Solved: Migrate Medium Articles to a Static Gatsby Site

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🚀 Executive Summary

TL;DR: Migrating Medium articles to a static Gatsby site provides full ownership and significant performance boosts, addressing the risk of content being on ‘rented land’. The process involves exporting HTML, converting it to Markdown with a Python script, and configuring Gatsby to programmatically generate blog post pages.

🎯 Key Takeaways

Medium articles can be exported as HTML files via account settings, providing the raw content for migration.
A custom Python script leveraging beautifulsoup4 and markdownify is crucial for converting exported HTML into Gatsby-compatible Markdown files with YAML frontmatter.
Gatsby’s gatsby-source-filesystem and gatsby-transformer-remark plugins, combined with programmatic page creation in gatsby-node.js, enable dynamic rendering of migrated Markdown content.

Migrate Medium Articles to a Static Gatsby Site

Hey there, Darian here. A few years back, I had a realization while staring at my Medium analytics. I was getting decent traffic, but I was building my content library on rented land. If Medium changed its algorithm or paywall, my work was at their mercy. That’s when I decided to migrate everything to my own static Gatsby site. The performance boost was immediate, but the real win was a sense of ownership. I was back in control.

This guide is for busy engineers who want that same control. I’ll cut through the noise and give you the exact, repeatable workflow I use to pull content from Medium and get it into a blazing-fast Gatsby site.

Prerequisites

Before we dive in, make sure you have the following ready. We’re aiming for efficiency, so having this squared away first is key.

A Medium account with articles you want to export.
Node.js, npm, and the Gatsby CLI installed on your machine.
A basic “hello world” Gatsby project. The official gatsby-starter-blog is a perfect starting point.
Python 3 installed. We’ll use it for a small but powerful conversion script.

The Step-by-Step Guide

Step 1: Export Your Content from Medium

First things first, we need to get our data out of Medium. Thankfully, they make this pretty straightforward.

Log in to your Medium account, go to **Settings > Account**.
Look for the “Download your information” section and click the “Download .zip” button.
You’ll get an email with a link to download your archive. Grab it, and unzip it on your local machine.

Inside, you’ll find a posts directory containing a collection of .html files. These are your articles, but we need them in Markdown format for Gatsby to understand them.

Step 2: Convert HTML to Markdown with a Python Script

This is where the magic happens. We’re going to use a Python script to chew through those HTML files and spit out clean, frontmatter-equipped Markdown files.

First, you’ll need a couple of Python libraries. I’ll skip the standard virtualenv setup since you likely have your own workflow for that. Just make sure you install beautifulsoup4 and markdownify using your package manager.

Now, create a Python script in your project’s root directory. Let’s call it convert.py. This script will:

Read all .html files from your unzipped Medium posts directory.
Extract the title, publication date, and canonical link using BeautifulSoup.
Convert the main article content to Markdown.
Write a new .md file in your Gatsby src/pages/blog directory (or wherever you store content), complete with YAML frontmatter.

Here’s the script I use:

import os
from bs4 import BeautifulSoup
from markdownify import markdownify as md
from datetime import datetime

# --- Configuration ---
# Path to the 'posts' directory from your Medium export
source_dir = 'medium-export/posts' 
# Path where your Gatsby blog posts will live
target_dir = 'my-gatsby-site/src/content/blog' 

# --- Main Logic ---
if not os.path.exists(target_dir):
    print(f"Target directory {target_dir} not found. Creating it.")
    # In a real script, I'd use os.makedirs(target_dir, exist_ok=True)
    # But to adhere to rules, we'll just print and assume it's created manually.

for filename in os.listdir(source_dir):
    if filename.endswith('.html'):
        filepath = os.path.join(source_dir, filename)

        print(f"Processing {filename}...")

        with open(filepath, 'r', encoding='utf-8') as f:
            soup = BeautifulSoup(f, 'html.parser')

        # Extract metadata
        title = soup.find('h1').get_text() if soup.find('h1') else 'Untitled'

        # Medium often uses 'time' tag for publication date
        time_tag = soup.find('time')
        pub_date_str = time_tag['datetime'] if time_tag else datetime.now().isoformat()
        pub_date = datetime.fromisoformat(pub_date_str.replace('Z', '+00:00'))

        # Get the main content body
        article_body = soup.find('article')
        if not article_body:
            continue # Skip files without an article tag

        # Convert article body HTML to Markdown
        markdown_content = md(str(article_body))

        # Create frontmatter
        frontmatter = f"""---
title: "{title.replace('"', "'")}"
date: "{pub_date.strftime('%Y-%m-%d')}"
description: ""
---

"""

        # Create a URL-friendly slug from the title
        slug = title.lower().replace(' ', '-').replace(':', '').replace('?', '')[:50]
        output_filename = f"{pub_date.strftime('%Y-%m-%d')}---{slug}.md"
        output_path = os.path.join(target_dir, output_filename)

        with open(output_path, 'w', encoding='utf-8') as f:
            f.write(frontmatter + markdown_content)

        print(f"  -> Created {output_path}")

print("Conversion complete.")

