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:
Step 3. Here’s everything you need to add memory to your LLM:
Python
TypeScript
from supermemory import Supermemoryclient = 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 contextprofile = 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 contextmessages = [{"role": "system", "content": f"User context:\n{context}"}, *conversation]# response = llm.chat(messages=messages)# Store conversation for future contextclient.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 contextconst 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:${profile.searchResults.results.map((r) => r.memory).join("\n")}`;// Build messages with memory-enriched contextconst messages = [{ role: "system", content: `User context:\n${context}` }, ...conversation];// const response = await llm.chat({ messages });// Store conversation for future contextawait 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.
