LexBench-Web-Fetch

LexBench-Web-Fetch evaluates whether a browser, extraction tool or hosted fetch service returns the content an AI agent needs from a URL. Its 550 task entries cover page bodies, structured records with usable links, resource discovery and file delivery. Each task declares what the returned answer must contain, so the same acceptance rules apply to every participant.

Use it to compare live-site fetching, diagnose incomplete extraction, or test a saved response after changing a parser. Browser Eval provides the scorer, execution adapters, evidence storage and comparison reports. Scoring saved answers requires no browser, provider account or LLM judge.

Task groups and source hierarchy

The viewer organizes Web Fetch tasks by their source groups. The original mixed webfetch suite has six groups: web-pages, documents, images, videos, challenge-pages and dynamic-examples. The other source suites appear as popular-sites, dynamic-content, access-restrictions and articles.

Viewer group	Source suite	Tasks	Scope
web-pages	webfetch	100	Page bodies, documentation and structured records; category retains the original finer classification
documents	webfetch	10	Complete document text, including embedded documents, tables and appendices
images	webfetch	10	Requested image URLs or delivered image files; image understanding is outside the task
videos	webfetch	10	Declared video resources, playback or download entries, files and metadata
challenge-pages	webfetch	10	Target content or qualifying records on sites with observed access challenges
dynamic-examples	webfetch	10	Public asynchronous, client-rendered, embedded-state and embedded-frame examples
popular-sites	popular-sites	100	Task-relevant stable content and qualifying named records or links on widely used websites
dynamic-content	dynamic-content	100	Content loaded after the initial response, including asynchronous requests and client-rendered records
access-restrictions	access-restrictions	100	Target content at URLs with previously observed access restrictions
articles	articles	100	Article bodies, titles, requested initial comments and post collections

Each task also retains its original track and finer category, such as technical documentation, developer portals or table structure. These describe the source workload. Delivery requirements separately identify bodies, structure, discovered resources and delivered files. The article group includes fixed articles and changing post collections. An access-restriction task tests delivery of its target content; a past restriction does not guarantee that the same site will challenge every later request.

Some entries overlap across subsets, including matching URL-and-prompt pairs. Declare the selected subsets and any deduplication rule before running a combined comparison. The catalog total counts task entries, not independent URLs.

What counts as a pass

A method receives the task URL and prompt. Its response is checked against the task's declared delivery requirements:

Delivery	Checks selected by the task
Body	Required facts and stable text, reference-body coverage, required regions and article endings
Structure	Qualifying records, names and destination URLs, fields owned by the same record, tables or document sections
Resources	Returned resource URLs, permitted variants, and target-only output requirements where declared
Files	Delivered bytes, declared file type and integrity requirements

Contracts can combine these checks. A successful request, nonempty answer or long response alone does not establish task completion. The scorer keeps binary acceptance separate from text-quality diagnostics. Missing reference coverage remains unavailable. Exact requirements and thresholds are in each task; the metrics guide explains their interpretation.

Web Fetch runs call the tested tools directly. An agent does not choose browser actions during execution. Preserve the requested initial-load scope: clicking, logging in, expanding hidden content or paginating changes the work unless a task explicitly permits it. In the article subset, a qualified displayed excerpt is scored as that excerpt, and changing feeds use their own post-collection contracts.

A published use of the benchmark

The Web Fetch benchmark comparison evaluates five browsers on an earlier 430-case live-site population and separately on WebMainBench's 545 saved pages. It uses the same URLs and task requirements for each browser, a 45-second task limit, and at most one retry for temporary network errors. The article reports a 96.7% request-success rate but a 54.4% task-pass rate for Chromium, illustrating how returned pages can still miss the requested content. It reports an 89.8% task-pass rate for Moli on that 430-case population.

Those results describe the article's versions, interfaces and execution conditions. They are an example of using per-task evidence to distinguish access failures from extraction failures, and are separate from scores on the current catalog. The article also explains why live fetching and extraction from fixed HTML answer different evaluation questions.

Browse and load the data

Each viewer group has one test split. With the Hugging Face datasets library installed:

import json
from datasets import load_dataset

tasks = load_dataset(
    "Lexmount/LexBench-Web-Fetch",
    "web-pages",
    split="test",
    revision="<full-Hugging-Face-commit>",
)
task = json.loads(tasks[0]["task"])
print(task["url"], task["task_prompt"])

Replace the revision with the full dataset commit you intend to use. Select another group by its viewer name in the table above.

