What is AI memory?
AI memory is the ability for an application to remember facts about a user across sessions, devices, and conversations. Without memory, every chat starts from zero. The model only knows what fits in the current context window.
Persistent AI memory stores preferences, history, and context outside the prompt. When a new message arrives, the system retrieves relevant memories and injects them into the LLM context automatically.
Databaset provides this as a managed API: store once, recall by meaning, inject into any model.
Why AI memory matters for product teams
- Personalization: assistants that remember name, role, stack, and preferences
- Continuity: users pick up where they left off days or weeks later
- Less prompt engineering: stop stuffing the same user facts into every request
- Better answers: the model sees only relevant context, not entire chat logs
- Lower token cost: retrieve 3 to 5 memories instead of sending full history
Memory vs context window
The context window is a hard limit on tokens the model can read at once. Stuffing entire chat history into every request is expensive, slow, and hits limits quickly.
Memory is selective. You store durable facts as discrete records. At query time, semantic search finds what matters for this specific question.
Think of context as short-term working memory and Databaset as long-term storage with intelligent retrieval.
How AI memory works (store, recall, inject)
- Store: save a fact tied to userId and optional appId or metadata
- Embed: Databaset creates vector embeddings for semantic search automatically
- Recall: on each user message, fetch top-k relevant memories by meaning
- Inject: prepend recalled memories to the system or user prompt before the LLM call
import { Databaset } from "@databaset/sdk";
const memory = new Databaset({ apiKey: process.env.DATABASET_API_KEY });
// Store a preference once
await memory.store({
userId: "user_123",
text: "Prefers PostgreSQL and dark mode UI",
});
// Later: recall before every LLM call
const { memories } = await memory.recall({
userId: "user_123",
query: userMessage,
limit: 5,
});When to use AI memory
- AI copilots and chatbots with returning users
- Customer support agents that need ticket and account history
- Sales and CRM assistants with lead context
- Education and coaching apps tracking learner progress
- Any product where users expect the AI to remember them
Get started with Databaset
Sign up free at app.databaset.com. Install the JavaScript or Python SDK, create an API key, and call store and recall from your backend.
Free tier includes 3,000 API calls per month. No credit card required. Full docs at docs.databaset.com.
Frequently asked questions
- Is AI memory the same as RAG?
- Related but different. RAG usually retrieves documents from a knowledge base. AI memory stores per-user facts and preferences. Many apps use both. See our RAG vs Memory guide for a full comparison.
- Do I need a vector database?
- Not with Databaset. We handle embeddings, indexing, and retrieval. You call store and recall over HTTP.
- Which LLMs work with Databaset?
- Any model that accepts text prompts: OpenAI GPT, Anthropic Claude, Google Gemini, Mistral, Llama, and local models via Ollama or vLLM.
Start building with Databaset
Add persistent AI memory in minutes. Free tier includes 3,000 API calls per month. No credit card required.