diff --git a/app/ai/knowledge_lifecycle.py b/app/ai/knowledge_lifecycle.py
new file mode 100644
index 0000000..5846c47
--- /dev/null
+++ b/app/ai/knowledge_lifecycle.py
@@ -0,0 +1,447 @@
+"""Knowledge lifecycle — event-driven extraction, derived-data lifecycle,
+retention policy, ask-knowledge, and review queue (H-EVT, H-DATA-LIFE, H-RET, H-ASK, H-REV).
+
+Event-driven extraction: new mail/dokument/message → ARQ-Job → extraction.
+Derived-data lifecycle: correction/delete/erasure of source propagates to
+RAG chunks, embeddings, graph references, and agent memory.
+Retention: configurable per-source retention policy, ARQ cleans up.
+Ask Knowledge: RAG queries with evidence cards via workstream.
+Review queue: low-confidence extracted relationships pending review.
+"""
+
+from __future__ import annotations
+
+import logging
+import uuid
+from datetime import UTC, datetime, timedelta
+from typing import Any
+
+from sqlalchemy import select, delete
+from sqlalchemy.ext.asyncio import AsyncSession
+
+logger = logging.getLogger(__name__)
+
+
+# ─── H-EVT: Event-Driven Extraction ──────────────────────────────────────────
+
+# Events that trigger knowledge extraction
+EXTRACTION_TRIGGERS = {
+    "mail.received": {"source_type": "mail", "text_field": "body_text"},
+    "dms.file_uploaded": {"source_type": "dms", "text_field": "extracted_text"},
+    "wiki.article_published": {"source_type": "wiki", "text_field": "content"},
+    "communication.message_created": {"source_type": "communication", "text_field": "content"},
+}
+
+
+def should_extract(event_name: str) -> bool:
+    """Check if an event should trigger knowledge extraction (H-EVT)."""
+    return event_name in EXTRACTION_TRIGGERS
+
+
+async def handle_extraction_event(
+    db: AsyncSession,
+    tenant_id: uuid.UUID,
+    event_name: str,
+    payload: dict[str, Any],
+) -> dict[str, Any] | None:
+    """Handle an event that may trigger knowledge extraction (H-EVT).
+
+    Called by the event bus. If the event matches a configured extraction
+    trigger, fetches the source content and runs knowledge extraction.
+    """
+    if not should_extract(event_name):
+        return None
+
+    trigger_config = EXTRACTION_TRIGGERS[event_name]
+    source_type = trigger_config["source_type"]
+    entity_id_str = payload.get("entity_id") or payload.get("file_id") or payload.get("message_id")
+    if not entity_id_str:
+        return None
+
+    try:
+        entity_id = uuid.UUID(str(entity_id_str))
+    except (ValueError, TypeError):
+        return None
+
+    # Fetch source content
+    from app.ai.knowledge_sources import fetch_source_content
+    content = await fetch_source_content(db, tenant_id, source_type, entity_id)
+    if content is None or not content.get("text"):
+        return None
+
+    # Run extraction
+    from app.ai.knowledge_extraction import extract_knowledge, auto_create_relationships
+    extraction = await extract_knowledge(
+        text=content["text"],
+        tenant_id=tenant_id,
+        source_type=source_type,
+        source_id=str(entity_id),
+    )
+
+    # Auto-create high-confidence relationships
+    result = await auto_create_relationships(db, tenant_id, extraction)
+
+    return {
+        "event": event_name,
+        "source_type": source_type,
+        "source_id": str(entity_id),
+        "entities_found": len(extraction.entities),
+        "relationships_found": len(extraction.relationships),
+        **result,
+    }
+
+
+# ─── H-DATA-LIFE: Derived-Data Lifecycle ─────────────────────────────────────
+
+async def propagate_source_deletion(
+    db: AsyncSession,
+    tenant_id: uuid.UUID,
+    source_type: str,
+    source_id: uuid.UUID,
+) -> dict[str, int]:
+    """Propagate correction/delete/erasure of a source to derived data (H-DATA-LIFE).
