- Updated: March 20, 2026
- 3 min read
End‑to‑End CI/CD for the OpenClaw Full‑Stack Template
End‑to‑End CI/CD for the OpenClaw Full‑Stack Template
In today’s AI‑agent boom, developers are racing to deploy self‑hosted assistants that are both powerful and reliable. Reliable automation is the backbone of such deployments, ensuring that code moves from commit to production without hiccups. This guide walks you through setting up a complete CI/CD pipeline for the one‑click‑deploy OpenClaw Rating API Edge template on UBOS, covering GitHub Actions, Docker, automated tests, and deployment.
Why CI/CD Matters for AI‑Agents
AI‑agents are becoming ubiquitous, but their value hinges on uptime and consistent performance. A broken deployment can cripple user experience and erode trust. By automating builds, tests, and deployments, you minimize human error and accelerate iteration cycles.
Prerequisites
- UBOS instance ready for deployment (host‑openclaw guide)
- GitHub repository containing the OpenClaw Rating API Edge template
- Docker installed locally (for building images)
- Basic knowledge of GitHub Actions syntax
1. Set Up the Repository
Clone the OpenClaw Rating API Edge template and push it to a new GitHub repository. Ensure the Dockerfile and test suite are present.
2. Create a GitHub Actions Workflow
Add a workflow file .github/workflows/ci-cd.yml:
name: CI/CD Pipeline
on:
push:
branches: [ main ]
pull_request:
branches: [ main ]
jobs:
build:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v2
- name: Build Docker image
run: |
docker build -t ubos/openclaw:${{ github.sha }} .
- name: Run tests
run: |
docker run --rm ubos/openclaw:${{ github.sha }} pytest
- name: Push image to registry
uses: docker/login-action@v2
with:
username: ${{ secrets.DOCKER_USERNAME }}
password: ${{ secrets.DOCKER_PASSWORD }}
- name: Push Docker image
run: |
docker push ubos/openclaw:${{ github.sha }}
deploy:
needs: build
runs-on: ubuntu-latest
steps:
- name: Deploy to UBOS
env:
UBOS_HOST: ${{ secrets.UBOS_HOST }}
UBOS_TOKEN: ${{ secrets.UBOS_TOKEN }}
run: |
curl -X POST \
-H "Authorization: Bearer $UBOS_TOKEN" \
-F "image=ubos/openclaw:${{ github.sha }}" \
https://$UBOS_HOST/api/v1/deploy
3. Automated Tests
Include a test suite (e.g., using pytest) that validates the API endpoints, database migrations, and any AI‑agent integration points. The workflow above runs these tests inside the Docker container.
4. Deploy to UBOS
The final step in the workflow triggers a deployment to your UBOS instance using a simple HTTP POST. Ensure you have generated an API token in UBOS and stored it as a secret.
5. Verify the Deployment
After the workflow completes, visit your UBOS-hosted URL to confirm the OpenClaw Rating API Edge is live. You can also monitor logs via the UBOS dashboard.
Conclusion
By automating the entire pipeline—from code commit to live deployment—you reduce friction, catch bugs early, and keep your AI‑agent services reliable. In a world where AI‑agents are everywhere, such reliability is not a luxury; it’s a necessity.
Ready to get started? Follow the host‑openclaw guide and set up your CI/CD pipeline today.