An open-source AI Gateway with integrated guardrails
Hi HN, I've been developing Portkey Gateway, an open-source AI gateway that's now processing billions of tokens daily across 200+ LLMs. Today, we're launching a significant update: integrated Guardrails at the gateway level. Key technical features: 1. Guardrails as middleware: We've implemented a hooks architecture that allows guardrails to act as middleware in the request/response flow. This enables real-time LLM output evaluation and transformation. 2. Flexible orchestration: The gateway can now route requests based on guardrail verdicts. This allows for complex logic like fallbacks…
In plain words
Portkey Gateway is an open-source AI gateway that routes requests across 200+ large language models while processing billions of tokens daily. Its latest update adds integrated guardrails that operate as middleware to evaluate and transform LLM outputs in real time. The system features flexible request routing based on guardrail verdicts, enabling fallbacks and conditional logic, plus a modular plugin system for various guardrail implementations. It is designed for developers and organizations managing multiple LLM integrations who need output validation and quality control at the gateway level.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi HN, I've been developing Portkey Gateway, an open-source AI gateway that's now processing billions of tokens daily across 200+ LLMs. Today, we're launching a significant update: integrated Guardrails at the gateway level. Key technical features: 1. Guardrails as middleware: We've implemented a hooks architecture that allows guardrails to act as middleware in the request/response flow. This enables real-time LLM output evaluation and transformation. 2. Flexible orchestration: The gateway can now route requests based on guardrail verdicts. This allows for complex logic like fallbacks to different models or prompts based on output quality. 3. Plugin system: We've designed a modular plugin system that allows integration of various guardrail implementations (e.g., anthropic/constrained-llm, microsoft/guidance). 4. Stateless design: The guardrails implementation maintains the gateway's stateless nature, ensuring scalability and allowing for easy horizontal scaling. 5. Unified API: Despite the added complexity, we've maintained our unified API across different LLM providers, now extended to include guardrail configurations. Implementation details: * The guardrails are implemented as async functions in the request pipeline. * We use a combination of regex and LLM-based evaluation for output validation. * The system supports both pre-processing (input modification) and post-processing (output filtering/transformation) guardrails. Performance impact: * Latency increase is minimal (<50ms) for most deterministic guardrails. * We've implemented caching mechanisms to reduce repeated evaluations. * Since the gateway lives on the edge, it avoids longer roundtrips Challenges we're still tackling: * Balancing strict guardrails with maintaining model creativity * Standardizing evaluation metrics across different types of guardrails * Handling guardrail false positives/negatives effectively We believe this approach of integrating guardrails at the gateway level provides a powerful tool for managing LLM behavior in production environments. The code is open-source, and we welcome contributions and feedback. We're particularly interested in hearing about specific use cases or challenges you've faced in implementing reliable LLM systems. Detailed documentation: https://portkey.wiki/guardrails What are your thoughts on this approach? Are there specific guardrail implementations or orchestration patterns you'd like to see added?
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