Sample Apps for AWS Generative AI CDK Constructs
February 12, 2026 ยท View on GitHub
This repo provides samples to demonstrate how to build your own Generative AI solutions using AWS Generative AI CDK Constructs.
Getting started
| Use Case | Description | Type | Language |
|---|---|---|---|
| SageMaker JumpStart model | This sample provides a sample application which deploys a SageMaker real-time endpoint hosting a Llama 2 foundation model developed by Meta from Amazon JumpStart, and an AWS Lambda function to run inference requests against that endpoint. | Backend | TypeScript |
| SageMaker Hugging Face model | This sample provides a sample application which deploys a SageMaker real-time endpoint hosting a model (Mistral 7B) from Hugging Face, and an AWS Lambda function to run inference requests against that endpoint. | Backend | TypeScript |
| SageMaker Hugging Face model on AWS Inferentia2 | This sample provides a sample application which deploys a SageMaker real-time endpoint hosting a model (Zephyr 7B) from Hugging Face, and an AWS Lambda function to run inference requests against that endpoint. This sample uses Inferentia 2 as the hardware accelerator. | Backend | TypeScript |
| SageMaker custom endpoint | This sample provides a sample application which deploys a SageMaker real-time endpoint hosting a model with artifacts stored in an Amazon Simple Storage Service (S3) bucket, and an AWS Lambda function to run inference requests against that endpoint. This sample uses Inferentia2 as the hardware accelerator. | Backend | TypeScript |
| SageMaker multimodal custom endpoint | This sample provides a sample application which deploys a SageMaker real-time endpoint hosting llava-1.5-7b, with artifacts stored in an Amazon Simple Storage Service (S3) bucket, a custom inference script, and an AWS Lambda function to run inference requests against that endpoint. | Backend | TypeScript |
| SageMaker image to video endpoint | This sample provides a sample application which deploys a SageMaker async endpoint hosting stable-video-diffusion-img2vid-xt-1-1, with artifacts stored in an Amazon Simple Storage Service (S3) bucket, a custom inference script, and an AWS Lambda function to run inference requests against that endpoint. | Backend | TypeScript |
| LLM on SageMaker in GovCloud PDT | This sample provides a sample application which deploys a SageMaker real-time endpoint hosting Falcon-40b on GovCloud PDT. | Backend | TypeScript |
| Amazon Bedrock Agents | This sample provides a sample application which deploys an Amazon Bedrock Agent and Knowledge Base backed by an OpenSearch Serverless Collection and documents in S3. It demonstrates how to use the Amazon Bedrock CDK construct. | Backend | TypeScript |
| Python Samples | This project showcases the utilization of the 'generative-ai-cdk-constructs' package from the Python Package Index (PyPI). | Backend | Python |
| .NET Samples | This project showcases the utilization of the 'Cdklabs.GenerativeAiCdkConstructs' package from nuget library. | Backend | .NET |
| Contract Compliance Analysis | This prototype can potentially help you make your contract compliance process more efficient, leveraging Generative Artificial Intelligence (GenAI) to evaluate contract clauses against predefined guidelines and to provide feedback on adherence to standards applicable to you, based on your instructions. This is achieved through workflows that leverages Large Language Models via Amazon Bedrock. | Backend + Frontend | Python for Backend, TypeScript (React) for Frontend |
| Multimodal RAG solution | This project showcases Amazon Bedrock multimodal features using Amazon Bedrock Data Automation (BDA) combined with Amazon Bedrock Knowledge Bases for a RAG-based chatbot. | Backend + Frontend | Python for Backend, TypeScript (React) for Frontend |
| RFP Answers with GenAI | This project automates RFP (Request for Proposal) responses by using Amazon Bedrock's LLMs to generate answers based on your company's existing knowledge base. Key features include direct Excel file processing for questions and answers, Bedrock AgentCore Runtime with agents and tools for sophisticated question answering, and S3 Vectors for simplified vector storage. | Backend + Frontend | Python for Backend, TypeScript (React) for Frontend |
| Code Expert | This project addresses the scalability limitations of manual code reviews by leveraging artificial intelligence to perform expert code reviews automatically. It leverages the Bedrock Batch Step Functions CDK construct. | Backend | Python for Backend and Demo, TypeScript for CDK |
| Bedrock Agent UI Wrapper | This sample provides a CDK construct that creates an API layer and frontend application for Amazon Bedrock Agents. It includes authentication with Amazon Cognito, agent trace streaming, and can be deployed locally or on ECS Fargate. | API layer + Frontend | Python |
| Stateless MCP Server on AWS Lambda | Sample MCP Server running natively on AWS Lambda and API Gateway without any extra bridging components or custom transports and a test MCP client. | API layer | TypeScript |
| Stateless MCP Server on ECS | Sample stateless MCP Server running natively on ECS Fargate and ALB without any extra bridging components or custom transports and a test MCP client. | API layer | TypeScript |
| Stateful MCP Server on ECS | Sample stateful MCP Server running natively on ECS Fargate and ALB without any extra bridging components or custom transports and a test MCP client. | API layer | TypeScript |
| Speech to speech | Real-time Speech to Speech solution with Amazon Nova Sonic, featuring a Java WebSocket server and React frontend. | Backend + Frontend | Python for Backend, TypeScript (React) for Frontend |
| Strands Lambda Durable | This folder contains demos showcasing the Strands Agents Python SDK integrated with AWS Lambda durable execution. | Backend | Python for Lambda, Ts for IaC |
Contributing
Please refer to the CONTRIBUTING document for further details on contributing to this repository.