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SaaS-Bench-docker

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Tags:
benchmark
agents
computer-use
llm-agents
browser-use
saas
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Cannot get the split names for the config 'default' of the dataset.
Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 88, in _split_generators
                  inferred_arrow_schema = pa.concat_tables(pa_tables, promote_options="default").schema
                                          ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 6321, in pyarrow.lib.concat_tables
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                  return check_status(status)
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowTypeError: Unable to merge: Field json has incompatible types: struct<manifests: list<item: struct<annotations: struct<io.containerd.image.name: string, org.opencontainers.image.ref.name: string>, digest: string, mediaType: string, size: int64>>, mediaType: string, schemaVersion: int64> vs list<item: struct<Config: string, LayerSources: struct<sha256:047dc79adc1bfe9134c7ac6353c33d91424c110d134f803543875318e28cec38: struct<digest: string, mediaType: string, size: int64>, sha256:12b4fc9650098c1cfc7695465ee8eb514f42ea0973c84163d56c909b5a273683: struct<digest: string, mediaType: string, size: int64>, sha256:17d6053e599e4d973c3a54616db8ad596ce2084a92e616793402447913dfe0bf: struct<digest: string, mediaType: string, size: int64>, sha256:1b017fce3d013302ef2e4ecbd84f1583b3deafc1cbed5486895f74ba7943965e: struct<digest: string, mediaType: string, size: int64>, sha256:1de3afcf4505f16ba8425741db24746f44c6362e730529c38b5bf758f4631113: struct<digest: string, mediaType: string, size: int64>, sha256:2da0f06325de241bea4ee3b8b1a806dcd33aeb6b47d10be4bd38367f275042c8: struct<digest: string, mediaType: string, size: int64>, sha256:2dae1660292a81623e7540f2f65b06a83dc7cd536e34a94b1360df53c486c01e: struct<digest: string, mediaType: string, size: int64>, sha256:319f61f0b03815093af51cd43e093acd5ee1113ee14075d0e1c4f01408298613: struct<digest: string, mediaType: string, size: int64>, sha256:346988dfd11e89c9bd4405ba273eb9f57db2c7b1de72630d2f79a6c11142f043: struct<digest: string, mediaType: string, size: int64>, sha256:37bbe73689207ff02a014ce07a384db97be7ba649cfaeb8d47cd199f742062ce: struct<digest: string, mediaType: string, size: int64>, sha256:4734b8f58c5fec85efaf9b6708b9a5ca97bf8379a1631569d90507d6ddf0dd12: struct<digest: string, mediaType: string, size: int64>, sha256:47c72449347ba568b5097f39353e66198fac34ba8f20d23cdbf35d2e50319d45: struct<digest: string, mediaType: string, size: int64>, sha256:4efaec3e46184d2e2dd5e27410ab49a04a46009d107190c27406a2a88c9cf1e7: struct<digest: string, mediaType: string, size: int64>, sha256:51a1421740f11650ca24c4feeac891e04bd16ccf4c23237b5d2c556622c5c813: struct<digest: string, mediaType: string, size: int64>, sha256:5a65ee14eecb33c3fcf8b2530b3328ca56e15bffaa091b6d0743de38ce981892: struct<digest: string, mediaType: string, size: int64>, sha256:5f70bf18a086007016e948b04aed3b82103a36bea41755b6cddfaf10ace3c6ef: struct<digest: string, mediaType: string, size: int64>, sha256:6e00429be7e5602b72b62a1df8df17d071b1d020a5e7fbe06a8c2e45f6f6fffa: struct<digest: string, mediaType: string, size: int64>, sha256:6e06255daf650e28ca445941328c8244ae0d15936f7574c6fcdba3951d33fa2c: struct<digest: string, mediaType: string, size: int64>, sha256:6f87f2710c8374a70ee3319c4905db9a63a68c9c83124e900f2b103b44986ccb: struct<digest: string, mediaType: string, size: int64>, sha256:75b4b9e336853fc00d637014861c0e4edb9bb8b802a5905c9953cd521f1fbc29: struct<digest: string, mediaType: string, size: int64>, sha256:823fe74d158192ebe7eae551a8ffd79d7bb3d6ba64e3998f6ddfcd7edc1b2aa6: struct<digest: string, mediaType: string, size: int64>, sha256:82502b057773e068fc6367b7a3244bb5fea4a5827d19e782594c10437e557a32: struct<digest: string, mediaType: string, size: int64>, sha256:8a7b091d050a6a1deff0ae19558f858b9b1cd67b77ba332abf668e797fbc8fa2: struct<digest: string, mediaType: string, size: int64>, sha256:8d41a085f56eec2e9c923738425fe7c53c692a7a3309fbbffd237662412f981f: struct<digest: string, mediaType: string, size: int64>, sha256:8d81b2a04b6239a65123acecc7d65ff36b5765fa577d242c194998d049621806: struct<digest: string, mediaType: string, size: int64>, sha256:8eb1bc9e2cf526e52827f7be5ceb1c86243e909244c327c9f7c277d6ebd7fa4d: struct<digest: string, mediaType: string, size: int64>, sha256:97964e11c0340e9872460634b63da4cd93beed61581d15f1ff0d943d6f8ceeaa: struct<digest: string, mediaType: string, size: int64>, sha256:a001fe1bdd46f5fd5cdb4120d1881bd3a282013ab1c0fc525b05807cc54fcdbb: struct<digest: string, mediaType: string, size: int64>, sha256:a880b0d2c39e00b70821d3626ac7cd2323c27287fc63ea369a7b065650266e61: struct<digest: string, mediaType: string, size: int64>, sha256:b3768fa87766119b9fffef5a15a34262ff82359be9643fea8b2c1860b89b7897: struct<digest: string, mediaType: string, size: int64>, sha256:cdb550749cc01cd06efe652656d55e5a891c29a8b1f7abfeb8e90140d3ded143: struct<digest: string, mediaType: string, size: int64>, sha256:cfcadd61a8d9ecf24b8505bd33e6f37dca8ef7cf3e1d5b6065ad5ad3e4fac44d: struct<digest: string, mediaType: string, size: int64>, sha256:f77cd126bcdfedb7ff4e195153ee4ee6ac48cbd420cdb5a74595148f70839557: struct<digest: string, mediaType: string, size: int64>>, Layers: list<item: string>, RepoTags: list<item: string>>>
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

