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Memory Architecture

Memory Architecture

Skill 4: Hybrid Memory Architectures and Knowledge Engineering

The cognitive foundation that transforms agents from simple question-answering systems into sophisticated knowledge workers.

Overview

Skill 4 represents the critical competency for designing sophisticated memory and knowledge systems that empower intelligent agents. Moving beyond simple Retrieval-Augmented Generation (RAG), this skill embraces a holistic discipline of knowledge engineering where memory is not just a database but a cognitive architecture inspired by human cognition.

The Three Sub-Skills

Sub-Skill Focus Area Key Concepts
4.1 Three-Tier Memory Cognitive model for agent memory Episodic, semantic, and procedural memory layers
4.2 Hybrid Retrieval Combining semantic search and structured traversal Vector embeddings, knowledge graphs, GraphRAG
4.3 Retrieval Optimization Advanced retrieval quality and efficiency Contextual embeddings, hierarchical retrieval, hybrid fusion

4.1 The Three-Tier Memory Architecture

Episodic Memory

  • Core Principle: Storing specific interaction histories with temporal and contextual indexing
  • Function: Captures "what happened when" of agent-user interactions
  • Key Technologies: Zep, Graphiti, temporal knowledge graphs
  • Use Cases: Multi-session conversations, personalized experiences, debugging interaction histories

Semantic Memory

  • Core Principle: Storing general world knowledge, facts, policies, and procedures
  • Function: The agent's "knowledge base" — facts and information about the world
  • Key Technologies: GraphRAG, hybrid vector + graph systems
  • Use Cases: Enterprise knowledge bases, policy retrieval, domain expertise, complex reasoning

Procedural Memory

  • Core Principle: Storing "how-to" knowledge — successful problem-solving patterns
  • Function: Learned skills and proven solution patterns
  • Implementation: Prompt templates, few-shot examples, cached execution plans
  • Use Cases: Workflow automation, best practice retrieval, solution reuse

4.2 Hybrid Retrieval: Vector + Graph

Vector Search for Breadth

  • Core Principle: Semantic similarity search using high-dimensional embeddings
  • Key Techniques: Dense embeddings, approximate nearest neighbor (HNSW, IVF), hybrid sparse-dense
  • Technologies: Weaviate, Pinecone, Qdrant
  • Use Cases: Document retrieval, semantic search, fuzzy matching, exploratory queries

Graph Traversal for Depth

  • Core Principle: Structured relationship traversal for multi-hop reasoning
  • Key Techniques: Entity extraction, relationship modeling, Cypher/SPARQL queries
  • Technologies: Neo4j, MemGraph
  • Use Cases: Complex reasoning, dependency analysis, relationship discovery, root cause analysis

Community Detection and Hierarchical Summarization

  • Core Principle: Clustering and pre-summarization for scalable global queries
  • Key Techniques: Louvain, Leiden algorithms for clustering
  • Use Cases: Large-scale knowledge bases, executive summaries, trend analysis

4.3 Contextual Embeddings and Retrieval Optimization

Contextual Embeddings

  • Core Principle: Embedding chunks with surrounding context for better semantic representation
  • Pattern: embed(f"{document_summary}\n\n{section_header}\n\n{chunk_text}")
  • Use Cases: Improving retrieval precision, reducing false positives

Hierarchical Retrieval

  • Core Principle: Multi-stage retrieval from coarse to fine
  • Pattern: Domain Selection → Document Retrieval → Chunk Extraction
  • Use Cases: Large document collections, latency-sensitive applications

Entity Extraction and Graph Construction

  • Core Principle: Automated graph building from unstructured text
  • Key Techniques: Named entity recognition (NER), relationship extraction, coreference resolution
  • Use Cases: Automated knowledge graph construction, relationship discovery

Hybrid Fusion Strategies

  • Core Principle: Optimally combining vector and graph retrieval results
  • Key Techniques: Reciprocal rank fusion (RRF), score normalization, learned-to-rank models
  • Use Cases: Hybrid search systems, result quality optimization

Transferable Competencies

Mastering Skill 4 requires proficiency in:

  • Cognitive Science: Memory models, knowledge representation, cognitive architectures
  • Information Retrieval: Vector search, ranking algorithms, precision/recall/NDCG
  • Graph Theory: Graph algorithms, community detection, path finding, centrality measures
  • Natural Language Processing: Entity extraction, relationship extraction, coreference resolution
  • Vector Databases: Embedding models, ANN algorithms, indexing strategies
  • Graph Databases: Cypher, SPARQL, graph modeling, query optimization

Common Pitfalls

  1. Vector-only thinking: Missing the power of structured relationships and multi-hop reasoning
  2. Poor chunking strategies: Creating chunks that lose context or are too large/small
  3. Ignoring temporal aspects: Not tracking when information was added or updated
  4. No contextual embeddings: Embedding chunks without surrounding context
  5. Flat retrieval: Not using hierarchical or multi-stage retrieval for efficiency
  6. Manual graph construction: Not automating entity extraction and relationship building
  7. No fusion strategy: Poorly combining vector and graph results
  8. Ignoring scalability: Not planning for growth in knowledge base size

Key Technologies

RAG Frameworks

  • LlamaIndex (comprehensive RAG with hybrid retrieval)
  • Haystack (pipeline-based RAG)
  • LangChain (extensive retrieval integrations)

Vector Databases

  • Weaviate (open-source with hybrid search)
  • Pinecone (managed, high performance)
  • Qdrant (open-source with advanced filtering)
  • Chroma (lightweight embedding database)

Graph Databases

  • Neo4j (industry-leading with Cypher)
  • MemGraph (high-performance in-memory)
  • Amazon Neptune (managed service)

Specialized Memory Systems

  • Zep (long-term memory for conversational AI)
  • Graphiti (temporal knowledge graphs)
  • GraphRAG (Microsoft Research's hybrid methodology)

The Bottom Line

Skill 4 is the cognitive foundation that transforms agents from simple question-answering systems into sophisticated knowledge workers capable of complex reasoning over vast information landscapes. Mastering hybrid memory architectures and knowledge engineering principles is essential for building truly intelligent agentic systems.

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