This builds on the basic Supermemory setup. Here the focus is the pattern — when to write, when to read, and what to keep out of memory.

What is the read-write pattern?

On every turn, two things happen. Before the model answers, you search the user’s namespace and inject the top memories as context. After the model replies, you extract durable facts from the turn and store them. Read first, answer, then write. Skip the write and memory never grows; skip the read and the bot ignores what it knows.

How do you decide what to store?

Store facts that stay relevant: preferences, account details, recurring goals. Do not store the whole transcript turn by turn — that bloats memory and makes retrieval noisy. A quick filter works: if a future session would benefit from knowing this, store it; if it only matters now, drop it.

• Store: “prefers email over phone”, “is on the Pro plan”, “builds with Python”.
• Skip: greetings, one-off questions, anything already in a system of record.

How do you inject memories without wrecking the prompt?

1. Query the namespace with the user’s latest message.
2. Take the top 3–5 matches, not all of them.
3. Format them as a short “known facts” block.
4. Keep it separate from instructions so the model does not confuse the two.

Too many memories crowd out the actual task.

How do you avoid stale or wrong memories?

Facts change. If a user switches plans, the old memory lingers unless you update it. Store memories with timestamps and prefer recent ones when two conflict. Build a path to correct or delete a memory when the user says so — nothing erodes trust faster than a bot insisting on something the user says is wrong.

What about privacy?

You are storing personal data in a third-party service. That triggers obligations: tell users what you store, give them a way to delete it, and mind regional rules. For sensitive or regulated data, confirm the vendor’s handling and whether self-hosting is required before you go live.

Supermemory vs. rolling your own

You can build the same loop on a vector database plus an embedding pipeline, and for a large team with strict control that may be worth it. Supermemory saves the plumbing — chunking, embeddings, retrieval — for a small monthly cost. Start with it, and migrate only if scale or compliance forces you.

Key Takeaways

• Read memories before answering; write durable facts after.
• Store preferences and state, not the whole transcript.
• Inject only the top few memories to keep prompts clean.
• Timestamp memories and allow corrections to prevent staleness.
• Mind privacy rules; self-host if data is regulated.

Last updated October 2026. More at Supermemory.

It fits chatbots, assistants and CRM-style tools where a user expects the product to remember them. The API is small enough to wire up in an afternoon, and there is a free tier for development. Sign up, grab a key, and you can store your first memory in minutes.

What is Supermemory?

Supermemory is a managed memory API. You organize data into containers or namespaces per user, add content, and search it with natural language. It returns relevant memories with metadata, so your model gets grounded context rather than a blank slate each session.

How do you get started?

1. Create an account and copy your API key.
2. Install the SDK or call the REST endpoint directly.
3. Create a container/namespace per user (e.g. user-42).
4. Add a memory: paste text, or pass a URL or file.
5. Search the namespace and inject the results into your prompt.

Namespaces keep one user’s memories from leaking into another’s results.

What can you store?

• Plain text notes and conversation snippets.
• URLs it fetches and parses.
• Uploaded files like PDFs and docs.

Files and pages get chunked automatically, so you do not manage splitting by hand.

How do you retrieve memories?

Send a query string and the namespace; get back the closest memories with scores and metadata. A common pattern is to fetch the top few before each model call and prepend them as context. Keep the count small — injecting 20 memories dilutes the answer more than it enriches it.

Is Supermemory free?

There is a free tier for building and testing, with usage limits on stored items and API calls. Paid plans raise the limits and add team features. Exact numbers shift, so check the current pricing page before you build a cost model around it. For prototypes, the free tier is usually enough.

What are the trade-offs?

You are sending user data to a third party, which matters if you handle sensitive content — check the data policy and whether you need self-hosting. Retrieval quality depends on what you store: garbage in, garbage recalled. And memory is not a substitute for a database; use it for fuzzy recall, keep hard facts in your own store.

Key Takeaways

• Supermemory is a hosted memory API for storing and recalling context.
• Organize data into a namespace or container per user.
• Accepts text, URLs and files; chunks content automatically.
• Free tier exists for development; paid plans raise limits.
• Popular for chatbots and assistants; keep hard facts in your own DB.

Last updated October 2026. Details at Supermemory.