How to Use Supermemory with Convex - April 2026

Most AI features forget everything the moment a session ends. That's not a product limitation. It's an architectural choice you probably made by accident. Convex gives you a reactive, real-time backend that's genuinely one of the best ways to ship full-stack TypeScript apps fast, but it's stateless by design for long-term user memory. This guide is about fixing that in under five minutes by wiring Supermemory into your Convex actions, so your agents actually remember who your users are across sessions.

TLDR:

• Supermemory adds persistent memory to Convex's reactive backend via actions in under 5 minutes
• Sub-300ms recall keeps memory retrieval invisible within Convex's real-time sync model
• 85.4% accuracy on LongMemEval-S means fewer hallucinations when injecting context into LLM calls
• SOC 2 Type 2 and full Docker self-hosting match Convex's security without introducing audit gaps
• Supermemory is a memory API that gives AI agents graph-based recall and user profiles across sessions

What It Means to Use Supermemory with Convex

Convex lets you express your entire backend in TypeScript, with libraries that guarantee real-time reflection of changes across frontend code, backend code, and database state. It's reactive, fast, and genuinely fun to build on. But there's a gap: Convex handles state within active sessions. It doesn't preserve what a user cared about three weeks ago, or what your AI agent learned from 50 prior conversations using context memory.

That's where Supermemory comes in.

Wire Supermemory into a Convex stack and you're adding a persistent, graph-based memory layer that sits alongside your reactive database and serverless functions. Your AI features can then recall past interactions, surface user preferences, and work with evolving knowledge without rebuilding context on every single request.

This is about treating memory as a first-class architectural layer, the same way you'd treat your database schema, so your agents actually know who users are over time.

How Supermemory Fits Into a Convex Stack

Convex gives you two core primitives: query and mutation functions. Queries are pure read-only operations. Mutations are transactions that read and write. Neither is designed to persist semantic memory or recall what a user said two months ago.

Actions are where Supermemory slots in. They're Convex's serverless escape hatch for external calls, sitting outside the sync engine. From an action, you call the Supermemory API to store new memories, retrieve context, and update user profiles. Then you pass that context back into your mutations or queries as needed.

The division of responsibility is clean:

• Convex mutations own transactional writes and reactive state
• Convex queries handle real-time reads across your frontend
• Supermemory actions handle memory graph updates, semantic retrieval, and user profile building

Nothing gets replaced. Supermemory fills the layer Convex intentionally leaves open: long-term context, historical understanding, and personalized recall across sessions.

Memory Capabilities That Matter for Convex Builders

Convex's reactive model sets a high bar for perceived performance. The memory layer you add has to keep up.

Sub-300ms Latency for Reactive Workflows

Convex uses WebSockets to maintain persistent connections, meaning UI updates feel instantaneous. Any memory retrieval that adds noticeable lag breaks that contract. Supermemory's recall time sits under 300ms, so when a Convex action fetches user context before generating an LLM response, the round trip stays invisible to users.

Memory Graph for Multi-Session User Understanding

Reactive queries don't know what happened last Tuesday. Supermemory's memory graph tracks relationships between memories across sessions, merges contradictions, and infers intent from past behavior. For conversational AI or collaborative tools built on Convex, agents carry genuine user understanding from one session to the next without re-scanning your transactional data.

User Profiles as Default Context

Every Convex action that calls an LLM needs context. Instead of passing historical data through every function manually, Supermemory user profiles inject static preferences and recent episodic memory automatically. Fewer tokens, better responses, and no boilerplate to maintain.

Performance Benchmarks in a Convex Context

Numbers matter when you're deciding whether to add any new layer to a production stack.

On the LongMemEval-S benchmark, Supermemory hits 85.4% overall accuracy, 92.3% on single-session user recall, and 89.7% on knowledge updates. For Convex apps where actions feed LLM calls, those figures mean fewer hallucinations and more accurate context injection per request.

Precision at retrieval rank one matters even more at scale. Supermemory hits 59.7% P@1 on LoCoMo versus 34.4% for competing providers, with 83.5% Recall@10 compared to 69.3%. When each Convex action may trigger multiple memory lookups, getting the first result right directly cuts unnecessary LLM calls and lowers token spend.

Then there's latency. Convex V8 isolates spin up in under 5ms. Supermemory recall completes in under 300ms. Zep clocks in at 4 seconds. Mem0 runs 7 to 8 seconds. For a reactive Convex app, that gap is the difference between a response that feels alive and one that feels broken.

Enterprise Readiness for Teams Shipping on Convex

Teams shipping on Convex already benefit from infrastructure that encrypts and replicates durably. Any dependency added to that stack gets held to the same bar. Supermemory clears it: SOC 2 Type 2, HIPAA, GDPR, with all data encrypted in transit and at rest. No new audit gaps introduced.

Convex supports self-hosting through its open-source backend. Supermemory matches that with full Docker-based self-hosting, so both layers can live within the same deployment boundary. For teams running Convex in a VPC, that means no external API calls leaving your network for memory operations.

For teams with existing vector infrastructure, Supermemory's pluggable vector store backends let you bring Pinecone, Weaviate, or Qdrant without abandoning prior tooling. You get the memory graph on top of what you already own.

Pricing and Scale Considerations for Convex Products

Three tiers cover the full range of Convex product stages.

One pricing structure covers API access, plugins, and enterprise features. No per-product billing. For Convex developers already juggling separate invoices for database, functions, and third-party services, that simplicity matters.

There's a cost angle worth noting too. Convex actions that call LLMs frequently can rack up token spend fast. Supermemory's retrieval passes only relevant context per request instead of re-injecting full conversation history each time. For teams where reducing LLM costs is a real concern, that compounds quickly at scale.

Getting Started: Supermemory + Convex

Convex developers work entirely in TypeScript, so the Supermemory TypeScript SDK is the right starting point. Install it with:

npm i supermemory

Grab your API key from console.supermemory.ai and initialize the client. From there, call Supermemory inside Convex actions, since that's where external API calls belong. Store a memory, retrieve context in a follow-up action, and pass it into your mutations or queries.

Basic setup takes about five minutes: authenticate, store a memory, retrieve it. That's the loop. Once memory-augmented context gets written to Convex via a mutation, every subscribed query updates automatically across connected clients. Supermemory handles what happened before. Convex propagates what's happening now.

For deeper work involving user profiles, memory graph traversal, or connector configuration, the API reference and cookbook cover production-ready patterns. If you're wiring up agents, this guide on building AI agents with memory is worth reading before you ship.

FAQ

Can I use Supermemory with Convex without breaking reactive performance?

Yes. Supermemory recall completes in under 300ms, so memory lookups from Convex actions stay invisible to users and won't disrupt your WebSocket-based reactive workflows.

Supermemory vs storing conversation history in Convex tables?

Supermemory builds a memory graph that tracks relationships across sessions, handles contradictions, and infers user intent over time. Convex tables give you transactional storage for active state, but they won't surface what a user said three weeks ago or merge knowledge updates automatically.

How do I actually wire Supermemory into a Convex stack?

Call the Supermemory API from inside Convex actions (not queries or mutations), since actions are designed for external calls. Store memories or retrieve context in the action, then pass that context back into your mutations or queries as needed.

What's the best way to reduce LLM token costs in Convex actions?

Supermemory retrieves only relevant context per request instead of re-injecting full conversation history every time, which cuts token spend fast when your Convex actions call LLMs frequently.

Does Supermemory support self-hosting like Convex?

Yes. Supermemory offers full Docker-based self-hosting, so both layers can run within the same deployment boundary, meaning no external API calls leaving your VPC for memory operations.