Phase 5.5-5.9: Plugin-Marketplace, Agent Memory, GraphRAG, Subagents, External Agent API

5.5 Plugin-Marketplace:
- New plugin: marketplace/ (models, routes, services, schemas, config)
- MarketplaceListing model (global, no tenant_id)
- Ed25519 signature verification via PluginSignature
- Endpoints: list, detail, install, verify, categories
- Config: MARKETPLACE_SERVER_URL setting

5.6 Agent Memory (persistent):
- New plugin: agent_memory/ (models, routes, services, schemas)
- AgentMemory model with embedding vector(768) + HNSW index
- store_memory() with auto-embedding
- retrieve_relevant_memories() with pgvector cosine similarity
- Semantic search endpoint

5.7 GraphRAG:
- New plugin: graph_rag/ (models, routes, services, provider, schemas)
- EntityRelationship model (source/target type+id, relationship_type, metadata)
- BFS graph traversal (bidirectional, configurable depth)
- GraphRAGSearchProvider registered in unified_search

5.8 Subagents / Multi-Agent:
- AgentCoordinator class (create_subtask, wait_for_subtask, aggregate, cancel)
- AgentSubtask model + migration 0002_agent_subtasks.sql
- 6 new API endpoints for subtask management
- Tools registered in AI tool registry

5.9 External Agent API:
- external_api.py: POST /run, GET /status, POST /stream (SSE)
- Bearer API token authentication
- Rate limiting: 10 req/min per token
- ExternalAgentRequest/Response schemas

3 new plugins registered in main.py and __init__.py
All files py_compile clean

app/plugins/builtins/agent_memory/models.py

@@ -0,0 +1,39 @@
+"""AgentMemory model for persistent agent memory with pgvector embeddings."""
+
+from __future__ import annotations
+
+import uuid
+
+from sqlalchemy import Index, String, Text
+from sqlalchemy.dialects.postgresql import UUID as PGUUID
+from sqlalchemy.orm import Mapped, mapped_column
+
+from app.core.db import Base, TenantMixin
+from app.models.owned_mixin import OwnedMixin
+
+
+class AgentMemory(Base, TenantMixin, OwnedMixin):
+    """Persistent agent memory with semantic search via pgvector.
+
+    Stores agent memories (facts, context, learned patterns) with
+    vector embeddings for semantic retrieval.
+    """
+
+    __tablename__ = "agent_memories"
+    __table_args__ = (
+        Index("ix_agent_memories_tenant_agent", "tenant_id", "agent_id"),
+        Index("ix_agent_memories_tenant_type", "tenant_id", "memory_type"),
+    )
+
+    id: Mapped[uuid.UUID] = mapped_column(
+        PGUUID(as_uuid=True), primary_key=True, default=uuid.uuid4
+    )
+    agent_id: Mapped[uuid.UUID] = mapped_column(
+        PGUUID(as_uuid=True), nullable=False, index=True
+    )
+    memory_type: Mapped[str] = mapped_column(
+        String(50), nullable=False, default="fact"
+    )
+    content: Mapped[str] = mapped_column(Text, nullable=False)
+    # embedding column is managed via raw SQL (pgvector extension)
+    # embedding vector(768) — see migration 0001_initial.sql