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Long-Term Memory

Master how AI agents store and retrieve knowledge across sessions using persistent memory systems

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Choosing the Right Storage System

Long-term memory requires persistent storage—a database that survives beyond the agent's session. But which database? The choice depends on what type of information you're storing and how you need to retrieve it.

Let's explore four major storage paradigms and when to use each.

Interactive: Storage Type Explorer

🗄️ SQL (Relational Databases)

Structured tables with rows and columns. Data is organized with strict schemas and relationships.

✅ Best For
  • • Structured data (users, transactions)
  • • ACID guarantees
  • • Complex joins
❌ Limitations
  • • Rigid schema
  • • Harder to scale horizontally
  • • Not ideal for unstructured data
-- Example: Storing conversation history
CREATE TABLE conversations (
id INT PRIMARY KEY,
user_id INT,
message TEXT,
timestamp DATETIME
);
Use Case:

Customer support agents tracking ticket history, user profiles, and structured interaction logs.

Interactive: Performance at Scale

See how query performance changes with data volume. Adjust the slider to simulate different scales.

1,000 records
10010K100K1M10M
Query Time
7.0ms
Excellent
Scalability
Good
Horizontal scaling ability
Storage Type
SQL
Small dataset

Storage Decision Matrix

Use CaseRecommendedWhy
User profiles & settingsNoSQLFlexible schema, fast writes
Conversation historySQLStructured, timestamped data
Document search (RAG)Vector DBSemantic similarity search
Knowledge graphsGraph DBComplex relationships
Hybrid systemMultiple DBsBest of all worlds

💡 Key Insight

There's no "best" database for all agents. Production systems often use multiple databases: SQL for structured data (users, transactions), vector DBs for semantic search (RAG), and NoSQL for flexible session storage. Choose based on your access patterns.

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