Datasets - bryel

A dataset is a curated set of records for supervised fine-tuning. Each record is one agent run, reconstructed into a chat-with-tools trajectory ({messages, tools}) — the training target. There’s no separate “expected” answer: SFT learns from the trajectory itself.

Build a dataset

Datasets are built from your real runs. Create one, then add the traces worth training on.

In the app
From a coding agent

Create a dataset

From Datasets, give it a name. Empty to start.

Add traces

Use the trace selector to filter your runs (by intent, status, cost, tools, and more), preview the matches, and add them. Each trace is reconstructed into a record with its full message + tool trajectory.

With the remote MCP and write access, an agent builds a set from your editor:

"Using bryel: create a dataset 'good-add-pricing', add every clean
add_pricing run from the last 30 days, and inspect the result."

That runs bryel_create_dataset → bryel_add_from_query → bryel_inspect_dataset. Adding from a query is idempotent per trace, so you can widen the filter and re-run without duplicating.

Judge quality before you train

A fine-tune is only as good as its data. bryel surfaces the dimensions that make or break an SFT set — in the dataset view and through bryel_inspect_dataset.

Balance

intent distribution

How records spread across intents. One intent dominating makes the model overfit it and forget the rest.

Redundancy

duplicate clusters

Near-identical inputs waste capacity and bias the model. bryel groups them so you can prune to a few exemplars.

Trajectory health

looping / errored

Runs that looped or errored teach the model bad behavior. They’re flagged so you can drop or repair them.

Length

token & step distribution

Records that exceed the fine-tune context window get truncated mid-trajectory; trivially short ones add no signal.

In the dataset view you can filter to duplicates or problematic records and prune them in place.

Export for training

Export a dataset to fine-tune JSONL — one {messages, tools} object per line, ready for a training run.

Export JSONL

From the dataset, choose Export JSONL — bryel streams the records to a file you can download. A coding agent can do the same with bryel_export_dataset, then poll bryel_export_status for the download URL.

Train

Feed the JSONL to your fine-tuning pipeline.

Build datasets from your editor

Connect a coding agent over the remote MCP to query, curate, and inspect datasets.

​Build a dataset

​Judge quality before you train

​Export for training

Build datasets from your editor

Build a dataset

Judge quality before you train

Export for training