Blog post drafted and edited by a local Ollama model

Newversion 1 · updated 2026-11-12

Blog post drafted and edited by a local Ollama model, picture 1

An n8n workflow that writes a blog post on your own computer. Set a topic, a tone and a length, and Ollama’s llama3:8b researches the topic, outlines it, writes a draft and edits it. No API key and no account are needed.

Blog post drafted and edited by a local Ollama model is an n8n workflow in the Writing category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page or copy its JSON, then import it into an n8n canvas. Nothing runs until you activate it in n8n.

At a glance

Runs in

Your own n8n on the machine that runs Ollama: the same file and steps
n8n in Docker: point the four requests at the host, since localhost inside a container is the container
n8n Cloud: it cannot reach localhost on your computer, so use an n8n of your own
Your editor or CMS: copy final_post from the run, or add a node after Final Output to save a file

Platform

n8nWorkflow automation, in n8n’s cloud or on your own server

Connects 1 app

How it works

Trigger

Manual run

It starts when you press Execute workflow, and there is no form. Three values are set in Set Blog Parameters:

The topic, a sample about self-hosting AI models in the file
The tone, “professional but conversational”
The word count, 1500

Actions

Ollama’s llama3:8b researches the topic and returns five key points
It turns the research into an outline with a title, H2 sections and a conclusion
It writes the full post in Markdown from the outline, in your tone and length
It edits the draft for clarity, grammar, flow and search
The finished post, the topic and the time are kept in one item

Result

A Markdown blog post in the final_post field, made and edited by a model on your own machine

Integrations

What you need before you start.

Ollama

Local language model

Runs llama3:8b four times through its generate endpoint: to research, to outline, to draft and to edit.

NeedsOllama running where n8n can reach it, with llama3:8b pulled. No key

Workflow check

Checked by Mindket on 13 Nov 2026

Steps: 7 steps, starting by hand
AI: No step calls an AI model
Sign-ins: No sign-in
API keys: No API key is left in the file
Step types: Mindket’s checker reads all 3 of its step types

Read from the workflow’s file, not from a run.

About

What it is

This n8n workflow writes a blog post with an AI model that runs on your own computer. You set a topic, a tone and a length in one node. Four calls to Ollama’s llama3:8b follow: research with five key points, an outline, a full draft and an edit pass. The last node holds the finished Markdown post, the topic and a time stamp. No key or account is involved. Seven nodes do the work.

Who it suits

Bloggers, small marketing teams and developers who want first drafts without paying for an AI service or sending their topics to one. Skip it if you need the quality of a large hosted model, or if your n8n cannot reach the computer that runs Ollama. Every post still needs a human edit and a fact check.

Who uses it, and how

Industry	Who uses it	How they use it	Example
Developer blogs	Engineers who document tools	Draft a how-to post on a topic they know, then correct it	A developer sets a topic about self-hosting and fixes the draft before posting.
Small business	Owners with no writing budget	Get a first draft for a service page or a news post	A bakery owner asks for a post on wedding cake sizes and rewrites it in their own voice.
Content agencies	Editors	Make quick outlines to show a client	An editor reads the outline from step two and sends it for approval before anything is written.
Internal documentation	Teams whose topics must stay in house	Write about subjects they would not send to a hosted model	A team drafts an internal explainer and its topic never leaves the office.

You need a little n8n: import a file and press Execute workflow. Most of the work is on the Ollama side, where you pull a 4.7 GB model. Nothing in the file states a setup time.

Where to use it

n8n has to reach Ollama at http://localhost:11434, so the two normally run on one machine.

Your own n8n on the machine that runs Ollama: the same file and steps
n8n in Docker: point the four requests at the host, since localhost inside a container is the container
n8n Cloud: it cannot reach localhost on your computer, so use an n8n of your own
Your editor or CMS: copy final_post from the run, or add a node after Final Output to save a file

Setup

Download saves a zip with workflow.json and Mindket’s setup notes in your downloads folder. Pull the model before the first run.

