Agent Memory vs RAG
Understanding when to use agent memory systems versus RAG (Retrieval-Augmented Generation) for your AI application
Agent memory and RAG solve different problems and often work together:
Agent Memory
Remembers user-specific information, preferences, and conversation history. Personal and evolving.
RAG
Retrieves factual knowledge from documents and databases. Shared and static (until docs update).
Agent Memory is like your friend remembering that you don't like mushrooms, that you're learning piano, and what you talked about last week. It's personal memories about YOU.
RAG is like your friend looking up facts in an encyclopedia when you ask a question. "What's the capital of France?" → They look it up → "Paris!" It's about finding facts, not remembering personal stuff.
Best together: Your friend remembers you're learning French (memory) AND looks up French grammar rules for you (RAG)!
| Aspect | Agent Memory | RAG |
|---|---|---|
| Primary Purpose | Remember user context, preferences, history | Retrieve factual knowledge from documents |
| Data Scope | User-Specific Each user has their own memory | Shared Knowledge Same documents for all users |
| Updates | Continuous: Every interaction adds to memory | Manual: When you update docs/knowledge base |
| Typical Use Cases |
|
|
Cost per User | Higher Scales with users × history | Lower Shared knowledge across users |
Retention | Short to long-term (days to years) | As long as documents exist |
| Privacy Concerns | High: Storing personal user data | Low: Public/company knowledge only |
Use Agent Memory When:
- Building personal assistants
Need to remember user preferences, habits, goals
- Conversation continuity matters
"Continue our discussion from yesterday"
- Tracking state over time
Project progress, learning journey, relationship building
- Personalized experiences
Adapting responses based on past interactions
Use RAG When:
- Answering from documentation
Product docs, FAQs, knowledge bases
- Factual accuracy is critical
Need to cite sources and ground responses
- Large, shared knowledge
Company wiki, product catalog, research papers
- Content updates frequently
Need latest info without retraining
The most powerful AI applications combine agent memory AND RAG:
Example: Customer Support AI
- Memory: "This is Sarah's third ticket about billing. She's on the Pro plan and prefers email over phone."
- RAG: "Let me look up the latest billing documentation to answer her question accurately."
Example: Code Assistant
- Memory: "You prefer TypeScript with strict types. Your project uses React with Next.js 14."
- RAG: "Here's how to do that based on the Next.js 14 API documentation..."
async function handleUserQuery(userId, query) {
// 1. Retrieve user memory (personal context)
const userMemory = await getMemory(userId, {
type: "episodic",
limit: 5
})
// 2. Retrieve relevant docs (shared knowledge)
const relevantDocs = await ragSearch(query, {
limit: 3
})
// 3. Combine both in prompt
const response = await llm.generate({
system: `You are a helpful assistant.
User context (remember this about the user):
${userMemory}
Relevant documentation:
${relevantDocs}`,
user: query
})
// 4. Store this interaction in memory
await storeMemory(userId, {
query,
response,
timestamp: Date.now()
})
return response
}Ask yourself:
"If a new user asked the same question, should they get the same answer?"
- ✅ Yes → Use RAG (factual, shared knowledge)
- ✅ No → Use Memory (personal, contextual)
- ✅ Both matter → Use both!