# PROJECT: memory-compactor

## Aim
Compact noisy conversational memory stretches (especially debug sessions) into durable resolved summaries while preserving recall quality.

## Current implementation
- HTTP client for custom mem0 server endpoints:
  - `POST /memories/all`
  - `POST /memories/raw`
  - `DELETE /memory/{id}`
- temporal clustering (`gap-minutes`)
- LLM segment extraction + summary writer
- dry-run reporting

## Operating mode
- default dry-run
- `--apply` to mutate memory state
- bounded deletions with `--max-deletes`

## Design notes
- write compacted summary before deleting old entries
- annotate summaries with provenance metadata (`segment_source_ids`, extraction)
- set `metadata.created_at` to the segment end timestamp so summary ordering stays intact

## Next steps
1. Add rollback snapshot file before apply
2. Add explicit per-user allowlist
3. Add cron integration helper (OpenClaw cron payload template)
4. Add quality checks pre/post compaction