feat: Aggiunge miglioramenti per memoria e pianificazione con integrazione MCP

- Aggiunto sistema SimplePersistentMemory per fatti, preferenze e cronologia conversazioni
- Aggiunto EnhancedPlanner con dipendenze task, priorità e tracciamento progresso
- Integrati strumenti di miglioramento nell'agente (remember, recall, preference, planning)
- Aggiunti comandi CLI /memory e /plan per accesso diretto da terminale
- Aggiunto client MCP per connessione a server Model Context Protocol
- Aggiornato README con documentazione completa per tutte le nuove funzionalità
- Corretto parsing comando creazione piano per gestire nomi multi-parola

Nuovi comandi:
- /memory remember <testo> - Ricorda un fatto
- /memory recall <query> - Cerca nella memoria
- /memory preference set/get - Gestisci preferenze
- /memory stats - Statistiche memoria
- /plan create <nome> <task1;task2;...> - Crea un piano
- /plan list - Elenca tutti i piani
- /plan progress <plan_id> - Mostra progresso piano
- /plan start <task_id> - Inizia un task
- /plan complete <task_id> - Completa un task
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# Enhancements for LangCode Agent based on LangChain Deep Agents analysis
This directory contains planned enhancements to bring LangCode Agent closer to the architecture patterns revealed in LangChain's Deep Agents framework.
## Planned Enhancements
1. **Enhanced Memory Systems**
- Add VectorStoreRetrieverMemory for semantic recall
- Implement SummaryBufferMemory for conversation summarization
- Add persistent memory storage for cross-session learning
2. **Advanced Planning & Reasoning**
- Implement plan-and-execute agent pattern
- Add self-reflection capabilities
- Improve task decomposition and tracking
3. **Production Observability**
- Add LangChain-style callbacks for tracing
- Implement tool usage monitoring
- Add performance metrics collection
4. **Advanced Tool Composition**
- Implement tool chaining capabilities
- Add async tool execution for parallelism
- Enhance input validation and error handling
5. **Agent Hierarchies**
- Develop specialist subagents for specific domains
- Implement agent routing based on task analysis
- Add agent-to-agent communication patterns
## Implementation Approach
Each enhancement will be implemented as a separate module that can be optionally integrated into the main agent, maintaining backward compatibility while extending capabilities.
The enhancements will follow the same patterns already established in the codebase:
- Modular design
- Configuration-driven behavior
- Clear separation of concerns
- Comprehensive error handling