Agentes de IA
promptfoo
Test prompts, agents and RAGs — LLM evals, red teaming and model comparisons from the CLI
★ 25,8 mil estrelasMIT
Ver o guia →agentdojo é um projeto de agentes de IA de código aberto de ethz-spylab (ethz-spylab/agentdojo no GitHub). Tem 897 estrelas no GitHub e 241 forks, é publicado sob a licença MIT e foi atualizado pela última vez no GitHub em 2 de junho de 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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Grátis e aberto: pegue o código-fonte, dê uma estrela no repositório e comece a criar.