- Updated: March 17, 2026
- 3 min read
Deploy OpenClaw Rating API on the Edge for Real‑Time Personalization
## Deploy OpenClaw Rating API on the Edge for Real‑Time Personalization
*Senior‑engineer level guide* covering:
1. **Exporting rating data** – how to extract and format rating data from OpenClaw for downstream consumption.
2. **Setting up the Python client** – installing the client library, authenticating, and making API calls.
3. **Integrating with Moltbook** – wiring the rating service into Moltbook’s recommendation pipeline.
4. **Deploying on edge nodes** – containerizing the service, configuring edge‑node orchestration, and ensuring low‑latency, real‑time personalization.
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### 1. Exporting Rating Data
bash
# Example command to export ratings to JSON
openclaw export –format json –output ./ratings.json
– Ensure the export includes user IDs, item IDs, and rating scores.
– Store the file in a location accessible to the edge deployment (e.g., an S3 bucket or a shared volume).
### 2. Setting Up the Python Client
bash
pip install openclaw-client
python
from openclaw_client import OpenClawClient
client = OpenClawClient(api_key=’YOUR_API_KEY’, endpoint=’https://api.openclaw.dev’)
# Load exported ratings
import json
with open(‘ratings.json’) as f:
ratings = json.load(f)
# Push ratings to the rating service
client.upload_ratings(ratings)
– Use environment variables for `API_KEY` and endpoint to keep credentials secure.
### 3. Integrating with Moltbook
Moltbook expects a REST endpoint that returns personalized recommendations.
python
from flask import Flask, request, jsonify
from openclaw_client import OpenClawClient
app = Flask(__name__)
client = OpenClawClient(api_key=’${API_KEY}’, endpoint=’${RATING_SERVICE_URL}’)
@app.route(‘/recommendations’, methods=[‘GET’])
def recommendations():
user_id = request.args.get(‘user_id’)
recs = client.get_recommendations(user_id=user_id, top_n=10)
return jsonify(recs)
if __name__ == ‘__main__’:
app.run(host=’0.0.0.0′, port=8080)
– Deploy this Flask app alongside Moltbook or as a micro‑service.
### 4. Deploying on Edge Nodes
#### Containerization
Create a `Dockerfile`:
dockerfile
FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
EXPOSE 8080
CMD [“python”, “app.py”]
Build and push the image:
bash
docker build -t ubos/openclaw-rating-edge:latest .
docker push ubos/openclaw-rating-edge:latest
#### Edge Orchestration (e.g., K3s)
yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: openclaw-rating-edge
spec:
replicas: 3
selector:
matchLabels:
app: openclaw-rating-edge
template:
metadata:
labels:
app: openclaw-rating-edge
spec:
containers:
– name: rating-service
image: ubos/openclaw-rating-edge:latest
ports:
– containerPort: 8080
env:
– name: API_KEY
valueFrom:
secretKeyRef:
name: openclaw-secret
key: api-key
– Use a local edge registry or a lightweight image distribution method to keep the deployment fast.
– Monitor latency and autoscale based on request volume.
—
### Internal Reference
For a deeper dive on hosting OpenClaw on UBOS, see the related guide: [Host OpenClaw on UBOS](/host-openclaw/).
—
**Conclusion**
By exporting rating data, leveraging the Python client, integrating with Moltbook, and deploying the rating service on edge nodes, developers can achieve real‑time, low‑latency personalization for their applications. This architecture scales horizontally across edge locations while keeping data privacy and response times optimal.