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NVIDIA-NeMo/Guardrails

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5,680 星标·601 分支·Python·other·19 次浏览docs.nvidia.com/nemo/guardrails/latest/index.html↗

Guardrails

Features

  • AI Safety Guardrails - Provides an open-source framework for defining and enforcing safety policies on LLM inputs, outputs, and dialog flows.
  • Guardrail Servers - Ships a dedicated HTTP server that exposes guardrail configurations via a chat completion endpoint.
  • Guardrailed Inference Executions - Provides a Python API and server to run LLM inference with enforced safety guardrails.
  • Guardrail-Enforced - Exposes an HTTP server with a chat completion endpoint that enforces guardrail policies.
  • Conversation Flow Design - Defines and enforces conversational paths and interaction patterns for multi-turn LLM conversations.
  • Dialog Flow Engines - Ships a Colang-based runtime engine for steering multi-turn LLM conversations through predefined interaction patterns.
  • Conversational Dialog Flow Languages - Defines conversational guardrails using a custom modeling language for dialog states and transitions.
  • Input Filters - Applies checks to user messages before they reach the LLM, allowing rejection or alteration of harmful input.
  • LLM Provider Integrations - Integrates with OpenAI, Anthropic, HuggingFace, and NVIDIA NIM through a unified interface.
  • Provider Abstraction Layers - Wraps multiple model providers behind a common interface that normalizes API differences.
  • LLM Input Safety Interceptions - Intercepts user inputs before they reach the model and blocks those violating defined safety or security policies.
  • Agent Steering Languages - Uses a modeling language to define conversational paths and enforce predefined interaction patterns.
  • Conversational Dialog Guardrails - Enforces conversational policies that control the flow and content of multi-turn LLM interactions.
  • Middleware-Style Guardrail Pipelines - Processes LLM requests and responses through a sequential chain of guardrail modules.
  • Content Moderation - Inspects generated responses and can reject or modify them to remove unsafe, sensitive, or off-topic content.
  • Security Policy Enforcers - Enforces configurable safety and security policies on LLM inputs and outputs to block harmful content.
  • Model Safety Filters - Inspects model responses against configurable rules and blocks or modifies content that violates safety policies.
  • Guardrail Policy Configurations - Declares guardrail rules, model connections, and dialog flows in YAML files parsed at runtime.
  • LLM Behavior Policy Configurations - Defines LLM behavior policies using YAML configuration files and Colang dialog flows.
  • Guardrail Plugin Architectures - Organizes safety checks as composable plugins that intercept inputs and outputs in the LLM pipeline.
  • Guardrail Execution Traces - Traces which rails activate during conversations and monitors LLM calls to debug flow execution and identify bottlenecks.
  • Guarded Tool Calls - Inspects inputs and outputs of custom actions called by the LLM to enforce safety and security constraints.
  • Guardrail Reliability Evaluation - Measures the accuracy and performance of safety, fact-checking, moderation, and hallucination guardrails.
  • LangChain Guardrail Integrations - Connects safety guardrails to LangChain chains, runnables, and LangGraph workflows.
  • LLM Tracing Systems - Captures user inputs and model outputs in trace events for detailed analysis with a configurable privacy toggle.
  • GenAI Trace Exporters - Exports guardrail execution traces using OpenTelemetry semantic conventions for GenAI observability.
  • RAG Context Pruning - Inspects and filters retrieved chunks before they are used to prompt an LLM in RAG pipelines.
  • Context Validators - Checks and filters retrieved chunks before they are used to prompt an LLM, preventing unsafe context injection.
  • RAG Context Validations - Intercepts retrieved document chunks and applies configurable checks before they reach the LLM prompt.
  • Production Deployments - Runs guardrail configurations as an API server, inside Docker, or as microservices for remote use.
  • OpenTelemetry-Integrated Monitors - Emits traces, logs, and metrics via OpenTelemetry for end-to-end observability into guardrail execution.
  • OpenTelemetry Exporters - Exports guardrail execution traces using OpenTelemetry semantic conventions for GenAI observability.
  • Security and Sandboxing - Toolkit for adding programmable safety guardrails.

Star 历史

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查看 Guardrails 的所有 30 个替代方案→

常见问题解答

nvidia-nemo/guardrails 的主要功能有哪些?

nvidia-nemo/guardrails 的主要功能包括:AI Safety Guardrails, Guardrail Servers, Guardrailed Inference Executions, Guardrail-Enforced, Conversation Flow Design, Dialog Flow Engines, Conversational Dialog Flow Languages, Input Filters。

nvidia-nemo/guardrails 有哪些开源替代品?

nvidia-nemo/guardrails 的开源替代品包括: katanemo/plano — Plano is an AI agent orchestrator and LLM gateway proxy that unifies access to multiple AI providers through a single… nvidia/isaac-gr00t. voltagent/voltagent. guardrails-ai/guardrails — Guardrails is a Python SDK that wraps calls to large language models with configurable validation pipelines,… nvidia/nemo-guardrails — NeMo-Guardrails is a toolkit for adding programmable safety constraints and dialogue boundaries to large language… superagent-ai/superagent — Superagent is an AI safety platform that protects applications from prompt injections, data leaks, and harmful outputs…