Why MemorySync
AI applications need useful context after the current request ends. MemorySync gives your application a managed way to capture, retrieve, update, and remove that context.
The problem it solves
A model only sees the context you send with the current request. Replaying full histories increases prompt size, while a basic database lookup does not rank information by meaning. MemorySync provides a memory API between your application and model so you can retrieve a small, relevant context set.
What you get
Capture durable facts and search them with natural-language queries.
Separate memory by project and the end user your application represents.
Update, summarize, export, or forget memory through documented APIs.
Apply key safety, isolation tests, error handling, and lifecycle controls.
When it is a good fit
- An assistant should remember user preferences across sessions.
- An agent should reuse outcomes or facts from earlier work.
- A support experience needs relevant customer context without replaying every conversation.
- A knowledge assistant needs semantic retrieval across connected content.
What it does not replace
| Need | Use |
|---|---|
| Current conversation tokens | Your model or orchestration framework |
| Transactional source of truth | Your application database |
| Document ownership and permissions | Your source system plus your authorization layer |
| Final answer generation | Your chosen model |
Evaluate with your workload
Use a representative set of user questions and expected facts. Measure retrieval relevance, end-to-end latency, prompt size, and deletion behavior before production. Do not rely on generic benchmark or cost claims for your own workload.