What you can do with Lossless Memory

4 use cases

• Keep every line of a personal AI's chats on your machine

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

  • Lossless Memory
  • Personal productivity
  • Intermediate
  View prompt

  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.

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• Pull the exact lines from a past evening or day

  Ask for a time phrase like yesterday evening and get the verbatim lines from that range in order.

  • Lossless Memory
  • Personal productivity
  • Intermediate
  View prompt

  Using the repo aru-labs/lossless-memory, run a time-scoped recall for the phrase <time phrase> against the logs in <target data directory> following examples/quickstart.md. Expected output: verbatim log lines from that range in chronological order with timestamps. Verify that every returned line falls inside the parsed date range.

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• Import Claude Code transcripts into a searchable memory

  Point the importer at Claude Code transcript files and build a time-indexed memory from them.

  • Lossless Memory
  • Claude Code
  • Personal productivity
  • Intermediate
  View prompt

  Using the repo aru-labs/lossless-memory, configure ingest_format as claude_code and import the transcript files at <transcript path> following examples/quickstart.md. Expected output: per-day JSONL logs containing the imported transcript lines with ts values. Verify that the record count matches the number of transcript messages you expect.

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• Keep a long-running assistant on topic after context compaction

  Maintain a short timestamped topic index that is injected into the model's context every turn.

  • Lossless Memory
  • Personal productivity
  • Advanced
  View prompt

  Using the repo aru-labs/lossless-memory, read docs/lll.md and set up an LLL topic index for <assistant name>. Expected output: a short list of timestamped topic markers that can be injected each turn. Verify that the human-set priority and completion marks are not edited by the model.

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How to use Lossless Memory

1. Check you have what it needs: 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.
2. Open aru-labs/lossless-memory on GitHub and read the README and license.
3. git clone https://github.com/aru-labs/lossless-memory
4. cd lossless-memory
5. pip install -e .
6. cp config.example.json config.json

These steps come from our top use case, Keep every line of a personal AI's chats on your machine, which also has a copy-paste agent prompt.

Lossless Memory alternatives · Best AI repos for Personal productivity

About this repo

• Who it is forWritten for a developer running one personal AI assistant on their own machine who wants its past conversations kept in full and searchable by time.
• Why it is hot
• LanguagePython
• Sourcegithub.com/aru-labs/lossless-memory · Back to Hot repos

Requirements

• 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

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