Turborepo Example Workflow

Full Example

This workflow demonstrates a complete DxCore + Turborepo integration running build and test across 4 parallel agents.

name: CI with DxCore
on: [push, pull_request]

env:
  DXCORE_URL: ${{ secrets.DXCORE_URL }}
  DXCORE_TOKEN: ${{ secrets.DXCORE_TOKEN }}

jobs:
  session:
    runs-on: ubuntu-latest
    outputs:
      session_id: ${{ steps.create.outputs.session_id }}
    steps:
      - name: Create DxCore session
        id: create
        run: |
          SESSION=$(dxcore ci create-session -c $DXCORE_URL -t $DXCORE_TOKEN)
          echo "session_id=$SESSION" >> "$GITHUB_OUTPUT"

      - name: Wait for coordinator
        run: dxcore ci wait -c $DXCORE_URL --timeout 300

  dispatch:
    needs: session
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: pnpm/action-setup@v4
      - run: pnpm install --frozen-lockfile
      - name: Dispatch graph
        run: |
          turbo run build test --dry=json | dxcore dispatch \
            -c $DXCORE_URL -s ${{ needs.session.outputs.session_id }} \
            -t $DXCORE_TOKEN -b turbo

  agent:
    needs: session
    runs-on: ubuntu-latest
    strategy:
      matrix:
        shard: [0, 1, 2, 3]
    steps:
      - uses: actions/checkout@v4
      - uses: pnpm/action-setup@v4
      - run: pnpm install --frozen-lockfile
      - name: Run DxCore agent
        run: |
          dxcore agent \
            -c $DXCORE_URL \
            -a agent-${{ matrix.shard }} \
            -s ${{ needs.session.outputs.session_id }} \
            -t $DXCORE_TOKEN \
            -b turbo

  finish:
    needs: [session, dispatch, agent]
    if: always()
    runs-on: ubuntu-latest
    steps:
      - name: Finish session
        run: |
          dxcore ci finish \
            -c $DXCORE_URL \
            -s ${{ needs.session.outputs.session_id }} \
            -t $DXCORE_TOKEN

What Happens

1. session — Creates a coordinator session and waits for readiness
2. dispatch — Pipes the Turborepo dry-run graph to the coordinator
3. agent (x4) — Each agent connects, receives tasks, and executes them
4. finish — Marks the session complete regardless of outcome

Scaling Agents

Change the matrix.shard array to add or remove agents. More agents means more parallelism, but only up to the number of independent tasks in your graph.

Next Steps

• Shard configuration
• Troubleshooting