How to import Claude Code transcripts into a searchable memory with Lossless Memory

Who it is forA developer who uses Claude Code daily and wants its past sessions searchable by time without writing a custom importer.

The problemPast coding sessions live in transcript files that cannot be searched by when they happened or by the exact words used.

The outcomeTranscript lines converted into timestamped records in the lossless log and indexed for time-scoped recall.

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

git clone https://github.com/aru-labs/lossless-memory
cd lossless-memory
pip install -e .
cp config.example.json config.json
In config.json, set ingest_format to "claude_code" and point the input path at your transcript files.
Run the ingest and index steps described in examples/quickstart.md, then confirm the records appear in the per-day logs.

Prompt for your agent

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.

Caveat. The README calls the Claude Code importer the path with the most mileage, but another app would need its own importer.