This repository implements an OpenAI-compatible REST API proxy for Amazon Bedrock, enabling applications built for OpenAI SDKs to seamlessly use Bedrock foundation models without code changes. The FastAPI-based service translates standard OpenAI API calls (chat completions, embeddings, models listing) into Amazon Bedrock runtime invocations and returns responses in OpenAI-compatible format. The architecture supports two deployment options: Lambda with API Gateway (serverless, cost-effective) or Fargate with ALB (lowest latency, no cold starts). Key features include streaming responses via SSE, tool/function calling, multimodal inputs, prompt caching for Claude/Nova models, reasoning mode for Claude 3.7 and DeepSeek R1, and interleaved thinking for Claude 4 models. Infrastructure is managed via CloudFormation templates with proper IAM roles and Secrets Manager integration for API key authentication. The codebase demonstrates solid engineering practices with clean separation of concerns, proper error handling, and comprehensive feature support. However, the absence of test files and limited documentation reduce overall quality. The project is actively maintained with recent commits and up-to-date dependencies.

This repository contains a collection of AWS Lambda extension examples and demonstrations across multiple programming languages (Go, Python, Node.js, Java, C#, Rust) and wrapper scripts (Bash, Python, Ruby). Each example illustrates how to build a Lambda extension that integrates deeply with the Lambda execution environment using the Extensions API for lifecycle event registration, and the Logs API/Telemetry API for observability data processing. The extensions perform tasks such as forwarding logs to external destinations (S3, Kinesis, Firehose, Elasticsearch, etc.), collecting and sending telemetry data, implementing custom runtime behavior via wrapper scripts, and caching data from AWS services like DynamoDB and Parameter Store. The repository serves as a practical guide for developers to understand and implement Lambda extensions for observability, security, and governance use cases.

This repository implements a serverless document chat application that allows users to upload PDF documents and ask natural language questions. The backend is composed of AWS Lambda functions orchestrated by Amazon API Gateway, S3, SQS, and DynamoDB. PDF processing uses LangChain with FAISS for vector embeddings and Amazon Bedrock (Claude Sonnet) for LLM inference. The frontend is a React SPA with TypeScript, TailwindCSS, and AWS Amplify authentication. Architecture follows an event-driven serverless pattern: upload triggers S3 event → Lambda extracts PDF metadata, stores in DynamoDB, sends SQS message → another Lambda generates embeddings and stores FAISS index in S3. Chat requests invoke a Lambda that loads the FAISS index, retrieves relevant context, and uses a ConversationalRetrievalChain with DynamoDB-backed memory. The code is clean and follows AWS best practices with Powertools logging, but lacks tests and hardcodes the Bedrock region to us-east-1. It is a solid reference implementation for building a RAG-based Q&A system on AWS.

This repository is a comprehensive, tool-agnostic skills and steering pack that teaches AI coding agents to perform AWS Well-Architected Framework reviews. It contains a curated reference corpus of over 2,000 markdown files (6 pillars, 307 best practices, 27 industry lenses) derived from official AWS documentation, plus per-tool adapters for 15 AI coding assistants (Claude Code, Cursor, Codex, GitHub Copilot, etc.). The repo also ships production-grade supporting tooling: a CI gating tool (wa-ci) that diffs structured review JSON against a baseline and blocks PRs on regressed gaps; an evaluation harness with blind multi-reviewer adversarial adjudication; a crawler to regenerate the reference corpus from AWS docs; and one-command installers. The core skill dispatches six parallel subagents (one per pillar) to keep the 2.2 MB corpus out of any single context window. Despite the extensive content, the repository has notable maintenance challenges: the reference snapshot will drift as AWS documentation evolves (users are responsible for regeneration); lens files are duplicated across `powers/` and `skills/` directories creating a sync hazard; and the flagship skill is explicitly deprecated in favor of a successor in Agent Toolkit for AWS. The evaluation harnesses and CI tooling are well-crafted and reusable, but the sheer scale of markdown files and platform-specific adapters makes ongoing synchronization complex.

This repository contains a full-stack web application deployed on AWS ECS with a CI/CD pipeline. The frontend is a Vue.js application that retrieves product data from a backend Node.js/Express API. The backend scans a DynamoDB table and returns product information, including image URLs stored in S3. Infrastructure is provisioned via Terraform, including networking, ECS cluster/services, ECR repositories, IAM roles, CodePipeline/CodeBuild/CodeDeploy for Blue/Green deployments, autoscaling, and supporting resources (S3, DynamoDB, SNS). The architecture separates concerns with modular Terraform modules and implements production-grade practices like health checks, Swagger documentation, and stress testing.