AI Safety Toolkit: 6 Open Source Tools to Harden Your LLM Stack
An AI safety toolkit for production: guardrails, injection defense, evaluation, and monitoring - with verified stars and setup notes.
💡 What You Will Learn
An AI safety toolkit for production: guardrails, injection defense, evaluation, and monitoring - with verified stars and setup notes.
AI safety is not one tool - it is a stack. You need input filters, output validators, evaluation harnesses, and monitoring, wired together. Here is the open-source AI safety toolkit we actually assemble for production LLM apps.
The Stack
Input defense: llm-guard (protectai/llm-guard, 3,202 stars) for injection detection and PII redaction. Policy layer: NVIDIA NeMo Guardrails (6,880 stars) for programmable input/output rails, or guardrails-ai (7,258 stars) for structured output validation. Evaluation: OpenAI Evals (19,115 stars) and DeepEval (confident-ai/deepeval, 17,443 stars) to measure safety regressions before release. Monitoring: Langfuse (32,615 stars) for tracing, cost, and quality checks in production.
Assembly order: guardrails in front (filter input), llm-guard on the side (injection scoring), evals in CI (safety regression tests), Langfuse watching production. Each tool does one job; together they make the stack auditable - which is what security reviews actually check.
Comparison
| Layer | Tool | Stars |
|---|---|---|
| Input filter | llm-guard | 3,202 |
| Policy rails | NeMo Guardrails | 6,880 |
| Output validation | Guardrails AI | 7,258 |
| Evaluation | OpenAI Evals | 19,115 |
| Monitoring | Langfuse | 32,615 |
FAQ
Q: Is this stack overkill for a side project?
A: Yes - a simple llm-guard filter plus logging is enough until you have real users and real risk.
Q: What should I add first?
A: llm-guard in front of the model and Langfuse for tracing - input defense and visibility are the highest ROI.
