# session_memory

Capture + retention layer for Helix Forge — the **Capture** stage of the loop in
[../docs/PRD-helix-forge.md](../docs/PRD-helix-forge.md), built to the
[../docs/DESIGN-session-memory.md](../docs/DESIGN-session-memory.md) spec.

It scans coding-agent session logs, normalizes them into one schema, distills a
compact per-session digest, and ages out raw bulk under a **storage budget**
(dropping sessions once analyzed and once space is needed) rather than a fixed
time window.

## Layout

```
session_memory/
  adapters/common.py   # shared Normalized bundle + helpers
  adapters/claude.py   # Tier0 -> Tier1 normalizers, one per flavor
  adapters/codex.py    #   (rollout {timestamp,type,payload}, flat call_id join)
  adapters/grok.py     #   (per-session dir: chat_history + events + updates)
  core/schema.py       # Session / SessionEvent / Cost
  core/store.py        # SQLite rows + blob-dir bodies (Tier1) + digests/patterns (Tier2)
  core/cursor.py       # incremental ingest cursors
  core/digest.py       # Tier1 -> Tier2 promotion + outcome heuristic
  core/retention.py    # budget-based eviction sweep
  ingest.py            # one sweep: discover -> normalize -> store -> digest -> evict
  detect/signals.py    # signal extractors over digests
  detect/cluster.py    # cluster signals -> candidate patterns + cross-flavor flag
  detect/__main__.py   # python -m session_memory.detect (ranked report)
  config.toml          # store paths, retention caps, sources, repo->domain map
```

The local store lives under `session_memory/.store/` (gitignored).

## Run a sweep

```bash
# from the repo root
python -m session_memory.ingest                 # ingest + analyze + evict
python -m session_memory.ingest --dry-run       # discover + parse only, writes nothing
python -m session_memory.ingest --config path/to/config.toml
```

Output reports `discovered / ingested / skipped_unchanged / analyzed` and a
retention line (`freed`, `final_usage`, and per-pass eviction counts). Sweeps are
idempotent — re-running skips unchanged files via the cursor.

## Scheduling (cadence)

Retention is budget-based; the `cadence` in `config.toml` only decides how often
the sweep *runs*. Trigger it with the repo scheduler, e.g. daily:

```bash
# Claude Code: schedule a daily routine that runs the sweep
/schedule "daily session-memory sweep" -- python -m session_memory.ingest
```

or a cron entry / `/loop` on a timer. Push-capture (agent Stop/SessionEnd hooks)
can also enqueue a sweep; see design §7.

## Detect candidate patterns

After ingesting, mine the digests for recurring problem/success patterns:

```bash
python -m session_memory.detect                 # ranked report, cross-flavor first
python -m session_memory.detect --json          # machine-readable candidates
python -m session_memory.detect --min-frequency 3
```

Candidates are persisted to a Tier 2 `patterns` table and are the input to the
Curate phase (Phase 2). Patterns whose evidence spans more than one agent flavor
are flagged `[CROSS-FLAVOR]` — the highest-value reuse targets.

## Retention knobs (`[retention]` in config.toml)

| Key | Meaning |
|-----|---------|
| `raw_soft_cap_bytes` | begin evicting **analyzed** sessions above this (oldest first) |
| `raw_hard_cap_bytes` | absolute Tier 1 ceiling; overflow path may, as a last resort, evict un-analyzed sessions and report `data_loss` |
| `raw_max_age_days` | backstop: analyzed raw older than this is evictable regardless of space |
| `distilled_cap_bytes` | Tier 2 ceiling — **alert only**, never auto-dropped |

**Invariant:** a session's raw bytes are never dropped before its Tier 2 digest
exists, except the explicitly-reported hard-cap overflow path.

## Tests

```bash
python -m pytest          # 26 tests: schema, adapter, store, digest, retention, ingest
```

## Status

- **Phase 0** (AGENTIC-WP-0002): schema, store, digest, budget retention, Claude
  adapter, ingest sweep.
- **Phase 1** (AGENTIC-WP-0003): Codex + Grok adapters, multi-file session merge,
  and the Detect pipeline (signals → clustering → cross-flavor candidate patterns).
- **Next — Phase 2 (Curate):** review/approve candidates into a versioned pattern
  catalog. **Phase 3 (Distribute) / Phase 4 (Measure)** follow per the PRD.