DevGenius is an AI-powered application that transforms project ideas into complete, ready-to-deploy AWS solutions. It leverages Amazon Bedrock and Claude AI models to provide architecture diagrams, cost estimates, infrastructure as code, and comprehensive technical documentation.

Conversational Solution Architecture Building: DevGenius enables customers to design solution architectures in a conversational manner. Users can create architecture diagrams (in draw.io format) and refine them interactively. Once the design is finalized, they can generate end-to-end code automation using CDK or CloudFormation templates, and deploy it in their AWS account with a single click. Additionally, customers can receive cost estimates for running the architecture in production, along with detailed documentation for the solution.
Build Solution Architecture from Whiteboard Drawings: For customers who already have their architecture in image form (e.g., whiteboard drawings), DevGenius allows them to upload the image. Once uploaded, DevGenius analyzes the architecture and provides a detailed explanation. Customer can then refine the design conversationally and, once finalized, generate end-to-end code automation using CDK or CloudFormation. Cost estimates and comprehensive documentation are also available.
DevGenius is built using a modern cloud-native architecture:
bash
git clone https://github.com/aws-samples/sample-devgenius-aws-solution-builder.git devgenius
cd devgenius
bash
npm install
bash
export AWS_REGION="us-west-2"
export BEDROCK_AGENT_ID="<REPLACE_ME_BEDROCK_AGENT_ID>"
export BEDROCK_AGENT_ALIAS_ID="<REPLACE_ME_BEDROCK_AGENT_ALIAS_ID>"
export S3_BUCKET_NAME="<REPLACE_ME_S3_BUCKET_NAME>"
export CONVERSATION_TABLE_NAME="<REPLACE_ME_CONVERSATION_TABLE_NAME>"
export FEEDBACK_TABLE_NAME="<REPLACE_ME_FEEDBACK_TABLE_NAME>"
export SESSION_TABLE_NAME="<REPLACE_ME_SESSION_TABLE_NAME>"
bash
streamlit run chatbot/agent.py
Build and run using Docker after replacing all the values that follow the pattern :
cd chatbot
docker build -t devgenius .
docker run -p 8501:8501 \
-e AWS_REGION="us-west-2" \
-e BEDROCK_AGENT_ID="<REPLACE_ME_BEDROCK_AGENT_ID>" \
-e BEDROCK_AGENT_ALIAS_ID="<REPLACE_ME_BEDROCK_AGENT_ALIAS_ID>" \
-e S3_BUCKET_NAME="<REPLACE_ME_S3_BUCKET_NAME>" \
-e CONVERSATION_TABLE_NAME="<REPLACE_ME_CONVERSATION_TABLE_NAME>" \
-e FEEDBACK_TABLE_NAME="<REPLACE_ME_FEEDBACK_TABLE_NAME>" \
-e SESSION_TABLE_NAME="<REPLACE_ME_SESSION_TABLE_NAME>" \
devgenius
DevGenius includes a CDK stack that deploys all required infrastructure:
bash
npm install -g aws-cdk
bash
npm install
bash
cdk bootstrap
bash
cdk deploy --all --context stackName=devgenius
bash
cdk destroy --all --context stackName=devgenius
This command will remove all AWS resources created by the stack. You'll be prompted to confirm before the deletion proceeds. Note that this action is irreversible and will delete all application data stored in the deployed resources.
The CDK stack deploys:
DevGenius uses Amazon Bedrock Agents with a custom Knowledge Base containing AWS documentation, whitepapers, and blogs. The agent is configured with specialized prompts to generate AWS solutions following best practices.
Knowledge base sources include:
Architecture information is stored as vector embeddings in Amazon OpenSearch Serverless, enabling semantic search and retrieval of relevant architectural patterns.
The application can generate both AWS CDK (TypeScript) and CloudFormation (YAML) templates for deploying the proposed solutions.
├── chatbot/ # Code for chatbot
├── agent.py # Main application entry point
├── cost_estimate_widget.py # Cost estimation functionality
├── generate_arch_widget.py # Architecture diagram generation
├── generate_cdk_widget.py # CDK code generation
├── generate_cfn_widget.py # CloudFormation template generation
├── generate_doc_widget.py # Documentation generation
├── layout.py # UI layout components
├── styles.py # UI styling
├── utils.py # Utility functions
├── Dockerfile # Container definition
├── requirements.txt # Python dependencies
├── lib/ # CDK stack definition
├── layer/ # Lambda layer containing dependencies
├── lambda/ # Lambda function code
└── edge-lambda/ # CloudFront Lambda@Edge function
DevGenius includes several security features:
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$ claude mcp add sample-devgenius-aws-solution-builder \
-- python -m otcore.mcp_server <graph>