What you need first

Need	Detail
n8n	Your own, on the machine that runs Ollama or one that can reach it
Ollama	Installed and running, by default at `http://localhost:11434`
The model	`llama3:8b`, which Ollama’s library page lists at 4.7 GB

In n8n, create a workflow, open its menu, choose Import from File and pick workflow.json.
In a terminal, run ollama pull llama3:8b and wait for it to finish.
Open http://localhost:11434 in a browser. Ollama answers Ollama is running.
Open Set Blog Parameters and set topic, tone and word_count. Try 300 words first.
If n8n runs elsewhere or in Docker, replace http://localhost:11434 in the four Ollama requests with an address n8n can reach.
Save and press Execute workflow. Open Final Output when the run ends.

How to use it

Change the topic in Set Blog Parameters and press Execute workflow. A run takes minutes, since every step waits for the model. The first two requests time out after two minutes and the last two after five. When it ends, open Final Output. final_post is the article in Markdown.

What to change for a different result

To change	Where
Topic, tone and length	`Set Blog Parameters`
The model	`model` in each of the four request bodies, `llama3:8b` in the file
How inventive it is	`temperature` in each body: 0.3 for research and editing, 0.4 for the outline, 0.7 for the draft
How long an answer can run	`num_predict`: 1000 for research and outline, 4000 for draft and edit
The prompts	The text in each request body

Example

The topic is the sample that ships in the file, a how-to on running AI models on your own server with Ollama, at 1500 words in a professional but conversational tone.

Step one returns five key points. Step two turns them into a title, H2 sections and a conclusion. Step three writes the post in Markdown with ## headings, and step four edits it. Final Output holds the post, the topic and the time of the run. Check every claim, since a small model can state a wrong fact in a smooth voice.

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 is a straight line of seven. Each Ollama step receives the answer before it and passes its own on. Try 300 words first to see how long your computer takes.

For experts. Each request calls Ollama’s /api/generate with streaming off, so the answer arrives as one JSON object and its response field holds the text.

Where to look closer

Node	What it does	What to know
`1. Research Topic (Ollama)`	Posts the topic and a research prompt to `llama3:8b`	Its body is built with `JSON.stringify` in an expression, so quotes in a topic travel safely
Steps 2 to 4	Each prompt carries the answer before it	They read the tone and the word count back from `Set Blog Parameters`
`Final Output`	A Set node with `final_post`, `topic` and `generated_at`	`generated_at` is the run’s time in ISO form

Good to know

Everything runs on your machine, so speed and quality depend on it.

Context: Ollama’s context-length page, read on 12 November 2026, gives a 4k context on a machine with under 24 GiB of VRAM. The edit step sends the whole draft back in, so a long draft can be cut before the model sees it. Raise the context length in Ollama’s settings if the edited post comes back short
Time: a slow computer can pass a timeout, and the run stops at that step
Accuracy: a small local model can state wrong facts. Check every claim before you publish
Your data: the topic and the text go only to the Ollama address in the requests. With Ollama on your machine, nothing reaches an outside service
Keys: none are needed, and the JSON holds none
A repair: the original copy sent its Ollama requests as GET, which /api/generate does not take, and read each reply from a field that stays empty. Mindket’s copy posts and reads response

README and files

README

Blog post drafted and edited by a local Ollama model

An n8n workflow that writes a blog post with a model running on your own computer. You set a topic, a tone and a length in
one node. Four calls to Ollama's llama3:8b then research the topic, outline the post, write a draft and edit it. No API
key and no account are involved.

What is inside

workflow.json: the workflow as n8n exports it, 7 nodes and no sticky notes. It holds no credential.
README.md: these notes.

What we changed from the original copy

The copy Mindket started from could not finish a run. Mindket fixed two things:

The four Ollama requests named no method, so n8n sent them as GET. Ollama's /api/generate takes POST only. Each
request now says POST.
Steps 2, 3 and 4 and Final Output read the previous answer with JSON.parse($json.data).response. n8n puts a reply in
data only when it is not JSON. Ollama's reply is JSON, which n8n parses for you, so data is empty and the parse
fails. They now read $json.response.

