docs(programming-interface): add "Persisting state across runs" page#2739
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DaveHanns wants to merge 1 commit into
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docs(programming-interface): add "Persisting state across runs" page#2739DaveHanns wants to merge 1 commit into
DaveHanns wants to merge 1 commit into
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Adds guidance for a common pattern that isn't currently covered in the docs: how a scheduled/cron-driven Actor can read data written by a previous run of itself. - New page under Programming interface with two recommended patterns (list previous runs via the API vs. named key-value store), a side-by-side comparison, and the caveats around named storages under limited permissions. - Cross-links from the Programming interface landing card grid. - Clarifies in the `actor.json` reference that `environmentVariables` is a JSON object (not an array) and documents the `@secretName` reference form. Surfaced during an evaluation of Apify surfaces for agent-driven Actor development. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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Summary
Adds a new page under Programming interface that documents a pattern the current docs don't cover: how a scheduled or cron-driven Actor can read data written by a previous run of itself.
Two patterns are presented with runnable JavaScript and Python snippets:
STATErecord from that run's default KVS.Actor.openKeyValueStore('name')pattern, together with a:::cautionblock that spells out the limited-permissions edge cases (stores created under full permissions aren't visible to limited-permissions runs; stores are per-user).A side-by-side comparison table helps the reader choose between the two.
Related touch-ups
actor.jsonreference: clarified thatenvironmentVariablesis a JSON object (not an array), and documented the"@secretName"reference form for values managed viaapify secrets add. This is where new users trip up first.index.mdx): added a card linking to the new page.Test plan
actor.json, environment variables, permissions, migration guide, key-value store, storage pages) resolve.Surfaced during an evaluation of Apify surfaces for agent-driven Actor development.