KI-Agenten
deepeval
Unit tests for LLMs — evaluate before you ship.
★ 18,7 Tsd. SterneApache-2.0
Anleitung ansehen →guardrails ist ein Open-Source-Projekt aus der Kategorie KI-Agenten von guardrails-ai (guardrails-ai/guardrails auf GitHub). Es hat 7.496 Sterne auf GitHub und 715 Forks, wird unter der Lizenz Apache-2.0 veröffentlicht und wurde zuletzt am 6. Oktober 2026 auf GitHub aktualisiert.
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.
Terminal security that stops malicious prompts, URLs, and payloads before they run.
Detect prompt injections and jailbreaks before they hit your LLM.
Programmable guardrails to keep LLM systems safe and on rails.
Fast, best-practice evaluations for AI model outputs.
Scan AI models for serialization attacks before you load them.
Die anderen Projekte der Kategorie KI-Agenten mit den meisten Sternen im LibreAI-Katalog — die realistische Auswahl, falls guardrails nicht das Richtige ist.
A personal AI that really does things — on any OS, with your data staying yours.
An agentic skills framework & software development methodology that works.
Agent harness optimization system with skills, memory and security for Claude Code, Codex and more
The agent that grows with you — a Python framework for adaptive AI agents.
DeepSeek's plugin-based agent harness where everything is a plugin.
Verwandte Projekte der Kategorie KI-Agenten im Katalog, die guardrails bei den Sternen am nächsten stehen — zum Weiterstöbern auf beiden Seiten.
Reef — infrastructure for continually self-improving agents: weight-training and harness-evolving deployments.
OpenTelemetry-native observability for LLM apps.
Honcho — Plastic Labs' memory library for stateful agents that learn about their users over time.
Programmable guardrails to keep LLM systems safe and on rails.
A batteries-included browser API so agents can automate the web.
Voyager — the open-ended embodied Minecraft agent that writes its own skills and keeps learning for life.
Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.