ИИ-агенты
promptfoo
Test prompts, agents and RAGs — LLM evals, red teaming and model comparisons from the CLI
★ 25,8 тыс. звёздMIT
Смотреть руководство →agentdojo — проект с открытым исходным кодом из категории «ИИ-агенты» от ethz-spylab (ethz-spylab/agentdojo на GitHub). У него 897 звёзд на GitHub и 241 форк, он распространяется по лицензии MIT и последний раз обновлялся на GitHub 2 июня 2026.
Test your agents against prompt-injection attacks.
Choose one real workload to test the project against.
Clone it locally and read through the getting-started guide.
Install the stack it needs and set any environment variables.
Execute the starter example and check results before going further.
Benchmark prompt-injection attacks.
Measure how well defenses hold.
Realistic, interactive agent scenarios.
From the SPy Lab.
Extensible evaluation code.
Permissive licensing.
Download the source and move into the project folder.
git clone https://github.com/ethz-spylab/agentdojo.git
cd agentdojoSet up a virtual environment and install dependencies.
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txtEvaluate an agent against the attack suites per the docs.
# see docs for the evaluation commandsIt is maintained by ETH Zurich's SPy Lab at ethz-spylab/agentdojo, with 241 forks.
Yes. It is released under the MIT license, which permits commercial use, modification, and redistribution. Read the LICENSE file for the exact terms.
You need model access for the agents under test; the harness itself is free.
Compare on scenario realism — its dynamic environments are the focus.
Follow the docs to run a baseline evaluation against your agent.
Test prompts, agents and RAGs — LLM evals, red teaming and model comparisons from the CLI
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Бесплатно и открыто — забирайте исходный код, ставьте звезду репозиторию и начинайте создавать.