This is a monorepo containing over 40 independent MCP (Model Context Protocol) servers for AWS services, hosted under awslabs/mcp. Each server exposes tools, resources, and prompts that allow AI agents (coding assistants, chatbots) to interact with AWS APIs, databases, documentation, and best practices. The architecture is a collection of standalone Python packages, each with its own entry point (server.py/main.py), tests, Dockerfile, and PyPI distribution. Servers are built using either FastMCP or the lower-level MCP Python SDK, with consistent patterns for read-only enforcement, error handling, AWS client creation, and user-agent tracking. The codebase demonstrates solid engineering with extensive test coverage, but suffers from significant copy-paste boilerplate across servers (healthcheck scripts, Context singletons, test_init.py) and inconsistent use of MCP frameworks. Notable high-craftsmanship components include the AWS API MCP server's custom CLI-to-IR parser and execution engine, the OpenAPI MCP server's pluggable authentication provider framework, and the DynamoDB MCP server's schema-driven code generation tool. The repository is actively maintained with frequent commits and issue triage, though some servers are marked as deprecated or in initial setup, and the announcement of a successor ('Agent Toolkit for AWS') suggests potential future migration.
Artillery is a cloud-scale load testing and functional testing framework for backend services and APIs. It supports multiple protocols (HTTP, WebSocket, Socket.IO, Playwright, PostHog) through a plugin-based engine architecture. The system is primarily a CLI tool (built on oclif) that executes YAML/JSON test scripts defining virtual user flows with configurable arrival phases, payloads, and assertions. It provides distributed execution across local worker threads, AWS Lambda, AWS ECS/Fargate, and Azure ACI, with a bill-of-materials (BOM) system that bundles test dependencies for cloud workers. Metrics are aggregated via a custom Simple Statistics Measurement System (SSMS) supporting counters, histograms (DDSketch), and rates, and can be published to Datadog, CloudWatch, Prometheus, OpenTelemetry, Splunk, New Relic, and other observability platforms. The codebase is a monorepo managed with Turborepo, containing the core runner, platform integrations, first-party plugins (expect, ensure, apdex, slack, fake-data, metrics-by-endpoint, publish-metrics, memory-inspector), and shared type definitions. Key entry points include the `artillery` binary (`packages/artillery/bin/run`), the core runner (`packages/artillery/lib/core/runner.ts`), and platform launchers (`packages/artillery/lib/launch-platform.ts`). External dependencies include AWS SDK, Azure SDK, Playwright, got (HTTP client), and various metric publishing SDKs. The architecture is event-driven, using EventEmitter for lifecycle events and plugin hooks, with a global `global.artillery` object for shared state.
Nitric is a multi-cloud framework that enables developers to define cloud infrastructure and application logic in code, then deploy to AWS, Azure, or GCP. The framework provides infrastructure-as-code capabilities for cloud resources (APIs, storage, queues, topics, secrets, etc.) with runtime implementations for handling cloud service events. It features a modular architecture with core framework logic, cloud provider implementations, and a documentation site. The system uses a plugin-based worker system for handling cloud events and provides consistent abstractions across cloud providers through well-defined interfaces.
This repository is the official AWS Toolkit extension for Visual Studio Code, providing deep integration with Amazon Web Services directly within the IDE. It enables developers to browse and manage AWS resources (S3, Lambda, CloudFormation, EC2, ECS, IoT, and dozens more), invoke Lambda functions, deploy serverless applications using AWS SAM, debug Lambda functions locally, and access Amazon Q (formerly CodeWhisperer) for AI-powered code suggestions, chat, security scanning, and code transformation. The codebase is a large monorepo organized into packages including the core extension logic, generated streaming API clients, a custom ESLint plugin, and webview-based UIs built with Vue.js. The architecture follows a modular activation pattern with centralized command registration, tree view providers for resource exploration, a wizard/prompter framework for multi-step user flows, and a telemetry system for operational metrics. It supports both Node.js and web (browser) extension targets. Despite its size, the codebase maintains consistent patterns, extensive test coverage, and clear separation of concerns, making it a well-crafted enterprise-grade IDE extension.
This Laravel package provides an Eloquent-like interface for AWS DynamoDB, allowing developers to use familiar ORM methods with DynamoDB while maintaining compatibility with Laravel's ecosystem. The package implements a sophisticated query builder that translates Eloquent-style conditions into efficient DynamoDB operations, intelligently choosing between Query and Scan operations and selecting optimal indexes when available. Key features include support for simple and composite keys, global/secondary indexes, asynchronous operations via AWS SDK promises, query scopes, and automatic attribute marshaling/unmarshaling. The architecture follows Laravel conventions with service providers, facades, and a base DynamoDbModel class. Notable components include a condition analyzer that optimizes query operations and a parser subsystem that correctly builds DynamoDB expression syntax with proper attribute name/value handling.
