3 个仓库
Comprehensive systems for enforcing safety and structural constraints on both AI inputs and outputs.
Distinct from Output Guardrails: Covers the overall implementation of both input and output guardrails, rather than just one side.
Explore 3 awesome GitHub repositories matching security & cryptography · LLM Guardrail Frameworks. Refine with filters or upvote what's useful.
NeMo-Guardrails is a toolkit for adding programmable safety constraints and dialogue boundaries to large language model conversational systems. It functions as security middleware that intercepts inputs and outputs to block prompt injections, jailbreaks, and sensitive data leaks, while providing a conversational dialogue manager to define structured interaction flows through configuration files. The framework includes a hallucination filter to screen model outputs for factual accuracy and a specialized modeling language for defining conversational flows and constraints. It provides capabiliti
Provides a comprehensive framework for enforcing safety and structural constraints on LLM inputs and outputs.
Agency Swarm is a multi-agent orchestration framework and development kit designed to coordinate specialized AI agents through defined communication patterns and handoffs. It functions as a system for managing agent swarms, providing an API gateway to expose these coordinated collectives as production-ready HTTP endpoints. The project distinguishes itself through its Model Context Protocol integration layer, which connects agents to external data sources and capabilities. It implements specialized orchestration patterns, such as the orchestrator-worker model and role-based delegation, to tran
Enforces input and output validation through schemas and checkpoints to ensure safe responses.
LLM Guard is a security firewall and guardrail framework designed to scan and sanitize inputs and outputs for large language models. It functions as a proxy gateway and security layer to block prompt injections, toxicity, and sensitive data leakage while ensuring that model interactions remain compliant with organizational policies. The system distinguishes itself through a modular scanner pipeline that utilizes local model orchestration to eliminate external network dependencies. It supports real-time security filtering via streaming chunk analysis and implements a fail-fast execution model
Provides a set of scanners for validating the consistency, relevance, and safety of LLM content.