dank-py helps you turn an existing Python agent into a Dockerized microservice with two commands. You do not need to rewrite your app. You can keep your Python code and run it in a clean service setup on Windows.
This page is for a non-technical user who wants to download the project and get it running from GitHub.
Use this link to visit the project page and download the files:
Before you start, make sure your Windows PC has these items:
- Windows 10 or Windows 11
- Internet access
- Docker Desktop installed
- Python 3.10 or newer
- Git, if you want to copy the project from GitHub
If you do not already have Docker Desktop, install it first. Docker is the tool that runs the service in a container on your computer.
dank-py takes a Python agent and packages it into a Docker service. That lets you run it like a small app that stays ready in the background.
It is useful when you want to:
- Run an agent as a service
- Keep your code in Python
- Avoid a full rewrite
- Add logs and tracing
- Use FastAPI-style service behavior
- Make the app easier to deploy later
Follow these steps on Windows:
- Open the GitHub link above.
- Download the project files to your computer.
- Unzip the files if they came in a ZIP folder.
- Open Docker Desktop and make sure it is running.
- Open a terminal in the project folder.
- Run the setup command shown in the project files.
- Run the start command to launch the service.
If you are not sure where the terminal is, use PowerShell or Windows Terminal.
After you download the files:
- Find the folder in File Explorer.
- Right-click inside the folder.
- Choose Open in Terminal or Open PowerShell window here.
- Keep that window open while you run the commands.
If you do not see that option, open Windows Terminal, then move to the folder with cd.
Use the project page here to get the files first:
Download or open the project on GitHub
After the files are on your computer, the usual flow is:
- Install the needed tools.
- Build the Docker image.
- Start the service.
- Open the local web address shown in the terminal.
A common command flow for this kind of project looks like this:
docker build -t dank-py .docker run -p 8000:8000 dank-py
If the project includes a helper command, use that instead. The goal is to start the app and open it in your browser or local tool.
If the app does not open right away, check these items:
- Docker Desktop is running
- You are in the correct folder
- The download finished fully
- Your network is working
- Port 8000 is free
If another app already uses port 8000, close that app or change the port in the run command.
When dank-py starts, it should expose a local service on your computer. In many cases, this means:
- A local web page
- A health check endpoint
- Agent logs in the terminal
- Traces or request output for each run
If the project includes a browser page, open the local address printed in the terminal. It often looks like http://localhost:8000.
dank-py fits well when you want to:
- Wrap an AI agent as a service
- Test an agent in a Docker container
- Share the same setup with other users
- Add visibility through tracing
- Keep the agent easy to move between machines
This setup helps when you want your Python agent to behave like a small app instead of a one-off script.
A typical project layout for dank-py may include:
- A main Python app file
- Docker files
- A config file
- A service file
- A requirements list
- Logs or tracing setup
If you open the folder, look for files with names like:
Dockerfilerequirements.txtmain.pyapp.pydocker-compose.yml
These files help Docker build and run the service.
If something goes wrong, try these steps:
- Restart Docker Desktop
- Reboot your PC
- Make sure virtualization is on in BIOS if needed
- Check that you ran the build command first
- Make sure you are in the right folder
- Use the address from the terminal
- Try
http://localhost:8000 - Check that the container is still running
- Close the app using that port
- Try another port, such as
8001
- Revisit the GitHub page
- Download the files again
- Make sure your browser did not block the file
dank-py is built for Python agents, Docker, FastAPI-style services, observability, and tracing. It aims to make an existing agent easier to run in a service setup without changing the core code.
It is a good fit if you want:
- A simple service wrapper
- A container-based run path
- Clear request tracing
- A small deployment path for AI tools
- Use Windows Terminal for easier command entry
- Keep Docker Desktop open while the app runs
- Do not move files after setup
- Use a short folder path, like
C:\dank-py - Avoid spaces in the folder name if you can
These small steps can make setup smoother on Windows.
A simple first-time flow looks like this:
- Open the GitHub page.
- Download the project files.
- Unzip them.
- Install Docker Desktop.
- Open the project folder.
- Run the build command.
- Run the start command.
- Open the local address in your browser.
Project page:
Download and setup start here:
For the easiest setup:
- Use the latest Windows update
- Keep Docker Desktop open
- Run commands from the project folder
- Watch the terminal for the local address
- Start with the default port
If you want to turn a Python agent into a Docker service with a simple path on Windows, use the GitHub link above and follow the setup steps