Social posts for four platforms from a local Ollama model

What it is

This n8n workflow turns one topic into four draft posts without sending your text to a cloud model. You set the topic, the brand voice and the audience in one Set step. A request goes to your own Ollama server, where llama3:8b answers with JSON. The JSON holds a post for X, a post for LinkedIn, a title and body for Reddit and a caption for Instagram. A Code step pulls the JSON out of the reply. A second request asks the same model to check grammar, tone, format and hashtags and to return corrected versions. The last Code step keeps the reviewed posts. Six nodes run it, beside one sticky note. Nothing is published anywhere.

Who it suits

Developers, small teams and privacy-minded creators who want first drafts from a model that runs on their own machine. Skip it if you need polished copy, because a small local model writes plain drafts that need an edit. Skip it too if you want the posts sent for you. This workflow only writes them.

You need to know n8n’s settings: import a file and run a workflow by hand. You also need Ollama running on a machine you control.

Where to use it

It needs an n8n that can reach your Ollama server, so it suits a self-hosted n8n on the same machine or network.

• Your own n8n server: the same file and steps, with Ollama beside it
• n8n Cloud: it cannot reach localhost on your computer, so the address of the server must be one that n8n can reach
• Ollama: the model server that writes and reviews the posts

Setup

Download saves a zip with workflow.json and Mindket’s setup notes in your downloads folder. Setup is one model download and one check of the address.

1. Start Ollama and run ollama pull llama3:8b.
2. In n8n, create a workflow, open its menu, choose Import from File and pick workflow.json.
3. Open Write Posts With Ollama and Review Posts With Ollama. If n8n does not run on the same machine as Ollama, or runs in Docker, replace http://localhost:11434 with an address that n8n can reach.
4. Open Content Parameters and write your own topic, brand voice and audience.
5. Run the workflow by hand and read the last node’s output.

How to use it

Change the three fields in Content Parameters, run the workflow and copy the drafts you like. The first run can be slow, because Ollama loads the model into memory.

Example

You keep the topic as “Benefits of self-hosting AI models” and run the workflow.

{"twitter": "...", "linkedin": "...", "reddit_title": "...", "reddit_body": "...", "instagram_caption": "...", "reviewed": true}

The last node returns one item with a draft for each of the four networks, plus the topic and the time. The field reviewed says whether the second pass could be read.

For newcomers and experts

If you are new. n8n draws a workflow as boxes joined by lines, and each box, a node, does one job. This one asks a model on your own computer to write posts, then asks it to check them. Start with the sample topic to see the shape of the answer.

For experts. Both requests use /api/generate with stream turned off.

Good to know

This is a drafting tool. Read every post before you publish it.

• Fixes: the two request nodes had no method, so n8n would have sent a GET to an endpoint that takes a POST. Both are now POST. The Code steps read the text from response, because a JSON reply is already parsed and has no data field. They still accept the old shape
• Address: localhost means the machine n8n runs on. In Docker or on n8n Cloud it is not your Ollama server, so use an address n8n can reach
• Model: Ollama’s library lists llama3:8b as updated two years ago. You can name a newer model in both request nodes
• Quality: a small model can break a rule such as the 280 characters of an X post. The review step asks it to check and does not guarantee it
• Platform rules: Mindket did not check each network’s current length or hashtag rules
• Not run: Mindket did not run it against Ollama. The two Code steps were tested only on sample replies on a local computer
• Credentials: the JSON holds none