feat: decision-log data layer for Decide mode (learning layer)

Storage + tools so Decide mode can learn instead of one-shot tie-breaking: log
the call Brian makes, resolve it later with the outcome, recall similar past calls
to ground new recommendations in his own track record.

- memory: decisions table + Decision dataclass + log/resolve/get/list/recall_decisions
  (embedding over situation+choice; embed failure never blocks a log)
- tools: log_decision / resolve_decision / recall_decisions handlers + specs
- tests: 9 covering roundtrip, resolve, open-only filter, similarity rank, tool layer

Data layer only — NOT wired into any mode's allow-list or the Decide card yet
(the prompt/taste part is left for Brian; see docs/DECISION_LOG.md). No behavior
changes until the tools are added to _DECIDE_TOOLS.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

docs/DECISION_LOG.md

@@ -0,0 +1,48 @@
+# Decision log — Decide mode's learning layer
+
+Built overnight on `feat/decision-log`. This is the **data layer + tools only**. The
+prompt/mode wiring (the taste part) is left for you on purpose — no persona/card edits
+were made.
+
+## The idea
+
+Decide mode is currently a one-shot tie-breaker. The learning layer gives it memory:
+log the call Brian actually makes, record how it turned out, and recall similar past
+calls so a new recommendation leans on his own track record instead of generic advice.
+
+Lifecycle: **log** (when the call is made) → **resolve** (later, with the outcome) →
+**recall** (next time something similar comes up).
+
+## What's built
+
+**Storage** (`lyra/memory.py`):
+- `decisions` table — situation, options, choice, rationale, confidence (1-5), tags,
+  embedding (over situation+choice), outcome, outcome_rating (-1/0/+1), resolved_at.
+- `Decision` dataclass (with a `.resolved` property).
+- `log_decision(...) -> id`, `resolve_decision(id, outcome, rating) -> bool`,
+  `get_decision(id)`, `list_decisions(limit, open_only)`,
+  `recall_decisions(query, k)` (cosine over embeddings, each hit carries `.score`).
+- Embedding failures never block a log (blob just stays NULL).
+
+**Tools** (`lyra/tools.py`) — handlers + specs, wired into `dispatch`:
+- `log_decision` (situation, choice, options?, rationale?, confidence?, tags?)
+- `resolve_decision` (decision_id, outcome, rating?)
+- `recall_decisions` (query, k?) — returns past calls with their verdicts
+
+**Tests** (`tests/test_decisions.py`) — 9, covering roundtrip, resolve, open-only
+filtering, similarity ranking, and all three tool handlers. Full suite green, ruff clean.
+
+## What's left for you (the wiring)
+
+1. **Allow-list** — add the three tools to `_DECIDE_TOOLS` in `lyra/modes.py`
+   (and decide whether `recall_decisions` also belongs in Study). One-liner, but it's
+   the gate that lets her actually call them.
+2. **Decide card guidance** — tell her *when* to use them: recall similar decisions
+   before recommending, log once Brian commits to a call, and circle back to resolve
+   open ones. This is the part I didn't want to touch without you (no bandaids).
+3. **Optional surfacing** — open/unresolved decisions are a natural thing for her to
+   raise (thought loop / ping), and a small UI panel could list them. Not built.
+4. **Optional auto-prompt to resolve** — the dream loop could notice decisions that
+   have been open a while and nudge for an outcome.
+
+Nothing here changes behavior until step 1 — the tools exist but no mode offers them.

lyra/memory.py

@@ -115,6 +115,26 @@ CREATE TABLE IF NOT EXISTS ratings (
     note TEXT
 );
 CREATE INDEX IF NOT EXISTS idx_ratings_created ON ratings(created_at);
+
+-- Decisions Lyra helped Brian make (Decide mode's learning layer). Logged when the
+-- call is made; resolved later with how it actually turned out; recalled by semantic
+-- similarity so a new call can lean on how similar ones went. embedding covers the
+-- situation + choice. Resolved rows (with an outcome) are the signal worth recalling.
+CREATE TABLE IF NOT EXISTS decisions (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    created_at TEXT NOT NULL,
+    situation TEXT NOT NULL,    -- what was being decided
+    options TEXT,               -- the alternatives weighed (free text / newline list)
+    choice TEXT NOT NULL,       -- the call that was made
+    rationale TEXT,             -- why
+    confidence INTEGER,         -- 1-5, how sure at the time (nullable)
+    tags TEXT,                  -- domain: poker | life | build | ... (comma-separated)
+    embedding BLOB,
+    outcome TEXT,               -- filled in on resolve: what actually happened
+    outcome_rating INTEGER,     -- -1 bad / 0 mixed / +1 good (nullable until resolved)
+    resolved_at TEXT
+);
+CREATE INDEX IF NOT EXISTS idx_decisions_created ON decisions(created_at);
 """
 
