docker/labs-ai-tools-for-devs

This README is an agentic workflow
# AI Tools for Developers
Agentic AI workflows enabled by Docker containers.
Just Docker. Just Markdown. BYOLLM.
## MCP
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
sh
# ...
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
VSCode Extension
Docs
# What is this?
This is a simple Docker image which enables infinite possibilities for novel workflows by combining Dockerized Tools, Markdown, and the LLM of your choice.
## Markdown is the language
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.
## Dockerized Tools
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
## Conversation Loop
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.
## Multi-Model Agents
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.
## Project-First Design
To get help from an assistant in your software development loop, the only context necessary is the project you are working on.
### Extracting project context
An extractor is a Docker image that runs against a project and extracts information into a JSON context.
## Prompts as a trackable artifact
Prompts are stored in a git repo and can be versioned, tracked, and shared for anyone to run in their own environment.
# Get Started
We highly recommend using the VSCode extension to get started. It will help you create prompts, and run them with your own LLM.
## Running your first loop
### VSCode
Install Extension
Get the latest release and install with
sh
code --install-extension 'labs-ai-tools-vscode- .vsix'
Running:
1. Open an existing markdown file, or create a new markdown file in VSCode.
> You can even run this markdown file directly!
2. Run command >Docker AI: Set OpenAI API Key to set an OpenAI API key, or use a dummy value for local models.