> ## Documentation Index
> Fetch the complete documentation index at: https://supermemory.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Quickstart

> Make your first API call to Supermemory - add and retrieve memories.

<Tip>
  **Using Vercel AI SDK?** Check out the [AI SDK integration](/integrations/ai-sdk) for the cleanest implementation with `@supermemory/tools/ai-sdk`.
</Tip>

<Tip>
  **Prefer to run it locally?** Supermemory is also a [self-hostable single binary](/self-hosting/overview) — `curl -fsSL https://supermemory.ai/install | bash` and you're running.
</Tip>

## Memory API

**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.

<img src="https://mintcdn.com/supermemory/nafXZdsbm5CLncox/images/create-api.png?fit=max&auto=format&n=nafXZdsbm5CLncox&q=85&s=a3216bf250253e6b33eb44a635a62b9d" alt="create api key" width="2772" height="1282" data-path="images/create-api.png" />

**Step 2.** Install the SDK and set your API key:

<Tabs>
  <Tab title="Python">
    ```bash theme={null}
    pip install supermemory
    export SUPERMEMORY_API_KEY="YOUR_API_KEY"
    ```
  </Tab>

  <Tab title="TypeScript">
    ```bash theme={null}
    npm install supermemory
    export SUPERMEMORY_API_KEY="YOUR_API_KEY"
    ```
  </Tab>
</Tabs>

**Step 3.** Here's everything you need to add memory to your LLM:

<Tabs>
  <Tab title="Python">
    ```python theme={null}
    from supermemory import Supermemory

    client = Supermemory()
    USER_ID = "dhravya"

    conversation = [
        {"role": "assistant", "content": "Hello, how are you doing?"},
        {"role": "user", "content": "Hello! I am Dhravya. I am 20 years old. I love to code!"},
        {"role": "user", "content": "Can I go to the club?"},
    ]

    # Get user profile + relevant memories for context
    profile = client.profile(container_tag=USER_ID, q=conversation[-1]["content"])

    static = "\n".join(profile.profile.static)
    dynamic = "\n".join(profile.profile.dynamic)
    memories = "\n".join(r.get("memory", "") for r in profile.search_results.results)

    context = f"""Static profile:
    {static}

    Dynamic profile:
    {dynamic}

    Relevant memories:
    {memories}"""

    # Build messages with memory-enriched context
    messages = [{"role": "system", "content": f"User context:\n{context}"}, *conversation]

    # response = llm.chat(messages=messages)

    # Store conversation for future context
    client.add(
        content="\n".join(f"{m['role']}: {m['content']}" for m in conversation),
        container_tag=USER_ID,
    )
    ```
  </Tab>

  <Tab title="TypeScript">
    ```typescript theme={null}
    import Supermemory from "supermemory";

    const client = new Supermemory();
    const USER_ID = "dhravya";

    const conversation = [
      { role: "assistant", content: "Hello, how are you doing?" },
      { role: "user", content: "Hello! I am Dhravya. I am 20 years old. I love to code!" },
      { role: "user", content: "Can I go to the club?" },
    ];

    // Get user profile + relevant memories for context
    const profile = await client.profile({
      containerTag: USER_ID,
      q: conversation.at(-1)!.content,
    });

    const context = `Static profile:
    ${profile.profile.static.join("\n")}

    Dynamic profile:
    ${profile.profile.dynamic.join("\n")}

    Relevant memories:
    ${profile.searchResults.results.map((r) => r.memory).join("\n")}`;

    // Build messages with memory-enriched context
    const messages = [{ role: "system", content: `User context:\n${context}` }, ...conversation];

    // const response = await llm.chat({ messages });

    // Store conversation for future context
    await client.add({
      content: conversation.map((m) => `${m.role}: ${m.content}`).join("\n"),
      containerTag: USER_ID,
    });
    ```
  </Tab>
</Tabs>

That's it! Supermemory automatically:

* Extracts memories from conversations
* Builds and maintains user profiles (static facts + dynamic context)
* Returns relevant context for personalized LLM responses

<Tip>
  **Optional:** Use the `threshold` parameter to filter search results by relevance score. For example: `client.profile(container_tag=USER_ID, threshold=0.7, q=query)` will only include results with a score above 0.7.
</Tip>

Learn more about [User Profiles](/user-profiles) and [Search](/search).