 _conn: sqlite3.Connection | None = None
@@ -184,6 +204,26 @@ class Era:
     score: float | None = None
 
 
+@dataclass
+class Decision:
+    id: int
+    created_at: str
+    situation: str
+    choice: str
+    options: str | None = None
+    rationale: str | None = None
+    confidence: int | None = None
+    tags: str | None = None
+    outcome: str | None = None
+    outcome_rating: int | None = None
+    resolved_at: str | None = None
+    score: float | None = None
+
+    @property
+    def resolved(self) -> bool:
+        return self.resolved_at is not None
+
+
 def _to_blob(vec: list[float]) -> bytes:
     return np.asarray(vec, dtype=np.float32).tobytes()
 
@@ -645,6 +685,87 @@ def backfill_journal_embeddings(limit: int | None = None) -> int:
     return n
 
 
+# --- decisions (Decide mode's learning layer) ---------------------------------
+
+def _row_to_decision(r: sqlite3.Row) -> Decision:
+    return Decision(
+        id=r["id"], created_at=r["created_at"], situation=r["situation"],
+        choice=r["choice"], options=r["options"], rationale=r["rationale"],
+        confidence=r["confidence"], tags=r["tags"], outcome=r["outcome"],
+        outcome_rating=r["outcome_rating"], resolved_at=r["resolved_at"],
+    )
+
+
+def log_decision(situation: str, choice: str, options: str | None = None,
+                 rationale: str | None = None, confidence: int | None = None,
+                 tags: str | None = None) -> int:
+    """Record a decision Brian made. Embeds situation+choice so similar future calls
+    can recall it. Returns the new row id. Resolve it later with resolve_decision."""
+    now = datetime.now(timezone.utc).isoformat()
+    try:
+        [emb] = llm.embed([f"{situation}\nChose: {choice}"])
+        blob = _to_blob(emb)
+    except Exception:
+        blob = None  # never block logging a decision on the embedder being down
+    conn = _connection()
+    with conn:
+        cur = conn.execute(
+            "INSERT INTO decisions (created_at, situation, options, choice, rationale, "
+            "confidence, tags, embedding) VALUES (?, ?, ?, ?, ?, ?, ?, ?)",
+            (now, situation, options, choice, rationale, confidence, tags, blob),
+        )
+    return int(cur.lastrowid)
+
+
+def resolve_decision(decision_id: int, outcome: str, outcome_rating: int | None = None) -> bool:
+    """Record how a past decision turned out. Returns False if the id is unknown."""
+    now = datetime.now(timezone.utc).isoformat()
+    conn = _connection()
+    with conn:
+        cur = conn.execute(
+            "UPDATE decisions SET outcome = ?, outcome_rating = ?, resolved_at = ? WHERE id = ?",
+            (outcome, outcome_rating, now, decision_id),
+        )
+    return cur.rowcount > 0
+
+
+def get_decision(decision_id: int) -> Decision | None:
+    r = _connection().execute("SELECT * FROM decisions WHERE id = ?", (decision_id,)).fetchone()
+    return _row_to_decision(r) if r else None
+
+
+def list_decisions(limit: int = 20, open_only: bool = False) -> list[Decision]:
+    """Recent decisions, newest first. open_only -> only those not yet resolved."""
+    sql = "SELECT * FROM decisions"
+    if open_only:
+        sql += " WHERE resolved_at IS NULL"
+    sql += " ORDER BY created_at DESC LIMIT ?"
+    rows = _connection().execute(sql, (limit,)).fetchall()
+    return [_row_to_decision(r) for r in rows]
+
+
+def recall_decisions(query: str, k: int = 5) -> list[Decision]:
+    """Top-k past decisions semantically similar to `query`, each with a `score` — so a
+    new call can lean on how similar ones went. Resolved rows carry the real signal."""
+    [q_vec] = llm.embed([query])
+    q = np.asarray(q_vec, dtype=np.float32)
+    rows = _connection().execute(
+        "SELECT * FROM decisions WHERE embedding IS NOT NULL"
+    ).fetchall()
+    if not rows:
+        return []
+    matrix = np.stack([_from_blob(r["embedding"]) for r in rows])
+    norms = np.linalg.norm(matrix, axis=1)
+    scores = (matrix @ q) / (norms * np.linalg.norm(q) + 1e-9)
+    top_idx = np.argsort(scores)[::-1][:k]
+    out = []
+    for i in top_idx:
+        d = _row_to_decision(rows[i])
+        d.score = float(scores[i])
+        out.append(d)
+    return out
+
+
 def get_setting(key: str, default: str | None = None) -> str | None:
     """A runtime setting value (UI-tunable), or `default` if unset."""
     r = _connection().execute("SELECT value FROM settings WHERE key = ?", (key,)).fetchone()

lyra/tools.py

@@ -81,6 +81,65 @@ def _thought_response(args: dict, ctx: dict) -> str:
             "next time I'm thinking.")
 
