AI Agents
WorkArena
WorkArena: ServiceNow's benchmark for how well web agents handle everyday knowledge-work tasks.
★ 275 starsNOASSERTION
View guide →A rigorous benchmark for LLMs acting as agents.
AgentBench is an open-source ai agents project by THUDM (THUDM/AgentBench on GitHub). It has 3,765 GitHub stars and 288 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 8 February 2026.
Pick one concrete task so you can judge whether the project fits.
Clone the repo and check the prerequisites listed in the README.
Install what it needs and configure credentials or model access.
Run a minimal example end to end before wiring it into your stack.
Tests models as actors in environments.
Peer-reviewed benchmark design.
Diverse tasks beyond static QA.
From a leading academic lab.
Runnable evaluation code.
Permissive licensing.
Download the source and move into the project folder.
git clone https://github.com/THUDM/AgentBench.git
cd AgentBenchSet up a virtual environment and install dependencies.
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txtEvaluate a model on a task subset per the docs.
# see README for the task runnersIt is maintained by THUDM at THUDM/AgentBench, with 288 forks.
Yes. It is released under the Apache-2.0 license, which permits commercial use, modification, and redistribution. Read the LICENSE file for the exact terms.
You need model access to evaluate; the harness itself is free.
AgentBench covers diverse agentic environments; SWE-bench targets real-world software engineering specifically.
Follow the repo docs to run a task subset against your model.
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Free and open — grab the source, star the repo, and start creating.