- Updated: March 24, 2026
- 4 min read
Production‑Ready GitHub Actions Workflow for the OpenClaw Agent Evaluation Framework
# Automating OpenClaw Agent Evaluation with GitHub Actions
*Published on the UBOS blog – a step‑by‑step, production‑ready guide.*
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## Why now?
The AI agent hype is at an all‑time high – from autonomous chat‑bots to self‑learning recommendation engines, developers are racing to benchmark their agents. To turn that hype into actionable insights, you need a reliable, repeatable evaluation pipeline. This guide shows you how to lock that pipeline into GitHub Actions, so every push, PR, or schedule automatically runs the **OpenClaw Agent Evaluation Framework** and surfaces the metrics you care about.
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## Prerequisites
1. A GitHub repository containing your agent code and a `Dockerfile` (or any runnable artifact).
2. Access to the **OpenClaw** evaluation scripts – either as a submodule or via a Docker image.
3. A UBOS account with write permissions to the blog (for the final publishing step).
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## 1. Workflow Overview
The workflow consists of four jobs:
| Job | Purpose |
|—–|———|
| `setup` | Checkout code, set up Python/Node, and cache dependencies. |
| `build` | Build the agent container (or binary) and push it to the GitHub Container Registry. |
| `evaluate` | Pull the built image, run the OpenClaw evaluation suite, and collect metrics. |
| `report` | Upload metrics as artifacts, post a comment on the PR, and optionally fail the run if thresholds are not met.
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## 2. Full `ci.yml` Example
Create the file `.github/workflows/ci.yml` in your repository:
yaml
name: OpenClaw Evaluation CI
on:
push:
branches: [ main ]
pull_request:
branches: [ main ]
schedule:
– cron: ‘0 2 * * *’ # nightly run
jobs:
setup:
runs-on: ubuntu-latest
steps:
– name: Checkout repository
uses: actions/checkout@v3
– name: Set up Python
uses: actions/setup-python@v4
with:
python-version: ‘3.11’
– name: Cache pip
uses: actions/cache@v3
with:
path: ~/.cache/pip
key: ${{ runner.os }}-pip-${{ hashFiles(‘requirements.txt’) }}
restore-keys: |
${{ runner.os }}-pip-
build:
needs: setup
runs-on: ubuntu-latest
permissions:
contents: read
packages: write
steps:
– name: Checkout repository
uses: actions/checkout@v3
– name: Log in to GitHub Container Registry
uses: docker/login-action@v2
with:
registry: ghcr.io
username: ${{ github.actor }}
password: ${{ secrets.GITHUB_TOKEN }}
– name: Build Docker image
run: |
docker build -t ghcr.io/${{ github.repository_owner }}/${{ github.repository }}:latest .
– name: Push Docker image
run: |
docker push ghcr.io/${{ github.repository_owner }}/${{ github.repository }}:latest
evaluate:
needs: build
runs-on: ubuntu-latest
steps:
– name: Pull built image
run: |
docker pull ghcr.io/${{ github.repository_owner }}/${{ github.repository }}:latest
– name: Run OpenClaw evaluation
env:
OPENCLAW_CONFIG: ${{ secrets.OPENCLAW_CONFIG }} # JSON/YAML config
run: |
docker run –rm \
-e OPENCLAW_CONFIG \
ghcr.io/${{ github.repository_owner }}/${{ github.repository }}:latest \
/app/run_evaluation.sh
– name: Collect metrics
id: metrics
run: |
# Assume the script writes metrics.json to the workspace
cat metrics.json
echo “::set-output name=metrics::$(cat metrics.json)”
report:
needs: evaluate
runs-on: ubuntu-latest
if: always()
steps:
– name: Upload metrics artifact
uses: actions/upload-artifact@v3
with:
name: evaluation-metrics
path: metrics.json
– name: Post comment on PR
if: github.event_name == ‘pull_request’
uses: thollander/actions-comment-pull-request@v2
with:
message: |
**OpenClaw Evaluation Results**\n
\n${{ steps.metrics.outputs.metrics }}\n
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
—
## 3. Required Inputs & Secrets
| Input | Description | Example |
|——-|————-|———|
| `OPENCLAW_CONFIG` (secret) | JSON/YAML configuration for the evaluation suite (datasets, metrics, thresholds). | `{ “datasets”: [“gym”, “atari”], “metrics”: [“score”, “latency”], “thresholds”: { “score”: 0.8 } }` |
| `GITHUB_TOKEN` (auto) | Token for pushing images and commenting on PRs. | – |
> **Tip:** Store `OPENCLAW_CONFIG` in the repository **Settings → Secrets** to keep it private.
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## 4. Metric Collection & CI Integration
* The `evaluate` job writes a `metrics.json` file containing raw scores, latency, and any custom KPI.
* The `report` job uploads this file as an artifact, making it downloadable from the Actions UI.
* If you want to fail the CI when a metric falls below a threshold, add a step after `Collect metrics`:
yaml
– name: Enforce thresholds
run: |
python -c “import json, sys; data=json.load(open(‘metrics.json’)); \
assert data[‘score’] >= 0.8, ‘Score below threshold'”
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## 5. Publishing the Guide
The article you are reading is now live on the UBOS blog. For a deeper dive into hosting the OpenClaw framework on UBOS, visit our dedicated page:
[OpenClaw on UBOS – Host Your Evaluation Framework]({{“https://ubos.tech/host-openclaw/”}})
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### 🎉 You’re all set!
Push this workflow to your repo, watch the actions run, and get instant, reproducible evaluation results for every change. Keep an eye on the AI‑agent hype curve – with this pipeline, you’ll always have data‑backed answers.
*Happy automating!*