Tomorrow: Hindsight 1.0 + Memlake. What breaks first when your agent's memory gets big?
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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.
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My team added a time-weighted decay function to retrieval working together with the vector distance. You could give that method a try. It helps move context down naturally so you don’t have to manually delete anything.