Why we ditched heavy Vector DBs: Libro Architecture & Benchmarks 🚀

Hey Product Hunt! 👋

While building Libro (ContextOS), we realized a massive bottleneck with existing AI memory frameworks like Mem0 and Zep: Network Latency & Memory Footprint.

Most agent memory tools default to heavy, managed databases (like Pinecone, Qdrant, or massive Postgres/Graphiti setups). Every time an agent tries to recall a memory, it requires a network hop to the DB, taking 50-200ms just for the query. At 10 million vectors (Float32), they chew through ~31 GB of RAM.

We wanted a lighter, faster engine.

So we built one.

To power Libro, we implemented a custom in-memory vector engine running on a serverless Hugging Face Space. Instead of Float32, we use bleeding-edge 4-bit quantization (Turbovec / TurboQuant).

Here are the results from our holistic system diagnostic:

⚡ Retrieval Latency: Sub-millisecond raw engine scanning (eliminating the DB network hop).

🗜️ Memory Footprint: 10 million 768-dim vectors fit into just 4GB of RAM (an 87% reduction in cost).

🎯 Accuracy: Maintained a 99.4% Recall@10 accuracy, beating standard FAISS IndexPQ.

(See the attached CLI benchmark screenshot for the full breakdown vs Mem0, Zep, and LangMem).

By running the index directly in RAM and syncing backups to Supabase, Libro achieves enterprise-grade context retrieval at a fraction of the infrastructure cost.

Would love to hear from other devs building AI agents—are you sticking with Float32 managed vector DBs, or are you exploring quantization for faster context retrieval?

Let me know your thoughts! 👇