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How to back up AI coding chat history before it is lost with AI Data Extractor

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

Credentials. Exported chats can contain API keys or secrets in code_context or tool_use fields, so scan the output before you share or store it elsewhere.

Who it is forA developer who has years of conversations in AI coding tools and fears an app update or cleanup will clear the local database.

The problemChat history is stored in each tool's own local format, so it is hard to keep a single copy of it.

The outcomeOne folder of timestamped JSONL files per source, plus an optional combined all_conversations.jsonl file.

What you need first

  • 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

Steps

  1. Run python extract.py --list to see which supported tools were found on this machine.
  2. Run python extract.py --all --merge to extract every found source and write all_conversations.jsonl.
  3. Check the new files in extracted_data/, one per source, with timestamps in the file names.
  4. Copy the extracted_data folder to encrypted storage, since it may hold client or proprietary work.

Prompt for your agent

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.

Caveat. The extractor reads each tool's storage as it exists today, and Windsurf and Trae use a heuristic that may return zero conversations after a storage change.

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