About AI Agents in Container
AI Agents in Container is a setup guide for running browser-use and related components in a Kasm desktop container, alongside Ollama and MCP server configuration. It documents connections for VS Code Insiders, Claude Desktop, Cursor, and Windsurf.
Key features
- 01Run a Kasm desktop container with Docker
- 02Set up browser-use and its web UI
- 03Install and run Ollama models
- 04Configure Puppeteer MCP server in several clients
Use cases
- 01Set up a containerized browser-use environment
- 02Connect Puppeteer MCP to a supported client
- 03Run Ollama with a selected model
MCP support
The guide shows how to build the Puppeteer MCP server and configure it for VS Code Insiders, Claude Desktop, Cursor, and Windsurf.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEAI Agents in Container
kasm Desktop
docker run \
--name browser-use \
--shm-size=4096m \
-w /mnt \
-p 8080:6901 \
-e VNC_PW=password \
-id kasmweb/ubuntu-noble-desktop:1.16.1
user: kasm_user
password: password
browser-use
Install latest version of python
docker exec -u root -it browser-use bash
visudo
kasm-user ALL=(ALL) NOPASSWD: ALL
passwd kasm-user
passwd root
exit
docker exec -it browser-use bash
sudo apt update -y
sudo apt install -y python3-venv git xvfb python3-full
web-ui
# Start in Desktop directory
cd /home/kasm-user/Desktop
# Remove any existing web-ui directory if it exists
sudo rm -rf web-ui
# Clone the repository
git clone https://github.com/browser-use/web-ui.git
cd web-ui
# Create and set up virtual environment in your home directory instead
python3 -m venv ~/web-ui-venv
source ~/web-ui-venv/bin/activate
# Install UV
curl -LsSf https://github.com/astral-sh/uv/releases/latest/download/uv-installer.sh | sh
source $HOME/.local/bin/env
# Verify environment
whoami
uv --version
python3 --version
# Install requirements using the virtual environment in home directory
uv pip install -r requirements.txt
# Install Playwright dependencies
playwright install-deps
playwright install
# Setup environment file
cp .env.example .env
# Run the application with Xvfb
xvfb-run -a --server-args="-screen 0 1280x1024x24" python webui.py --ip 0.0.0.0 --port 7788
# Access the application at:
# http://localhost:7788
# Remove existing venv
deactivate
cd /home/kasm-user/Desktop
sudo rm -rf web-ui
Install OLLAMA
curl -fsSL https://ollama.com/install.sh | sh
ollama serve &
http://localhost:11434
ollama pull deepseek-r1:8b
ollama pull deepseek-r1:8b # I used this for docker
ollama run deepseek-r1:8b
ollama list
ollama stop deepseek-r1:8b
ollama rm deepseek-r1:8b
MAC OS
- Install latest version of python
brew install python3
git clone https://github.com/browser-use/web-ui.git
# Install UV Package https://docs.astral.sh/uv/getting-started/installation/
cd web-ui
python3 -m venv .venv
source .venv/bin/activate
curl -LsSf https://github.com/astral-sh/uv/releases/latest/download/uv-installer.sh | sh
source $HOME/.local/bin/env
uv --version
python3 --version
uv pip install -r requirements.txt
playwright install-deps
playwright install
cp .env.example .env
python webui.py --ip 127.0.0.1 --port 7788
http://localhost:7788
Install OLLAMA on MAC OS
https://ollama.com/download/mac
ollama pull deepseek-r1:8b # I used this for Mac OS
ollama run deepseek-r1:8b
Vscode Insiders
Google AI Studio (API Key)
MCP ServerS
-
https://github.com/punkpeye/awesome-mcp-servers?tab=readme-ov-file#file-systems
-
This provides internet access, so you can get latest documentation and update code accordingly
git clone https://github.com/modelcontextprotocol/servers.git
cd servers
docker build -t mcp/puppeteer -f src/puppeteer/Dockerfile .
- vscode Insider Settings
{
"mcp": {
"inputs": [],
"servers": {
"puppeteer": {
"command": "docker",
"args": ["run", "-i", "--rm", "--init", "-e", "DOCKER_CONTAINER=true", "mcp/puppeteer"]
}
}
}
}
- Claude Desktop Settings
{
"mcpServers": {
"puppeteer": {
"command": "docker",
"args": ["run", "-i", "--rm", "--init", "-e", "DOCKER_CONTAINER=true", "mcp/puppeteer"]
}
}
}
- Cursor
{
"mcpServers": {
"puppeteer": {
"command": "docker",
"args": ["run", "-i", "--rm", "--init", "-e", "DOCKER_CONTAINER=true", "mcp/puppeteer"]
}
}
}
- Codium Windsurf
{
"mcpServers": {
"puppeteer": {
"command": "docker",
"args": ["run", "-i", "--rm", "--init", "-e", "DOCKER_CONTAINER=true", "mcp/puppeteer"]
}
}
}
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.