A source-backed collection of papers, models, datasets, benchmarks and tools for agents that use computers.

English · 简体中文

Search the website · Papers · Topics · Data · Agent guide · Contribute

If this collection is useful, a star on GitHub helps others find it.

Curated entries were reviewed against a first-party source snapshot; records awaiting review have their bibliographic identity checked; candidates are discovered identifiers awaiting resolution. What “verified” means · Coverage ledger

Website reconstruction	Browser agents
Can AI agents rebuild websites through browser exploration? ★ Star on GitHub	Open-source benchmark for browser AI agents on daily tasks. ★ Star on GitHub

Featured: Awesome Reverse Engineering Anything — a collection of tools, case studies and research for reverse-engineering websites, games, apps, native binaries, firmware and protocols.

cd Awesome-CUA
python3 scripts/catalog.py search "grounding" --kind paper --status curated --limit 0 --json
python3 scripts/catalog.py search --kind benchmark --environment web --status curated --limit 0 --json
python3 scripts/catalog.py search --kind dataset --environment android --status curated --limit 0 --json
python3 scripts/catalog.py search --kind framework --status curated --limit 0 --json
python3 scripts/export_rag.py --output dist/rag-documents.jsonl
python3 scripts/catalog.py get arxiv:2404.07972 --related --json

2,170 curated resources · 1,673 reviewed papers and surveys · 104 discovery documents

Evidence through 2026-11-07T05:19:53Z. 453 records awaiting review have verified bibliographic metadata, and 2,062 discovered candidates await resolution. 1,308 scope exclusions retain their original metadata in the archive.

By platform: Web 858 · Desktop 364 · Windows 68 · macOS 36 · Linux 57 · Android 350 · iOS 26 · HarmonyOS 4 · Cross-platform 206

Coverage spans web, desktop, Android, iOS, Windows, macOS, Linux, and cross-platform work. Topics include grounding, screen understanding, planning, memory, long-horizon workflows, reinforcement learning, data generation, safety, robustness, and evaluation.

Download the catalog, feeds and RAG documents; the data hub also lists sources, review states and schemas.

• llms.txt - Navigation file for language models.
• llms-full.txt - Curated annotations in Markdown.
• Data hub - Catalog, RAG documents, citations, topics, comparisons, sources and schemas in six groups.
• Catalog JSONL - Complete catalog with one resource per line.
• Download manifest - Export counts, byte sizes and SHA-256 checksums.
• Atom feed - Research updates as an Atom feed.
• Hugging Face dataset - Read-only mirror; metadata_only and github_candidates are unreviewed.
• Agent and RAG guide - Guide for agents and RAG systems.

Clone the complete catalog:

git clone https://github.com/reacher-z/Awesome-CUA.git

Agent instructions · Retrieval documents

Discovery is scheduled every hour at minute 7 UTC. Actual scan results · Schedule and safeguards.

Read the research survey — Methods, evaluation, human agency, and reusable research assets. English working manuscript with a versioned bibliography and reading register.

The most-mentioned curated resources per category: the count is how many other cataloged resources mention each one in their reviewed first-party source (README, model or dataset card). It reflects connectedness inside this catalog. Every category page lists all reviewed entries. Surveys come first, newest first.

Newest editorial approvals of models, datasets, benchmarks, systems, and tools (newest first). New papers appear by date in the topic hubs.

Browse the interactive website to search and filter all resources, inspect source evidence, switch between English and Chinese, or download the complete catalog. Website development and publishing.

The English data hub and 中文数据中心 organize public downloads into six groups: complete catalog, RAG documents, citations, topics and comparisons, sources and review status, and schemas. Cloning still gives you the complete source catalog; the hub provides exports from its published snapshot.

The download manifest records each listed export's actual count and unit, scope, byte size, and SHA-256 fingerprint. Compare catalog_sha256 across manifests before combining files from different downloads.

Reviewed research · Computer-use agent guides · Static resource directory · Search and AI discovery

• Catalog JSONL: one resource per line, with stable IDs, typed fields, provenance, scoped licenses, and review status.
• Reference graph: outbound references from primary sources.
• Discovery registry and candidates: source snapshots and where each identifier was found.
• Review queue: unresolved evidence, unindexed discoveries, and proposals requiring editorial work. Discovered links reviewed as non-resources or as other addresses of indexed resources stay listed with a resolution and their reason.
• JSON Schema and coverage JSON: machine-checkable contracts and coverage accounting.
• BibTeX and CSL JSON: reviewed-paper citations with recorded authors, dates, and versions. Each resource page also provides its own record.json.

# Python 3.11+. Search and export require only the standard library.
cd Awesome-CUA
python3 scripts/catalog.py search "grounding" --kind model
python3 scripts/catalog.py search --kind dataset --environment android
python3 scripts/catalog.py search --kind product --status curated --json
python3 scripts/catalog.py get arxiv:2404.07972 --related --json
python3 scripts/catalog.py build
# Creates dist/catalog.sqlite and dist/catalog.json, with no network calls.
python3 scripts/export_rag.py --output dist/rag-documents.jsonl
python3 scripts/export_citations.py --format bibtex --output dist/citations.bib
# Add --include-metadata to include every active record, with its review state intact.

Use SQLite to join resources, environment/topic facets, and reviewed relations. See query examples. data/catalog.jsonl is committed; SQLite and JSON array exports are reproducible local build products.

A paper, repository, dataset release, model checkpoint, product, and publisher can be separate related resources. Model families and individual checkpoints are explicitly linked where reviewed.

Code, data, weights, and document licenses are separate. Missing values are null.

python3 -m venv .venv
.venv/bin/python -m pip install --require-hashes -r requirements-dev.txt
.venv/bin/python scripts/catalog.py validate
.venv/bin/python scripts/catalog.py build --check
.venv/bin/python -m unittest discover -s tests -v
.venv/bin/python scripts/catalog.py build
node --test tests/web.test.mjs
.venv/bin/python scripts/build_site.py

These commands match the checks that Catalog quality runs on every push and pull request, in the same order. They need Python 3.11+ and Node.js 24+, the engines version in package.json (CI uses Python 3.12 and Node.js 24). The web tests use only the built-in Node.js test runner, so nothing needs to be installed with npm. npm run test:web and npm run build:web are shortcuts for the last two commands.

Network discovery and verification are explicit commands; ordinary builds are offline. Changed primary snapshots require another editorial review before publication. Maintenance guide · Methodology · Data dictionary

The source registry credits the community collections and surveys used for discovery. Bibliographic records use retained arXiv, publisher, and author sources; publication observations preserve edition-specific titles, authors, and date precision. Selected assets are checked against publisher repositories, documentation, and model/dataset cards. Upstream READMEs, papers, model weights, and datasets are not redistributed here.

Original repository code and annotations use the MIT license. Linked resources retain their own terms.

To cite the catalog, use CITATION.cff or GitHub's "Cite this repository" button. The data changes with every scan, so pin the exact version you used. Each v* tag has a data release with versioned downloads built from the tagged commit: the catalog as JSONL, JSON and SQLite, the coverage ledger, topic and profile exports, and their SHA256SUMS. Cite the release tag, or record the commit from git rev-parse HEAD in your clone, or link files at that commit, such as https://github.com/reacher-z/Awesome-CUA/blob/<commit>/data/catalog.jsonl. Cutting a release.