Add AI Council architecture: Tier 2/3/Graph implementation + Integration Plan

Architecture (Agent 1):
- hermes_memory/tier2/{schema,facts,entities,relations,timeline}.py
- hermes_memory/tier3/{backend,chroma_backend,embedder}.py
- hermes_memory/graph/nx_store.py
- hermes_memory/api/memory_api.py (unified API)
- hermes_memory/cron/{consolidate,embed_queue,graph_refresh,prune}.py
- hermes_memory/config.py + pyproject.toml

Integration Plan (Agent 3):
- INTEGRATION_PLAN.md: Memory Provider Plugin strategy
- Hermes Core needs minimal changes
- sync_turn() + prefetch() hooks
- Skills integration via nextlevel_search/remember

Auto-Extraction (Agent 2):
- ARCHITECTURE.md: Full extraction pipeline docs
- Chunking, Pre-Filter, LLM Prompts, Classification
- Entity-Linking, Temporal Reasoning, Deduplication

All files: Python syntax checked, ECC standards applied.

hermes_memory/cron/__init__.py

@@ -0,0 +1,8 @@
+"""Cron-Jobs für Hermes Memory Maintenance."""
+
+from hermes_memory.cron.consolidate import run_consolidate
+from hermes_memory.cron.embed_queue import run_embed_queue
+from hermes_memory.cron.graph_refresh import run_graph_refresh
+from hermes_memory.cron.prune import run_prune
+
+__all__ = ["run_consolidate", "run_embed_queue", "run_graph_refresh", "run_prune"]

hermes_memory/cron/consolidate.py

@@ -0,0 +1,14 @@
+"""Fakten-Deduplizierung & Konflikt-Auflösung."""
+
+import logging
+from hermes_memory.api.memory_api import MemoryAPI
+
+logger = logging.getLogger(__name__)
+
+
+def run_consolidate(profile: str = "default") -> dict:
+    """Führt Deduplizierung und Maintenance aus."""
+    api = MemoryAPI(profile=profile)
+    result = api.consolidate()
+    logger.info("Consolidation complete: %s", result)
+    return result

hermes_memory/cron/embed_queue.py

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+"""Embedding-Job-Queue für Tier 3."""
+
+import logging
+import time
+from hermes_memory.api.memory_api import MemoryAPI
+from hermes_memory.tier2.schema import connect
+from hermes_memory.config import load_config
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+
+def run_embed_queue(profile: str = "default", batch_size: int = 50) -> dict:
+    """Verarbeitet pending Embeddings aus der Queue."""
+    config = load_config(profile)
+    db_path = Path(config["tier2"]["db_path"].format(
+        HERMES_HOME=Path.home() / ".hermes",
+        profile=profile,
+    ))
+    conn = connect(db_path)
+    rows = conn.execute(
+        "SELECT id, fact_id, content, source_type, session_id, message_id FROM embedding_queue WHERE processed = 0 ORDER BY queued_at LIMIT ?",
+        (batch_size,),
+    ).fetchall()
+
+    if not rows:
+        return {"processed": 0}
+
+    api = MemoryAPI(profile=profile)
+    processed = 0
+    for row in rows:
+        try:
+            api.semantic_index(
+                text=row["content"],
+                source_type=row["source_type"],
+                session_id=row["session_id"],
+                message_id=row["message_id"],
+            )
+            conn.execute("UPDATE embedding_queue SET processed = 1 WHERE id = ?", (row["id"],))
+            processed += 1
+        except Exception as e:
+            logger.error("Embedding failed for queue id %s: %s", row["id"], e)
+
+    conn.commit()
+    return {"processed": processed}

hermes_memory/cron/graph_refresh.py

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+"""Graph-Rebuild Job."""
+
+import logging
+from hermes_memory.api.memory_api import MemoryAPI
+
+logger = logging.getLogger(__name__)
+
+
+def run_graph_refresh(profile: str = "default") -> dict:
+    """Baut Knowledge Graph aus Tier 2 neu auf."""
+    api = MemoryAPI(profile=profile)
+    result = api.graph_rebuild()
+    logger.info("Graph refresh complete: %s", result)
+    return result

hermes_memory/cron/prune.py

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+"""Prune — Alte/archivierte Daten entfernen."""
+
+import logging
+import time
+from pathlib import Path
+
+from hermes_memory.config import load_config
+from hermes_memory.tier2.schema import connect
+
+logger = logging.getLogger(__name__)
+
+
+def run_prune(profile: str = "default") -> dict:
+    """Entfernt archivierte Fakten und alte Embeddings."""
+    config = load_config(profile)
+    db_path = Path(config["tier2"]["db_path"].format(
+        HERMES_HOME=Path.home() / ".hermes",
+        profile=profile,
+    ))
+    conn = connect(db_path)
+    now = time.time()
+    ttl = config["limits"]["fact_ttl_days"] * 86400
+
+    # Archivierte Fakten älter als TTL löschen
+    cur = conn.execute(
+        "DELETE FROM facts WHERE is_archived = 1 AND updated_at < ?",
+        (now - ttl,),
+    )
+    deleted_facts = cur.rowcount
+
+    # Verarbeitete Queue-Einträge älter als 7 Tage löschen
+    cur = conn.execute(
+        "DELETE FROM embedding_queue WHERE processed = 1 AND queued_at < ?",
+        (now - 7 * 86400,),
+    )
+    deleted_queue = cur.rowcount
+
+    conn.commit()
+    logger.info("Pruned %s facts, %s queue entries", deleted_facts, deleted_queue)
+    return {"deleted_facts": deleted_facts, "deleted_queue": deleted_queue}