 
+def _log_decision(args: dict, ctx: dict) -> str:
+    situation = (args.get("situation") or "").strip()
+    choice = (args.get("choice") or "").strip()
+    if not situation or not choice:
+        return "Need both what was being decided and the call you landed on."
+    conf = args.get("confidence")
+    try:
+        conf = int(conf) if conf is not None else None
+    except (TypeError, ValueError):
+        conf = None
+    did = memory.log_decision(
+        situation=situation, choice=choice,
+        options=(args.get("options") or "").strip() or None,
+        rationale=(args.get("rationale") or "").strip() or None,
+        confidence=conf, tags=(args.get("tags") or "").strip() or None,
+    )
+    logbus.log("info", "decision logged (tool)", id=did)
+    return (f"Logged decision #{did}. When you know how it played out, tell me and "
+            "I'll close the loop on it.")
+
+
+def _resolve_decision(args: dict, ctx: dict) -> str:
+    try:
+        did = int(args.get("decision_id"))
+    except (TypeError, ValueError):
+        return "Which decision? I need its id (#number)."
+    outcome = (args.get("outcome") or "").strip()
+    if not outcome:
+        return "Tell me how it turned out so I can record the outcome."
+    rating = args.get("rating")
+    try:
+        rating = int(rating) if rating is not None else None
+    except (TypeError, ValueError):
+        rating = None
+    if not memory.resolve_decision(did, outcome, rating):
+        return f"(couldn't find decision #{did})"
+    logbus.log("info", "decision resolved (tool)", id=did, rating=rating)
+    return f"Closed the loop on decision #{did}. That goes into how I weigh the next one."
+
+
+def _recall_decisions(args: dict, ctx: dict) -> str:
+    query = (args.get("query") or "").strip()
+    if not query:
+        return "Give me the gist of the call you're weighing and I'll pull similar past ones."
+    hits = memory.recall_decisions(query, k=int(args.get("k") or 4))
+    if not hits:
+        return "No comparable past decisions on record yet."
+    lines = []
+    for d in hits:
+        head = f"#{d.id} ({d.created_at[:10]}): {d.situation} → {d.choice}"
+        if d.resolved:
+            verdict = {1: "went well", 0: "mixed", -1: "went badly"}.get(d.outcome_rating, "resolved")
+            head += f" — {verdict}: {d.outcome}"
+        else:
+            head += " — outcome still open"
+        lines.append(head)
+    return "\n".join(lines)
+
+
 # name -> {spec (OpenAI function tool), handler}
 TOOLS: dict[str, dict] = {
     "journal_write": {
@@ -481,6 +540,34 @@ TOOLS.update({
         {"thread_id": {**_N, "description": "The thread id (#number) of the thought he reacted to."},
          "brian_said": {**_S, "description": "What Brian said / his take, in your words."}},
         ["thread_id", "brian_said"])},
+    "log_decision": {"handler": _log_decision, "spec": _f(
+        "log_decision",
+        "Record a real decision Brian lands on (especially in Decide mode) so it can "
+        "inform future calls. Capture it once he's settled — what he was deciding, the "
+        "call, and why. Outcome comes later via resolve_decision.",
+        {"situation": {**_S, "description": "What was being decided, in Brian's terms."},
+         "choice": {**_S, "description": "The call he landed on."},
+         "options": {**_S, "description": "The alternatives weighed (optional, newline/free text)."},
+         "rationale": {**_S, "description": "Why this call (optional)."},
+         "confidence": {**_N, "description": "How sure he was, 1-5 (optional)."},
+         "tags": {**_S, "description": "Domain, comma-separated: poker | life | build | ... (optional)."}},
+        ["situation", "choice"])},
+    "resolve_decision": {"handler": _resolve_decision, "spec": _f(
+        "resolve_decision",
+        "Close the loop on a previously logged decision once Brian knows how it turned "
+        "out. This is what makes the decision log learn — outcomes sharpen future calls.",
+        {"decision_id": {**_N, "description": "The decision id (#number)."},
+         "outcome": {**_S, "description": "What actually happened, in Brian's terms."},
+         "rating": {**_N, "description": "How it went: 1 good / 0 mixed / -1 bad (optional)."}},
+        ["decision_id", "outcome"])},
+    "recall_decisions": {"handler": _recall_decisions, "spec": _f(
+        "recall_decisions",
+        "Pull past decisions similar to one Brian's weighing now, with how they turned "
+        "out — so you can ground a recommendation in his own track record rather than "
+        "generic advice. Use it when he's deciding something with precedent.",
+        {"query": {**_S, "description": "The gist of the current call / situation."},
+         "k": {**_N, "description": "How many to pull (default 4)."}},
+        ["query"])},
     "start_session": {"handler": _start_session, "spec": _f(
         "start_session",
         "Begin a live poker session. Call when Brian sits down to play.",

