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How to keep every line of a personal AI's chats on your machine with Lossless Memory

Store raw conversation logs verbatim in local JSONL files and build search indexes from them.

Credentials. The logs store conversation text verbatim, so any passwords or keys pasted into chats will be kept in plain files on disk.

Who it is forA developer who talks to a personal AI every day and wants nothing summarized, so the exact wording and timestamps survive across sessions.

The problemSummaries and similarity search drop the exact words and the time they were said, so the assistant later forgets what was actually decided.

The outcomeA local folder of per-day JSONL logs plus SQLite indexes, all rebuildable from the logs, with every record stamped with its time.

What you need first

  • Python with pip, and git to clone the repository
  • A machine you own; the README describes no server or cloud component
  • Conversation logs as per-day JSONL files with ts, role, and text fields, or Claude Code transcript files
  • Optional: a local sentence-transformers embedding model if you want semantic search as a fallback

Steps

  1. git clone https://github.com/aru-labs/lossless-memory
  2. cd lossless-memory
  3. pip install -e .
  4. cp config.example.json config.json
  5. Edit config.json to set your names, paths, and timezone (an IANA name such as America/New_York).
  6. Follow examples/quickstart.md to ingest the sample conversation and build the indexes.

Prompt for your agent

Using the repo aru-labs/lossless-memory, set up the memory layer on <target machine> following examples/quickstart.md. Expected output: the sample conversation ingested into per-day JSONL logs and the exact and vector indexes built under data_dir. Verify that the raw log files exist and that the 7-field records include a ts value.

Caveat. The README says it has only run for one person on one machine, so there is no multi-user setup to rely on.

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