Decomposing the OpenClaw Full‑Stack Template into Microservices: A Step‑by‑Step Guide

1. Introduction

Developers and DevOps engineers often start with a monolithic “one‑click‑deploy” template because it accelerates prototyping. However, as applications scale, the need for a microservice‑first architecture becomes inevitable. This guide walks you through the systematic extraction of the three core pillars—CI/CD, security, and monitoring—from the OpenClaw Full‑Stack Template, turning them into autonomous services that can be versioned, scaled, and reused across projects.

By the end of this article you will be able to:

• Isolate CI/CD logic into a dedicated microservice.
• Separate authentication, secret management, and policy enforcement into a security microservice.
• Deploy a self‑contained monitoring stack that feeds metrics to OpenClaw agents.
• Integrate the new services with Clawd.bot and Moltbot for automated orchestration.
• Run a quick demo using the Moltbook sandbox.

All examples assume you have a working UBOS homepage environment and basic familiarity with Docker, Kubernetes, and GitOps.

2. Overview of the OpenClaw Full‑Stack Template

The OpenClaw template bundles four logical layers:

1. Application code (Node.js, Python, or Go).
2. CI/CD pipeline powered by GitHub Actions and UBOS’s Workflow automation studio.
3. Security services (OAuth2, JWT, secret vault).
4. Monitoring stack (Prometheus, Grafana, Loki).

All layers are orchestrated by a single docker‑compose.yml file, which makes local development painless but hides the boundaries needed for true microservice independence.

Before we split the monolith, let’s visualize the current architecture:

┌─────────────────────┐
│   OpenClaw UI (SPA) │
└───────▲───────▲─────┘
        │       │
   ┌────┴─────┐ ┌─────┴─────┐
   │ CI/CD    │ │ Security │
   └────▲─────┘ └─────▲─────┘
        │            │
   ┌────┴─────┐ ┌─────┴─────┐
   │ Monitoring│ │ App Code │
   └───────────┘ └───────────┘

Our goal is to replace the two middle boxes with independent services that expose clean APIs and can be deployed on separate clusters if needed.

3. Extracting CI/CD as an Independent Microservice

The CI/CD pipeline is the engine that builds, tests, and releases code. To decouple it, follow these steps:

3.1 Create a Dedicated Repository

Spin up a new Git repository named openclaw-ci-cd. Move all GitHub Action workflows, Dockerfiles, and UBOS pipeline scripts into .github/workflows/ of this repo.

3.2 Containerize the Pipeline

Wrap the pipeline in a Docker image that can be invoked as a service:

FROM ubos/ci-base:latest
COPY . /pipeline
WORKDIR /pipeline
ENTRYPOINT ["/usr/local/bin/run-pipeline.sh"]

Push the image to your container registry (e.g., Docker Hub or GitHub Packages).

3.3 Expose a RESTful Trigger API

Implement a tiny FastAPI wrapper that accepts a POST request with a Git commit SHA and starts the pipeline:

from fastapi import FastAPI, Body
import subprocess

app = FastAPI()

@app.post("/trigger")
def trigger(commit: str = Body(...)):
    subprocess.run(["/pipeline/run-pipeline.sh", commit])
    return {"status": "pipeline started"}

3.4 Deploy the Service

Deploy the CI/CD microservice to a Kubernetes namespace called cicd using the Web app editor on UBOS. Example manifest:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: cicd-service
  namespace: cicd
spec:
  replicas: 2
  selector:
    matchLabels:
      app: cicd
  template:
    metadata:
      labels:
        app: cicd
    spec:
      containers:
      - name: cicd
        image: ghcr.io/yourorg/openclaw-ci-cd:latest
        ports:
        - containerPort: 8000

Now any OpenClaw agent can call POST /trigger to start a build, making the pipeline truly independent.

4. Extracting Security as an Independent Microservice

Security in the original template is tightly coupled with the UI and the CI/CD service. To achieve isolation, we’ll create a Security Gateway that handles authentication, authorization, and secret management.

4.1 Choose a Standards‑Based Stack

Leverage OpenAI ChatGPT integration for AI‑driven risk analysis, and combine it with Chroma DB integration for vector‑based policy storage.

4.2 Implement OAuth2 Provider

Deploy Keycloak as a containerized service. Expose the standard OAuth2 endpoints (/auth, /token, /userinfo) and configure OpenClaw agents to use the client_credentials flow.

4.3 Secret Vault Integration

Use ElevenLabs AI voice integration as a demonstration of how the security service can protect API keys for third‑party AI services. Store secrets in HashiCorp Vault and expose a /secrets endpoint that returns encrypted payloads only after proper JWT validation.

4.4 Deploy the Security Service

Deploy the security gateway into a security namespace. Example Helm values:

keycloak:
  replicaCount: 2
  resources:
    limits:
      cpu: "500m"
      memory: "512Mi"
vault:
  replicaCount: 1
  storage: 5Gi

All downstream microservices—including CI/CD and monitoring—will now authenticate via this gateway, ensuring a single source of truth for identity.

5. Extracting Monitoring as an Independent Microservice

Monitoring provides observability, alerting, and feedback loops for automated agents. By extracting it, you enable OpenClaw agents to query metrics without coupling to the application stack.

