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Guardrails
LLM Frameworks
An open-source toolkit for adding programmable guardrails to LLM-based conversational applications
Python
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Latest v0.22.0 · 12d ago
Security brief →
Features
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Add customizable guardrails to control LLM output behavior
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Protect against jailbreaks, prompt injections, and other LLM vulnerabilities
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Enable safe integration of models with external tools and services
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Steer conversations along predefined paths for consistent interaction
Config change
v0.22.0
Breaking risk
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Auth
Usage reporting, LangChain optional, IORails enhancements
v0.21.0
Breaking risk
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Breaking changes
- Removed stream_usage from streaming metadata capture
Notable features
- IORails Input/Output rail engine with parallel execution
- check_async method for standalone rail validation
- OpenAI-compatible server with v1/models endpoint
v0.20.0
Breaking risk
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Breaking changes
- Removed streaming field from configuration
Notable features
- Nemotron reasoning-capable content safety models
- GLiNER for open-source PII detection
- Multilingual refusal messages in content safety rails
v0.19.0
Breaking risk
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Notable features
- LangChain 1.x compatibility
- Content blocks API support for reasoning traces and tool calls
v0.18.0
Breaking risk
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Breaking changes
- Reasoning traces extracted to separate field instead of prepended to message
- stream_async now raises error if output rail streaming disabled
- Replaced reasoning trace extraction with LangChain additional_kwargs
Notable features
- BotThinking events for processing reasoning traces
- Embedding providers: Azure OpenAI, Cohere, Google
- In-memory LFU caches for content-safety, topic-control, jailbreak models
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