Libretto vs Metrics SQL by Rill

Libretto is an open-source browser automation toolkit built by Saffron Health to solve a critical problem with AI-driven web agents: non-determinism. Standard agent-controlled browsers using Playwright or Puppeteer routinely fail 20-30% of the time on production workflows because they rely on LLM judgment for timing and element selection. Libretto replaces that with a record-replay system that captures precise interaction timing and DOM fingerprints, achieving a reported 95% success rate on identical workflows. The library works by recording a "golden path" of a browser session — capturing not just actions but the exact CSS selectors, visual context, and timing windows during which those actions are valid. On replay, it verifies each step against expected page state before proceeding, and falls back to an LLM-assisted recovery mode when pages drift (e.g., after a UI update). Saffron Health built it to maintain integrations with EHR portals that change frequently and where failure has compliance consequences. Saffron open-sourced Libretto after using it internally for 18 months across 40+ healthcare software integrations. The HN thread highlighted the appeal for fintech, legal, and healthcare automation where reliability, not just capability, is the product. The toolkit targets TypeScript/Node.js environments and integrates cleanly with existing Playwright infrastructure.

Metrics SQL is a SQL-based semantic layer from Rill Data that solves a specific and painful problem: AI agents that query your data warehouse tend to hallucinate aggregation logic, producing metrics that look plausible but are mathematically wrong. Metrics SQL lets analysts define business metrics once — revenue, MAU, conversion rate, ROAS — in a governed definition layer, and then exposes those definitions as queryable SQL tables. Every dashboard, notebook, and AI agent resolves from the same source. The technical approach is elegant: rather than inventing a new DSL, Metrics SQL extends SQL itself. An agent that knows SQL can query `SELECT * FROM metrics.weekly_revenue` and get correctly computed numbers without needing to know how revenue is defined, which tables it joins, or how edge cases like refunds are handled. The semantic layer intercepts the query, applies the governed definition, and returns correct results. The implications for AI-native data stacks are significant. Currently, one of the biggest failure modes for AI analysts and BI agents is inconsistent metric computation — different agents or dashboards produce different numbers for 'revenue' because they implement aggregation logic differently. Metrics SQL addresses this at the infrastructure level, not by improving agent prompting.