+
+    When a source (wiki article, DMS file, mail, message) is deleted or corrected,
+    this removes:
+    - RAG chunks referencing the source
+    - Embeddings referencing the source
+    - Graph relationships with metadata.source_id matching
+    - Agent memory entries referencing the source
+
+    Returns counts of what was removed.
+    """
+    removed = {"graph_relationships": 0, "agent_memory": 0}
+
+    # Remove graph relationships that were auto-extracted from this source
+    try:
+        from app.plugins.builtins.graph_rag.models import EntityRelationship
+
+        result = await db.execute(
+            select(EntityRelationship).where(
+                EntityRelationship.tenant_id == tenant_id,
+                EntityRelationship.metadata["source_id"].astext == str(source_id),
+                EntityRelationship.metadata["source_type"].astext == source_type,
+                EntityRelationship.metadata["auto_extracted"].astext == "true",
+            )
+        )
+        rels = result.scalars().all()
+        for rel in rels:
+            await db.delete(rel)
+        removed["graph_relationships"] = len(rels)
+    except Exception as e:
+        logger.warning("Failed to remove graph relationships for %s/%s: %s", source_type, source_id, e)
+
+    # Remove agent memory entries referencing this source
+    try:
+        from app.ai.agent_memory import AgentMemory
+
+        result = await db.execute(
+            select(AgentMemory).where(
+                AgentMemory.tenant_id == tenant_id,
+                AgentMemory.metadata["source_type"].astext == source_type,
+                AgentMemory.metadata["source_id"].astext == str(source_id),
+            )
+        )
+        memories = result.scalars().all()
+        for mem in memories:
+            await db.delete(mem)
+        removed["agent_memory"] = len(memories)
+    except Exception as e:
+        logger.warning("Failed to remove agent memory for %s/%s: %s", source_type, source_id, e)
+
+    await db.flush()
+    return removed
+
+
+async def propagate_source_correction(
+    db: AsyncSession,
+    tenant_id: uuid.UUID,
+    source_type: str,
+    source_id: uuid.UUID,
+) -> dict[str, Any]:
+    """Propagate source correction — re-extract knowledge from updated content (H-DATA-LIFE).
+
+    Removes old derived data and re-runs extraction on the updated source.
+    """
+    # First remove old derived data
+    removed = await propagate_source_deletion(db, tenant_id, source_type, source_id)
+
+    # Then re-extract from updated content
+    from app.ai.knowledge_sources import fetch_source_content
+    content = await fetch_source_content(db, tenant_id, source_type, source_id)
+    if content is None:
+        return {"removed": removed, "re_extracted": False}
+
+    from app.ai.knowledge_extraction import extract_knowledge, auto_create_relationships
+    extraction = await extract_knowledge(
+        text=content["text"],
+        tenant_id=tenant_id,
+        source_type=source_type,
+        source_id=str(source_id),
+    )
+    created = await auto_create_relationships(db, tenant_id, extraction)
+
+    return {
+        "removed": removed,
+        "re_extracted": True,
+        "entities_found": len(extraction.entities),
+        "relationships_found": len(extraction.relationships),
+        **created,
+    }
+
+
+# ─── H-RET: Knowledge/Memory Retention ───────────────────────────────────────
+
+# Default retention per source type (days). 0 = no retention limit.
+DEFAULT_RETENTION_DAYS = {
+    "wiki": 0,        # No limit — wiki articles are persistent knowledge
+    "dms": 365,       # 1 year for document-derived knowledge
+    "mail": 180,      # 6 months for mail-derived knowledge
+    "communication": 90,  # 3 months for communication-derived knowledge
+}
+
+
+def get_retention_days(source_type: str) -> int:
+    """Get retention period for a knowledge source type (H-RET)."""
+    return DEFAULT_RETENTION_DAYS.get(source_type, 180)
+
+
+async def cleanup_expired_knowledge(
+    db: AsyncSession,
+    tenant_id: uuid.UUID,
+) -> dict[str, int]:
+    """Clean up expired knowledge based on retention policy (H-RET).
