AIエージェント
deepeval
Unit tests for LLMs — evaluate before you ship.
★ 18.7k スターApache-2.0
ガイドを見る →guardrailsは、guardrails-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: guardrails-ai/guardrails）。GitHubスターは7,496、フォークは715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6日です。
Put guardrails on LLMs: validate outputs before they reach users.
Write down the outcome you want in one sentence.
Clone it locally and check the README.
Get dependencies and tooling in place per the docs.
Follow the minimal example, then adapt it to your needs.
Check LLM output against your rules.
Express constraints declaratively.
Re-ask when the model strays.
Enforce schemas, not hopes.
Catch disallowed content pre-send.
Fits the standard LLM stack.
Download the source and move into the project folder.
git clone https://github.com/guardrails-ai/guardrails.git
cd guardrailsIsolate dependencies, then install requirements.
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txtLaunch using the entry point shown in the README.
python -m guardrails # check README for the exact commandIt is an open-source project at guardrails-ai/guardrails with 715 forks. Check the contributors tab and recent commits to judge how active maintenance is.
Yes — Apache 2.0 allows commercial use and modification. Keep the license and notice files, and note it includes a patent grant.
You define rules for LLM outputs; the framework validates and retries or corrects when outputs break them.
Validation adds some latency — the trade-off for safer outputs. Measure it for your use case.
Clone the repo and follow the README quickstart — the docs at https://www.guardrailsai.com/docs walk through setup.
Unit tests for LLMs — evaluate before you ship.
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