NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational applications. Guardrails (or "rails" for short) are specific ways of controlling the output of a large language model, such as not talking about politics, responding in a particular way to specific user requests, following a predefined dialog path, using a particular language style, extracting structured data, and more. NeMo Guardrails enables developers building LLM-based applications to easily add programmable guardrails between the application code and the LLM. Key benefits of adding programmable guardrails include: Building Trustworthy, Safe, and Secure LLM-based Applications: you can define rails to guide and safeguard conversations; you can choose to define the behavior of your LLM-based application on specific topics and prevent it from engaging in discussions on unwanted topics. Connecting models, chains, and other services securely: you can connect an LLM to other services (a.k.a. tools) seamlessly and securely. Controllable dialog: you can steer the LLM to follow pre-defined conversational paths, allowing you to design the interaction following conversation design best practices and enforce standard operating procedures (e.g., authentication, support). NeMo Guardrails provides several mechanisms for protecting an LLM-powered chat application against common LLM vulnerabilities, such as jailbreaks and prompt injections. NeMo Guardrails integrates seamlessly with LangChain. You can easily wrap a guardrails configuration around a LangChain chain (or any Runnable). You can also call a LangChain chain from within a guardrails configuration.
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Handle multi-step workflows autonomously
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AI agents combine large language models with tools, memory, and decision-making logic to autonomously complete multi-step tasks without constant human guidance.
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APIs, databases, external services the agent can call
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Short-term (conversation) and long-term (persistent) memory
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