agentic-resources/session_memory/README.md

# 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)
  curate/schema.py     # SolutionPattern artifact + per-flavor rendering hints
  curate/catalog.py    # versioned, files-first Pattern Catalog (dedup on id)
  curate/gating.py     # promotion evidence bar + bloat guard
  curate/review.py     # discuss/approve/reject -> promote workflow
  curate/decisions.py  # hub decision audit trail (graceful local-queue fallback)
  curate/__main__.py   # python -m session_memory.curate (interactive / --auto-approve)
  catalog/             # the committed Pattern Catalog (source of truth)
  distribute/base.py   # Artifact + Distributor protocol + idempotent snippet markers
  distribute/claude.py # CLAUDE.md (or skill) renderer    } per-flavor edges
  distribute/codex.py  # AGENTS.md renderer                } (agnostic body,
  distribute/grok.py   # native instruction renderer       }  different targets)
  distribute/proposals.py  # scoping + proposed-not-applied output + active registry
  distribute/__main__.py   # python -m session_memory.distribute
  measure/metrics.py   # fleet metrics + persisted baseline snapshots
  measure/effect.py    # before/after per-pattern effectiveness
  measure/__main__.py  # python -m session_memory.measure
  config.toml          # store paths, retention caps, sources, repo->domain map, curate gate
```

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.

## Curate candidates into the Pattern Catalog

Review detect candidates into versioned **Solution Patterns** held in the
files-first catalog (`session_memory/catalog/`). The flow is **detect → curate →
(Phase 3) distribute**; `curate` refreshes candidates by running detect first.

```bash
python -m session_memory.curate                 # interactive review (a/r/d per candidate)
python -m session_memory.curate --auto-approve  # batch: promote all that clear the evidence bar
python -m session_memory.curate --json          # machine-readable result
```

- **Promotion** writes a `SolutionPattern` file (id = source candidate key, so
  re-promoting the same candidate dedups; content changes bump the semver and
  archive the prior version to `<id>.history.jsonl`).
- The **evidence bar** (`[curate.gate]`) sets two floors: a promote floor and a
  stricter *distribution* floor. A thin-but-real candidate lands `provisional`;
  one clearing the distribution floor lands `approved` + `distribution_ready`.
- A **bloat guard** flags duplicate / near-duplicate candidates so the catalog
  stays lean.
- Re-review is **idempotent** — a remembered decision is skipped unless the
  candidate's evidence changed; a prior reject is not re-surfaced.
- Each final promote/reject is recorded as a **hub decision**; if the hub is
  offline the decision is queued to `[curate].decision_queue` for later sync
  (the same after-the-fact pattern used in Phase 1).

### Curate knobs (`[curate]` / `[curate.gate]` in config.toml)

| Key | Meaning |
|-----|---------|
| `catalog_dir` | committed Pattern Catalog dir (source of truth) |
| `review_log` / `decision_queue` | remembered decisions + pending hub decisions (gitignored) |
| `min_frequency` / `min_sessions` / `min_cost_impact` | floor to promote at all |
| `dist_require_cross_flavor` | require cross-flavor evidence to be distribution-eligible |
| `dist_min_frequency` / `dist_min_cost_impact` | stricter floor for `distribution_ready` |

## Distribute patterns as per-flavor proposals

Render approved catalog patterns into per-flavor artifacts — **proposed, never
auto-applied** (HITL). Completes the loop: **detect → curate → distribute**.

```bash
python -m session_memory.distribute                 # proposals for all repos/flavors
python -m session_memory.distribute --repo state-hub --flavor claude
python -m session_memory.distribute --json
```

- Only `approved` + `distribution_ready` patterns are rendered; each pattern's
  `Scope` (repos/domains/flavors) decides where it lands (FR-X2).
- Each flavor renders the **same agnostic body** to its own target (Claude →
  `CLAUDE.md`/skill, Codex → `AGENTS.md`, Grok → native) via `rendering_hints`
  (FR-A3); blocks carry stable `BEGIN/END` markers so re-running updates in place.
- Output goes to `session_memory/proposals/<repo>/<target>` (gitignored,
  regenerated) — a reviewable diff a human applies (FR-X3). The committed
  `distribute/active_patterns.json` records which pattern+version is proposed in
  which `(repo, flavor)` (FR-X4).

## Measure effectiveness (closing the loop)

Track whether the fleet is getting cheaper / more reliable, and whether a
distributed pattern actually helped.

```bash
python -m session_memory.measure --label "baseline"      # snapshot + trend
python -m session_memory.measure --since 2026-06-07      # before/after a change
python -m session_memory.measure --no-save --json
```

- A **snapshot** (infra-overhead share, error rate, schema-thrash, token
  percentiles, success rate) is appended to `measure/baselines.jsonl` to build a
  trend (FR-M3).
- `--since DATE` splits sessions before/after a change and diffs the metrics, with
  an `improved` verdict per metric (FR-M1/FR-M2) — so ineffective patterns can be
  retired. Recorded pre-fix baseline (2026-06-07): 27 sessions, infra-overhead
  median 11.7 %, error rate 0.96, schema-thrash 8 sessions.

## 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          # schema, adapters, store, digest, retention, ingest, detect, curate
```

## 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).
- **Phase 2** (AGENTIC-WP-0004): Curate — Solution Pattern schema, versioned
  files-first Pattern Catalog, discuss/approve/reject review with an evidence bar +
  bloat guard, and hub-decision audit trail.
- **Detect hardening** (AGENTIC-WP-0005): session-quality filter + tool-mix /
  infra-overhead signals. **Error mining** (AGENTIC-WP-0006): recurring error
  fingerprints → root-cause patterns.
- **Phase 3** (AGENTIC-WP-0007): Distribute — per-flavor distributor adapters
  render approved patterns into proposed (HITL) artifacts, scoped by repo/domain,
  with an active-pattern registry.
- **Phase 4** (AGENTIC-WP-0009): Measure — fleet baseline/trend + before/after
  per-pattern effectiveness. The Capture → Detect → Curate → Distribute → Measure
  loop is closed.