Retrieval is where memory meets action. The best memory system in the world is useless if you can’t find what you need when you need it. Right memory + right time + right context = intelligent response.
The Retrieval Pipeline
From query to enhanced response: Query → Encode → Rank → Select → Inject → Generate → Response. “What’s my budget?” → encode the query → rank matching memories → select relevant ones (Budget: $500, Saved: Jan 2024, Category: Travel) → inject into context → LLM generates → “Your budget is $500 for travel.”
Three Retrieval Strategies
Similarity-Based: Find memories closest to query in embedding space. The classic vector search approach—encode query, find nearest neighbors, return top matches. Methods: Cosine similarity, kNN, FAISS, Pinecone. Simple and fast but can miss semantically related but lexically different matches.
Structured Query: Use metadata, filters, and relationships. SQL-style precision: SELECT * FROM memories WHERE type=’budget’. Methods: SQL, GraphQL, Cypher, Filters. Precise when you know what you’re looking for but requires structured data.
Hybrid Retrieval: Combine multiple strategies + LLM reranking. Query flows through semantic, keyword, and graph pathways; LLM reranks combined results. Methods: RAG-Fusion, HyDE, Self-Query. Best results but highest complexity.
Quality Factors
Relevance: Right memories found—the fundamental requirement.
Recency: Prefer recent information when freshness matters.
Importance: Weight by significance, not just similarity.
Diversity: Avoid redundancy in retrieved context.
Key Insight
Retrieval is where memory meets action. The best memory in the world is useless if you can’t find it when you need it. Invest in retrieval quality—it’s the difference between an agent that knows and an agent that can use what it knows.
Read the full analysis: The AI Agents Memory Ecosystem
Source: Hu et al. (2025) “Memory in the Age of AI Agents” arXiv:2512.13564









