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63 lines
2.5 KiB
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63 lines
2.5 KiB
Text
---
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title: "Overview — What is Supermemory?"
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sidebarTitle: "Overview"
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description = "Add long-term memory to your LLMs with three integration paths: AI SDK, Memory API, or Memory Router."
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---
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Supermemory gives your LLMs long-term memory. Instead of stateless text generation, they recall the right facts from your files, chats, and tools, so responses stay consistent, contextual, and personal.
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## How does it work? (at a glance)
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- You send Supermemory text, files, and chats.
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- Supermemory [intelligently indexes them](/how-it-works) and builds a semantic understanding graph on top of an entity (e.g., a user, a document, a project, an organization).
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- At query time, we fetch only the most relevant context and pass it to your models.
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We offer three ways to add memory to your LLMs:
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### Memory API — full control
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- Ingest text, files, and chats (supports multi-modal); search & filter; re-rank results.
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- Modelled after the actual human brain's working with smart forgetting, decay, recency bias, context rewriting, etc.
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- API + SDKs for Node & Python; designed to scale in production.
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<Info>
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You can reference the full API documentation for the Memory API [here](/api-reference/manage-memories/add-memory).
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</Info>
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### AI SDK
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- Native Vercel AI SDK integration with `@supermemory/tools/ai-sdk`
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- Memory tools for agents or infinite chat for automatic context
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- Works with streamText, generateText, and all AI SDK features
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```typescript
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import { streamText } from "ai"
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import { supermemoryTools } from "@supermemory/tools/ai-sdk"
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const result = await streamText({
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model: anthropic("claude-3"),
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tools: supermemoryTools("YOUR_KEY")
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})
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```
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<Info>
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The AI SDK is recommended for new projects using Vercel AI SDK. The Router works best for existing **chat applications**, whereas the Memory API works as a **complete memory database** with granular control.
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</Info>
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### Memory Router — drop-in proxy with minimal code
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- Keep your existing LLM client; just append `api.supermemory.ai/v3/` to your base URL.
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- Automatic chunking and token management that fits your context window.
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- Adds minimal latency on top of existing LLM requests.
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<Note>
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All three approaches share the **same memory pool** when using the same user ID. You can mix and match based on your needs.
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</Note>
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## Next steps
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Head to the [**Router vs API**](/routervsapi) guide to understand the technical differences between the two and pick what’s best for you with a simple 4-question flow.
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