Agents
deepteam
Open-source red-teaming framework that stress-tests LLMs and AI agents.
★ 3.0k starsApache-2.0
View guide →Unit tests for LLMs — evaluate before you ship.
deepeval is an open-source ai agents project by confident-ai (confident-ai/deepeval on GitHub). It has 18,695 GitHub stars and 2,032 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 7 October 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.
A library of evaluation metrics for model outputs.
Integrates with pytest-style workflows.
Catch quality drops between model versions.
Maintained by confident-ai with docs and cloud options.
Apache-2.0 licensed.
Designed to run in automated pipelines.
Install DeepEval from PyPI.
pip install deepevalDefine metrics over your model's outputs in a test file.
# see README for metric examples
# test_example.pyExecute the evaluation suite.
deepeval test run test_example.pyIt is maintained by confident-ai at confident-ai/deepeval, with 2,032 forks.
The open-source framework is free under Apache-2.0; a cloud tier also exists.
Core evals run locally; some metrics may call models through your own API keys.
Compare with Ragas, LangSmith evals, and others on metrics coverage and workflow fit.
Install the package, write a test file with your metrics, and run it.
Open-source red-teaming framework that stress-tests LLMs and AI agents.
Put guardrails on LLMs: validate outputs before they reach users.
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