feat: Autonomy Core v1 — Lyra's evolving self-state

Give Lyra a model of *herself* (vs the profile/narrative which model Brian):

- persona: a real origin/identity — she's an AI and knows it (Bender/C-3PO
  style), with the Cortex/NeoMem lineage as her actual past, so "how were you
  made" stops falling through to generic-assistant deflection.
- memory: self_state table (JSON blob) + get/set_self_state.
- lyra/self_state.py: evolving first-person inner state (mood, valence, energy,
  confidence, curiosity, self_narrative, relationship, reflections). render_for_
  context injects it; reflect() updates it from recent activity. `lyra-reflect`.
- chat.build_messages injects her interiority right after the persona — she
  speaks from a continuous self, not a reset.

The state -> behavior -> reflection -> updated state loop is the substrate for
the emergence experiment. Verified: reflection shifted mood curious->reflective
and produced genuine first-person self-observations.

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

lyra/memory.py

@@ -7,6 +7,7 @@ thousands of rows; swap in a vector index when that stops being true.
 """
 from __future__ import annotations
 
+import json
 import sqlite3
 from dataclasses import dataclass
 from datetime import datetime, timezone
@@ -70,6 +71,14 @@ CREATE TABLE IF NOT EXISTS narrative (
     content TEXT NOT NULL,
     updated_at TEXT NOT NULL
 );
+
+-- Autonomy Core: Lyra's evolving self-state (mood, energy, her own first-person
+-- self-narrative, reflections). Stored as a JSON blob. Single row (id='lyra').
+CREATE TABLE IF NOT EXISTS self_state (
+    id TEXT PRIMARY KEY,
+    data TEXT NOT NULL,
+    updated_at TEXT NOT NULL
+);
 """
 
 _conn: sqlite3.Connection | None = None
@@ -434,6 +443,23 @@ def get_narrative(narrative_id: str = "current") -> str | None:
     return r["content"] if r else None
 
 
+def get_self_state(state_id: str = "lyra") -> dict | None:
+    conn = _connection()
+    r = conn.execute("SELECT data FROM self_state WHERE id = ?", (state_id,)).fetchone()
+    return json.loads(r["data"]) if r else None
+
+
+def set_self_state(state: dict, state_id: str = "lyra") -> None:
+    now = datetime.now(timezone.utc).isoformat()
+    conn = _connection()
+    with conn:
+        conn.execute(
+            "INSERT INTO self_state (id, data, updated_at) VALUES (?, ?, ?) "
+            "ON CONFLICT(id) DO UPDATE SET data=excluded.data, updated_at=excluded.updated_at",
+            (state_id, json.dumps(state), now),
+        )
+
+
 def recall_eras(query: str, k: int = 2) -> list[Era]:
     """Top-k month digests most similar to `query` (time-based context)."""
     [q_vec] = llm.embed([query])