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120 lines
3.2 KiB
Text
120 lines
3.2 KiB
Text
---
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title: Quickstart
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description: Make your first API call to Supermemory - add and retrieve memories.
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---
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<Tip>
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**Using Vercel AI SDK?** Check out the [AI SDK integration](/ai-sdk/overview) for the cleanest implementation with `@supermemory/tools/ai-sdk`.
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</Tip>
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## Memory API
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**Step 1.** Sign up for [Supermemory's Developer Platform](http://console.supermemory.ai) to get the API key. Click on **API Keys -> Create API Key** to generate one.
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**Step 2.** Install the SDK and set your API key:
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<Tabs>
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<Tab title="Python">
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```bash
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pip install supermemory
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export SUPERMEMORY_API_KEY="YOUR_API_KEY"
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```
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</Tab>
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<Tab title="TypeScript">
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```bash
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npm install supermemory
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export SUPERMEMORY_API_KEY="YOUR_API_KEY"
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```
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</Tab>
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</Tabs>
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**Step 3.** Here's everything you need to add memory to your LLM:
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<Tabs>
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<Tab title="Python">
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```python
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from supermemory import Supermemory
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client = Supermemory()
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USER_ID = "dhravya"
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conversation = [
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{"role": "assistant", "content": "Hello, how are you doing?"},
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{"role": "user", "content": "Hello! I am Dhravya. I am 20 years old. I love to code!"},
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{"role": "user", "content": "Can I go to the club?"},
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]
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# Get user profile + relevant memories for context
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profile = client.profile(container_tag=USER_ID, q=conversation[-1]["content"])
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context = f"""Static profile:
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{"\n".join(profile.profile.static)}
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Dynamic profile:
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{"\n".join(profile.profile.dynamic)}
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Relevant memories:
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{"\n".join(r.content for r in profile.search_results.results)}"""
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# Build messages with memory-enriched context
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messages = [{"role": "system", "content": f"User context:\n{context}"}, *conversation]
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# response = llm.chat(messages=messages)
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# Store conversation for future context
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client.add(
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content="\n".join(f"{m['role']}: {m['content']}" for m in conversation),
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container_tag=USER_ID,
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)
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```
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</Tab>
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<Tab title="TypeScript">
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```typescript
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import Supermemory from "supermemory";
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const client = new Supermemory();
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const USER_ID = "dhravya";
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const conversation = [
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{ role: "assistant", content: "Hello, how are you doing?" },
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{ role: "user", content: "Hello! I am Dhravya. I am 20 years old. I love to code!" },
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{ role: "user", content: "Can I go to the club?" },
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];
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// Get user profile + relevant memories for context
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const profile = await client.profile({
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containerTag: USER_ID,
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q: conversation.at(-1)!.content,
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});
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const context = `Static profile:
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${profile.profile.static.join("\n")}
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Dynamic profile:
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${profile.profile.dynamic.join("\n")}
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Relevant memories:
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${profile.searchResults.results.map((r) => r.content).join("\n")}`;
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// Build messages with memory-enriched context
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const messages = [{ role: "system", content: `User context:\n${context}` }, ...conversation];
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// const response = await llm.chat({ messages });
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// Store conversation for future context
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await client.memories.add({
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content: conversation.map((m) => `${m.role}: ${m.content}`).join("\n"),
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containerTag: USER_ID,
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});
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```
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</Tab>
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</Tabs>
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That's it! Supermemory automatically:
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- Extracts memories from conversations
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- Builds and maintains user profiles (static facts + dynamic context)
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- Returns relevant context for personalized LLM responses
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Learn more about [User Profiles](/user-profiles) and [Search](/search/overview).
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