Show HN: Fast Database for Agents (github.com)

Kalki, a newly announced fast database designed specifically for the needs of autonomous agents, addresses common issues with traditional vector databases that often mismanage noisy log data. Utilizing a Tablet Architecture, Kalki separates semantic indexing from raw data storage, enhancing query speed. By summarizing agent logs with a large language model (LLM) before indexing, it efficiently manages the vast volumes of log entries generated by these agents, which run 24/7 and can produce millions of entries. This innovative approach tackles problems like "The Noise Problem," where indexing every raw thought token creates imprecise searches, and "The Decompression Tax," which slows down retrieval by needing to decompress entire data pages. Kalki's architecture includes a two-part storage system with compressed summaries and metadata alongside the raw data, allowing for high-performance query capabilities. It employs a Log-Structured Merge-Tree (LSM) architecture to maintain high signal quality and minimizes token reading through efficient payload retrieval. With features like Semantic Predicate Pushdown and background compaction, Kalki drastically improves retrieval precision while reducing computational load. Its benchmarks showcase the database's impressive Query Per Second (QPS) performance and low latency, solidifying its position as a powerful tool for the AI/ML community dealing with autonomous agents.