Installation¶

wger-agent is a standard Python package and a prebuilt container image. Pick the path that matches how you want to run it.

Requirements¶

Python 3.11 – 3.14.
A reachable Wger Workout Manager instance — see Backing Platform to deploy one locally.

From PyPI (recommended)¶

pip install wger-agent

Optional extras¶

The base install ships the MCP server runtime. Install the extra for what you need:

Extra	Install	Pulls in
(base)	`pip install wger-agent`	`agent-utilities[mcp]` — the FastMCP MCP-server runtime
`agent`	`pip install "wger-agent[agent]"`	Pydantic-AI agent + Logfire tracing
`all`	`pip install "wger-agent[all]"`	MCP server, agent, and Logfire tracing
`test`	`pip install "wger-agent[test]"`	`pytest`, `pytest-asyncio`, `pytest-cov`, `pytest-xdist`

# Typical: run the MCP server and the graph agent together
pip install "wger-agent[all]"

From source¶

git clone https://github.com/Knuckles-Team/wger-agent.git
cd wger-agent
pip install -e ".[all]"          # editable install with every extra

With uv:

uv pip install -e ".[all]"
uv run wger-mcp

Prebuilt Docker image¶

A multi-stage, slim image is published on every release (entrypoint wger-mcp):

docker pull knucklessg1/wger-agent:latest

docker run --rm -i \
  -e WGER_URL=https://your-wger:8000 \
  -e WGER_API_KEY=your_api_key \
  knucklessg1/wger-agent:latest        # stdio transport (default)

For an HTTP server with a published port, or to run the agent server, see Deployment.

Verify the install¶

wger-mcp --help
wger-agent --help
python -c "import wger_agent; print(wger_agent.__version__)"

Next steps¶

Deployment — run it as a long-lived MCP server and agent behind Caddy + DNS.
Usage — call the tools, the API, and the agent CLI.
Configuration — every environment variable.