Two teams describe the same symptom — "our agent forgets" — and reach for opposite tools. One adds a vector database and starts dumping conversation turns into it. The other adds mem0 and lets it decide what to keep. Six weeks later the first team has quietly hand-built a fact-extraction pipeline around their vector DB, and the second is fighting an LLM write policy they can't fully see. Both picked a reasonable tool for the wrong half of the problem.

The two things are not competitors. They live at different layers, and knowing which layer your problem is at settles the choice in about a minute.

A vector database is a retrieval primitive#

LanceDB, Chroma, and Qdrant do one job with precision: store embeddings and metadata, return nearest neighbors, filter by fields, and — increasingly — rank by full-text terms too. What they store is exactly what you hand them. They will index a contradicting fact right next to the fact it contradicts and rank both, because resolving that isn't their job. The write path is cheap: an embedding call and an insert.

That precision is the feature when you already know what belongs in the store — documents, chunks, product records, event logs. You control the schema, the scoping, and the write policy, and nothing calls a language model behind your back. The failure mode is subtler: the day you start writing extraction logic, dedup logic, "is this worth remembering" logic, and per-user namespacing around your vector DB, you've begun re-implementing a memory layer by hand — usually worse than the ones that already exist.

A memory layer is a policy on top of a store#

mem0 and Zep don't replace the vector database — they sit above one and add the intelligence the database deliberately lacks. Hand mem0 a raw conversation and it uses an LLM at write time to pull out atomic facts, then decides whether each one is new, an update to something it already knows, or a contradiction that should overwrite the old value. Zep goes further and threads those facts onto a temporal knowledge graph so "what was true then" and "what's true now" stay distinct. Both give you user, session, and agent scoping out of the box.

The database finds things. The memory layer decides what is worth finding later. That decision is the entire product — and the entire cost.

Because that decision runs a model on the write path, a memory layer buys you managed forgetting, contradiction resolution, and scoping at the price of latency, tokens, and a write policy you don't fully control. When your input is open-ended dialogue that has to become durable, self-correcting, per-user state, that's work you'd otherwise build yourself — so paying for it is the bargain. When your input is documents you could have indexed as-is, it's overhead wrapped around a call you didn't need.

The part that surprises people: it's not either/or#

The cleanest way to hold this is that "use a memory layer" and "own your vector database" are compatible, not opposing, choices. mem0 and its peers run on a pluggable store, and most let you point them at your own backend — your own Qdrant, PGVector, or otherwise — so you keep data ownership and residency while the memory layer supplies the policy on top. The stack is layered: retrieval primitive at the bottom, remember/forget policy above it, your agent above that.

So the real question isn't "LanceDB or mem0." It's:

Retrieval is a solved primitive with several good implementations. Memory is a policy about that primitive — what to keep, what to merge, what to let go, and whose it is. Pick the layer your problem actually lives at, and the tool falls out for free.