Automate PostgreSQL + YouTube
The production database your reports should come from. Long-form content worth repurposing everywhere else. Put an agent between them and the hand-off stops being manual: it reads, decides, acts, and shows its work.
Report Assembler is a ready-made agent built around PostgreSQL; point its last step at YouTube to finish this hand-off.
Automations teams run
These are not field mappings. An agent reads the PostgreSQL side, works out what it means, and only then decides what — if anything — should happen in YouTube.
When a scheduled query runs in PostgreSQL…
…an agent can pull transcripts and stats in YouTube — after reading the context and deciding it should.
When a threshold is crossed in PostgreSQL…
…an agent can draft descriptions and chapters in YouTube — after reading the context and deciding it should.
When a video is published in YouTube…
…an agent can run parameterised queries in PostgreSQL — with the reasoning recorded in the run’s receipt.
When a comment arrives in YouTube…
…an agent can write validated rows in PostgreSQL — with the reasoning recorded in the run’s receipt.
Copy this PostgreSQL → YouTube workflow
Start with a custom-api call to {host}:5432 and the youtube-api node. Configure the PostgreSQL source payload, the agent input, and the YouTube 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": "2ccb1d0e-4e28-4e01-a780-f2e107cfa182",
"name": "PostgreSQL → YouTube",
"nodes": [
{
"id": "a4d27c84-19f9-43d4-a452-58021951db90",
"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": "1fdd851a-973d-4513-ac2a-366968c6ea62",
"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 PostgreSQL item. Decide whether it warrants action in YouTube, 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": "c171590a-d630-41de-adf7-1522325ed738",
"text": "Configure destination in YouTube",
"x": 512,
"y": 528,
"isEditing": false,
"type": "youtube-api",
"icon": "connect",
"category": "action",
"isSelected": false,
"parameters": {
"action": "ADD_VIDEO_TO_PLAYLIST",
"playlistId": "CONFIGURE_PLAYLISTID"
},
"description": "Setup template for YouTube API. Interact with the YouTube API to manage videos, comments, playlists, and more. Configure credentials, inputs and output mapping before use.",
"error": null,
"isActive": false,
"output": null,
"outputs": {}
}
],
"edges": [
{
"id": "0e1ad757-2883-4795-a905-0cc43f7a3c86",
"start": {
"id": "a4d27c84-19f9-43d4-a452-58021951db90",
"type": "output"
},
"end": {
"id": "1fdd851a-973d-4513-ac2a-366968c6ea62",
"type": "input"
},
"startX": 800,
"startY": 168,
"endX": 512,
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{
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"type": "output"
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"end": {
"id": "c171590a-d630-41de-adf7-1522325ed738",
"type": "input"
},
"startX": 800,
"startY": 360,
"endX": 512,
"endY": 552
}
],
"zoomLevel": 1,
"canvasOffsetX": 0,
"canvasOffsetY": 0,
"isTinyNodeMode": false
}Configure YouTube API
Interact with the YouTube API to manage videos, comments, playlists, and more.
- action — The action to perform on YouTube Current example:
ADD_VIDEO_TO_PLAYLIST. - playlistId — The ID of the playlist Current example:
CONFIGURE_PLAYLISTID.
Install Report Assembler
Report Assembler is a working agent built around PostgreSQL. Install it, point its last step at YouTube, 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
- Download AGNT Community Core — free and local-first.
- Connect PostgreSQL with a local endpoint on your own machine and YouTube with one OAuth sign-in; both land in the local vault.
- Start from a marketplace workflow, or drop a custom-api node and youtube-api onto the canvas with an agent between them.
- Put an approval gate on anything consequential, then run it — the receipt shows what was read in PostgreSQL and what was written to YouTube.
PostgreSQL and YouTube — common questions
Can I connect PostgreSQL to YouTube without writing code?
No code is required. Add both connections in the vault (PostgreSQL uses a local endpoint on your own machine, YouTube uses one OAuth sign-in), then either install a marketplace workflow for this pair or drag the two nodes onto the canvas and put an agent between them.
How is this different from a field-mapping PostgreSQL to YouTube automation?
A field mapping copies values and breaks on the first record nobody anticipated. The middle step here reads the PostgreSQL item in full and decides — whether it matters, how to summarise it, and what belongs in YouTube. Deciding to do nothing is a valid outcome, which no mapping can express.
Does my PostgreSQL and YouTube 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: PostgreSQL, YouTube, and your chosen model provider — which can be a local one.
What does a PostgreSQL and YouTube run leave behind?
A receipt: what was read in PostgreSQL, which tools ran with which arguments, what the agent concluded, and what it wrote to YouTube. Anything consequential can sit behind an approval gate so a person signs off before it happens.