tests/test_decisions.py

@@ -0,0 +1,103 @@
+"""Decision log (Decide mode's learning layer): log -> resolve -> recall, + tools."""
+from __future__ import annotations
+
+import importlib
+
+import pytest
+
+
+@pytest.fixture
+def mem(tmp_path, monkeypatch):
+    monkeypatch.setenv("LYRA_DB_PATH", str(tmp_path / "test.db"))
+    from lyra import llm
+    # Deterministic, content-dependent embeddings so recall ordering is meaningful:
+    # "cleveland"/"tournament" cluster on axis 0, "stocks"/"money" on axis 1.
+    def fake_embed(texts):
+        out = []
+        for t in texts:
+            t = t.lower()
+            poker = sum(w in t for w in ("tournament", "cleveland", "poker", "buy-in"))
+            money = sum(w in t for w in ("stocks", "money", "invest", "sell"))
+            out.append([float(poker), float(money), 0.1])
+        return out
+    monkeypatch.setattr(llm, "embed", fake_embed)
+    import lyra.memory as memory
+    importlib.reload(memory)
+    return memory
+
+
+def test_log_and_get_roundtrip(mem):
+    did = mem.log_decision(
+        situation="Play the Cleveland turbo tournament tomorrow?",
+        choice="Yes, but only the noon flight",
+        options="skip it / noon flight / both flights",
+        rationale="20-min levels suit my aggression; one flight caps the variance",
+        confidence=4, tags="poker,tournament",
+    )
+    d = mem.get_decision(did)
+    assert d.situation.startswith("Play the Cleveland")
+    assert d.choice == "Yes, but only the noon flight"
+    assert d.confidence == 4 and d.tags == "poker,tournament"
+    assert not d.resolved and d.outcome is None
+
+
+def test_resolve_closes_the_loop(mem):
+    did = mem.log_decision(situation="Sell the stocks now?", choice="Hold")
+    assert mem.resolve_decision(did, "Recovered 12% the next week", outcome_rating=1)
+    d = mem.get_decision(did)
+    assert d.resolved and d.outcome_rating == 1
+    assert "Recovered" in d.outcome and d.resolved_at is not None
+
+
+def test_resolve_unknown_id_is_false(mem):
+    assert mem.resolve_decision(999, "n/a") is False
+
+
+def test_list_open_only_filters_resolved(mem):
+    a = mem.log_decision(situation="A?", choice="x")
+    mem.log_decision(situation="B?", choice="y")
+    mem.resolve_decision(a, "done", 0)
+    assert {d.situation for d in mem.list_decisions(open_only=True)} == {"B?"}
+    assert len(mem.list_decisions()) == 2
+
+
+def test_recall_ranks_by_similarity(mem):
+    mem.log_decision(situation="Which Cleveland tournament flight?", choice="noon")
+    mem.log_decision(situation="Should I sell the stocks?", choice="hold")
+    hits = mem.recall_decisions("another poker tournament buy-in", k=2)
+    assert hits[0].situation.startswith("Which Cleveland")   # poker cluster ranks first
+    assert hits[0].score >= hits[1].score
+
+
+# --- tool layer ---------------------------------------------------------------
+
+def test_log_decision_tool_persists(mem):
+    from lyra import tools
+    out = tools.dispatch("log_decision",
+                         {"situation": "Move the MI50 to auto clocks?", "choice": "yes",
+                          "confidence": "3", "tags": "build"})
+    assert "#1" in out
+    d = mem.get_decision(1)
+    assert d.choice == "yes" and d.confidence == 3 and d.tags == "build"
+
+
+def test_log_decision_tool_requires_both_fields(mem):
+    from lyra import tools
+    assert "Need both" in tools.dispatch("log_decision", {"situation": "just this"})
+
+
+def test_resolve_decision_tool(mem):
+    from lyra import tools
+    did = mem.log_decision(situation="X?", choice="y")
+    out = tools.dispatch("resolve_decision",
+                         {"decision_id": did, "outcome": "worked out", "rating": "1"})
+    assert f"#{did}" in out
+    assert mem.get_decision(did).outcome_rating == 1
+
+
+def test_recall_decisions_tool_surfaces_outcomes(mem):
+    from lyra import tools
+    did = mem.log_decision(situation="Cleveland tournament again?", choice="play")
+    mem.resolve_decision(did, "min-cashed", outcome_rating=0)
+    out = tools.dispatch("recall_decisions", {"query": "poker tournament tomorrow"})
+    assert "Cleveland" in out and "mixed" in out