- Updated: March 22, 2026
- 3 min read
Building a CI/CD Pipeline for OpenClaw Metrics on UBOS
## Introduction
As senior engineers, we often need to turn raw evaluation data into actionable improvements for our AI agents. This guide walks you through constructing a robust CI/CD pipeline on UBOS that automatically consumes OpenClaw Agent Evaluation Framework metrics, generates updated model checkpoints, and redeploys the support agent—all with minimal manual intervention.
## Prerequisites
– A running UBOS instance with admin access.
– OpenClaw Agent Evaluation Framework integrated into your development workflow.
– Docker and Git installed on your build server.
## Pipeline Overview
1. **Metric Collection** – OpenClaw publishes evaluation metrics to a JSON endpoint after each test run.
2. **Automated Trigger** – A webhook or cron job fetches the latest metrics and pushes them to a Git repository.
3. **Model Checkpoint Generation** – A Docker‑based job parses the metrics, decides whether a new checkpoint is warranted, and runs the training script to produce an updated model artifact.
4. **Artifact Storage** – The new checkpoint is stored in UBOS’s object store and version‑tagged in Git.
5. **Redeployment** – UBOS’s deployment service detects the new version tag, pulls the updated container image, and rolls out the new agent without downtime.
## Step‑by‑Step Implementation
### 1. Set Up the Metrics Webhook
Create a simple Node‑RED flow that polls the OpenClaw metrics endpoint (`/metrics/latest`). When new data is detected, push the JSON payload to a private Git repo:
bash
curl -X POST -H “Content-Type: application/json” \
-d @metrics.json https://git.example.com/ubos/openclaw-metrics.git
### 2. Build the Checkpoint Job
Dockerfile example:
Dockerfile
FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY train.py .
CMD [“python”, “train.py”, “–metrics”, “/data/metrics.json”]
The job reads the metrics, decides on a threshold, and outputs `model.ckpt`.
### 3. Store the Checkpoint
After the container finishes, push the checkpoint to UBOS’s object store and commit a new tag:
bash
ubos storage upload model.ckpt –bucket checkpoints
git tag -a v$(date +%Y%m%d%H%M) -m “Automated checkpoint”
git push –tags
### 4. Trigger Redeployment
UBOS watches the repository for new tags. When a tag appears, it pulls the latest Docker image and updates the running service:
bash
ubos deploy update –service openclaw-agent –image ubos/openclaw-agent:latest
The rollout is zero‑downtime thanks to UBOS’s built‑in rolling update strategy.
## Contextual Link
For a deeper dive into hosting OpenClaw on UBOS, see our internal guide: https://ubus.tech/host-openclaw/.
## Conclusion
By chaining metric collection, automated model training, and seamless redeployment, you create a feedback loop that continuously improves your support agent. This CI/CD pattern scales with your data volume and keeps your AI services fresh without manual bottlenecks.
*Happy coding!*
Andrii Bidochko
CTO UBOS
Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.