Automate Airtable + Linear
Structured records with an API worth automating. Fast issue tracking that deserves fast automation. Put an agent between them and the hand-off stops being manual: it reads, decides, acts, and shows its work.
Document Extractor is a ready-made agent built around Airtable; point its last step at Linear to finish this hand-off.
Automations teams run
Each of these runs with a reasoning step between the two tools, so what reaches Linear is a decision about the Airtable event rather than a transcription of it.
When a record is created in Airtable…
…an agent can create issues with full context in Linear — after reading the context and deciding it should.
When a field changes in Airtable…
…an agent can update status and assignees in Linear — after reading the context and deciding it should.
When a view gains a row in Airtable…
…an agent can write cycle summaries in Linear — after reading the context and deciding it should.
When an issue is created in Linear…
…an agent can create and update records in Airtable — with the reasoning recorded in the run’s receipt.
When a status changes in Linear…
…an agent can query views for reporting in Airtable — with the reasoning recorded in the run’s receipt.
When a cycle ends in Linear…
…an agent can link records across tables in Airtable — with the reasoning recorded in the run’s receipt.
Copy this Airtable → Linear workflow
Start with a custom-api call to api.airtable.com and a custom-api call to api.linear.app. Configure the Airtable source payload, the agent input, and the Linear operation. The sequential template does not implement conditional writes or approval by itself.
This is a schema-checked starting point, not an end-to-end tested automation. Replace every CONFIGURE_ value, choose the operations you intend, connect credentials and map source outputs into later inputs. An edge orders steps; it does not supply a condition, an approval gate or a field mapping. Add and test those controls explicitly before any write. Cloud APIs and model providers may receive data. Keep the workflow inactive until tested with fixtures.
{
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"name": "Airtable → Linear",
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"type": "trigger-timer",
"icon": "connect",
"category": "trigger",
"isSelected": false,
"parameters": {
"fireOnStart": "No",
"scheduleType": "Interval",
"schedule": "Every 15 Minutes"
},
"description": "Setup template for Timer Trigger. This trigger node fires the workflow at specified intervals or at a specific time. Configure credentials, inputs and output mapping before use.",
"error": null,
"isActive": false,
"output": null,
"outputs": {}
},
{
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"text": "Review source and draft",
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"y": 336,
"isEditing": false,
"type": "agnt-agent",
"icon": "connect",
"category": "action",
"isSelected": false,
"parameters": {
"agentId": "CONFIGURE_AGENTID",
"message": "Read the Airtable item. Decide whether it warrants action in Linear, and explain why.\nSource input: CONFIGURE_SOURCE_INPUT. Produce a reviewable draft only; do not take external actions."
},
"description": "Setup template for Agent Chat. Chat with an AI agent from your agent library. Select an agent and send messages to interact with it within your workflow. Configure credentials, inputs and output mapping before use.",
"error": null,
"isActive": false,
"output": null,
"outputs": {}
},
{
"id": "5872e78d-133c-4bea-a6a4-fff9a7424bf0",
"text": "Call Linear",
"x": 512,
"y": 528,
"isEditing": false,
"type": "custom-api",
"icon": "connect",
"category": "action",
"isSelected": false,
"parameters": {
"url": "https://api.linear.app/graphql",
"method": "GET",
"authType": "None",
"query": "",
"headers": "{}"
},
"description": "Setup template for Custom API Request. This action node makes a custom API request to any endpoint with configurable method, headers, and authentication. Configure credentials, inputs and output mapping before use.",
"error": null,
"isActive": false,
"output": null,
"outputs": {}
}
],
"edges": [
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"type": "output"
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{
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"type": "output"
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"end": {
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"type": "input"
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"startX": 800,
"startY": 360,
"endX": 512,
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],
"zoomLevel": 1,
"canvasOffsetX": 0,
"canvasOffsetY": 0,
"isTinyNodeMode": false
}Configure Custom API Request
This action node makes a custom API request to any endpoint with configurable method, headers, and authentication.
- url — The URL of the API endpoint Current example:
https://api.linear.app/graphql. - method — The HTTP method for the request Current example:
GET. - authType — The type of authentication Current example:
None. - query — Query parameters for the request (e.g., 'key1=value1&key2=value2') Current example:
. - headers — Optional headers for the request. e.g., {"Content-Type": "application/json"} Current example:
{}.
Install Document Extractor
Document Extractor is a working agent built around Airtable. Install it, point its last step at Linear, and you have this hand-off without building it from an empty canvas.
Runs on your own machine · See what it does
Build it in AGNT
- Install AGNT — it runs on your own machine and needs no account to start.
- Authorise Linear (an API key pasted into the vault) and Airtable (an API key pasted into the vault). Nothing is stored anywhere but your disk.
- Describe the job in plain language, or paste the definition above; custom-api does the reading and custom-api the writing.
- Gate anything you would not want done unattended, then let it run and read the first receipt end to end.
Airtable and Linear — common questions
Can I connect Airtable to Linear without writing code?
You can build it entirely from the canvas. Authorise Airtable with an API key pasted into the vault, Linear with an API key pasted into the vault, then tell the agent what pipeline trackers that stay current should look like once Linear is involved. Code is an option for the unusual cases, never a requirement for the common ones.
How is this different from a field-mapping Airtable to Linear automation?
The difference shows up on the messy inputs. A rule chain needs every case enumerated in advance; an agent handles pipeline trackers that stay current the way a colleague would — reading the Airtable context, judging it, then writing to Linear with the reasoning recorded.
Does my Airtable and Linear data leave my machine?
AGNT Community Core is local-first, so there is no vendor middleman holding your Airtable or Linear tokens. The runtime talks directly to both services. Choose a local model and the content of what it reads never leaves the machine at all.
What does a Airtable and Linear run leave behind?
Every run is recorded end to end — the Airtable items examined, the decision and its reasoning, the resulting Linear write. That record is what makes it reasonable to let this run unattended, and what lets you widen its remit on evidence.
