RAG vs Memory

RAG (Retrieval-Augmented Generation) retrieves relevant documents or context for each query, enabling LLMs to generate grounded responses from a knowledge base. Agent memory, in contrast, is persistent, structured context that accumulates over time—tracking entities, relationships, state changes, and history across sessions.

RAG is stateless: each query is independent, retrieval happens per request, and no context carries forward unless explicitly re-retrieved. Agent memory is stateful: it remembers prior interactions, learns from experience, tracks ownership and causality, and evolves continuously. Both are valuable, but they solve different problems.

The outcome is understanding when RAG is sufficient (document Q&A, knowledge lookup) and when agent memory is required (stateful workflows, continuity, learning).

Why it matters

• Clarifies architectural decisions: Choosing RAG vs. memory depends on whether you need stateless retrieval or stateful continuity.
• Prevents over-engineering: Not every AI application needs agent memory—RAG is simpler for document Q&A use cases.
• Highlights limitations: RAG doesn't track state or learn—if users expect "remember what I said yesterday," RAG fails.
• Enables hybrid approaches: Combine RAG (for document knowledge) with agent memory (for workflow state and history)—best of both.
• Improves user expectations: If you're using RAG, tell users it's stateless—if you promise memory, implement it properly.
• Informs cost and complexity: Agent memory requires more infrastructure (graphs, timelines, entity linking) than RAG—understand tradeoffs.

How it works

RAG workflow:

• User query → Embed query → Vector search for similar documents → Retrieve top K docs → Pass to LLM → Generate response → Discard context.
• Next query starts fresh: no memory of prior interactions unless session history is manually included in prompts.

Agent memory workflow:

• User interaction → Extract entities, facts, and events → Store in knowledge graph and timeline → Link to existing memory → Update indexes.
• Next interaction: Query memory for relevant history → Assemble context (facts, relationships, timeline) → Agent reasons over accumulated memory → Response reflects past interactions.
• Memory persists and evolves across sessions.

Comparison & confusion to avoid

Examples & uses

RAG use case: Company knowledge base Q&A
User asks: "What's our refund policy?" RAG embeds the query, retrieves the 3 most similar policy documents, and LLM generates an answer. Next query: "How do I reset my password?" RAG retrieves different docs—no memory of prior query.

Agent memory use case: Multi-session coding assistant
Day 1: User asks agent to refactor authentication logic. Agent extracts facts, stores in memory. Day 5: User says "add OAuth to that auth change." Agent recalls prior refactor from memory, understands "that" refers to it, and applies OAuth changes without re-explanation.

Hybrid: Customer support agent
RAG retrieves help articles for user questions. Agent memory tracks customer history: prior issues, escalations, preferences. Response combines retrieved docs (RAG) + customer context (memory) for personalized support.

Best practices

• Use RAG for stateless knowledge lookup: If users ask one-off questions about documents, RAG is faster and simpler.
• Use agent memory for stateful workflows: If users expect continuity ("remember what I asked yesterday"), implement proper memory.
• Combine both for best results: RAG for document knowledge + agent memory for workflow state = powerful hybrid.
• Don't fake memory with RAG: Re-sending full conversation history in every prompt is not memory—it's a workaround that hits limits fast.
• Be explicit about limitations: If using RAG-only, document that the system is stateless—set user expectations correctly.
• Plan for scaling: RAG scales to millions of documents easily; agent memory requires careful graph and timeline architecture.

Common pitfalls

• Assuming RAG equals memory: RAG retrieves context per query but doesn't persist state—users will notice the lack of continuity.
• Over-engineering with memory: If the use case is simple document Q&A, RAG is sufficient—don't build unnecessary complexity.
• No temporal awareness in RAG: RAG doesn't know "what changed last week"—if you need time-based reasoning, you need memory.
• Ignoring relationship queries: RAG retrieves similar docs but can't answer "who owns tasks blocking Project X"—memory with knowledge graphs can.
• Session history workaround: Appending all prior messages to prompts is brittle—implement proper session or agent memory instead.

See also

• Agent Memory — Persistent, stateful context across sessions
• Semantic Retrieval — Intelligent retrieval used by both RAG and memory
• Stateful Agent — Agents that require memory, not just RAG
• Vector Database — Storage layer used in RAG systems
• Agent Memory Platform — Infrastructure for stateful memory

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See how Graphlit combines RAG with Agent Memory → Agent Memory Platform