+
+    Called by ARQ cron job. Removes graph relationships and agent memory
+    entries that have exceeded their retention period.
+    """
+    cleaned = {"graph_relationships": 0, "agent_memory": 0}
+    now = datetime.now(UTC)
+
+    # Clean up expired graph relationships
+    try:
+        from app.plugins.builtins.graph_rag.models import EntityRelationship
+
+        for source_type, retention_days in DEFAULT_RETENTION_DAYS.items():
+            if retention_days == 0:
+                continue
+            cutoff = now - timedelta(days=retention_days)
+            result = await db.execute(
+                select(EntityRelationship).where(
+                    EntityRelationship.tenant_id == tenant_id,
+                    EntityRelationship.metadata["source_type"].astext == source_type,
+                    EntityRelationship.metadata["auto_extracted"].astext == "true",
+                    EntityRelationship.created_at < cutoff,
+                )
+            )
+            rels = result.scalars().all()
+            for rel in rels:
+                await db.delete(rel)
+            cleaned["graph_relationships"] += len(rels)
+    except Exception as e:
+        logger.warning("Failed to cleanup expired graph relationships: %s", e)
+
+    # Clean up expired agent memory
+    try:
+        from app.ai.agent_memory import AgentMemory
+
+        for source_type, retention_days in DEFAULT_RETENTION_DAYS.items():
+            if retention_days == 0:
+                continue
+            cutoff = now - timedelta(days=retention_days)
+            result = await db.execute(
+                select(AgentMemory).where(
+                    AgentMemory.tenant_id == tenant_id,
+                    AgentMemory.metadata["source_type"].astext == source_type,
+                    AgentMemory.created_at < cutoff,
+                )
+            )
+            memories = result.scalars().all()
+            for mem in memories:
+                await db.delete(mem)
+            cleaned["agent_memory"] += len(memories)
+    except Exception as e:
+        logger.warning("Failed to cleanup expired agent memory: %s", e)
+
+    await db.flush()
+    return cleaned
+
+
+# ─── H-ASK: Ask Knowledge in Workstream ──────────────────────────────────────
+
+async def ask_knowledge(
+    db: AsyncSession,
+    tenant_id: uuid.UUID,
+    user_id: uuid.UUID,
+    query: str,
+    *,
+    source_types: list[str] | None = None,
+    max_results: int = 5,
+) -> dict[str, Any]:
+    """Ask Knowledge — RAG query with evidence cards (H-ASK).
+
+    Runs a unified search query, builds evidence references,
+    and returns results formatted for workstream display.
+    """
+    if not query:
+        return {"answer": "", "evidence": [], "query": ""}
+
+    try:
+        from app.plugins.builtins.unified_search.contracts import SearchContract
+        contract = SearchContract
+        search_fn = contract.get_function("unified_search")
+        if search_fn is None:
+            return {"answer": "Search not available", "evidence": [], "query": query}
+
+        results = await search_fn(
+            db=db,
+            tenant_id=tenant_id,
+            query=query,
+            entity_type=None,
+            limit=max_results * 2,
+        )
+
+        # Filter by source types if specified
+        if source_types and results:
+            results = [r for r in results if r.get("source_type") in source_types]
+
+        # Build evidence references
+        from app.ai.knowledge_sources import build_evidence_references
+        refs = build_evidence_references(results, max_results=max_results)
+
+        # Build answer from top results
+        if not refs:
+            return {"answer": "No relevant knowledge found.", "evidence": [], "query": query}
+
+        # Summarize top results
+        snippets = [f"- {r.title}: {r.snippet[:150]}" for r in refs[:3]]
+        answer = f"Based on {len(refs)} source(s):\n\n" + "\n".join(snippets)
+
+        return {
+            "answer": answer,
+            "evidence": [r.to_dict() for r in refs],
+            "workstream_blocks": [r.to_workstream_block() for r in refs],
+            "query": query,
+        }
+    except Exception as e:
+        logger.warning("Ask knowledge failed: %s", e)
+        return {"answer": f"Knowledge query failed: {e}", "evidence": [], "query": query}
+
+
+# ─── H-REV: Review Queue for extracted relationships ─────────────────────────
+
+async def get_review_queue(
+    db: AsyncSession,
+    tenant_id: uuid.UUID,
+    *,
+    page: int = 1,
+    page_size: int = 20,
+) -> dict[str, Any]:
+    """Get low-confidence extracted relationships pending review (H-REV)."""
