Agentes de IA
WorkArena
WorkArena: ServiceNow's benchmark for how well web agents handle everyday knowledge-work tasks.
★ 275 estrelasNOASSERTION
Ver o guia →ClawBench é um projeto de agentes de IA de código aberto de TIGER-AI-Lab (TIGER-AI-Lab/ClawBench no GitHub). Tem 975 estrelas no GitHub e 62 forks, é publicado sob a licença Apache-2.0 e foi atualizado pela última vez no GitHub em 5 de outubro de 2026.
An open benchmark for browser AI agents on everyday tasks.
Start with one concrete task rather than a grand architecture.
Clone the repo and check the examples folder for a quickstart.
Install packages, set environment variables, and point it at your model.
Execute the demo workflow, then adapt it to your own data.
Daily chores, not toy problems.
Compare agents apples-to-apples.
Purpose-built for web agents.
Inspect and extend the tasks.
From TIGER-AI-Lab.
Free to use and adapt.
Download the source and move into the project folder.
git clone https://github.com/TIGER-AI-Lab/ClawBench.git
cd ClawBenchCreate a virtual environment and install the requirements.
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txtTreat these as starting points - the README documents the exact commands.
python examples/quickstart.pyTIGER-AI-Lab/ClawBench is its home on GitHub, with 62 forks so far. The contributors tab and recent commits show how actively it is maintained.
Yes - it is Apache-2.0 licensed, so it is free for commercial use with attribution.
Running the evaluation is free; the model under test may use your own paid API keys.
ClawBench is one specific benchmark for browser agents; Harbor is a framework for running eval programs. Run ClawBench inside Harbor-style workflows for the best of both.
Follow the README quickstart, then browse the examples directory for patterns that match what you want to build.
WorkArena: ServiceNow's benchmark for how well web agents handle everyday knowledge-work tasks.
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Grátis e aberto: pegue o código-fonte, dê uma estrela no repositório e comece a criar.