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Marti844
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SaaS-Bench-docker

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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/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 90, in _split_generators
                  inferred_arrow_schema = pa.concat_tables(pa_tables, promote_options="default").schema
                                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 6319, in pyarrow.lib.concat_tables
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_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<sha256:0419a2c79fe3076abc1f421db6ec9fbfc705c15c62397f6ec1553195a472feac: struct<digest: string, mediaType: string, size: int64>, sha256:0e9561121601abac7643543d82000c52279dc746ef9bca9c90d0a028bf2953dc: struct<digest: string, mediaType: string, size: int64>, sha256:102ea32e0609cd854458a91add95b98f4fd3d0d3fa10ca4a734ac9ccddd6e76a: struct<digest: string, mediaType: string, size: int64>, sha256:1157aa1c473d3cd8bf2ce734850242b17601a65bce61c2123cb85e5452aca6d1: struct<digest: string, mediaType: string, size: int64>, sha256:1b527432e55d3c4e1a1323eb88b2a1f05abebf375a0a5ef0999f4884d242f048: struct<digest: string, mediaType: string, size: int64>, sha256:29473d95d06283b4febf9e7a67b080bcaa1f50155032d6bbe811b69d5c0d2c8e: struct<digest: string, mediaType: string, size: int64>, sha256:499e7a3a977cb4bad3e6a0387810cb73ae5dcfe908a9e5b940aacdfd64af4f48: struct<digest: string, mediaType: string, size: int64>, sha256:5ca28657b8d62b3f3c36151d858c4299f147e962f579cd48495bc23ecfcd47e9: struct<digest: string, mediaType: string, size: int64>, sha256:62e97cc660d529898f4ac07c003a888835f4917f40b263a719c21a10ac8c89fe: struct<digest: string, mediaType: string, size: int64>, sha256:7b034e607fe417aa2b9d6f24aae96317bbb4618e9007dccd893654d3028bd32d: struct<digest: string, mediaType: string, size: int64>, sha256:8747ddf1dc456d701301acb4efe193ccb56ee38f9392a39153e0671befdf1d2c: struct<digest: string, mediaType: string, size: int64>, sha256:975e45ff98ca18ebc9ac508f39e8b979f2f6d6ec56484764ca4429acc8c03cb1: struct<digest: string, mediaType: string, size: int64>, sha256:abe743884ea0f81e9c440e82f83e69d90f4a9c127436d36b77b0aae5eb96ce25: struct<digest: string, mediaType: string, size: int64>, sha256:af7f4a7b7e664c3ed836a23f4bb262c36a8d849ebddcfccf7edcdc523fbd5ce7: struct<digest: string, mediaType: string, size: int64>, sha256:f2c82d70eed479681124abd5551ab2808e4db7dd359422197e060bf49b212e33: struct<digest: string, mediaType: string, size: int64>, sha256:fde1622bb3aa78b9d60f37d0a5502e0be8b5774b3cf590bfa1e13c0d1750b424: 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 65, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                         ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/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.

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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 (≈ 52 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 2.86 GB
mw-bigcapital.tar Bigcapital 2.74 GB
mw-booklore.tar BookLore 1.45 GB
mw-code-server.tar code-server 4.60 GB
mw-elabel.tar e-label 1.86 GB
mw-farmos.tar farmOS 1.05 GB
mw-grocy.tar Grocy 273 MB
mw-hrms.tar HRMS 5.47 GB
mw-mattermost.tar Mattermost (+Postgres) 1.42 GB
mw-mediacms.tar MediaCMS 1.64 GB
mw-metabase.tar Metabase 847 MB
mw-onlyoffice.tar OnlyOffice Community 9.74 GB
mw-openemr.tar OpenEMR 2.61 GB
mw-openproject.tar OpenProject 2.11 GB
mw-opnform.tar OpnForm 474 MB
mw-owncloud.tar ownCloud 1.97 GB
mw-photoprism.tar PhotoPrism 3.56 GB
mw-pretix.tar Pretix 2.23 GB
mw-recipya.tar Recipya 593 MB
mw-roundcubemail.tar Roundcube Mail 1.25 GB
mw-siyuan.tar SiYuan Notes 2.85 GB
mw-twenty.tar Twenty CRM 1.97 GB
mw-watcharr.tar Watcharr 239 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:

huggingface-cli download anonymous8722/SaaS-Bench \
    --repo-type dataset --local-dir docker \
    --include "*.tar"

System requirements

  • Disk: ≥ 60 GB free for the image archives plus 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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