Building custom model provider for Strands Agents with LLMs hosted on SageMaker AI endpoints

This article explains how to build custom model parsers for Strands Agents when deploying LLMs on SageMaker AI endpoints using custom serving frameworks like SGLang, vLLM, or TorchServe.

• Custom serving frameworks return OpenAI-compatible responses; Strands expects Bedrock Messages API format
• Response format mismatch causes parsing errors like "TypeError: 'NoneType' object is not subscriptable"
• Use awslabs/ml-container-creator to automate SageMaker BYOC deployment projects
• Deploy Llama 3.1 with SGLang on SageMaker using generated build and deployment scripts
• Extend SageMakerAIModel class with custom stream() method to translate response formats
• Custom parsers enable seamless integration between custom-hosted models and Strands Agents SDK
• Complete implementation available in companion GitHub repository

Custom model parsers solve the compatibility gap between SageMaker's flexible model hosting and Strands Agents' API expectations, enabling organizations to use preferred serving frameworks without sacrificing agent integration.