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awesome-devcontainers

A curated list of awesome tools and resources about dev containers for common programming languages and technology stacks to boost your developer productivity 🚀
https://github.com/manekinekko/awesome-devcontainers

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  • AI

    • AI devcontainers

      • Generative AI for Beginners - Microsoft's devcontainer for learning LLM application development with Python, Jupyter notebooks, model APIs, and course dependencies.
      • AI Agents for Beginners - Microsoft's agent-development course environment with Python, Node.js, .NET, and notebook support. Includes Microsoft Agent Framework and MCP SDK dependencies for agent and tool-calling examples.
      • Jupyter Data Science Notebooks - Community devcontainer template based on Jupyter Docker Stacks, with selectable base images for dataset exploration, preprocessing, visualization, and machine learning.
      • Data Science with Python and R - Microsoft's Python, R, and Jupyter development environment for statistics and notebook-based experimentation. The published image is documented as x86-64.
      • PyTorch CPU/CUDA Devcontainers - PyTorch's CPU and CUDA development configurations for building, debugging, and contributing to PyTorch itself, rather than a lightweight model-training starter. CUDA usage requires compatible NVIDIA hardware, drivers, and the NVIDIA Container Toolkit.
    • AI stack recipes

      • TypeScript AI App (stack recipe) - Suggested stack: Node.js, TypeScript, pnpm, and AI SDK for streaming chat, structured outputs, and tool-calling applications. Hosted model APIs do not require a local GPU.
      • MCP Development Lab (stack recipe) - Suggested stack: a Python or TypeScript Model Context Protocol SDK, Node.js, and MCP Inspector for creating and debugging servers, tools, resources, and prompts.
      • Local LLM with Ollama (stack recipe) - Suggested stack: a Python or Node.js workspace, an Ollama service container, and persistent model storage for local inference on CPU or supported GPUs. For GPU acceleration on macOS, run Ollama on the host rather than inside Docker Desktop.
      • Hugging Face Fine-Tuning (stack recipe) - Suggested stack: PyTorch, Transformers, Datasets, Accelerate, and PEFT, optionally with TRL, for reproducible fine-tuning and LoRA experiments. A compatible GPU is recommended for practical LLM workloads.
      • vLLM Inference Lab (stack recipe) - Suggested stack: a Python development workspace, a vLLM service, and client benchmarks for OpenAI-compatible model serving, batching, and throughput experiments. Choose the service image and runtime configuration for the host's supported hardware.
      • LLM Evaluation Lab (stack recipe) - Suggested stack: Node.js, Python, promptfoo, and versioned evaluation datasets for comparing prompts and models, checking outputs, and catching quality regressions in CI. Hosted model evaluation does not require a local GPU.
  • Articles

  • Samples

  • Tools

    • VS Code Remote Development - An extension pack that lets you open any folder in a container, on a remote machine, or in WSL and take advantage of VS Code's full feature set.
    • Pieces - device copilot that helps you capture, enrich, and reuse code, streamline collaboration, and solve complex problems through a contextual understanding of your workflow.
    • Unofficial devcontainer CLI - An experimental CLI to improve the experience of working with Visual Studio Code dev containers.
    • vscli - A CLI/TUI which makes it easy to launch vscode projects from the terminal, with a focus on dev containers. Also supports projects with multiple dev containers.
    • tyedev - Improve Devcontainer Creation - An interactive experience for the creation of a devcontainer. Provides ability to search for [features](https://containers.dev/features) and [templates](https://containers.dev/templates). And more!
    • KubeStellar Console - Multi-cluster Kubernetes dashboard with AI-powered operations, real-time observability, and CNCF project integrations across edge and cloud clusters.
    • aicontainer - A CLI that drops a sandboxed dev container into any project for running AI coding agents (Claude Code, Codex) in auto-approve mode, with filesystem isolation, a filtered Docker socket, and an opt-in outbound firewall.
    • mirabilis - One-command launcher that runs Claude Code fully autonomously in an isolated Docker dev container — cross-platform (macOS/Linux/WSL2), with a terminal UI and persistent agent memory.
    • decolint - A linter for Dev Container definitions, Features, and Templates, with checks for correctness, security, and reproducibility.
    • devcontainer-devops - A DevOps and Infrastructure-as-Code dev container (Terraform, Terragrunt, Azure CLI, Ansible, Kubernetes, PowerShell, .NET) with a `ws` command that opens several repositories as roots of one multi-root workspace in a single container.
    • Ivy Tendril - Open-source agentic software factory with an amazing UI that handles parallel Git worktrees for you, complete with programmatic verifications and fast review loops.
    • Devsy - Accelerate engineering velocity with Devsy, a workspace management tool for deploying devcontainers on Docker, Kubernetes, cloud providers (e.g., AWS, GCP, Azure, and more), and SSH remote hosts.
    • AGRO - A CLI that creates a durable Docker dev container workspace for AI coding agents on a laptop or remote VM, installs Claude Code, Codex, Pi, OpenCode, and other harnesses on demand, gives parallel agents separate git worktrees, and supports attaching from VS Code Dev Containers.
  • Tutorials

    • Dev Containers from Beginner to Advanced - A free, self-paced course in three levels — from opening your first Dev Container to designing multi-service setups, mirroring pre-commit hooks in CI, and auditing supply-chain risk. No sign-up required.
  • Videos