AIエージェント
deepteam
Open-source red-teaming framework that stress-tests LLMs and AI agents.
★ 3.0k スターApache-2.0
ガイドを見る →deepevalは、confident-aiが公開しているオープンソースのAIエージェントプロジェクトです（GitHub: confident-ai/deepeval）。GitHubスターは18,695、フォークは2,032で、Apache-2.0ライセンスの下で公開されています。GitHubでの最終更新は2026年10月7日です。
Unit tests for LLMs — evaluate before you ship.
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.
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LibreAIカタログに掲載されたAIエージェントプロジェクトの中で最もスターの多いもの — deepevalが合わなかった場合に検討したい、現実的な候補リストです。
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