How to Use Supermemory with Convex
Keep Convex as your persistent application database and call Supermemory from actions for scoped retrieval, profiles, and cross-session context.
Convex already persists application data. It can store conversation history, preferences and records across sessions, and it supports vector search. Supermemory adds memory extraction, retrieval and maintained user context alongside that application data.
Use Convex for the application's authoritative state and a Convex action for calls to an external memory service. Keep the boundary explicit so a failed memory request does not silently change a user's account state or lose the original conversation.
What each part is responsible for
| Component | Responsibility in this design |
|---|---|
| Convex database | Conversation records, ownership, permissions and application state |
| Convex queries | Read stored application data for the UI |
| Convex mutations | Transactional changes to that data |
| Convex actions | External memory and model calls, with explicit failure handling |
| Supermemory | Ingested context, memory retrieval and maintained profiles |
The Convex database is persistent, and its vector-search API is a native retrieval option. Compare that option with a managed memory service using the same questions and source data. Neither storing embeddings nor choosing a memory API eliminates the need to evaluate answers.
Put external calls in an action
Convex actions can call third-party services and invoke queries or mutations to interact with the database. They are the appropriate boundary for a Supermemory request. A mutation can schedule work, but should not make an external API call as if it were part of the same database transaction.
Before implementing the action, define its input contract. The browser may supply a question and a conversation identifier. It must not choose an arbitrary user's memory scope. Authenticate the request, load the conversation through an authorized query, and derive the memory scope from trusted tenant and user identifiers.
Keep the Supermemory key in server-side environment configuration. If the SDK requires Node-specific APIs, use the runtime supported by the Convex action and that package version. A dependency installing successfully is not proof that it runs in every serverless environment.
Separate retrieval from ingestion
For an existing conversation, the request path can retrieve relevant context and pass it to the answering model. Store the original messages in Convex according to the application's retention policy. Record which source IDs and versions supplied the answer so a disputed response can be investigated.
For new source content, enqueue ingestion with a stable source identity. Record the returned document ID and processing state. An accepted write is not proof that extracted memories are ready for the next request. Use the documented readiness signal for the operation being called; do not promise a fixed processing time for every document.
Retry interrupted work without multiplying source records. A useful job record distinguishes pending, submitted, ready and failed work. Test a lost response after a successful submission as well as a failed request. The ingestion guide covers version and retry handling.
Retrieve profiles deliberately
Supermemory documents relatively stable and changing context in its user profiles. A direct API call does not automatically inject that profile into every Convex action. Your application must request it, bound the context, and pass appropriate evidence to the model.
Use profiles for relevant preferences and background. Check live entitlements, payments and permissions against their authoritative records. A remembered account status should never grant access to a feature.
The search documentation defines its current result types and search modes. Choose the mode for the evidence you need, then handle the corresponding memory or document fields.
Measure latency at the application boundary
Memory retrieval adds work to the request path. A reactive database subscription does not make an external memory lookup instantaneous. Measure retrieval duration, model time to first output and total response time separately, including failures and cold starts.
Build a small test set from the questions your users ask and retain the baseline without external memory. If the new path improves continuity, measure the added latency and cost under the same load.
For illustration, three sequential lookups of 150 ms each consume 450 ms before other work. Parallel calls may reduce elapsed time when they are independent, but they still consume capacity and can fail separately.
Plan deployment and operating costs
Review current Supermemory pricing for the operations used by the integration. Ingestion, retrieval and the answering model are different parts of the budget. Smaller retrieved context may lower model input costs, but it can also omit evidence; compare cost per successful task.
For a private deployment, inspect the self-hosting options and their supported configuration. Check the supported features and network requirements of that deployment. Model and embedding providers can still be external dependencies.
A deployment's compliance obligations depend on its actual data flows, controls and agreements. Adding a vendor does not automatically close every audit requirement.
Test one complete workflow
Use two synthetic users with different preferences. Save a preference, wait for processing, and start a second conversation for the same user. Inspect the retrieved evidence and answer. Repeat for the other user to check separation.
Then correct the preference, simulate a retrieval outage, and delete the test data through the supported lifecycle. Verify the application displays an honest state in each case. Use these cases as the regression suite when expanding to more application routes.
Get a Supermemory API key when you are ready to evaluate that path. Start with one authorized action and compare it with the context your Convex application already stores.
Frequently asked questions
Does Convex already persist conversation history?
Yes. Convex can persist application records across sessions. Supermemory adds a separate memory service; it is not required merely to keep a transcript.
Where should the Supermemory API call run?
Use an authenticated Convex action for the external call. Derive the memory scope from authorized application records and keep API keys on the server.
How should you evaluate the integration?
Measure retrieval, generation, answer support and cost on the same workload before and after adding memory. Include a new session, a corrected preference, another user's scope and a retrieval outage.