Agentes de IA
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An open-source speech language model from Hume.
★ 1,0k estrellasNOASSERTION
Ver la guía →autoevals es un proyecto de agentes de IA de código abierto de braintrustdata (braintrustdata/autoevals en GitHub). Tiene 1.050 estrellas en GitHub y 88 bifurcaciones, se publica bajo la licencia MIT y se actualizó por última vez en GitHub el 7 de octubre de 2026.
Fast, best-practice evaluations for AI model outputs.
Write down the task or workflow you want the agent to handle.
Clone the repository and read the README's prerequisites.
Install dependencies and configure keys or endpoints.
Run the sample script and check the output against the docs.
Common eval patterns out of the box.
Braintrust's eval expertise packaged.
A library, not a platform.
Use models to score models.
Drops into test suites.
Free for any use.
Download the source and move into the project folder.
git clone https://github.com/braintrustdata/autoevals.git
cd autoevalsCreate 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.pyThe project lives at braintrustdata/autoevals and has attracted 88 forks. Browse its commit history and contributor list for a sense of maintenance pace.
Yes - it is MIT licensed, so you can use, modify, and distribute it freely, including in commercial projects.
Running the evaluation is free; the model under test may use your own paid API keys.
AutoEvals is a lightweight scoring library; Harbor is a full evaluation framework. Start with AutoEvals for quick scoring, graduate to Harbor for a complete eval program.
The README and the official docs site are the fastest path: run the quickstart example first, then adapt it to your use case.
An open-source speech language model from Hume.
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An open-source speech language model from Hume.
Gratis y abierto: consigue el código fuente, dale una estrella al repositorio y empieza a crear.