docker-compose.yml

# lillycoder dev sandbox.
#
# Quick start (from this folder):
#
#   docker compose up -d
#   docker compose exec lillycoder bash
#
# That drops you into /workspace inside the container, with lillycoder
# installed in editable mode (so any edit to lib/lillycoder/ on the host
# is live inside). Run:
#
#   lillycoder --api http://host.docker.internal:11434/v1
#
# (or whatever port your local LLM server is on). The container has
# host.docker.internal pointing at your host's gateway by default on
# Docker Desktop; on Linux Docker Engine, the line below adds it
# explicitly.
#
# Files you create from inside (in /workspace) appear under
# ./WORKINGDIR/ on the host. If lillycoder goes haywire, damage stays
# in WORKINGDIR/.

services:
  lillycoder:
    build:
      context: .
      dockerfile: Dockerfile
    image: lillycoder:dev
    volumes:
      # Source: hot-reloaded
      - .:/opt/lillycoder
      # Working area: drop test files here from the host
      - ./WORKINGDIR:/workspace
      # Persistent config across runs
      - ${HOME}/.config/lillycoder:/root/.config/lillycoder
    environment:
      # If you also use the parent ai-on-device project's models, you can
      # point your LLM server at them. Lillycoder itself does not need
      # access to model files.
      - PYTHONDONTWRITEBYTECODE=1
      - PYTHONUNBUFFERED=1
    extra_hosts:
      # Lets you talk to the host's LLM server (e.g. ollama on the host)
      # from inside the container on Linux Docker Engine.
      - "host.docker.internal:host-gateway"
    working_dir: /workspace
    stdin_open: true
    tty: true
    entrypoint:
      - bash
      - -c
      - |
        pip install --quiet -e /opt/lillycoder
        echo "🦊 lillycoder dev sandbox"
        echo "   /workspace is your scratch (mounted from ./WORKINGDIR on host)"
        echo "   point lillycoder at a host LLM server, e.g.:"
        echo "     lillycoder --api http://host.docker.internal:11434/v1"
        exec bash