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Description
Closes #8
This PR implements issue #8 by adding a comprehensive long-term memory system with FAISS-based vector storage for Agent Arena. The implementation uses a clean three-layer architecture: Layer 1 provides a pure vector store (LongTermMemory) that works with text and metadata using sentence-transformers for embeddings and FAISS for similarity search, Layer 2 offers generic object storage (SemanticMemory) that works with any Python objects via converter functions, and Layer 3 delivers domain-specific adapters (RAGMemory and RAGMemoryV2) optimized for agent observations. The system includes full persistence support for saving and loading memory indices, configurable FAISS index types including Flat and IVF for different performance requirements, semantic retrieval with similarity thresholds, and comprehensive test coverage with 46 unit tests for the core memory system plus integration tests demonstrating the full architecture. RAGMemoryV2 is the recommended implementation that leverages all three layers for clean separation of concerns and maximum reusability. Documentation includes detailed API references, architecture diagrams, usage examples, and migration guides.