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Datasets:
akashnaren
/
agent-ui-sft

Tasks:
Text Generation
Languages:
English
Size:
n<1K
Tags:
agents
tool-use
ui
sft
License:
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    TypeError
Message:      Couldn't cast array of type list<item: struct<role: string, content: string, tool_calls: list<item: struct<id: string, type: string, function: struct<name: string, arguments: string>>>, tool_call_id: string>> to string
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2312, in cast_table_to_schema
                  cast_array_to_feature(
                  ~~~~~~~~~~~~~~~~~~~~~^
                      table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                      feature,
                      ^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ~~~~^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2152, in cast_array_to_feature
                  return array_cast(
                      array,
                  ...<2 lines>...
                      allow_decimal_to_str=allow_decimal_to_str,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
                  return func(array, *args, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2016, in array_cast
                  raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}")
              TypeError: Couldn't cast array of type list<item: struct<role: string, content: string, tool_calls: list<item: struct<id: string, type: string, function: struct<name: string, arguments: string>>>, tool_call_id: string>> to string

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Agent UI SFT

Small synthetic supervised fine-tune (SFT) set for agent tool-use. It studies the public research question in akashnaren/agent-ui-metrics: what is the most efficient UI for agents to interact with applications and tools?

Scope (read this first): rows are original lab fiction. They are not production Tesla data, not customer records, and not operational telemetry from any employer. Identifiers such as lab-w3, wf-synth-44, and tr-dom-9 are invented for the harness.

Author Akash Premkumar (akashnaren)
License Apache-2.0
Hub files train.jsonl (80), test.jsonl (20)
Mirror Kaggle: akashpnaren/agent-ui-sft
Related agent-ui-human, agent-ui-efficiency-scores, agent-ui-mode-pairs, ui-mode-router, Space

Schema

Each JSONL line is one object:

field type meaning
id string Stable id, unique across splits (e.g. aui-cli-001, aui-dom-014)
messages list[object] OpenAI-style chat: system, user, then assistant (often with tool_calls), tool results, final assistant answer
tools list[object] JSON-schema tool definitions available for that example
ui_mode string enum cli | structured_api | dom_click | form
task_type string Short label (e.g. workflow_signal, telemetry_diagnose, form_fill, ui_efficiency_score)

Split balance (checkable on Hub)

split rows per ui_mode
train 80 20 × each of 4 modes
test 20 5 × each of 4 modes

Tools by ui_mode (as published)

ui_mode tools in traces
cli run_cli, score_ui_trace
structured_api call_json_api, workflow_op
dom_click list_interactive, click_node, read_region
form get_form_schema, patch_form, submit_form

Observed message-list lengths on the published 100 rows (load the JSONL to reproduce): CLI and structured API averages ~5 messages; DOM and form averages ~7–8 (more hops for the same class of lab job).

How to load 

from datasets import load_dataset

ds = load_dataset("akashnaren/agent-ui-sft")
print(ds)
print(ds["train"][0]["id"], ds["train"][0]["ui_mode"])

Local files (after huggingface-cli download or cloning the dataset repo):

from datasets import load_dataset

ds = load_dataset("json", data_files={
    "train": "train.jsonl",
    "test": "test.jsonl",
})

Pandas / Polars:

import pandas as pd
train = pd.read_json("train.jsonl", lines=True)
print(train["ui_mode"].value_counts())

Example row (abbreviated)

From published train.jsonl, id aui-cli-001 (ui_mode=cli, task_type=ui_efficiency_score):

  • user: score lab trace tr-ui-104 for tokens / turns / recoveries (CLI vs form bakeoff).
  • tool result (fiction): {"trace_id":"tr-ui-104","tokens":1840,"turns":6,"recoveries":1,"ui_mode":"form","task":"restart_worker"}
  • final assistant: notes that form path is expensive vs one-shot CLI for that lab job.

Numbers inside tool results are staged lab fiction, not measurements from a live cluster.

Intended use

  • Smoke LoRA / SFT on a small open model with OpenAI-style messages + tools.
  • Teach routing among cli, structured_api, dom_click, and form.
  • Lab exercises: Temporal-like workflow ops, diagnostics-style reads, DOM selector recovery, form validation.

Not intended as: a general tool-use corpus, a production eval suite, or a rehost of Moonshot/Kimi weights or proprietary traces.

Limitations

  • 100 rows total — enough for a smoke LoRA, not a general agent corpus.
  • Synthetic English only.
  • Workflow / metrics / token counts in tool payloads are staged, not live-cluster measurements.
  • No dedicated safety / prompt-injection curriculum beyond ordinary lab refusals to invent PII or webhooks.
  • Does not contain employer or customer data.

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