SaaS-Bench Docker Images

Docker image archives for the SaaS-Bench benchmark — a suite of 23 self-hosted SaaS applications used to evaluate computer-use LLM agents on real, multi-step business workflows.

This repository hosts the prebuilt .tar images (≈ 63 GB total) so you can reproduce the benchmark environment without rebuilding each app from source. The eval harness, task definitions, and verifiers live in the main SaaS-Bench repository.

Paper: SaaS-Bench: Can Computer-Use Agents Leverage Real-World SaaS to Solve Professional Workflows?

Overview

SaaS-Bench evaluates browser-driving LLM agents on 106 task instances across 6 domains, running on 23 self-hosted SaaS applications. Each task asks the agent to complete a multi-step workflow (e.g. create a purchase order, configure a project board, schedule a patient visit); a per-task verify.py script then inspects the running application's state (DB rows, API responses, filesystem) and returns a pass/fail.

Track Domain Tasks Representative apps
uni-m BOF 15 Twenty, Bigcapital, HRMS, Pretix
uni-m HA 16 OpenEMR, OnlyOffice, OpnForm
uni-m SEPM 31 Baserow, OpenProject, code-server, Metabase
uni-m TCDW 12 OnlyOffice, Mattermost, RoundcubeMail, ownCloud
multi-m AASC 12 Grocy, farmOS, Recipya, e-label
multi-m IMC 20 SiYuan, Watcharr, BookLore, PhotoPrism, MediaCMS

Domains: BOF = Business Operations & Finance · HA = Healthcare & Administration · SEPM = Software Eng. & Project Mgmt. · TCDW = Team Comms & Document Workflows · AASC = Agriculture, Authoring & Supply Chain · IMC = Information Mgmt. & Creative.

Contents

23 Docker image archives (mw-*.tar) covering every app used by the benchmark.

File App / Stack Size
mw-baserow.tar Baserow 3.07 GB
mw-bigcapital.tar Bigcapital 2.94 GB
mw-booklore.tar BookLore 1.55 GB
mw-code-server.tar code-server 8.46 GB
mw-elabel.tar e-label 2.00 GB
mw-farmos.tar farmOS 1.13 GB
mw-grocy.tar Grocy 286 MB
mw-hrms.tar HRMS 5.88 GB
mw-mattermost.tar Mattermost (+Postgres) 1.53 GB
mw-mediacms.tar MediaCMS 1.76 GB
mw-metabase.tar Metabase 889 MB
mw-onlyoffice.tar OnlyOffice (4-image stack) 10.46 GB
mw-openemr.tar OpenEMR 4.48 GB
mw-openproject.tar OpenProject 2.27 GB
mw-opnform.tar OpnForm 548 MB
mw-owncloud.tar ownCloud 2.14 GB
mw-photoprism.tar PhotoPrism 3.82 GB
mw-pretix.tar Pretix 2.39 GB
mw-recipya.tar Recipya 624 MB
mw-roundcubemail.tar Roundcube Mail 1.34 GB
mw-siyuan.tar SiYuan Notes 3.06 GB
mw-twenty.tar Twenty CRM 2.11 GB
mw-watcharr.tar Watcharr 250 MB

Each tar already contains the :latest tag; image names follow the mw-<app>[-<component>] convention so loaders can resolve them deterministically.

Download 

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="Marti844/SaaS-Bench-docker",
    repo_type="dataset",
    local_dir="docker/images",
    allow_patterns=["*.tar"],
)

Or with the CLI:

hf download Marti844/SaaS-Bench-docker \
    --repo-type dataset --local-dir docker/images \
    --include "*.tar"

System requirements

  • Disk: ≥ 130 GB free — ~63 GB for the archives plus the loaded images.
  • RAM: ≥ 500 GB recommended if you run the full eval with the default 4-way parallelism — most stacks bundle their own DB / search / document-server, so total memory grows quickly under concurrency.
  • Host OS: Linux. Tested on Ubuntu 22.04 and Alibaba Cloud Linux.
  • Docker: 24+ with the compose plugin.

Licensing

  • This card: Apache 2.0.
  • Each bundled Docker image retains the license of its upstream project (e.g. OnlyOffice — AGPLv3, Mattermost — MIT/AGPLv3 dual, OpenEMR — GPLv3, etc.). The images are redistributed for benchmarking convenience only. Verify upstream terms before any non-research use.
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