How are Supermemory and AI Memory positioned differently?
Supermemory presents itself as memory and continual learning infrastructure for agents, with user profiles, graph memory, connectors and a developer platform. Its pricing uses a monthly usage balance across plans. AI Memory presents a tighter application-memory contract: add events, extract and merge durable facts, retrieve by user and agent scope, compress context and delete on request.
The distinction matters when the product does not need a personal knowledge workspace. More connector and profile surface can be valuable, but it also introduces more product decisions about ingestion, identity and what should be surfaced to the end user.
| Requirement | Supermemory | AI Memory |
|---|---|---|
| Managed memory API | Yes | Yes |
| User profiles and connectors | Broad public surface | Focused application memory |
| Graph memory | Yes | Yes |
| Compression and token budget | Evaluate current docs | Core memory operation |
| Portability | Check current export options | JSON graph export |
| Best fit | Broad personalization and connectors | Scoped product and agent memory |
When is Supermemory the better choice?
Choose Supermemory when the product needs extensive connectors, a rich user profile layer or a broader personal knowledge experience. It may also fit teams that want to consolidate many external sources into a single user context and are prepared to evaluate how those connectors map to their privacy model.
The public plan structure and feature set are moving quickly. Verify current ingestion, retention, export and multi-tenant behavior against the exact product requirement rather than relying on a comparison page from the past.
- You need many ready-made data connectors.
- A user-profile surface is central to the experience.
- The product needs a broad personal knowledge layer.
- The team is comfortable with a larger platform surface.
- Its current API passes your scope and deletion tests.
When is AI Memory the better fit?
Choose AI Memory when the product needs a compact memory layer behind one or more agents, not an end-user knowledge workspace. It fits teams that want controlled context compression, explicit user and tenant scopes, framework neutrality and a direct export path.
The simpler surface is an advantage when support and engineering need to explain what the agent remembers. Every durable fact can be inspected, corrected and deleted without navigating a larger ingestion ecosystem.
- The agent needs scoped facts instead of a general content vault.
- A small API surface reduces integration and review cost.
- The team wants token-budgeted context rather than raw document retrieval.
- Python and TypeScript coverage are enough.
- Export and deletion must be easy to demonstrate.
How should multi-tenant memory be tested?
Create two tenants with similar users and overlapping topics. Write facts that should be distinguishable, then query as each user. The retrieved set must contain zero records from the other tenant. Repeat with a shared agent identity to prove that agent context does not bypass the tenant boundary.
Test deletion by tenant and deletion by individual user. A user-level delete must not remove another user’s records, while a tenant delete must remove every related event, derived record and graph edge. Check exports after each operation.
- Cross-tenant queries must return zero foreign records.
- Agent identity must not weaken tenant or user scope.
- Shared project memory needs an explicit membership model.
- Deleted users must not remain in profile summaries or exported files.
- Backups must replay deletion requests before returning to service.
What should a Supermemory migration preserve?
Preserve source events, durable facts, entity relations, timestamps, validity windows, user identity mapping and deletion state. The migration should classify which content is application memory and which is document knowledge that should remain in a separate retrieval system.
Run a sample import and compare retrieval results before switching traffic. Keep a rollback export and a bounded dual-write period. Do not import raw connector content into memory unless it is useful for the next task and permitted by the privacy policy.
Which product should an application team choose?
If you need broad connectors, profiles and a personal knowledge surface, evaluate Supermemory. If you need a focused memory API behind agents with clear scopes, compression and export, evaluate AI Memory. Run the same private evaluation set and include migration cost in the decision.
Frequently asked questions
Is AI Memory a direct replacement for Supermemory?
Not for every use case. Products that depend on broad connectors, profiles or a personal knowledge workspace may need Supermemory’s larger surface. AI Memory fits application and agent memory with a smaller contract.
Can I use both products together?
It is technically possible, but running two memory systems increases identity, retention, deletion and cost complexity. Use both only when they serve clearly different jobs.
Does AI Memory support multi-tenancy?
AI Memory uses user, agent, session and tenant scopes, with tenant isolation and audit history available on the Scale plan. Validate the exact setup during your evaluation.
How do I compare pricing fairly?
Model the same number of users, writes, retrievals, storage and retention months in both systems. Include migration and operational cost, not only the monthly plan price.
What is the most important migration test?
Run retrieval, relation preservation, cross-tenant isolation and deletion after importing a representative sample. A successful export file does not prove a successful migration.