AI Memory Databases: What to Store and How to Choose

An AI memory database stores information that an application can retrieve after the current conversation ends. It may hold messages, selected facts, summaries, relationships or the saved state of a task. The database preserves records; application logic decides what to save and when an assistant should use it.

That is different from keeping a visible record of the work people agreed on. fenbs keeps projects, tasks, Decisions and rules, progress and History for connected assistants. A developer may use a memory database inside an application while the team uses fenbs to remember why it was built that way.

What should an AI memory database contain?

Start with the future question, then work backward to the record needed to answer it. A support assistant remembering a preferred contact method needs different fields from an assistant resuming an unfinished calculation. Saving every message can preserve evidence, but it does not automatically identify the answer that currently applies.

Keep a generated interpretation distinct from a person's statement. If someone says they may prefer a phone call, a stored fact should preserve that uncertainty. A later assistant cannot recover a missing qualification just because the database retains the sentence confidently.

Do you need vector storage, ordinary tables or a graph?

Ordinary database tables suit facts you can identify directly: an account's settings, a task's current step or a record with a known ID. They make explicit fields and exact conditions easy to reason about. A small memory feature may need nothing more, especially when people edit the facts themselves.

Vector storage holds embeddings, numerical representations that help search for similar meaning. This can help when a person asks a question using different words from the saved material. pgvector↗ (opens in a new tab) adds vector similarity search to PostgreSQL, so vectors and ordinary records can live in the same database.

A graph represents entities and the relationships between them. It may help when questions depend on several connected facts, such as which supplier handled a project after a team change. The guide to an AI memory graph covers that separate design choice. Avoid adding one merely because the word memory sounds more sophisticated with a graph attached.

These approaches can coexist. Keep an original document in one record, an extracted fact in another and a search representation pointing back to both. The important part is a stable relationship between them, so a correction can reach all the places that influence a later answer.

How does the right information reach the assistant?

Retrieval should begin by establishing whose information the request may use. Then the application selects relevant records, checks whether they are current and gives the assistant a limited, readable selection. A search result is evidence to examine, not permission to ignore the application's instructions.

Meaning-based search alone can retrieve an old policy because it uses nearly the same words as the replacement. Combine relevance with explicit fields such as project, record type and current status. Qdrant's filtering documentation↗ (opens in a new tab) shows conditions on stored metadata; your application must still supply the correct conditions for the signed-in person.

Test questions whose answer should be absent. If the application has no approved shipping exception, the assistant should say that instead of borrowing a similar exception from another customer. Good retrieval includes knowing when the stored evidence does not answer the question.

For agreed project rules, a visible record is often the clearer starting point. fenbs shows rules first in project context, alongside Where we left off and current work, rather than asking an assistant to infer agreement from whichever conversation fragment it finds.

What happens when a remembered fact changes?

Design correction before collecting a large history. A changed address, replaced policy and withdrawn permission are different events. Decide whether to replace a value, mark a previous version as historical or remove information entirely. Preserve the source relationship so a person can understand why an answer changed.

A deletion request can involve more than a row. Check summaries, search indexes, caches and queued processing jobs that could recreate it. Also distinguish the live system from backup retention. Write down what stops appearing immediately and what follows a separate removal schedule, then verify the actual behavior.

Give updates stable identifiers and a way to detect retries. If a job runs twice after a network failure, it should not quietly create two competing current facts. When two people edit the same record, the application needs a deliberate conflict policy rather than treating whichever write arrives last as unquestionable truth.

Which permissions and checks should shape the choice?

Access belongs in the service and storage design, before private text reaches the model. Separate accounts and projects, validate the request and enforce the current permission for each operation. Hiding a search result in the interface does not protect it from another client calling the same service.

PostgreSQL row security policies↗ (opens in a new tab) can restrict which rows ordinary queries return or change. Its documentation also explains exceptions for privileged roles. Test using the same database role the application will actually run under; a policy demonstrated with a different role is incomplete evidence.

• Restart test: saved information survives a real application restart.
• Correction test: the current answer changes, while historical answers remain distinguishable when required.
• Access test: two fictional accounts with similar records never receive each other's information.
• Removal test: deleted information no longer appears in retrieved context or new summaries.
• Source test: a reviewer can trace a returned claim to the record that supports it.

The team AI memory guide covers the ownership questions around shared information. For continuing access across applications, portable AI memory explains a different problem from choosing the physical storage engine.

How to do this with fenbs

Keep the database implementation and the team's work record connected by clear references. Use fenbs for decisions, test outcomes and the next implementation step. It does not provide a vector database, embeddings or a memory storage API for your product.

1. Create a free account and add the application as a project.
2. Add tasks for retrieval, correction, access and removal checks. Put the expected behavior in each plan and the actual result in its test record.
3. Record the choices people make in Decisions and rules, including which records may be stored and who owns their upkeep.
4. Connect a supported assistant through Integrations, ask it to read the project context, and save remaining checks in Where we left off before stopping.

fenbs is free for one person, no credit card. Choose another tool when you need a database to embed inside your application. Use the memory tools hub to compare the surrounding components without confusing storage with a complete memory system.