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",
+]

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()

backend/src/config/memory_config.py

@@ -0,0 +1,69 @@
+"""Configuration for memory mechanism."""
+
+from pydantic import BaseModel, Field
+
+
+class MemoryConfig(BaseModel):
+    """Configuration for global memory mechanism."""
+
+    enabled: bool = Field(
+        default=True,
+        description="Whether to enable memory mechanism",
+    )
+    storage_path: str = Field(
+        default=".deer-flow/memory.json",
+        description="Path to store memory data (relative to backend directory)",
+    )
+    debounce_seconds: int = Field(
+        default=30,
+        ge=1,
+        le=300,
+        description="Seconds to wait before processing queued updates (debounce)",
+    )
+    model_name: str | None = Field(
+        default=None,
+        description="Model name to use for memory updates (None = use default model)",
+    )
+    max_facts: int = Field(
+        default=100,
+        ge=10,
+        le=500,
+        description="Maximum number of facts to store",
+    )
+    fact_confidence_threshold: float = Field(
+        default=0.7,
+        ge=0.0,
+        le=1.0,
+        description="Minimum confidence threshold for storing facts",
+    )
+    injection_enabled: bool = Field(
+        default=True,
+        description="Whether to inject memory into system prompt",
+    )
+    max_injection_tokens: int = Field(
+        default=2000,
+        ge=100,
+        le=8000,
+        description="Maximum tokens to use for memory injection",
+    )
+
+
+# Global configuration instance
+_memory_config: MemoryConfig = MemoryConfig()
+
+
+def get_memory_config() -> MemoryConfig:
+    """Get the current memory configuration."""
+    return _memory_config
+
+
+def set_memory_config(config: MemoryConfig) -> None:
+    """Set the memory configuration."""
+    global _memory_config
+    _memory_config = config
+
+
+def load_memory_config_from_dict(config_dict: dict) -> None:
+    """Load memory configuration from a dictionary."""
+    global _memory_config
+    _memory_config = MemoryConfig(**config_dict)