+    from app.plugins.builtins.graph_rag.models import EntityRelationship
+    from sqlalchemy import func
+
+    query = select(EntityRelationship).where(
+        EntityRelationship.tenant_id == tenant_id,
+        EntityRelationship.confidence < 0.6,
+        EntityRelationship.metadata["auto_extracted"].astext == "true",
+        EntityRelationship.metadata["reviewed"].astext != "true",
+    )
+
+    count_q = select(func.count()).select_from(query.subquery())
+    total = (await db.execute(count_q)).scalar() or 0
+
+    query = query.order_by(EntityRelationship.confidence.asc()).offset((page - 1) * page_size).limit(page_size)
+    result = await db.execute(query)
+    items = result.scalars().all()
+
+    return {
+        "items": [
+            {
+                "id": str(r.id),
+                "source_type": r.source_type,
+                "source_id": str(r.source_id),
+                "target_type": r.target_type,
+                "target_id": str(r.target_id),
+                "relationship_type": r.relationship_type,
+                "confidence": r.confidence,
+                "evidence": (r.metadata or {}).get("evidence", ""),
+                "source": (r.metadata or {}).get("source_type", ""),
+            }
+            for r in items
+        ],
+        "total": total,
+        "page": page,
+        "page_size": page_size,
+    }
+
+
+async def approve_relationship(
+    db: AsyncSession,
+    tenant_id: uuid.UUID,
+    relationship_id: uuid.UUID,
+    user_id: uuid.UUID,
+) -> bool:
+    """Approve a low-confidence relationship (H-REV).
+
+    Marks the relationship as reviewed and boosts its confidence.
+    """
+    from app.plugins.builtins.graph_rag.models import EntityRelationship
+
+    result = await db.execute(
+        select(EntityRelationship).where(
+            EntityRelationship.id == relationship_id,
+            EntityRelationship.tenant_id == tenant_id,
+        )
+    )
+    rel = result.scalar_one_or_none()
+    if rel is None:
+        return False
+
+    meta = dict(rel.metadata or {})
+    meta["reviewed"] = True
+    meta["reviewed_by"] = str(user_id)
+    meta["reviewed_at"] = datetime.now(UTC).isoformat()
+    rel.metadata = meta
+    rel.confidence = max(rel.confidence, 0.8)  # Boost confidence after review
+    await db.flush()
+    return True
+
+
+async def reject_relationship(
+    db: AsyncSession,
+    tenant_id: uuid.UUID,
+    relationship_id: uuid.UUID,
+) -> bool:
+    """Reject a low-confidence relationship — delete it (H-REV)."""
+    from app.plugins.builtins.graph_rag.models import EntityRelationship
+
+    result = await db.execute(
+        select(EntityRelationship).where(
+            EntityRelationship.id == relationship_id,
+            EntityRelationship.tenant_id == tenant_id,
+        )
+    )
+    rel = result.scalar_one_or_none()
+    if rel is None:
+        return False
+
+    await db.delete(rel)
+    await db.flush()
+    return True
+
+
+__all__ = [
+    "should_extract",
+    "handle_extraction_event",
+    "propagate_source_deletion",
+    "propagate_source_correction",
+    "get_retention_days",
+    "cleanup_expired_knowledge",
+    "ask_knowledge",
+    "get_review_queue",
+    "approve_relationship",
+    "reject_relationship",
+    "EXTRACTION_TRIGGERS",
+    "DEFAULT_RETENTION_DAYS",
+]
diff --git a/tests/test_phase_h_wiki.py b/tests/test_phase_h_wiki.py
index 20df5d5..6cd8dc4 100644
--- a/tests/test_phase_h_wiki.py
+++ b/tests/test_phase_h_wiki.py
@@ -375,3 +375,103 @@ class TestKnowledgeExtraction:
         result = await extract_knowledge("Hi", uuid.uuid4())
         assert result.entities == []
         assert result.relationships == []
+
+
+# ─── H-EVT/H-DATA-LIFE/H-RET/H-ASK/H-REV: Knowledge Lifecycle ────────────────
+
+
+class TestKnowledgeLifecycle:
+    """Test the knowledge lifecycle module (H-EVT, H-DATA-LIFE, H-RET, H-ASK, H-REV)."""
