Automate Linear + PostgreSQL
Fast issue tracking that deserves fast automation. The production database your reports should come from. Put an agent between them and the hand-off stops being manual: it reads, decides, acts, and shows its work.
Meeting Follow-Through is a ready-made agent built around Linear; point its last step at PostgreSQL to finish this hand-off.
Automations teams run
Unlike field-mapping automation, the step in the middle is an agent: it can summarise, classify, judge and draft, not merely copy values from Linear into PostgreSQL.
When an issue is created in Linear…
…an agent can run parameterised queries in PostgreSQL — after reading the context and deciding it should.
When a status changes in Linear…
…an agent can write validated rows in PostgreSQL — after reading the context and deciding it should.
When a cycle ends in Linear…
…an agent can produce report extracts in PostgreSQL — after reading the context and deciding it should.
When a scheduled query runs in PostgreSQL…
…an agent can create issues with full context in Linear — with the reasoning recorded in the run’s receipt.
When a threshold is crossed in PostgreSQL…
…an agent can update status and assignees in Linear — with the reasoning recorded in the run’s receipt.
Linear to PostgreSQL, setup template
Start with a custom-api call to api.linear.app and a custom-api call to {host}:5432. Configure the Linear source payload, the agent input, and the PostgreSQL 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.
{
"id": "6c868ee6-dfb3-4be5-ab5d-7913460e5e52",
"name": "Linear → PostgreSQL",
"nodes": [
{
"id": "2d4b4f48-524e-4740-a9d5-1976a0349590",
"text": "Run on a schedule",
"x": 512,
"y": 144,
"isEditing": false,
"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": {}
},
{
"id": "9e864044-ceeb-4d8d-a347-e5c923ac4202",
"text": "Review source and draft",
"x": 512,
"y": 336,
"isEditing": false,
"type": "agnt-agent",
"icon": "connect",
"category": "action",
"isSelected": false,
"parameters": {
"agentId": "CONFIGURE_AGENTID",
"message": "Read the Linear item. Decide whether it warrants action in PostgreSQL, 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": "5f59571e-6b2f-4d6f-a4bc-b6772e8cf282",
"text": "Call PostgreSQL",
"x": 512,
"y": 528,
"isEditing": false,
"type": "custom-api",
"icon": "connect",
"category": "action",
"isSelected": false,
"parameters": {
"url": "postgresql://{user}:{password}@{host}:5432/{database}",
"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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"start": {
"id": "2d4b4f48-524e-4740-a9d5-1976a0349590",
"type": "output"
},
"end": {
"id": "9e864044-ceeb-4d8d-a347-e5c923ac4202",
"type": "input"
},
"startX": 800,
"startY": 168,
"endX": 512,
"endY": 360
},
{
"id": "87643b66-c1c3-48f2-af12-4d143933182c",
"start": {
"id": "9e864044-ceeb-4d8d-a347-e5c923ac4202",
"type": "output"
},
"end": {
"id": "5f59571e-6b2f-4d6f-a4bc-b6772e8cf282",
"type": "input"
},
"startX": 800,
"startY": 360,
"endX": 512,
"endY": 552
}
],
"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:
postgresql://{user}:{password}@{host}:5432/{database}. - 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 Meeting Follow-Through
Meeting Follow-Through is a working agent built around Linear. Install it, point its last step at PostgreSQL, and you have this hand-off without building it from an empty canvas.
Runs on your own machine · See what it does
What building this looks like
- Get AGNT Community Core. Free, local, no sign-up needed to run a workflow.
- Add both connections to the vault — Linear takes an API key pasted into the vault, PostgreSQL takes a local endpoint on your own machine.
- Configure the nodes above, or start from a marketplace workflow and edit it. The agent in the middle is the part worth spending time on.
- Run it once with an approval gate in place. The receipt tells you what it read, what it concluded, and what it changed in PostgreSQL.
Linear and PostgreSQL — common questions
Can I connect Linear to PostgreSQL without writing code?
Yes. Linear connects with an API key pasted into the vault and PostgreSQL with a local endpoint on your own machine, both from the local credential vault. You then describe the job in plain language — “when an issue is created, run parameterised queries” — and the agent handles the rest.
How is this different from a field-mapping Linear to PostgreSQL automation?
A field mapping copies values and breaks on the first record nobody anticipated. The middle step here reads the Linear item in full and decides — whether it matters, how to summarise it, and what belongs in PostgreSQL. Deciding to do nothing is a valid outcome, which no mapping can express.
Does my Linear and PostgreSQL data leave my machine?
Your credentials stay in a vault on your own disk rather than in someone else’s cloud. The only calls made are the ones the workflow needs: Linear, PostgreSQL, and your chosen model provider — which can be a local one.
What does a Linear and PostgreSQL run leave behind?
You get an auditable trail rather than a success flag. It shows the Linear context the agent worked from, the judgment it made about metrics pulled straight from source, and the exact change applied in PostgreSQL, with approvals attached where you required them.