Before you start

An n8n instance on the same machine as Ollama, or one that can reach it. n8n Cloud cannot reach localhost on your
computer.
Ollama, installed and running. By default it answers at http://localhost:11434.
The model llama3:8b. Ollama's library page lists it at 4.7 GB.

Set it up

In n8n, create a workflow, open its menu and choose Import from File. Pick workflow.json.
In a terminal, run ollama pull llama3:8b and wait until it finishes.
Open http://localhost:11434 in a browser. Ollama answers Ollama is running.
Open Set Blog Parameters and set topic, tone and word_count. Try 300 words first.
If n8n runs on another machine or in Docker, replace http://localhost:11434 in the four Ollama requests with an
address n8n can reach.
Save and press Execute workflow. Open Final Output when the run ends.

How a run goes

Manual Trigger starts the run when you press Execute workflow.
Set Blog Parameters holds three text values: the topic, the tone and the word count. The file ships with a sample topic
about self-hosting AI models, the tone "professional but conversational" and 1500 words.
1. Research Topic (Ollama) asks for five key points with supporting evidence, as a numbered list. Temperature 0.3,
at most 1000 tokens, a two-minute timeout.
2. Create Outline (Ollama) turns that research into a title, H2 sections with key points and a conclusion, aimed at
the word count. Temperature 0.4, at most 1000 tokens, a two-minute timeout.
3. Write Draft (Ollama) writes the whole post in Markdown from the outline, in the tone you chose, with an
introduction, a conclusion, tips and examples. Temperature 0.7, at most 4000 tokens, a five-minute timeout.
4. Edit & Polish (Ollama) edits the draft for clarity, grammar, flow and search. Temperature 0.3, at most 4000
tokens, a five-minute timeout.
Final Output keeps three fields: final_post, topic and generated_at, the time of the run.

Change the look

Topic, tone and length: Set Blog Parameters.
The model: model in each of the four request bodies.
How inventive it is: temperature in each body.
How long an answer can run: num_predict in each body.
The prompts: the text in each request body. Each one starts with the role it gives the model.
The time allowed: timeout under each request's options, in milliseconds.

Good to know

Ollama's context-length page, read on 12 November 2026, gives a 4k context on a machine with under 24 GiB of VRAM. The
edit step sends the whole draft back in, so a long draft can be cut before the model sees it. Raise the context length
in Ollama's settings if the edited post comes back short.
The first two steps wait up to two minutes and the last two up to five. A slow computer can time out, and the run
stops at that step.
A small local model can state wrong facts in a smooth voice. Check every claim before you publish.
The topic and the text go only to the Ollama address in the requests. With Ollama on your own machine, nothing reaches
an outside service, and no key is needed.
The post stays in the run's data. Copy final_post from Final Output, or add a node after it to save a file.
The JSON holds no credential and no key.

Files

NOTICE.md  595 B
README.md  4 KB
workflow.json  7 KB

3 files, 12 KB, downloaded as a zip.

What’s new

Version 1 · 2026-11-12

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Blog post drafted and edited by a local Ollama model

At a glance

Platform

How it works

Trigger

Actions

Result

Integrations

Ollama

Workflow check

About

What it is

Who it suits

Where to use it

Setup

How to use it

Example

For newcomers and experts

Good to know

README and files

Blog post drafted and edited by a local Ollama model

What is inside

What we changed from the original copy

Before you start

Set it up

How a run goes

Change the look

Good to know

What’s new

Reviews

Write a review

More from Mindket

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Blog post drafted and edited by a local Ollama model

Overview

At a glance

Platform

How it works

Trigger

Actions

Result

Integrations

Ollama

Workflow check

About

What it is

Who it suits

Where to use it

Setup

How to use it

Example

For newcomers and experts

Good to know

README and files

Blog post drafted and edited by a local Ollama model

What is inside

What we changed from the original copy

Before you start

Set it up

How a run goes

Change the look

Good to know

What’s new

Reviews

Write a review

More from Mindket

Huashu Design

Guizang PPT Skill

Claude Code Skills & Plugins

Similar in Writing

t5-base-grammar-correction

Hy-MT2-1.8B-1.25Bit-GGUF

Qwen3.8-27B-Longbook27-v2