
labs-ai-tools-for-devs
An MCP server & prompt runner for all of Docker. Simple Markdown. BYO LLM.
Stars: 126

This repository provides AI tools for developers through Docker containers, enabling agentic workflows. It allows users to create complex workflows using Dockerized tools and Markdown, leveraging various LLM models. The core features include Dockerized tools, conversation loops, multi-model agents, project-first design, and trackable prompts stored in a git repo.
README:
This README is an agentic workflow
Agentic AI workflows enabled by Docker containers.
Just Docker. Just Markdown. BYOLLM.
Any prompts you write and their tools can now be used as MCP servers
Use serve mode with --mcp
flag. Then, register prompts via git ref or path with --register <ref>
# ...
serve
--mcp
--register github:docker/labs-ai-tools-for-devs?path=prompts/examples/generate_dockerfile.md
--register /Users/ai-overlordz/some/local/prompt.md
# ...
Source for many experiments in our LinkedIn newsletter
This is a simple Docker image which enables infinite possibilities for novel workflows by combining Dockerized Tools, Markdown, and the LLM of your choice.
Humans already speak it. So do LLM's. This software allows you to write complex workflows in a markdown files, and then run them with your own LLM in your editor or terminal...or any environment, thanks to Docker.
OpenAI API compatiable LLM's already support tool calling. We believe these tools could just be Docker images. Some of the benefits using Docker based on our research are enabling the LLM to:
- take more complex actions
- get more context with fewer tokens
- work across a wider range of environments
- operate in a sandboxed environment
The conversation loop is the core of each workflow. Tool results, agent responses, and of course, the markdown prompts, are all passed through the loop. If an agent sees an error, it will try running the tool with different parameters, or even different tools until it gets the right result.
Each prompt can be configured to be run with different LLM models, or even different model families. This allows you to use the best tool for the job. When you combine these tools, you can create multi-agent workflows where each agent runs with the model best suited for that task.
With Docker, it is possible to have frontier models plan, while lightweight local models execute.
To get help from an assistant in your software development loop, the only context necessary is the project you are working on.
An extractor is a Docker image that runs against a project and extracts information into a JSON context.
Prompts are stored in a git repo and can be versioned, tracked, and shared for anyone to run in their own environment.
We highly recommend using the VSCode extension to get started. It will help you create prompts, and run them with your own LLM.
Install Extension
Get the latest release and install with
code --install-extension 'labs-ai-tools-vscode-<version>.vsix'
Running:
- Open an existing markdown file, or create a new markdown file in VSCode.
You can even run this markdown file directly!
-
Run command
>Docker AI: Set OpenAI API Key
to set an OpenAI API key, or use a dummy value for local models. -
Run command
>Docker AI: Select target project
to select a project to run the prompt against. -
Run command
>Docker AI: Run Prompt
to start the conversation loop.
Instructions assume you have a terminal open, and Docker Desktop running.
- Set OpenAI key
echo $OPENAI_API_KEY > $HOME/.openai-api-key
Note: we assume this file exists, so you must set a dummy value for local models.
- Run the container in your project directory
docker run
--rm \
--pull=always \
-it \
-v /var/run/docker.sock:/var/run/docker.sock \
--mount type=volume,source=docker-prompts,target=/prompts \
--mount type=bind,source=$HOME/.openai-api-key,target=/root/.openai-api-key \
vonwig/prompts:latest \
run \
--host-dir $PWD \
--user $USER \
--platform "$(uname -o)" \
--prompts "github:docker/labs-githooks?ref=main&path=prompts/git_hooks"
See docs for more details on how to run the conversation loop.
#docker:command=build
docker build -t vonwig/prompts:local -f Dockerfile .
Now, for the agentic workflow...
You are an expert at reading readmes.
Use curl to get the readme for https://github.com/docker/labs-ai-tools-for-devs before answering the following questions.
What is this project?
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