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0.1.12

Introducing Clone Request & Experiments, a powerful new feature that enables you to A/B test prompts, compare models, and iterate on LLM requests directly from your traces. Clone any finished trace into an isolated experiment where you can safely tweak parameters, switch models, or modify prompts without affecting the original request.

The Experiment feature provides two editing modes: a Visual Editor for intuitive prompt tweaking and a JSON Editor for precise parameter control. Edit system and user messages, switch models on the fly, adjust temperature and other parameters, and run experiments with side-by-side comparison of tokens, costs, and outputs—all without ever touching your original trace.

Experimenting with visual editor

Perfect for prompt engineering, model comparison, parameter tuning, and iterative debugging. Learn more in the Clone and Experiment documentation.

Other improvements in this release:

  • Enhanced breakpoint management with global intercept and resume capabilities
  • Improved tracing with better parent span ID handling and status recording
  • Stream implementation refactoring for better LLM provider support
  • Better output recording and model finish reason handling

Features​

  • Feat: Refine status recording in Actix middleware for improved tracing (@karolisg) (3957790)
  • Feat: Add output recording and model finish reason handling in OpenAI model event (@karolisg) (adffb0c)
  • Feat: Improve tracing by adding parent span ID handling in Actix middleware (@karolisg) (c23ed70)
  • Feat: Enhance breakpoint management with event handling for resume actions (@karolisg) (8137465)
  • Feat: Add functionality to list all pending breakpoints and their requests (@karolisg) (23f4d68)
  • Feat: Implement continue_all endpoint for managing multiple breakpoints (@karolisg) (4c5a117)
  • Feat: Add global breakpoint management to intercept all requests (@karolisg) (4917e0c)
  • Feat: Integrate breakpoint management into chat completion execution (@karolisg) (8b57e4d)
  • #186 Feat: Refactor stream implementation for llm providers (@karolisg)

Bug Fixes​