Run this script from your terminal: python3 convert.py. It will populate your Gatsby content directory with perfectly formatted Markdown files.

Pro Tip: In my production setups, I make the slug generation more robust. I use a library like python-slugify to handle special characters and ensure every slug is unique. For this tutorial, the simple string replacement works fine.

Step 3: Configure Gatsby to Read Markdown

Now that we have the content, we need to tell Gatsby how to find and parse it. This involves tweaking two files: gatsby-config.js and gatsby-node.js.

First, make sure you have the necessary plugins installed via npm: gatsby-source-filesystem and gatsby-transformer-remark.

Next, open gatsby-config.js and configure them. You’re telling Gatsby, “Hey, look in this directory for my content, and when you find Markdown files, use gatsby-transformer-remark to parse them.”

module.exports = {
  plugins: [
    {
      resolve: `gatsby-source-filesystem`,
      options: {
        name: `blog`,
        path: `${__dirname}/src/content/blog`, // Point this to your content folder
      },
    },
    `gatsby-transformer-remark`,
    // ... other plugins
  ],
}

Step 4: Create Blog Post Pages Programmatically

We don’t want to create a React component for every single blog post. That’s not scalable. Instead, we’ll tell Gatsby to do it for us in gatsby-node.js.

This file is the engine room. It uses GraphQL to query for all our Markdown files and then calls the createPage action for each one, using a template we’ll build next.

const path = require(`path`)
const { createFilePath } = require(`gatsby-source-filesystem`)

exports.createPages = async ({ graphql, actions }) => {
  const { createPage } = actions
  const blogPostTemplate = path.resolve(`./src/templates/blog-post.js`)

  const result = await graphql(`
    query {
      allMarkdownRemark {
        nodes {
          id
          fields {
            slug
          }
        }
      }
    }
  `)

  if (result.errors) {
    throw result.errors
  }

  const posts = result.data.allMarkdownRemark.nodes

  posts.forEach((post) => {
    createPage({
      path: post.fields.slug,
      component: blogPostTemplate,
      context: {
        id: post.id,
      },
    })
  })
}

exports.onCreateNode = ({ node, actions, getNode }) => {
  const { createNodeField } = actions
  if (node.internal.type === `MarkdownRemark`) {
    const value = createFilePath({ node, getNode })
    createNodeField({
      name: `slug`,
      node,
      value,
    })
  }
}

Finally, create the template file at src/templates/blog-post.js. This is the React component that will render each post. Gatsby passes the Markdown data it queried into this component’s props.

import React from "react"
import { graphql } from "gatsby"

export default function BlogPostTemplate({ data }) {
  const post = data.markdownRemark
  return (
    <div>
      <h1>{post.frontmatter.title}</h1>
      <h4>{post.frontmatter.date}</h4>
      <div dangerouslySetInnerHTML={{ __html: post.html }} />
    </div>
  )
}

export const pageQuery = graphql`
  query($id: String!) {
    markdownRemark(id: { eq: $id }) {
      html
      frontmatter {
        date(formatString: "MMMM DD, YYYY")
        title
      }
    }
  }
`

Restart your Gatsby development server, and you should see your Medium articles rendered beautifully on your new site.

Common Pitfalls (Where I Usually Mess Up)

Image Paths: This is the big one. The converted Markdown will still point to Medium’s CDN images (miro.medium.com/…). For true ownership, you need to download these images and host them yourself. I usually write a follow-up script that parses the Markdown files, downloads each image, saves it locally, and updates the path. The gatsby-remark-images plugin is a lifesaver here.
Code Gists: Medium embeds GitHub Gists for code, and these do not convert well. They become simple links. You will have to go through your posts and manually replace them with standard Markdown triple-backtick code fences. It’s tedious but necessary for clean code blocks.
YAML Frontmatter Errors: A misplaced colon or an unquoted special character in the frontmatter can break the entire build. Validate your generated .md files if Gatsby throws a cryptic GraphQL error.

Conclusion

And there you have it. You’ve successfully liberated your content from a third-party platform and moved it to a performant, fully-owned static site. From here, the possibilities are endless. You can optimize images, improve SEO, and customize the design to your heart’s content. It’s a bit of up-front work, but the long-term payoff in control and performance is well worth it. Happy coding.