Track every LLM experiment. Understand what works.

Prompt engineering is still engineering

Prompt inputs and structureStore every system, user, and tool message—plus chain logic—so you can retrace exactly what the model saw.

Model settings (e.g. temperature, provider)Snapshot hyperparameters, model names, and API endpoints for bullet-proof reproducibility.

Outputs and scores (human or LLM-graded)Log raw responses alongside rubric-based or reference-based scores to quantify quality at a glance.

Test results from evaluation suitesAttach pass/fail status and metric deltas from bias, safety, and robustness tests to each run.

Version comparisons over timeDiff prompts, settings, and results across checkpoints to see what changed—and why it matters.

Compare LLM runs side-by-side

Tag, organize, and annotate prompts

Evaluate outputs with rubrics or LLM-as-a-judge

Share results across teams and experiments

Works across OpenAI, Anthropic, Hugging Face, and LangChain ecosystems

What is LLM experiment tracking?

What tools support LLM tracking?

Can I compare results across models or providers?

$ openlayer push