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Memory Retrieval Strategies

Master how AI agents retrieve relevant memories to support intelligent decision-making and personalized responses

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Core Retrieval Methods

AI agents use different retrieval methods to search through memories. Each method has trade-offs between accuracy, speed, and complexity. Understanding these approaches helps you choose the right strategy for your agent's needs.

Interactive: Compare Retrieval Methods

Found 4 results:

Fixed authentication bug in login flow

Score: 0.95
High

User authentication system design patterns

Score: 0.88
High

Implemented OAuth authentication flow

Score: 0.82
High

Debugging JavaScript code in production

Score: 0.72
Med
🧠 Semantic search: Understands meaning via embeddings. Finds related content even without exact word matches.

📊 Method Comparison

1. Keyword/Text Matching

Traditional

Uses exact or fuzzy string matching (BM25, TF-IDF) to find memories containing query words.

✓ Pros: Fast, explainable, works without embeddings
✗ Cons: Misses synonyms, no semantic understanding

2. Semantic/Vector Search

Modern

Encodes query and memories as embeddings, retrieves based on cosine similarity in vector space.

✓ Pros: Understands meaning, finds semantically similar content
✗ Cons: Requires embeddings, slower, less explainable

3. Hybrid Search

Best Practice

Combines keyword matching (sparse vectors) with semantic search (dense vectors) for optimal results.

✓ Pros: Best of both worlds, precise + semantic
✗ Cons: More complex, requires tuning weights

🔧 Other Retrieval Techniques

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Metadata Filtering: Pre-filter memories by date, source, user, or category before semantic search to narrow results.
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Graph-Based Retrieval: Traverse knowledge graphs to find related memories through entity relationships (e.g., "Python" → "Django" → "Web Framework").
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Reranking: Retrieve large candidate set with fast method, then rerank top results with more expensive model (e.g., cross-encoder).
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Temporal Decay: Weight memories by recency using exponential decay function (newer = higher weight).
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