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Export Your ChatGPT History & Import It Into LLMOneStop (In 5 Minutes)

Hero graphic showing drag-and-drop import of conversations.json from ChatGPT into the LLMOneStop dashboard.
#chatgpt-export#import-transcripts#ai-chat-migration

TL;DR: Export your ChatGPT data, unzip the archive, locate conversations.json, then upload it to LLMOneStop > Settings > Data Management (provider: OpenAI). Your chats will appear under the folder OpenAI_imported.

Email
Subject:

Move your ChatGPT history to LLMOneStop in minutes

Hi there,

Your ideas shouldn’t be trapped in one app. Follow this quick guide to export your ChatGPT history and import it into LLMOneStop—no copy‑paste marathon required. If you get stuck, reply to this email and we’ll help 24×7.

—The LLMOneStop Team

Migrate ChatGPT Conversations to LLMOneStop

What you’ll need

  • Access to your ChatGPT account (OpenAI).
  • An LLMOneStop account.
  • A few minutes for the export email to arrive.

Step 1 — Export your OpenAI data (ChatGPT)

Use the official export flow exactly as shown below.

  1. Sign in to ChatGPT

  2. Open Settings

    At the bottom left corner (sidebar) click your profile email/name → Settings.

  3. Go to Data Controls

    Click the Data Controls menu.

  4. Request the export

    Under Export Data click Export.

  5. Confirm

    In the confirmation dialog click Confirm export.

  6. Check your email

    You should receive an email with your data. Note: The link in the email expires after 24 hours.

  7. Download and unzip

    Click Download data export — this saves a .zip file. Unzip it, and note that both chat.html and conversations.json are directly inside the unzipped folder (not inside any subdirectory).
Tip
Can’t find conversations.json?

Where is it in the export?

Look under:

conversations.json is in the main unzipped folder, right alongside chat.html.

Step 2 — Import into LLMOneStop

  1. Open Settings

    In the bottom left corner (sidebar), click on your profile email/name, then click Settings.


  2. Access Data Management

    In the Settings modal, click Data Management from the left menu.


    Opening LLMOneStop Settings from profile menu in sidebar

  3. Select provider

    Under Import Chat History, use the provider dropdown and choose OpenAI.

  4. Upload your file

    Drag and drop conversations.json into the upload zone (or click to browse) and start the import.

  5. Find your chats

    When processing finishes, your sessions appear in the folder OpenAI_imported inside LLMOneStop.

Troubleshooting

  • Import stalled: Refresh the page and retry the upload. Very large archives can take a few minutes.
  • Wrong file: Ensure you selected conversations.json, not chat.html.
  • Re-run import: Delete the OpenAI_imported folder and import again.

What exactly comes over?

Data TypeStatusNotes
Messages✅User & assistant text intact
Timestamps✅Original order preserved
Code blocks✅Syntax highlighting retained
Image uploads🚫Requires manual re-attachment*
Folders🚫All chats grouped under OpenAI_imported

*LLMOneStop strips potentially unsafe HTML. Embed images manually if needed.

A peek inside the raw file

conversations.json
[
  {
    "title": "Round button corners Tailwind",
    "create_time": 1744663272.112496,
    "update_time": 1744663725.933911,
    "mapping": {
      "client-created-root": {
        "id": "client-created-root",
        "message": null,
        "parent": null,
        "children": ["941fe0d5-a1ce-43bd-bf26-f847929ad038"]
      },
      "941fe0d5-a1ce-43bd-bf26-f847929ad038": {
        "id": "941fe0d5-a1ce-43bd-bf26-f847929ad038",
        "message": {
          "id": "941fe0d5-a1ce-43bd-bf26-f847929ad038",
          "author": { "role": "system", "name": null, "metadata": {} },
          "create_time": null,
          "update_time": null,
          "content": { "content_type": "text", "parts": [""] },
          "status": "finished_successfully",
          "end_turn": true,
          "weight": 0.0,
          "metadata": { "is_visually_hidden_from_conversation": true },
          "recipient": "all",
          "channel": null
        },
        "parent": "client-created-root",
        "children": ["0998f1d2-8df4-457b-8a67-f8b013decb31"]
      }, ...
]

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Discussion (3)

Join the conversation

Michael Roberts
2 days ago • AI Enthusiast

Great article! I've been trying to decide between Claude and GPT-4 for my project, and your breakdown of their strengths was incredibly helpful. I especially appreciated the section on context window comparisons.

Sarah Johnson
3 days ago • Data Scientist

I've been using multiple LLMs in my workflow for different tasks, exactly as you suggested. Using Claude for creative writing and GPT-4 for coding has been a game-changer for my productivity. Would love to see a follow-up article on how to create effective pipelines between different models!

James Chen
5 days ago • Software Engineer

Have you tested the code generation capabilities of these models with TypeScript specifically? I'm curious how they handle type definitions and generics. My experience has been mixed so far.

John DoeAuthor
4 days ago

Great question, James! I've been exploring this exact topic for a follow-up article. In my testing, Claude 3 Opus and GPT-4 both handle TypeScript quite well, but they have different strengths. Claude tends to produce more maintainable type definitions for complex objects, while GPT-4 seems better with generics. I'll share more comprehensive findings in my next article!

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