itself with the next cursor. Each run does one page of work well inside its own timeout, and the chain stops when there is nothing left.
src/logic-functions/sync-contacts-page.ts

Fan out per record

When the work is naturally per-item, enqueue one job per item and let the workers process them in parallel instead of looping inline.

Good practice for long-running work

Two rules cover almost every long job: recurse instead of looping, and process a bounded chunk per run. A run that tries to do everything is the failure mode — it hits the timeout, and with a retry it starts the whole thing again from zero. Instead, size one chunk so it comfortably finishes inside timeoutSeconds, persist your position, and enqueue the next run.
src/logic-functions/enrich-companies-batch.ts
What makes this hold up:
  • Size the chunk from the slowest item, not the average. CHUNK_SIZE × worst-case item time has to fit in timeoutSeconds with room to spare, or the tail of a chunk is lost when the run is cut off.
  • Make the terminating condition explicit. Recurse only while a full chunk came back. A chain that stops on “no results” alone will keep going forever if the source ever returns a short page mid-way.
  • Persist progress before enqueuing the next run, so a failed link restarts from the last completed chunk instead of the beginning.
  • Keep each chunk idempotent. Reprocessing one chunk after a retry must not double-write — key writes on the record or external id you are processing.
  • Prefer a chunked chain over one giant fan-out when the work hits a rate-limited third party: a chain with delayMs paces itself, whereas thousands of jobs enqueued at once all become eligible immediately.
Retries re-run the whole handler. Keep enqueued handlers idempotent before setting retryLimit above 0.