Libretto vs tldr MCP Gateway

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.

tldr is a local proxy that sits between your AI coding harness and upstream MCP servers, solving one of the most underappreciated problems in agentic workflows: context bloat from tool schema proliferation. When you connect GitHub MCP, filesystem MCP, and a few others, you can easily be sending 24,000+ tokens of tool schemas to the model before any work begins. Instead of passing all those schemas directly, tldr exposes exactly five wrapper tools to the model: search_tools, execute_plan, call_raw, inspect_tool, and get_result. The model learns which underlying tools exist on-demand through search_tools, then calls them through the proxy. GitHub MCP's 24,473-token schema surface compresses to 3,482 tokens — an 86% reduction. Output responses are further compressed through field stripping, a 4,096-token cap, and a 64KB byte limit. This is a genuinely practical solution for power users running multi-MCP setups who've noticed degraded performance as their tool count grows. The tradeoff is one extra hop of indirection, but the token savings pay for themselves in improved model attention and lower API costs.