This Terraform module provisions an AWS SNS topic and a Lambda function (Python 3.13) that forwards SNS notifications to a Slack channel via webhook. The Lambda function parses and formats various AWS event types—CloudWatch alarms, GuardDuty findings, Security Hub findings, AWS Health events, AWS Backup notifications, and generic messages—into structured Slack attachments. It supports encrypted webhook URLs via KMS, custom Lambda functions, VPC configuration, and SNS subscription filtering. The module is part of the terraform-aws-modules ecosystem and follows standard Terraform module conventions with comprehensive CI/CD, pre-commit hooks, and semantic release. The Python code includes unit tests with snapshot testing, linting, and type checking, demonstrating solid engineering practices.
Kamaji is a Kubernetes operator that transforms any Kubernetes cluster into a management cluster capable of orchestrating multiple independent tenant control planes. It provisions and manages Tenant Control Plane (TCP) custom resources—essentially running full Kubernetes control planes (kube-apiserver, kube-scheduler, kube-controller-manager) as pods inside a management cluster, using unmodified upstream Kubernetes components and kubeadm for bootstrapping. The operator extends Kubernetes via three CRDs: TenantControlPlane (namespace-scoped), DataStore (cluster-scoped, backing store abstraction), and KubeconfigGenerator. It supports multiple datastore drivers (etcd, MySQL, PostgreSQL, NATS via Kine) with TLS, basic auth, and live migration. The architecture features multiple controllers (main TCP reconciler, datastore lifecycle, certificate lifecycle, KubeconfigGenerator, telemetry), mutating/validating webhooks, and a per-TCP 'soot' manager that handles kubeadm phases, addons (CoreDNS, kube-proxy, Konnectivity), and write permissions. It integrates with Cluster API as a control plane provider and exposes Prometheus metrics, a Grafana dashboard, and optional telemetry. The codebase is a standard Go-based controller-runtime operator with Helm charts for deployment.
This repository is a comprehensive, production-focused AI system design guide, not a software application. It contains over 170 markdown files organized into 19 chapters covering the full lifecycle of building and operating AI systems: foundations (transformers, tokenization), model landscape (selection, pricing), training and adaptation (fine-tuning, LoRA, RLHF), inference optimization (KV cache, speculative decoding, batching), prompting and context engineering, retrieval systems (RAG, chunking, vector databases, hybrid search, GraphRAG), agentic systems (ReAct, MCP, multi-agent orchestration, durable execution), memory and state management, frameworks and tools (LangChain, LangGraph, LlamaIndex, DSPy, Semantic Kernel), document processing, infrastructure and MLOps (CI/CD, AI gateways, FinOps), security and access control, reliability and safety (guardrails, ensemble methods, governance), evaluation and observability (LLM evaluation, benchmarks), design patterns and anti-patterns, and 20+ real-world case studies. It also includes interview preparation materials (question bank, answer frameworks, whiteboard exercises, job market trends) and transition guides for engineers moving into AI roles. The content is current as of mid-2026, referencing specific model versions, pricing, and protocols (MCP 2.0, A2A). While it contains no executable code, it serves as an authoritative reference for engineers designing and deploying AI systems in production.
PullPreview is a GitHub Action implemented in Go that provisions ephemeral preview environments for pull requests. It manages cloud instances (AWS Lightsail or Hetzner), installs Docker or Kubernetes (k3s), and deploys applications using Docker Compose or Helm. The action integrates deeply with GitHub PR workflows: it reacts to labels, synchronizes SSH keys from repository collaborators, updates PR comments with deployment status and preview URLs, and performs scheduled cleanup of dangling instances. The codebase is organized around a provider abstraction pattern, with concrete implementations for Lightsail and Hetzner that share common orchestration logic for instance lifecycle, SSH access, and deployment. It is a self-contained CLI binary that runs as a composite action step, bundling all dependencies into a single executable.
