SQLite as AI Workflow Backbone: Logging, Scheduling, and Private Vectors

SQLite is quietly becoming the AI workflow backbone. Logging, scheduling, and private vectors are all happening on a lightweight, on-device store that keeps things shipshape.

Logging LLM interactions with SQLite SQLite stores every event from LLM apps as structured JSON with timing and metadata, and cryptographic hashes verify integrity. A DevTools-style inspector surfaces everything in real time ^[1]. The pattern is demonstrated with models from Anthropic, OpenAI, and Ollama, showing how a single store can power end-to-end conversations and debugging.

Distributed cron processing with SQLite backing Streaky tackles reliable scheduling with distributed cron processing via Cloudflare Service Bindings, giving each user its own isolated Worker ^[2]. It relies on a D1-based queue with atomic operations to prevent duplicate work during overlaps ^[2]. Security is baked in: GitHub tokens aren’t stored, webhooks are AES-256-GCM encrypted, and a Rust proxy routes notifications through Koyeb ^[2]. The backend uses Cloudflare Workers + D1 (SQLite) to keep state lightweight and portable ^[2].

Private vectors and local vector DB with encryption edgelesssys’ Privatemode for Chrome Demo pairs a local document store with a vector DB, with data staying encrypted even in memory and remote attestation validating the backend ^[3]. The on-device privacy story scales to inference via Privatemode AI, running on confidential hardware with AMD SEV-SNP and Nvidia H100, and featuring the model gpt-oss-120b in a carefully architected setup ^[3].

Bottom line: SQLite-backed logging, SQLite-backed scheduling, and private vector workflows are powering lean, on-device AI tooling today.

References