5.1 Choose a Lightweight Stack

We’ll use the classic Prometheus + Grafana + Loki trio, but each component will run in its own pod and expose a clean API.

5.2 Deploy Prometheus as a Scrape‑Only Service

Create a ServiceMonitor that scrapes metrics from the CI/CD and security services. Example:

apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: openclaw-monitor
  namespace: monitoring
spec:
  selector:
    matchLabels:
      app: cicd
  endpoints:
  - port: http
    interval: 15s

5.3 Grafana Dashboards as a Service

Package dashboards as JSON files and mount them via ConfigMap. Expose Grafana’s /api/dashboards endpoint so agents can fetch visualizations programmatically.

5.4 Loki for Log Aggregation

Configure Loki to ingest logs from all namespaces using the promtail sidecar. The /loki/api/v1/query_range endpoint will be used by Clawd.bot to answer “What happened during the last deployment?” queries.

5.5 Secure the Monitoring API

All monitoring endpoints must validate JWTs issued by the security gateway (see Section 4). This prevents unauthorized data leakage.

With CI/CD, security, and monitoring now independent, the OpenClaw template has transformed into a true microservice ecosystem.

6. Integrating the New Microservices with OpenClaw Agents (Clawd.bot, Moltbot)

OpenClaw agents are built on top of the AI marketing agents framework. They communicate via HTTP/REST and can invoke any registered microservice.

6.1 Register Service Endpoints

Update the agents-config.yaml file to include the new services:

services:
  cicd:
    url: http://cicd-service.cicd.svc.cluster.local:8000
  security:
    url: http://keycloak.security.svc.cluster.local:8080
  monitoring:
    url: http://prometheus.monitoring.svc.cluster.local:9090

6.2 Extend Clawd.bot for Deployment Queries

Clawd.bot can now answer natural‑language questions like “Deploy version 1.2.3 to staging”. It translates the request into a POST /trigger call to the CI/CD service, then polls the monitoring API for success metrics.

6.3 Empower Moltbot with Security Audits

Moltbot, the security‑focused agent, uses the security gateway to run on‑demand vulnerability scans. Example flow:

1. User asks Moltbot: “Are any secrets exposed in the last commit?”
2. Moltbot calls /secrets with the commit SHA.
3. Results are enriched with AI‑generated risk scores from the ChatGPT and Telegram integration.

6.4 Event‑Driven Glue with UBOS Workflow Automation

Leverage the Workflow automation studio to create triggers that fire when monitoring alerts cross thresholds, automatically invoking Moltbot to remediate security incidents.

7. Quick Demo Using Moltbook

The Moltbook sandbox is a pre‑configured UBOS environment that ships with all three microservices already containerized. Follow these steps to see the architecture in action:

7.1 Spin Up the Moltbook Environment

Run the following command in your terminal (requires Docker Desktop):

docker run -d -p 8080:80 ubos/moltbook:latest

7.2 Trigger a Build via Clawd.bot

Open the Moltbook UI at http://localhost:8080, navigate to the “Agents” tab, and type:

Deploy version v0.9.1 to production

Clawd.bot will call the CI/CD microservice, which builds the Docker image and pushes it to the registry.

7.3 Verify Security Checks with Moltbot

After the build finishes, ask Moltbot:

Scan the new image for secret leaks

Moltbot queries the security gateway, runs a Trivy scan, and returns a concise report.

7.4 Observe Metrics in Grafana

Open the Grafana dashboard (default: http://localhost:8080/grafana) and locate the “CI/CD Pipeline Latency” panel. You’ll see real‑time latency numbers that were fed back to Clawd.bot for the final status message.

This end‑to‑end flow demonstrates how the three microservices collaborate with OpenClaw agents to deliver a fully automated, observable, and secure deployment pipeline.

8. Benefits of the Microservice Architecture

• Scalability: Each service can be horizontally scaled based on its own load profile.
• Team Autonomy: Dev, SecOps, and SRE teams own distinct codebases, reducing merge conflicts.
• Technology Heterogeneity: CI/CD can stay in Python, security in Go, and monitoring in Rust without forcing a single language stack.
• Improved Observability: Dedicated monitoring service provides a single source of truth for metrics and logs.
• Rapid Innovation: New OpenClaw agents can be added without touching the core application.
• Cost Efficiency: Pay‑as‑you‑go cloud resources per service, rather than over‑provisioning a monolith.

For a deeper dive into cost modeling, see the UBOS pricing plans page, which outlines per‑service billing options.

9. Conclusion

By extracting CI/CD, security, and monitoring into independent microservices, you transform the OpenClaw Full‑Stack Template from a convenient starter kit into a production‑grade, modular platform. The new architecture empowers Clawd.bot and Moltbot to orchestrate deployments, enforce policies, and surface real‑time insights without ever touching the underlying application code.

Adopt the step‑by‑step process outlined above, experiment with the Moltbook demo, and you’ll be ready to scale your AI‑driven automation across teams, regions, and even multiple cloud providers.

10. Further Reading

Ready to host your own OpenClaw instance? Follow the official guide on the OpenClaw hosting page for detailed deployment instructions, pricing, and support options.