Convox Rack is a self-hosted platform-as-a-service that runs in an AWS account and manages the full lifecycle of containerized applications. The repository is a Go monolith with three coordinated surfaces: a CLI (cmd/convox), an HTTP/WebSocket API server (cmd/rack + pkg/api), and a generated Go SDK (sdk). All operations funnel through the Provider interface defined in pkg/structs/provider.go, and the main production implementation in provider/aws drives CloudFormation, ECS, ECR, DynamoDB, S3, KMS, SSM, CloudWatch Logs, SNS/SQS, and Lambda. In production, the rack creates per-app CloudFormation stacks, builds Docker images through ECS tasks, persists builds and releases in DynamoDB/S3, and promotes releases by updating stacks. Primary entry points are cmd/convox for the CLI, cmd/rack for the API server, cmd/monitor for background workers, and the Lambda functions under provider/aws/lambda for custom CloudFormation resources, autoscaling, instance lifecycle draining, and syslog forwarding. The CLI also supports local development mode via pkg/start and pkg/sync, which builds manifests in Docker and incrementally syncs files into running containers. The manifest layer has two generations: pkg/manifest1 handles legacy docker-compose-style definitions, while pkg/manifest implements the newer convox.yml format with flexible YAML parsing, NLB validation, timers, and internal/external port handling. The codebase contains substantial test infrastructure, including golden-output CLI tests, SDK contract tests, generated mock providers, and the awsutil request/response replay harness. However, it also has accumulated drift: two parallel manifest implementations, version-gated compatibility branches in the CLI/SDK, an embedded CA trust store from 2017, AWS SDK v1 dependencies, and some provider methods that remain unimplemented. Some tests are skipped or commented out. These factors keep the code from being exemplary, but the overall architecture is coherent, professional, and actively maintained. This is purely an application repository; there is no data-science or research component. The only data involved is operational state such as builds, releases, environment variables, and logs, stored in AWS services. Reusable pieces include generic TTL caching, order-preserving YAML unmarshalling, CloudFormation stack helpers, KMS envelope encryption, and a replay-based AWS test harness.
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 implements a live streaming solution on AWS using AWS Elemental MediaLive for transcoding, MediaPackage for packaging, and Amazon CloudFront for content delivery. It is deployed via AWS CloudFormation using the AWS CDK (v2) and includes Lambda-backed custom resources to manage MediaLive inputs, channels, and MediaPackage endpoints. A demo React-based video player is optionally deployed to an S3 bucket behind CloudFront. The solution supports multiple input types (RTP, RTMP, URL pull, MediaConnect) and three encoding profiles (1080p, 720p, 540p). The codebase is well-structured with unit tests, CDK nag suppressions, and a build pipeline for AWS Solutions publishing. It is actively maintained and uses current AWS SDK v3 and Node.js 22 runtimes.
StackMaster is a mature Ruby CLI gem for managing AWS CloudFormation stacks. It provides a rich command set (apply, diff, drift, delete, events, outputs, resources, status, validate, lint, nag, compile, tidy, init) and supports multiple template formats (JSON, YAML, ERB, SparkleFormation DSL, CfnDsl) through a pluggable compiler registry. A standout feature is the parameter resolution pipeline: 13+ pluggable resolvers (SSM Parameter Store, ACM certificates, AMI finders, stack outputs, 1Password, EJSON, environment variables, security groups, SSO group IDs) that fetch values at apply/compile time, making cross-account and cross-region stack composition practical. The architecture is cleanly modular — commands are standalone classes sharing a base module, template compilers and parameter resolvers are registered via simple registry interfaces, and the stack event streamer provides live sync feedback during updates. The codebase demonstrates solid engineering craftsmanship: a custom Erubis subclass generates CloudFormation Fn::Join arrays from ERB templates, a role assumer caches STS credentials per (account, role) and temporarily swaps Aws.config to support cross-account operations, and a generic PagedResponseAccumulator cleanly handles AWS SDK pagination. Test coverage is extensive — RSpec unit specs plus Cucumber/Aruba feature tests exercise every command and resolver against stubbed AWS responses, and a dedicated in-memory test driver (TestDriver::CloudFormation/S3) enables isolated testing without network calls. The changelog is meticulously maintained with per-release notes, and the gem supports a wide Ruby range (2.4–4.0). However, there are signs of accumulated technical debt: a large .rubocop_todo.yml exclusions list indicates code quality lapses, the codebase relies on autoload (deprecated in Ruby 3.2+), and heavy stubbing in tests may mask real AWS API integration regressions. Dependency pins on SparkleFormation (~> 3) and CfnDsl (~> 1) could become brittler as those upstream libraries evolve. This is a maintainer-mode tool — stable, well-documented, and battle-tested, but not rapidly evolving.