Column	Meaning
id	Task identifier within its source suite
input	Source URL
source_suite	Original scorer suite ID; the first six viewer groups all retain webfetch
track	Original source-group value from the task definition
category	Original finer category, preserved exactly as authored
family	Declared delivery kind, when present
language	Target or reference-content language, taken from source annotations or reviewed frozen references; mul marks multiple languages and zxx marks URL-only or binary targets
task	JSON serialization of the public task definition, including its prompt, acceptance requirements and external reference bindings

The viewer supports inspection and integration. Full body references and capture metadata are external: the public row cannot replace a complete executable task that requires them.

definitions/ contains the public definitions, catalogs and input manifests. definitions.tar.gz packages the same files for installation. manifest.json records source provenance and file hashes. Frozen catalogs retain their original dataset IDs and task languages, including historical method names inside some IDs; the same acceptance rules still apply to every method.

Validate, score and run

From a Browser Eval checkout with uv installed:

git clone https://github.com/lexmount/browser-eval.git
cd browser-eval
git checkout f488414a6610a9871f3a6d2d8615b58090f16184
uv sync --frozen
uv run --frozen python -m harness.benchmarks.data
uv run --frozen lexbench-webfetch list
uv run --frozen lexbench-webfetch validate

Browser Eval downloads the dataset revision selected by its data lock and verifies the archive and file hashes. Public downloads require no access token. The benchmark integration guide covers revision selection and offline caching.

For tasks that need external reference inputs, obtain an approved matching copy and export a verified executable suite:

uv run --frozen lexbench-webfetch export \
  --suite webfetch --source /absolute/path/approved-inputs \
  --output /absolute/path/new-input-directory

The scorer's --suite webfetch exports the complete mixed suite containing all six viewer groups. It does not select only web-pages. Use task IDs or a predeclared filtered executable dataset to run one group. The other four viewer groups retain their own scorer suite IDs. Export verifies the declared inputs before writing a new directory. See reference inputs and access.

Score a saved response using its complete task definition:

uv run --frozen lexbench-webfetch score \
  --task /absolute/path/task.json \
  --response /absolute/path/answer.txt

Add --metadata for saved structured answer fields and --file for delivered file bytes. The Python API, lexbench_webfetch.evaluation.evaluate_response, applies the same acceptance rules.

After exporting webfetch, run one diagnostic task with the HTTP baseline:

uv run --frozen browser-harness-eval run \
  --dataset /absolute/path/new-input-directory/dataset.json \
  --provider-config configs/providers.quickstart.json --providers http \
  --case-ids static-simple --output /absolute/path/new-evidence

The HTTP baseline does not execute JavaScript. For browser setup and a report you can inspect, follow the runnable local example. Formal comparisons use the certified workflow in the Web Fetch evaluation guide.

Reporting and limitations

Report passed/evaluated counts for the declared population, unavailable results and the selected benchmark and scorer revisions. Retain failed calls and timeouts. State timing boundaries, retry policy, local or remote execution, and conversion steps; keep all-call timing separate from timing conditional on success. Optional independent verification has its own disclosed scope and does not run automatically.

Live sites change with location, time, session state and access policy. The suite emphasizes task-relevant stable content, but a frozen reference can still become outdated. Investigate source drift before changing a verdict. Updating task requirements or scoring code does not retroactively update an old measurement. Record the immutable dataset commit, task/reference hashes and scorer source identity with each experiment. Detailed collection, correction and reporting rules are maintained in the Web Fetch guide.

These are curated workloads, not a representative sample of every website. The article suite's initial-load scope does not measure hidden continuations or interaction-dependent content. Text and link checks do not measure visual fidelity or autonomous browser use. This dataset contains task inputs and requirements; it does not ship provider measurements or establish a standing browser ranking.

Sources, related benchmarks and rights

LexBench-Web-Fetch was developed as the Web Fetch benchmark component of Browser Eval. Its task definitions retain source URLs and reference bindings. Project-authored material uses Apache-2.0; original website text, documents, media and captures retain their source rights. Full captures, screenshots, private logs and measured provider outputs are excluded. A public URL or content hash is not permission to redistribute the underlying material. Read THIRD_PARTY.md, NOTICE and the notices included with the selected snapshot.

The following projects have separate populations and terms; they are not subsets of this catalog:

• WebMainBench, maintained by OpenDataLab, evaluates main-content extraction from saved web pages. Browser Eval's LexBench-WebMainBench dataset records its selected corpus and corrections.
• WCXB is the Web Content Extraction Benchmark.
• WebArena and WebArena-Verified supply interactive tasks and evaluators used in separate browser workflows.
• LexBench-Headless-Browser evaluates headless-browser runtime and automation capabilities.

For search evaluation, use LexBench-Web-Search. Moli is one browser that can be evaluated with Web Fetch tasks, and WebFetch CLI is a client for a hosted fetch service. Report creation and sharing are covered by Browser Eval's reporting documentation.

Citation

Cite Browser Eval and record the exact dataset revision used in your experiment. The canonical project citation is CITATION.cff.

@software{browser_eval_2026,
  author = {{Browser Eval contributors}},
  title = {Browser Eval: Benchmarking Browsers and Web Tools for AI Agents},
  note = {Evaluation harness},
  year = {2026},
  url = {https://github.com/lexmount/browser-eval}
}