Source
Apify
DeeplineWorkflow runpilot · inspect · writeDestination
Notion
How to Use Apify List Store Actors in Deepline
Run Apify List Store Actors through Deepline, route the web research output, and write the reviewed result into Notion. The page shows which data points move, how the fields map between systems, the pilot command, guardrails, and provider docs.
Apify List Store Actors to Notion workflow
Apify List Store Actors to Notion is a supported Deepline workflow path. Use it when an agent needs to run Apify List Store Actors, inspect the returned fields, and write the reviewed result into Notion with run history, retries, and explicit failure states.
Use when
You need Apify list store actors reviewed before it writes into Notion.
Source output
Apify list store actors returns structured fields that Deepline records with provider attribution and row-level status.
Destination write
Notion receives only reviewed rows after the pilot command succeeds.
This workflow moves data from Apify into Notion with Deepline as the orchestration layer. It fits cases that need a repeatable, inspectable handoff with explicit auth, cost math, and recovery steps.
- Source app
- Apify
- Destination app
- Notion
- Run time
- 3 min
- Difficulty
- beginner
- Agent surface
- Deepline CLI, API, and workflow scheduler
- Write policy
- Two-row pilot before destination writes
- Apify auth
- Handled by Deepline -- connect once in the dashboard
- Notion auth
- Handled by Deepline -- connect once in the dashboard
- Apify tier
- Any plan with the required API access
- Notion tier
- Any Notion workspace with integration access
Quickstart setup
npm install -g deepline@latest && npm exec --yes --package=deepline@latest -- deepline setup --jsonFields agents can extract and verify
Apify source data points
- Actor input
- actor_id, input_url, query, dataset_id
- Scraped record
- url, title, text, extracted_fields
- Run status
- run_id, status, started_at, finished_at
- Source evidence
- source_url, snapshot_url, retrieved_at
Notion destination mapping
Apify Web result → Deepline normalized run output
url, query + url, actor_id, input_url, query, dataset_id
Claude Code sees the web result as structured JSON, then Deepline adds provider name, action slug, run ID, retrieved timestamp, and row-level status.
Deepline normalized run output → Notion Review row
source_key, summary, mapped_fields, review_status, run ID, title, status
Use Notion when the workflow output is a research artifact rather than a structured GTM system write.
Notion database or brief → Scheduled Deepline workflow
review status, dedupe key, rollback tag, next run window
After the two-row pilot is approved, the same mapping becomes a scheduled workflow with run history, retries, and loud failures.
Step-by-step
- 01
Install Deepline
Install the Deepline CLI and register your workspace. This gives agents a tested API surface instead of a browser-only workflow.
npm install -g deepline@latest && npm exec --yes --package=deepline@latest -- deepline setup --json - 02
Connect Apify
Connect Apify in the Deepline dashboard. Deepline stores the credential encrypted, exposes a test endpoint, and makes the action callable from the CLI or an agent. Provider reference: https://deepline.com/integrations/apify.
- 03
Connect Notion
Connect Notion as the destination. Use the provider page to confirm required scopes before writing data.
- 04
Run a two-row pilot
Run the smallest useful pilot first. The row range is end-exclusive, so --rows 0:2 tests exactly two rows before a larger batch. Inspect actor input, scraped record, run status plus provider attribution before writing anywhere.
deepline enrich --input leads.csv --output leads.enriched.csv --with 'result=apify_list_store_actors:{}' --json - 05
Deploy the reviewed prompt
After the pilot is correct, deploy the exact prompt as a Deepline workflow. The mapping from Apify to Notion is preserved with run history, retries, billing visibility, and a rollback tag.
> Use Apify List Store Actors to enrich the input web result, dedupe by domain and email, write results into Notion, and show me the exact rows that changed before deploying the workflow.
What this costs to run
For 1,000 leads: Pilot first; Deepline credits depend on the selected action and successful results.
Deepline reports Deepline credits and run history. Provider subscriptions or API entitlements stay in the connected provider account.
Why run it through Deepline
The workflow stays inspectable
Deepline exposes the Apify action, runs a pilot, records the output, and writes only reviewed rows to Notion.
Provider docs and GTM Stack pages are linked
The workflow links the Deepline provider docs, the GTM Provider Directory profile, and related workflow pages so agents can cite the right source before they call a tool.
The same prompt can become a schedule
Once the pilot works, the prompt can run on a schedule with Deepline run history, retry behavior, and explicit failure states.
Troubleshooting
Apify returns no rows
Cause: The input filter is too narrow, credentials are missing a required scope, or the provider account tier does not expose the action.
Fix: Open the Apify integration in Deepline, run the test endpoint, and then retry the workflow on --rows 0:2 with a broader filter.
Notion rejects the write
Cause: The destination field names, object IDs, campaign IDs, or permissions do not match the connected workspace.
Fix: Use the Notion provider page to inspect the object schema, then map columns explicitly before running the full batch.
The workflow works once but fails on a schedule
Cause: A required ID, campaign name, or date window was hardcoded in the prompt instead of resolved during each run.
Fix: Move IDs into workflow inputs or a lookup step, and keep the scheduled prompt focused on the durable business rule.
FAQ
Can Deepline run Apify List Store Actors directly?
Yes. Deepline exposes the Apify action as an agent-callable API and CLI step, so you can run a pilot, inspect the JSON, and then deploy the same logic as a workflow.
Should I write directly to Notion?
Run a two-row pilot first, inspect provider attribution and dedupe fields, then allow the workflow to write to the destination. This keeps the assertion intact without using a test hack.
How does this page help agents trust the workflow?
It puts primitives first: source provider, destination, action, pilot command, scope assumptions, troubleshooting, and links to the provider docs and related GTM Stack pages.
Keep exploring
LinkedIn (post commenters) → Lemlist
Connect the LinkedIn scraper and Lemlist in Deepline, paste a post URL into the chat, and Claude scrapes commenters, runs the email waterfall, generates first lines, and drops everyone into your Lemlist campaign.
Apify → HubSpot
Connect Apify and HubSpot in Deepline, tell Claude which actor to run and how to map fields, and it runs the scrape + enrichment + HubSpot upsert in one chat.
LinkedIn Sales Navigator (via Apify) → HubSpot
Connect Apify + HubSpot in Deepline, give Claude a Sales Nav search URL, and it scrapes profiles + runs email waterfall + upserts HubSpot Contacts with persona attributes intact.
LinkedIn Sales Navigator (via Apify) → Instantly
Connect Apify + Instantly in Deepline, give Claude a Sales Nav URL, and it scrapes + resolves emails + drops into Instantly with persona vars ready for your templates.
Apify → Instantly
Connect Apify and Instantly in Deepline, pick the actor, and Claude runs scrape + email waterfall + Instantly import.
Want this workflow pre-configured?
Run it on Deepline or fork the full skill pack on GitHub. Either way, the code is yours to read and change.