Hyperparam, the workbench for LLM datasets
Inspect, curate, and compare LLM datasets directly in the browser
Explore chat logs, agent traces, tool calls, and other LLM outputs using natural language queries. Fast, intuitive, and built for real AI workflows.
Hyperparam runs entirely client-side, without servers or heavy infrastructure.
Live Demo: All of Wikipedia in the Hyperparam Viewer
This is a demo of what you can do with parquet files being read directly in the browser. A parquet file with all of the English Wikipedia articles is stored in S3 and rows are retrieved on demand using hyparquet.
Debug failures across real-world LLM outputs
- Open multi-gigabyte Parquet or JSONL files directly in the browser.
- Inspect full LLM datasets to understand how models behave across real production inputs.
- Apply LLM-based scoring and filtering to surface failures and regressions.
- Compare outputs before and after prompt, tool, or model changes.
- Export curated subsets, no backend needed.
More insight with less effort
Use AI-assisted scoring, labeling, and filtering to surface patterns, identify failures, and understand LLM behavior across entire datasets. Use LLM-as-a-judge to validate updates consistently and at scale.
Performance and security start in your browser
Run everything directly in your browser for fast, responsive workflows and a local-first approach that avoids unnecessary infrastructure.
Bring the file formats you already work with
Work with Parquet, JSONL, or CSV files, and export curated datasets in the same formats for downstream evaluation or training workflows.
Scroll billions of rows like it's nothing
Advanced virtualization lets you scroll, filter, and inspect multi-gigabyte LLM datasets smoothly, even at very large row counts.
Query your data the way you think
Use natural-language queries to explore, filter, and compare LLM outputs without writing SQL or custom scripts.
Human-in-the-loop dataset curation
Combine AI-assisted workflows with manual review to make curation decisions visible, inspectable, and reversible at the row level.
If you work with LLM datasets, Hyperparam is your AI workbench
- AI engineers — Debug failures, evaluate changes, and compare outputs before shipping.
- Product teams — Review conversations, identify failure patterns, and assess user experience impact.
- Data scientists — Extract insight from unstructured text data at scale.
- ML researchers — Prepare and validate datasets for training and evaluation.
Watch a DemoFAQ
What makes Hyperparam different from other dataset tools?
Hyperparam runs in the browser, which supports fast, interactive work on LLM datasets. Use it to inspect, curate, and compare real production data without backend setup.
Does my data stay on my machine?
By default, yes. You can work in the browser so your data stays on your machine. If you log in, you can choose to upload files to access AI features and export.
Do I need to install anything?
No installation is required. Open Hyperparam in your browser and drop a dataset to start working.
Can Hyperparam handle large datasets?
Yes. Hyperparam uses virtualization to support smooth inspection of multi-gigabyte datasets, even at billions of rows.
Can I export curated datasets?
Yes. Hyperparam exports transformed datasets as Parquet, JSONL, or CSV files so you can plug them back into your pipeline.
Try it now
Sign in now for beta access.