Apify → Airtable Results Pusher

Push results from the most recent run of any Apify task or actor directly into Airtable — safely, predictably, and without duplicate runs.

This actor is designed as workflow glue between Apify and Airtable.
It does not scrape, rerun tasks, or process historical data.

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Why use this actor

• Works with Apify Tasks and Actors
• Uses ONLY the most recent run
• Optional SUCCEEDED-only safety check
• Prevents duplicate pushes with a run-lock
• Simple, predictable create-only writes
• Writes only fields that already exist in Airtable
• Designed for production workflows

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How it works (high level)

1. Paste your Apify Task or Actor URL (or ID)
2. Enter your Airtable Base ID and Table Name
3. Run the actor

Only the latest run is ever used.
The same run will never be pushed twice.

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⚠️ Important recommendation (read this first)

Use one Airtable table per Apify task or actor

For best results, it is strongly recommended to:

• Create a new Airtable table for each Apify task or actor
• Avoid mixing outputs from multiple scrapers into the same table

Why this matters

Different actors produce different fields.

Airtable:

• Has strict column types
• Does not handle dynamic schemas well

Mixing schemas leads to:

• Column type errors
• Skipped fields
• Partial writes
• Hard-to-debug failures

Keeping one table per actor ensures:

• Clean schema
• Predictable writes
• Easier automation
• Fewer errors

This is standard practice in production data pipelines.

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Quick start (2 minutes)

1. Run an Apify task or actor at least once
2. Create a new Airtable table
   • Default primary field is fine
3. Generate an Airtable personal access token
4. Paste:
   • Apify Source URL
   • Airtable Base ID
   • Airtable Table Name
5. Run the actor

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How Airtable columns are handled (very important)

This actor does NOT create Airtable columns automatically

Instead, it follows a safe, intentional schema model:

• The actor inspects the Airtable table
• It checks which columns already exist
• It writes only fields that match existing column names
• All other fields are skipped

This prevents:

• Accidental schema pollution
• Invalid column types
• Unusable tables

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Partial schema matching (key concept)

You do not need to add every dataset field as a column.

Rules:

• At least 2 matching columns must exist
• If fewer than 2 fields match:
  • No records are written
  • The actor logs exactly which fields you may want to add

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⭐ How to create missing columns (step-by-step)

If the actor cannot find enough matching columns:

1. Open the Run logs in Apify
2. Look for messages like:

Not enough matching columns. Airtable has: name, company You may want to add columns like: email, job_title, profile_url, location

3. Go to your Airtable table
4. Create any columns you want using those names
   (you do NOT need to add them all)
5. Rerun the actor

As soon as 2 or more fields match, the actor will:

• Write all matching fields
• Skip the rest
• Proceed normally

This gives you full control over your Airtable schema.

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What this actor does

• Pulls results from ONLY the most recent run
• Writes new Airtable records (no upserts)
• Flattens nested JSON into Airtable-friendly fields
• Writes only matching Airtable columns
• Skips unknown fields safely
• Prevents duplicate runs from being processed twice
• Handles Airtable rate limits safely

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What this actor does NOT do

• ❌ Does not start or rerun Apify tasks or actors
• ❌ Does not scrape or crawl websites
• ❌ Does not process historical runs
• ❌ Does not auto-create Airtable columns
• ❌ Does not dedupe rows inside Airtable

If you need scraping or enrichment, run those actors before this one.

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Required inputs

Apify Source URL

Paste one of:

• Task URL
• Actor URL
• Task ID
• Actor ID

Only the most recent run is used.

Airtable credentials

• API Key (personal access token)
• Base ID (e.g. appXXXXXXXXXXXXXX)
• Table Name

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Deduplication (run-lock)

Run-lock prevents pushing the same Apify run more than once.

This makes the actor safe to:

• schedule
• rerun
• automate

⚠️ This is run-level deduplication, not row-level deduplication.
New runs will always create new records.

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Pricing

Billed per Airtable record processed.

• $1 per 1,000 records
• Example: 2,500 records → $2.50
• A small actor start fee may also apply

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Common issues

“No runs found”

Run your Apify task or actor at least once.

“Latest run status is not SUCCEEDED”

Fix the upstream run and rerun it.

“No records written”

Check the logs:

• You likely have fewer than 2 matching Airtable columns
• The logs will list suggested column names to add

Airtable column errors

Usually caused by:

• Wrong column type
• Mixing schemas in one table

Creating a new table per actor almost always resolves this.

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Recommended workflow

Apify Task or Actor (scheduled) ↓ Apify → Airtable Results Pusher (this actor) ↓ Airtable (CRM, ops, pipelines, reporting)

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Summary

This actor is intentionally simple and opinionated:

• You control the Airtable schema
• The actor never mutates it
• Logs tell you exactly what to add
• Runs are safe, repeatable, and predictable

If you want a reliable bridge between Apify and Airtable — without surprises — this actor is built for that.