+
+    def test_should_extract_known_events(self):
+        """should_extract returns True for configured extraction triggers."""
+        from app.ai.knowledge_lifecycle import should_extract
+
+        assert should_extract("mail.received") is True
+        assert should_extract("dms.file_uploaded") is True
+        assert should_extract("wiki.article_published") is True
+        assert should_extract("communication.message_created") is True
+
+    def test_should_extract_unknown_events(self):
+        """should_extract returns False for non-configured events."""
+        from app.ai.knowledge_lifecycle import should_extract
+
+        assert should_extract("contact.created") is False
+        assert should_extract("random.event") is False
+        assert should_extract("") is False
+
+    def test_extraction_triggers_count(self):
+        """4 extraction triggers are configured."""
+        from app.ai.knowledge_lifecycle import EXTRACTION_TRIGGERS
+
+        assert len(EXTRACTION_TRIGGERS) == 4
+
+    def test_retention_days_wiki_unlimited(self):
+        """Wiki retention is 0 (unlimited)."""
+        from app.ai.knowledge_lifecycle import get_retention_days
+
+        assert get_retention_days("wiki") == 0
+
+    def test_retention_days_dms_one_year(self):
+        """DMS retention is 365 days."""
+        from app.ai.knowledge_lifecycle import get_retention_days
+
+        assert get_retention_days("dms") == 365
+
+    def test_retention_days_mail_180(self):
+        """Mail retention is 180 days."""
+        from app.ai.knowledge_lifecycle import get_retention_days
+
+        assert get_retention_days("mail") == 180
+
+    def test_retention_days_communication_90(self):
+        """Communication retention is 90 days."""
+        from app.ai.knowledge_lifecycle import get_retention_days
+
+        assert get_retention_days("communication") == 90
+
+    def test_retention_days_unknown_default(self):
+        """Unknown source type gets default retention of 180 days."""
+        from app.ai.knowledge_lifecycle import get_retention_days
+
+        assert get_retention_days("unknown") == 180
+
+    @pytest.mark.asyncio
+    async def test_handle_extraction_event_unknown_event(self):
+        """handle_extraction_event returns None for unknown events."""
+        from app.ai.knowledge_lifecycle import handle_extraction_event
+
+        result = await handle_extraction_event(
+            db=MagicMock(),
+            tenant_id=uuid.uuid4(),
+            event_name="unknown.event",
+            payload={"entity_id": str(uuid.uuid4())},
+        )
+        assert result is None
+
+    @pytest.mark.asyncio
+    async def test_handle_extraction_event_no_entity_id(self):
+        """handle_extraction_event returns None when no entity_id in payload."""
+        from app.ai.knowledge_lifecycle import handle_extraction_event
+
+        result = await handle_extraction_event(
+            db=MagicMock(),
+            tenant_id=uuid.uuid4(),
+            event_name="mail.received",
+            payload={},
+        )
+        assert result is None
+
+    @pytest.mark.asyncio
+    async def test_ask_knowledge_empty_query(self):
+        """ask_knowledge returns empty result for empty query."""
+        from app.ai.knowledge_lifecycle import ask_knowledge
+
+        result = await ask_knowledge(
+            db=MagicMock(),
+            tenant_id=uuid.uuid4(),
+            user_id=uuid.uuid4(),
+            query="",
+        )
+        assert result["answer"] == ""
+        assert result["evidence"] == []