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packages/server/tests/local/memory-lane-cron.test.ts

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+import { describe, it, expect, beforeEach, afterEach } from 'vitest';
+import { MindDB, FrameStore, SessionStore, type LLMCallFn } from '@waggle/core';
+import { runMemoryLaneExtraction } from '../../src/local/memory-lane-cron.js';
+
+/**
+ * W4.3d — memory-lane extraction cron routine (plan §5 W4.3 extraction side).
+ * LLM mocked; covers the watermark contract, the min-frames skip, lane-frame
+ * self-feeding exclusion, and idempotency across runs.
+ */
+
+describe('runMemoryLaneExtraction', () => {
+  let db: MindDB;
+  let frames: FrameStore;
+  let gopId: string;
+
+  const mockLLM: LLMCallFn = async (prompt: string) => {
+    if (prompt.includes('synthesis-level memory facts')) {
+      return '{"facts":[{"category":"preference","speaker":"Ana","text":"User preference: Ana prefers dark mode"}]}';
+    }
+    if (prompt.includes('datable events')) {
+      return '{"events":[{"session_date":"2026-05-08","cue":"yesterday","event_date":"2026-05-07","text":"Ana visited the dentist"}]}';
+    }
+    if (prompt.includes('profile card')) {
+      return '{"profiles":[{"speaker":"Ana","card":"Ana is a designer."}]}';
+    }
+    if (prompt.includes('Extract named entities from the FRAMES')) {
+      // Same entity mentioned in two frames — exercises findEntityByName dedup.
+      return [
+        '{"frame_id": 1, "name": "Hive Mind", "type": "project"}',
+        '{"frame_id": 2, "name": "Hive Mind", "type": "project"}',
+      ].join('\n');
+    }
+    return '{}';
+  };
+
+  beforeEach(() => {
+    db = new MindDB(':memory:');
+    frames = new FrameStore(db);
+    gopId = new SessionStore(db).create().gop_id;
+  });
+
+  afterEach(() => {
+    db.close();
+  });
+
+  function seedSourceFrames(n: number): void {
+    for (let i = 0; i < n; i++) {
+      frames.createIFrame(gopId, `Conversation note ${i}: Ana said something useful about topic ${i}.`, 'normal', 'user_stated');
+    }
+  }
+
+  it('skips when fewer than the minimum new frames exist', async () => {
+    seedSourceFrames(2);
+    const r = await runMemoryLaneExtraction(db, mockLLM);
+    expect(r.skipped).toBe(true);
+    expect(r.framesProcessed).toBe(0);
+  });
+
+  it('extracts lanes and advances the watermark', async () => {
+    seedSourceFrames(8);
+    const r = await runMemoryLaneExtraction(db, mockLLM);
+    expect(r.skipped).toBe(false);
+    expect(r.framesProcessed).toBe(8);
+    expect(r.written).toMatchObject({ factsWritten: 1, eventsWritten: 1, profilesWritten: 1 });
+
+    const raw = db.getDatabase();
+    const event = raw.prepare(
+      `SELECT created_at FROM memory_frames WHERE content LIKE '[mind-event]%'`
+    ).get() as { created_at: string };
+    expect(event.created_at).toBe('2026-05-07T00:00:00.000Z');
+  });
+
+  it('writes KG entities over the same window; findEntityByName dedups to one row', async () => {
+    seedSourceFrames(8);
+    const r = await runMemoryLaneExtraction(db, mockLLM);
+    expect(r.skipped).toBe(false);
+    // Two mentions of "Hive Mind": one create + one seen_count bump.
+    expect(r.kgEntitiesWritten).toBe(2);
+    expect(r.errors).toHaveLength(0);
+
+    const raw = db.getDatabase();
+    const rows = raw.prepare(
+      `SELECT entity_type, properties FROM knowledge_entities WHERE name = 'Hive Mind'`
+    ).all() as Array<{ entity_type: string; properties: string }>;
+    expect(rows).toHaveLength(1); // deduped, not duplicated
+    expect(rows[0].entity_type).toBe('project');
+    expect(JSON.parse(rows[0].properties)).toMatchObject({ seen_count: 2, source: 'cognify-llm' });
+  });
+
+  it('second run with no new frames skips (watermark holds)', async () => {
+    seedSourceFrames(8);
+    await runMemoryLaneExtraction(db, mockLLM);
+    const r2 = await runMemoryLaneExtraction(db, mockLLM);
+    // lane frames written by run 1 are excluded (no self-feeding), and the
+    // watermark has moved past the 8 source frames → nothing new.
+    expect(r2.skipped).toBe(true);
+  });
+
+  it('excludes [Loop:] automation tick frames from extraction (#13)', async () => {
+    seedSourceFrames(8);
+    for (let i = 0; i < 3; i++) {
+      frames.createIFrame(gopId, `[Loop: nightly-digest] tick ${i}: processed 4 items.`, 'normal', 'agent_inferred');
+    }
+    const r = await runMemoryLaneExtraction(db, mockLLM);
+    expect(r.skipped).toBe(false);
+    // Only the 8 real conversation frames are fed to the LLM; loop ticks stay out.
+    expect(r.framesProcessed).toBe(8);
+  });
+
+  it('processes genuinely new content on a later run', async () => {
+    seedSourceFrames(8);
+    await runMemoryLaneExtraction(db, mockLLM);
+    for (let i = 0; i < 6; i++) {
+      frames.createIFrame(gopId, `Fresh note ${i}: Ana planned the spring offsite agenda item ${i}.`, 'normal', 'user_stated');
+    }
+    const r2 = await runMemoryLaneExtraction(db, mockLLM);
+    expect(r2.skipped).toBe(false);
+    expect(r2.framesProcessed).toBe(6);
+  });
+});