Which Database Should Your AI Agent Use?
Short answer: Postgres for state, a vector store for recall, Redis for cache, and an analytics database for telemetry. Most agent stacks need two of those, not one, and the choice is less consequential than the amount written about it suggests.
This page answers that question properly first. There is a second question underneath it that almost nobody asks, and it matters more than the engine choice — but it is at the bottom, after the part you came for.
What agents actually store
"Database for an AI agent" collapses four unrelated workloads that happen to arrive at the same time. Separate them and the choices become obvious:
| What is being stored | Access pattern | Reasonable default |
|---|---|---|
| Application and session state | Small reads and writes, transactional, must be exact | Postgres |
| Semantic recall over documents | Approximate nearest-neighbour over embeddings | pgvector, or a dedicated vector store at scale |
| Hot context and rate limits | Sub-millisecond, ephemeral, high churn | Redis |
| Prompt/response logs and token accounting | High-volume append, aggregate reads | ClickHouse, DuckDB/MotherDuck, or Postgres until it hurts |
| Business facts the agent produced | Read by people and later agent runs as truth | See the last section |
Start with Postgres, and mean it
For the large majority of agent projects, Postgres alone is the correct first answer, and adding a second system on day one is premature. It handles state, relations, JSON, full-text search, and — through pgvector — embeddings, well past the scale most agent products ever reach.
Two specific things people get wrong here:
Reaching for a dedicated vector database too early. pgvector with HNSW is fine into the millions of vectors. A separate vector service buys you recall performance you cannot measure yet, at the cost of a second consistency domain and a second thing to operate. Mature managed Postgres now ships vector support as a matter of course; adding a separate system just for vectors reads as unnecessary complexity in most 2026 architectures.
Logging prompts and responses into the same Postgres as your state. This is the one that actually bites. Token accounting and full prompt/response capture are append-heavy and grow without bound; put them next to your transactional tables and you will be vacuuming and partitioning under pressure six months later. Give telemetry its own home early — an analytics database is cheap to add and expensive to retrofit.
When a dedicated vector store earns its place
- Tens of millions of vectors or more, with latency targets you are actually measuring.
- Heavy metadata filtering combined with ANN search, where pgvector's query planning stops cooperating.
- Frequent full re-embedding cycles you want isolated from your primary database's load.
Below that threshold, the operational cost of a second system outweighs the recall difference. Measure before you migrate.
When Redis is worth it
Session context, tool-call rate limiting, deduplication windows, and queue state. Redis earns its place when you are doing something per-request that Postgres would serve fine but not at the latency you want. It is not a system of record, and treating it as one is how agent teams lose a day's work to an eviction policy.
Disclosure, since it is relevant: Busabase itself runs on Postgres — PGlite when it runs locally, standard Postgres in Cloud. We are not neutral about the engine question, but we are boring about it, which is the point of this section.
The question underneath the question
Now the part that is usually missing.
Every option above answers where do the bytes go. None of them answers why should anyone believe the bytes. And for a growing share of agent work, that second question is the one that actually decides whether the project works.
Consider what happens after your engine choice is settled. An agent enriches 200 customer records. It writes them to Postgres, correctly, with perfect permissions. Some of them are wrong — not malformed, not rejected by a constraint, just false. Confidently, plausibly false.
Your database did its job. Every check passed. The rows are there. And nothing in your stack can tell a later reader which of those 200 rows a human ever looked at.
This is not a database problem, which is why choosing a different database does not fix it. Constraints validate shape. Row-level security validates permission. Neither validates whether a value is true, and an agent's most expensive failure mode is being wrong while being perfectly well-formed and fully authorized.
What that implies for your stack
If your agent's writes are disposable — scratch state, cache, logs, drafts a person reads immediately — none of this matters. Pick Postgres and move on.
If your agent's writes become facts that other things read — the next agent run, a report, a customer-facing page, an API consumer — then somewhere in your architecture you need a place where a write can be inspected before it counts. That can be a review queue you build yourself on top of Postgres, a staging table with a promotion step, or a system that does it natively.
What it cannot be is nothing at all, with the engine's constraints standing in for a decision nobody made.
Frequently asked questions
Is Postgres good enough for AI agents?
For most projects, yes, including vector search via pgvector. Add a second system when you have a measurement that says you need one.
Do I need a vector database?
Only above roughly the ten-million-vector mark, or with heavy metadata filtering alongside ANN search. Below that, pgvector is usually the right call.
Where should I store prompts and responses?
Not in your transactional database. Append-heavy, unbounded-growth telemetry deserves its own store from early on.
What database does Busabase use?
Postgres — PGlite locally, standard Postgres in Cloud. Busabase is not an alternative to your application's database; it is where agent-produced records live while people decide whether they are true.
Can one database do all of this?
Postgres comes closest and is the right starting point. The workloads diverge as you scale, and telemetry is usually the first to need its own home.
Next step
If the "which engine" question is settled and the "why should anyone believe it" question is the one you are now stuck on, that is the subject of the next page.
What makes a store a system of record for AI agents · Compare Busabase