Blog

AI Memory with 95% Fewer Tokens, Without Graphs or LLM Bookkeeping

Full-history answer quality with 95% fewer input tokens on LongMemEval, using source-preserving retrieval and LambdaDB.

Native Reranking with Jev for More Relevant Results

Use native reranking with Jev in LambdaDB, define relevance criteria in your search request, and evaluate ranking quality on your own data.

Adding Context to LambdaDB Collections

Use Collection descriptions and metadata tags to help people and agents understand data purpose and scope, with a code-search example.

Introducing Data Versioning in LambdaDB

Branch from current or historical data, pin snapshots, and route reads for AI knowledge and memory with LambdaDB.

Precise Hybrid Search with LambdaDB

Qdrant vs. LambdaDB: A 10M-Vector Benchmark

From Context Engineering to Harness Engineering

Build AI Agent Memory in 5 Minutes

What Is a Serverless Vector Database?

Why AI Agents Should Sleep Cheap

Vector vs Full-text vs Hybrid Search: When to Use Which

Vector vs Full-text vs Hybrid Search

🩺 Persly-AI × LambdaDB: Strengthening Trust in Medical AI Through Better Data Infrastructure

Persly-AI × LambdaDB

“Serverless” Database Is Dead - It’s Time to Evolve