Quickstart - supermemory | Memory API for the AI era

Using Vercel AI SDK? Check out the AI SDK integration for the cleanest implementation with @supermemory/tools/ai-sdk.

Prefer to run it locally? Supermemory is also a self-hostable single binary — curl -fsSL https://supermemory.ai/install | bash and you’re running.

Memory API

Step 1. Sign up for Supermemory’s Developer Platform to get the API key. Click on API Keys -> Create API Key to generate one.

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

Python
TypeScript

pip install supermemory
export SUPERMEMORY_API_KEY="YOUR_API_KEY"

npm install supermemory
export SUPERMEMORY_API_KEY="YOUR_API_KEY"

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

Python
TypeScript

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,
)

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,
});

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

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.

Learn more about User Profiles and Search.

​Memory API

Memory API