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/app_config.py

@@ -7,6 +7,7 @@ from dotenv import load_dotenv
 from pydantic import BaseModel, ConfigDict, Field
 
 from src.config.extensions_config import ExtensionsConfig
+from src.config.memory_config import load_memory_config_from_dict
 from src.config.model_config import ModelConfig
 from src.config.sandbox_config import SandboxConfig
 from src.config.skills_config import SkillsConfig
@@ -82,6 +83,10 @@ class AppConfig(BaseModel):
         if "summarization" in config_data:
             load_summarization_config_from_dict(config_data["summarization"])
 
+        # Load memory config if present
+        if "memory" in config_data:
+            load_memory_config_from_dict(config_data["memory"])
+
         # Load extensions config separately (it's in a different file)
         extensions_config = ExtensionsConfig.from_file()
         config_data["extensions"] = extensions_config.model_dump()