A homemade RAG vs Wemory
Retrieval-augmented generation is one component of Wemory, the same way an index is one component of a database. A homemade RAG — embeddings, pgvector, a search endpoint — solves retrieval over a corpus you give it. It does not solve where the corpus comes from (capture), whether its contents are still true (curation), who may see what (governance), or how every teammate's agent uses it (recall integration). Wemory ships all five layers as a product, maintained, for 20 USD per seat.
What this setup is good at
The instinct is sound: RAG is the right building block, and a competent engineer can stand up embeddings plus a vector store in a weekend. It will demo well. If your need is genuinely narrow — semantic search over a fixed, clean document set for a handful of users — a homemade RAG is a reasonable thing to build, and you will learn a lot doing it.
A homemade RAG vs Wemory at a glance
As teams typically run each setup — unusually disciplined teams may do better.
| A homemade RAG | Wemory | |
|---|---|---|
| Retrieval | Yes — that is the part RAG covers | Yes — semantic recall over the team's memory |
| Capture (where the corpus comes from) | Your problem: exporters, hooks, and pipelines you build and babysit | Automatic from coding sessions, meetings (Granola), documents |
| Curation (is it still true?) | None — stale and contradictory chunks retrieve confidently | Decisions extracted, conflicts resolved, knowledge verified and superseded |
| Governance | DIY auth, per-corpus at best | Per-project access to named people; self-hosted edition |
| Total cost | An engineer owns the pipeline forever | 20 USD per seat, first month free |
Where the objection is right
“Wemory is just a RAG” is right about the retrieval layer: under recall there are embeddings and a vector database, like under most search there is an inverted index. And a homemade RAG is a legitimate project when the corpus is fixed and clean, the users are few, and someone wants to learn the stack. That is where the equivalence ends.
The four layers a RAG doesn't have
Capture: a RAG retrieves over the corpus you feed it — so someone must build and maintain exporters for coding sessions, meeting transcripts, and documents, per tool, per format, forever. In practice the corpus becomes “whatever we remembered to ingest”, which is exactly the failure mode a memory layer exists to prevent. Wemory's capture is automatic and session-native: the decisions and reasoning inside AI sessions are recorded as they happen.
Curation: retrieval has no opinion about truth. A RAG over a growing corpus will confidently return the deprecated architecture, the abandoned pricing draft, and the decision that was reversed last month — similarity says nothing about validity. Wemory maintains a knowledge hierarchy: raw session traces, auto-extracted decisions, and team-verified knowledge, with conflicts surfaced and resolved, so recall returns the current truth first.
Governance and recall: a memory shared by a whole team needs per-project access control (finance and HR context restricted to named people), and it needs to show up inside every teammate's agent — not behind a bespoke endpoint one person knows how to query. Wemory provides both, plus a self-hosted edition when the data must stay home. The build-vs-buy question is not whether your engineer can build a RAG — it is whether you want them to own a memory platform as a permanent side job.
Which should you choose?
Build a RAG if you need semantic search over a fixed corpus and want the learning experience. Choose Wemory if what you actually need is team memory: automatic capture, curated truth, access control, and recall in every teammate's tools — without staffing it.
Frequently asked questions
So is Wemory just a RAG or not?+
Retrieval is one of Wemory's five layers. The others — automatic capture from sessions and meetings, curation that resolves conflicts and verifies knowledge, per-project governance, and recall inside every teammate's AI tools — are what make memory usable by a team, and they are exactly what a homemade RAG leaves as an exercise.
Our engineer can build this in a weekend. Why pay?+
The weekend gets you retrieval. Capture pipelines for every tool, a curation process for stale and conflicting knowledge, access control, agent integrations, and upgrades are the actual product — and they are a permanent maintenance job. 20 USD per seat is usually cheaper than the fraction of an engineer the homemade version quietly consumes.
We already have a pgvector setup. Is it wasted?+
Not at all — it means your team understands the value of semantic recall. Wemory's self-hosted edition runs on your own infrastructure (PostgreSQL + pgvector under the hood), so the operational skills transfer directly; what you gain is the capture, curation, and governance layers on top.
What does Wemory cost?+
20 USD per seat per month, first month free, no card required, prorated seats with no commitment.