AI Agent Memory Systems Explained: RAG, Vector, SQLite & File-Based

Comparison Table

This table summarizes the key tradeoffs across all five memory system types. Use it to quickly identify which approach fits your constraints.

How Popular Agents Handle Memory

Different AI agents have adopted different memory strategies based on their design philosophy and target users. Here is how the most widely used agents approach the memory problem in production.

Worked Example: Building a Hybrid Memory System

The most robust agent memory combines multiple layers, each optimized for a different type of data. Here is a conceptual architecture for a hybrid memory system that handles key facts, conversation history, and knowledge retrieval.

Architecture Overview

The hybrid system uses three layers working together:

1. Layer 1 — Persistent Key Facts (File-Based): A MEMORY.md file stores critical user preferences, project conventions, and long-lived decisions. Read at every session start. Updated when the agent identifies new persistent facts.
2. Layer 2 — Conversation History (SQLite + FTS5): Every conversation turn is stored in a SQLite database with full-text search indexing. The agent can search past conversations by keyword to recall specific discussions, decisions, or code snippets.
3. Layer 3 — Knowledge Base (Vector Search): Documentation, code comments, and reference materials are embedded and stored in a vector database. When the agent needs domain knowledge, it performs semantic search to find relevant chunks and injects them into the prompt.

How It Works in Practice

// Conceptual hybrid memory flow
async function buildContext(userMessage: string) {
  // Layer 1: Always load persistent facts
  const keyFacts = await readFile('MEMORY.md');

  // Layer 2: Search conversation history for relevant past context
  const pastConversations = await sqlite.search(
    extractKeywords(userMessage), { limit: 5 }
  );

  // Layer 3: Semantic search knowledge base
  const relevantDocs = await vectorDB.similaritySearch(
    await embed(userMessage), { topK: 3 }
  );

  // Compose final context for LLM prompt
  return {
    systemContext: keyFacts,
    conversationMemory: pastConversations,
    knowledgeContext: relevantDocs,
  };
}

This architecture gives you the reliability of file-based memory (key facts are always available), the precision of keyword search (find exact past conversations), and the flexibility of semantic search (discover relevant knowledge even with different terminology). The tradeoff is increased complexity — you need to maintain three separate systems and handle potential inconsistencies between them.

Frequently Asked Questions