Container Tool Kit (CTK) is a web application that provides a visual editor for Docker Compose and Kubernetes manifest files. Users can create and manage projects by dragging and dropping services, volumes, and networks onto a canvas, editing node configurations through detailed forms, and viewing the resulting YAML/JSON in a synchronized code editor. The backend is built with FastAPI and PostgreSQL, handling project persistence, user authentication (JWT-based), manifest generation (including calling kompose for Kubernetes), and project import from remote YAML URLs. The frontend is a React/TypeScript single-page application using jsPlumb for the interactive canvas, Formik for form management, and Monaco Editor for code editing. The application supports both anonymous and authenticated usage, with features like autosave, visibility toggles, and pagination for project lists.
Bash-my-AWS is a CLI toolset designed to manage AWS resources using Bash functions and aliases. It wraps the AWS CLI with convenience functions for listing, filtering, and manipulating resources like EC2, S3, and CloudFormation. The architecture is a monolithic Bash library sourced into the user's shell environment, with modularized logic in `lib/*-functions`. It relies heavily on `awscli`, `jq`, and standard Unix utilities. The tool implements a pipe-skimming pattern (`skim-stdin`) that allows chaining commands (e.g., `instances | instance-stop`), enhancing usability for shell scripting. It includes a Python-based caching proxy (`bin/cachews`) to reduce API latency. While well-structured for Bash, it carries inherent risks due to the language's limitations, including security vulnerabilities in SSH handling and reliance on `eval` for argument inference. The repository is actively maintained with recent commits and a comprehensive test suite using a custom BDD-style framework. However, technical drift is moderate due to the rapid evolution of AWS APIs and Bash-specific security concerns. The codebase is suitable for engineers comfortable with Bash scripting who need a robust set of AWS management utilities.
This repository implements a GitHub Action that triggers AWS CodeBuild builds. It starts a build using the AWS SDK, streams CloudWatch Logs to the GitHub Action output, and waits for the build to complete. The action handles rate limiting with exponential backoff and jitter, and supports cancelling builds via signal handling. The core logic is in `code-build.js`, which exports functions for building, waiting for completion, and processing inputs. The action is designed to be used in GitHub workflows but can also be run locally via the `local.js` script.
Troposphere is a mature, well-established Python library for programmatically generating AWS CloudFormation templates. It provides a rich set of Python classes that map to AWS resource types, allowing developers to define infrastructure as code using familiar Python constructs. The library covers hundreds of AWS services through auto-generated modules derived from the official CloudFormation resource specification, with per-service validators and patches to correct naming conflicts and maintain backward compatibility. Its architecture centers on base classes (AWSObject, AWSProperty, Template) that handle serialization, validation, and CloudFormation intrinsic functions. The project also includes a template generator for converting existing JSON/YAML templates back into Python objects, utility helpers for user data scripts, and optional integration with the awacs library for IAM policy documents. While the core is manually maintained, the vast majority of service modules are auto-generated, necessitating regular regeneration to keep pace with AWS changes. The library is extensively tested with example-based regression tests and unit tests for critical services and validators.
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.
LocalStack is a cloud service emulator that provides a local AWS environment for testing and development. It runs as a containerized application that mocks AWS services (Lambda, S3, DynamoDB, Step Functions, etc.) through a modular, plugin-based architecture. The system receives AWS API calls via local endpoints, routes them to service-specific mocks, and returns AWS-compatible responses. Key components include a runtime manager for service lifecycle, CloudFormation resource providers for infrastructure simulation, and a Step Functions ASL engine for workflow execution. The codebase demonstrates strong modularity, extensive use of design patterns (plugin, resource provider, state machine), and comprehensive testing. Although the repository is marked as archived (consolidated into a unified distribution), the code exhibits high craftsmanship with clean abstractions and reusable patterns.
SST is a full-stack deployment framework and CLI tool for building and deploying serverless applications on AWS and Cloudflare. It uses Pulumi for infrastructure as code, provides a rich set of high-level components (Functions, Buckets, Queues, Databases, Auth, etc.), and supports multiple runtimes (Node.js, Python, Go, Rust) and frontend frameworks (Next.js, Remix, Astro, SvelteKit, SolidStart, etc.). The CLI offers live development mode with hot-reload, a terminal multiplexer, and SSH tunneling for VPC resources. The codebase is a monorepo with a Go CLI, TypeScript platform components, and SDKs in JavaScript, Python, Rust, and Go. While it demonstrates solid engineering patterns like a typed event bus, component transform system, and resource linking, it suffers from some deprecated components, code duplication across framework-specific SSR site components, and a high maintenance surface due to the sheer number of providers and examples.