Build your own RAG memory layer vs MyMemory
A practical build-vs-buy framework for agent memory: vector databases, ingestion pipelines, search quality, and when a hosted Memory API is the faster path.
· 2 min read
Quick answer
Building agent memory yourself means owning ingestion, embeddings, hybrid search, a chat layer, a product surface and operations, not just adding a vector index. Build when you have strict data-residency needs or an existing search team; use a hosted Memory API like MyMemory when you need ingest, search and chat in weeks and don't want to staff a memory-infrastructure team.
Teams evaluating agent memory usually start with "we'll use Postgres and pgvector" or "we'll plug in an open-source memory library." Both can work. Both hide a lot of work.
This is a build-vs-buy checklist for a production memory layer, and where MyMemory fits if you want to ship faster.
What "build" actually includes
Rolling your own stack is much more than enabling a vector extension:
- Ingestion: PDFs, HTML, tweets, audio; chunking; deduplication.
- Embeddings: model choice, batching, re-embedding when you upgrade models, cost controls.
- Search: hybrid dense and keyword retrieval, recency, filters.
- Chat layer: grounded answers with citations, streaming, quotas.
- Product surface: a browser extension, a dashboard, API keys, shared collections.
- Agent protocols: MCP tools and a stable API.
- Operations: auth, multi-tenancy, observability, evaluation.
When building makes sense
Choose DIY when:
- you have strict data-residency requirements and can't use a hosted API,
- your memory schema is highly proprietary (custom graphs, compliance tagging),
- you already run a mature search team and only need another index, or
- agent memory is a small slice of a larger platform you own end to end.
When a hosted Memory API makes sense
A hosted API wins when:
- you need ingest, search and chat in weeks, not quarters,
- users save content from the browser and expect a searchable inbox,
- AI tools like Cursor and Claude need memory through MCP without custom bridges.
Side-by-side
| Dimension | DIY RAG stack | MyMemory |
|---|---|---|
| Time to first retrieval | Weeks to months | Minutes, with the quickstart |
| Browser capture | Build an extension | Chrome extension included |
| Hybrid search and recency | Custom SQL and tuning | Built in |
| MCP for AI tools | Write a server | Included |
| Hosting | You run everything | Hosted only |
| Ongoing cost | Engineering time and infrastructure | Plans on the pricing page |
A hybrid approach
Many teams use a hosted memory API for user-facing ingest and search, and keep proprietary analytics in their own warehouse. Apps and agents call /v1/add and /v1/search; spaces keep each product area or customer separate.
Decision worksheet
Answer honestly:
- Can you staff two or more engineers for three or more months on memory infrastructure?
- Do users need a UI to save and browse memories?
- Will AI tools like Cursor or Claude be a primary interface?
- Does search quality on messy, user-saved content matter to your product?
If you answered "no" to the first question and "yes" to most of the rest, a hosted Memory API is usually cheaper than building.
Try before you commit
curl -X POST https://api.mymemory.dev/v1/add \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"content": "Build vs buy evaluation", "metadata": {"source": "blog"}}'
Then run a search on the same content you'd use for a DIY prototype and compare relevance and latency side by side.