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What you can do with AI Data Extractor

A free, standard-library-only Python toolkit that extracts your own local chat history from AI coding assistants into normalized JSONL files.

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jatechan/ai-data-extractor on GitHub

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What do you want to do?

4 use cases

  • Back up AI coding chat history before it is lost

    Export local chats from supported AI coding tools into timestamped JSONL files you keep yourself.

    View prompt
    Using the repo jatechan/ai-data-extractor, run `python extract.py --all --merge` in the <project folder> directory. Report the number of conversation lines written to each file in extracted_data/ and confirm that all_conversations.jsonl exists. Verify that each JSONL line parses as valid JSON with a messages array.

  • Turn your coding chats into a fine-tuning dataset

    Extract, secret-scan, and load your assistant conversations as a training set for a chat model.

    View prompt
    Using the repo jatechan/ai-data-extractor, extract the sources cursor and claude_code, run detect-secrets on the output, and load the JSONL files in <project folder>/extracted_data with the datasets library. Report how many conversations remain after filtering for assistant messages. Verify that no record still contains a flagged secret before using it for training.

  • Measure your AI coding usage across tools and projects

    Count sessions, projects, and models over time from your extracted assistant conversations.

    View prompt
    Using the repo jatechan/ai-data-extractor, read extracted_data/all_conversations.jsonl from <project folder> and count unique session_id values grouped by source and by month of the timestamp. Output a CSV table with those counts. Verify that the total of session counts equals the number of unique session_id values in the file.

  • Collect Aider chat logs from all your project folders

    Find every Aider transcript across your project directories and export them to JSONL.

    View prompt
    Using the repo jatechan/ai-data-extractor, run the aider source with --search-path set to <project folder>, and report how many project transcripts were found and written. Verify that each output line includes a messages array and a project_path value that matches one of the searched folders.

How to use AI Data Extractor

  1. Check you have what it needs: Python 3.9 or newer installed on the machine that holds the chat history; The AI coding tools installed under your own user account, with local chat history present; Permission to read and store your own local data, and a secure place to keep the output.
  2. Open jatechan/ai-data-extractor on GitHub and read the README and license.
  3. Run python extract.py --list to see which supported tools were found on this machine.
  4. Run python extract.py --all --merge to extract every found source and write all_conversations.jsonl.
  5. Check the new files in extracted_data/, one per source, with timestamps in the file names.
  6. Copy the extracted_data folder to encrypted storage, since it may hold client or proprietary work.

These steps come from our top use case, Back up AI coding chat history before it is lost, which also has a copy-paste agent prompt.

AI Data Extractor alternatives · Best AI repos for Software and DevOps

About this repo

Requirements

  • Python 3.9 or newer installed on the machine that holds the chat history
  • The AI coding tools installed under your own user account, with local chat history present
  • Permission to read and store your own local data, and a secure place to keep the output
  • For the fine-tuning case: the Hugging Face datasets library and a tokenizer for your target model
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Questions about AI Data Extractor

What is AI Data Extractor?

A free, standard-library-only Python toolkit that extracts your own local chat history from AI coding assistants into normalized JSONL files. It is an open-source project on GitHub: https://github.com/jatechan/ai-data-extractor.

Who is AI Data Extractor for?

Developers who use AI coding assistants on their own machine and want to back up, analyze, or reuse their own chat history.

What can I do with AI Data Extractor?

4 use cases so far, for example: Back up AI coding chat history before it is lost; Turn your coding chats into a fine-tuning dataset; Measure your AI coding usage across tools and projects. Each has steps and a copy-paste agent prompt.

What do I need to run AI Data Extractor?

Python 3.9 or newer installed on the machine that holds the chat history; The AI coding tools installed under your own user account, with local chat history present; Permission to read and store your own local data, and a secure place to keep the output; For the fine-tuning case: the Hugging Face datasets library and a tokenizer for your target model.

What are alternatives to AI Data Extractor?

Other hot AI repos in Software and DevOps: Harness2, iFixAi, Orca, Diagram Design, AI Memory.

Is AI Data Extractor safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.