Hi all, Chris from Vectorize here. Tomorrow (Oct 14) we're launching Hindsight 1.0, our open-source memory for AI agents, together with Hindsight Memlake, a Rust storage engine for Enterprise customers with very large memory volumes.
Before launch day, a question for anyone running agents with long-term memory: what broke first as it grew? For us it was recall latency. On one 200k-memory bank, recall on Postgres had crept up to 5.3 s; on Memlake it's 113 ms, and the engine holds 100M memories under 100 ms p99.
Curious whether others hit the same wall or something else entirely: cost, relevance drift, stale facts. Happy to share what we learned, and we'd love to see you at the launch tomorrow.
Hindsight is open-source memory that lets AI agents learn from experience, #1 on every dataset of the Agent Memory Benchmark. Today it's 1.0, and the API is a promise.
We're also launching Hindsight Memlake, a storage engine written in Rust that keeps agent memory in object storage: 100M memories at under 100 ms p99, with no database, consensus cluster or leader.
Self-host it free, start on Hindsight Cloud with free credits, or run it in your own cloud with Enterprise.
Vectorize is a data platform for retrieval augmented generation (RAG). It combines RAG evaluation to identify the best way to vectorize your data with a cloud-scale RAG pipeline engine. Vectorize populates your vector database and keeps your vector data fresh.
