feat: add global memory mechanism for personalized conversations

Implement a memory system that stores user context and conversation history
in memory.json, uses LLM to summarize conversations, and injects relevant
context into system prompts for personalized responses.

Key components:
- MemoryConfig for configuration management
- MemoryUpdateQueue with debounce for batch processing
- MemoryUpdater for LLM-based memory extraction
- MemoryMiddleware to queue conversations after agent execution
- Memory injection into lead agent system prompt

Note: Add memory section to config.yaml to enable (see config.example.yaml)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

backend/src/config/__init__.py

@@ -1,5 +1,13 @@
 from .app_config import get_app_config
 from .extensions_config import ExtensionsConfig, get_extensions_config
+from .memory_config import MemoryConfig, get_memory_config
 from .skills_config import SkillsConfig
 
-__all__ = ["get_app_config", "SkillsConfig", "ExtensionsConfig", "get_extensions_config"]
+__all__ = [
+    "get_app_config",
+    "SkillsConfig",
+    "ExtensionsConfig",
+    "get_extensions_config",
+    "MemoryConfig",
+    "get_memory_config",
+]