KI-Agenten
archestra
Archestra: the enterprise AI platform — chat UI, LLM gateway, and MCP gateway with guardrails
★ 4,4 Tsd. SterneNOASSERTION
Anleitung ansehen →Guardrails ist ein Open-Source-Projekt aus der Kategorie KI-Agenten von NVIDIA-NeMo (NVIDIA-NeMo/Guardrails auf GitHub). Es hat 7.264 Sterne auf GitHub und 865 Forks, wird unter der Lizenz NOASSERTION veröffentlicht und wurde zuletzt am 7. Oktober 2026 auf GitHub aktualisiert.
Programmable guardrails to keep LLM systems safe and on rails.
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.
Define behavior rules in config.
Validate both sides of the conversation.
Keep agents on permitted subjects.
From the NeMo team.
Built for dialogue systems.
Pushed in October 2026.
Download the source and move into the project folder.
git clone https://github.com/NVIDIA-NeMo/Guardrails.git
cd GuardrailsCreate 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 NVIDIA-NeMo/Guardrails and has attracted 865 forks. Browse its commit history and contributor list for a sense of maintenance pace.
GitHub does not assert a license for this repo, so treat the code as all rights reserved until the author adds one.
The toolkit is free; only the models it guards might come from a paid provider.
Different jobs: Guardrails enforces runtime safety rules; PyRIT probes for vulnerabilities before deployment. Mature teams use both - test with PyRIT, enforce with Guardrails.
The README and the official docs site are the fastest path: run the quickstart example first, then adapt it to your use case.
Archestra: the enterprise AI platform — chat UI, LLM gateway, and MCP gateway with guardrails
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Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.