feat: add Apple Container support with comprehensive documentation and dev tools

Add native Apple Container support for better performance on macOS while
maintaining full Docker compatibility. Enhance documentation with memory system
details, development guidelines, and sandbox setup instructions. Improve dev
experience with container image pre-pulling and unified cleanup tools.

Key changes:
- Auto-detect and prefer Apple Container on macOS with Docker fallback
- Add APPLE_CONTAINER.md with complete usage and troubleshooting guide
- Document memory system architecture in CLAUDE.md
- Add make setup-sandbox for pre-pulling container images
- Create cleanup-containers.sh for cross-runtime container cleanup
- Update all related documentation (README, SETUP, config examples)

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

backend/CLAUDE.md

@@ -40,6 +40,17 @@ deer-flow/
     └── custom/                # Custom skills (gitignored)
 ```
 
+## Important Development Guidelines
+
+### Documentation Update Policy
+**CRITICAL: Always update README.md and CLAUDE.md after every code change**
+
+When making code changes, you MUST update the relevant documentation:
+- Update `README.md` for user-facing changes (features, setup, usage instructions)
+- Update `CLAUDE.md` for development changes (architecture, commands, workflows, internal systems)
+- Keep documentation synchronized with the codebase at all times
+- Ensure accuracy and timeliness of all documentation
+
 ## Commands
 
 **Root directory** (for full application):
@@ -202,7 +213,49 @@ Configuration priority:
   5. `TitleMiddleware` - Generates conversation titles
   6. `TodoListMiddleware` - Tracks multi-step tasks (if plan_mode enabled)
   7. `ViewImageMiddleware` - Injects image details for vision models
-  8. `ClarificationMiddleware` - Handles clarification requests (must be last)
+  8. `MemoryMiddleware` - Automatic context retention and personalization (if enabled)
+  9. `ClarificationMiddleware` - Handles clarification requests (must be last)
+
+**Memory System** (`src/agents/memory/`)
+- LLM-powered personalization layer that automatically extracts and stores user context across conversations
+- Components:
+  - `updater.py` - LLM-based memory updates with fact extraction and file I/O
+  - `queue.py` - Debounced update queue for batching and performance optimization
+  - `prompt.py` - Prompt templates and formatting utilities for memory updates
+- `MemoryMiddleware` (`src/agents/middlewares/memory_middleware.py`) - Queues conversations for memory updates
+- Gateway API (`src/gateway/routers/memory.py`) - REST endpoints for memory management
+- Storage: JSON file at `backend/.deer-flow/memory.json`
+
+**Memory Data Structure**:
+- **User Context** (current state):
+  - `workContext` - Work-related information (job, projects, technologies)
+  - `personalContext` - Preferences, communication style, background
+  - `topOfMind` - Current focus areas and immediate priorities
+- **History** (temporal context):
+  - `recentMonths` - Recent activities and discussions
+  - `earlierContext` - Important historical context
+  - `longTermBackground` - Persistent background information
+- **Facts** (structured knowledge):
+  - Discrete facts with categories: `preference`, `knowledge`, `context`, `behavior`, `goal`
+  - Each fact includes: `id`, `content`, `category`, `confidence` (0-1), `createdAt`, `source` (thread ID)
+  - Confidence threshold (default 0.7) filters low-quality facts
+  - Max facts limit (default 100) keeps highest-confidence facts
+
+**Memory Workflow**:
+1. **Post-Interaction**: `MemoryMiddleware` filters messages (user inputs + final AI responses only) and queues conversation
+2. **Debounced Processing**: Queue waits 30s (configurable), batches multiple updates, resets timer on new updates
+3. **LLM-Based Update**: Background thread loads memory, formats conversation, invokes LLM to extract:
+   - Updated context summaries (1-3 sentences each)
+   - New facts with confidence scores and categories
+   - Facts to remove (contradictions)
+4. **Storage**: Applies updates atomically to `memory.json` with cache invalidation (mtime-based)
+5. **Injection**: Next interaction loads memory, formats top 15 facts + context, injects into `<memory>` tags in system prompt
+
+**Memory API Endpoints** (`/api/memory`):
+- `GET /api/memory` - Retrieve current memory data
+- `POST /api/memory/reload` - Force reload from file (invalidates cache)
+- `GET /api/memory/config` - Get memory configuration
+- `GET /api/memory/status` - Get both config and data
 
 ### Config Schema
 
@@ -215,6 +268,15 @@ Models, tools, sandbox providers, skills, and middleware settings are configured
 - `skills.container_path`: Container mount path (default: `/mnt/skills`)
 - `title`: Automatic thread title generation configuration
 - `summarization`: Automatic conversation summarization configuration
+- `memory`: Memory system configuration
+  - `enabled`: Master switch (boolean)
+  - `storage_path`: Path to memory.json file (relative to backend/)
+  - `debounce_seconds`: Wait time before processing updates (default: 30)
+  - `model_name`: LLM model for memory updates (null = use default model)
+  - `max_facts`: Maximum facts to store (default: 100)
+  - `fact_confidence_threshold`: Minimum confidence to store fact (default: 0.7)
+  - `injection_enabled`: Inject memory into system prompt (boolean)
+  - `max_injection_tokens`: Token limit for memory injection (default: 2000)
 
 **Extensions Configuration Schema** (`extensions_config.json`):
 - `mcpServers`: Map of MCP server name to configuration
@@ -307,6 +369,29 @@ For models with `supports_vision: true`:
 - `view_image_tool` added to agent's toolset
 - Images automatically converted and injected into state
 
+### Memory System
+
+Persistent context retention and personalization across conversations:
+- **Automatic Extraction**: LLM analyzes conversations to extract user context, facts, and preferences
+- **Structured Storage**: Maintains user context, history, and confidence-scored facts in JSON format
+- **Smart Filtering**: Only processes meaningful messages (user inputs + final AI responses)
+- **Debounced Updates**: Batches updates to minimize LLM calls (configurable wait time)
+- **System Prompt Injection**: Automatically injects relevant memory context into agent prompts
+- **Cache Optimization**: File modification time-based cache invalidation for external edits
+- **Thread Safety**: Locks protect queue and cache for concurrent access
+- **REST API**: Full CRUD operations via `/api/memory` endpoints
+- **Frontend Integration**: Memory settings page for viewing and managing memory data
+
+**Configuration**: Controlled via `memory` section in `config.yaml`
+- Enable/disable memory system
+- Configure storage path, debounce timing, fact limits
+- Control system prompt injection and token limits
+- Set confidence thresholds for fact storage
+
+**Storage Location**: `backend/.deer-flow/memory.json`
+
+See configuration section for detailed settings.
+
 ## Code Style
 
 - Uses `ruff` for linting and formatting