Export OpenClaw Token‑Bucket Usage Data and Visualize with Moltbook Feed Metrics

Introduction

Developers building on the UBOS platform often need to combine usage analytics from multiple sources. The OpenClaw Rating API provides a token‑bucket model that tracks request quotas, while Moltbook delivers rich feed‑level metrics such as click‑through rates and content freshness. This guide walks you through:

• Exporting token‑bucket usage data via the OpenClaw Rating API.
• Fetching Moltbook feed metrics using the UBOS CLI.
• Merging the two datasets with a concise Python script.
• Creating interactive visualizations with Plotly and Grafana.
• Publishing the final article on ubos.tech with proper SEO markup.

By the end of this tutorial, you’ll have a reproducible pipeline that can be scheduled as a nightly job, giving product managers real‑time insight into both quota consumption and content performance.

Prerequisites

Make sure you have the following before you start:

• Access to an OpenClaw instance hosted on UBOS with a valid API key.
• UBOS CLI installed (ubos-cli) and authenticated.
• Python 3.9+ with pandas, requests, and plotly libraries.
• Grafana (optional) for dashboard‑level visualizations.
• Basic knowledge of JSON and RESTful APIs.

For a quick start with UBOS, check out the UBOS templates for quick start page.

Exporting Token‑Bucket Usage Data from OpenClaw Rating API

API Endpoint Details

The Rating API exposes a /v1/rating/token-bucket endpoint that returns a JSON array of bucket records. Each record contains:

Example CURL Command

curl -X GET "https://api.openclaw.io/v1/rating/token-bucket" \
     -H "Authorization: Bearer YOUR_API_KEY" \
     -H "Accept: application/json" \
     -o token_bucket.json

If you prefer the UBOS CLI, the same request can be issued with:

ubos api call /v1/rating/token-bucket \
     --header "Authorization: Bearer $OPENCLAW_KEY" \
     --output token_bucket.json

Sample Response

[
  {
    "bucket_id": "bkt_001",
    "client_id": "client_abc",
    "tokens_used": 1245,
    "reset_time": "2026-04-01T00:00:00Z"
  },
  {
    "bucket_id": "bkt_002",
    "client_id": "client_xyz",
    "tokens_used": 987,
    "reset_time": "2026-04-01T00:00:00Z"
  }
]

Save the JSON file; it will be the first input for the merge step.

Retrieving Moltbook Feed Metrics

Moltbook stores feed‑level statistics in a PostgreSQL table called feed_metrics. The UBOS CLI provides a convenient db export command to pull this data as CSV.

CLI Commands

# Export the last 30 days of feed metrics
ubos db export --table feed_metrics \
     --where "date >= CURRENT_DATE - INTERVAL '30 days'" \
     --format csv \
     --output moltbook_metrics.csv

The resulting CSV contains the following columns:

• feed_id – Unique identifier of the feed.
• impressions – Number of times the feed was shown.
• clicks – Click‑through count.
• ctr – Click‑through rate (clicks / impressions).
• date – Metric date (UTC).

Sample Data Format

feed_id,impressions,clicks,ctr,date
feed_01,15000,375,0.025,2026-03-20
feed_02,8200,164,0.020,2026-03-20
feed_03,23000,690,0.030,2026-03-20

Store moltbook_metrics.csv alongside token_bucket.json for the next step.

Merging Data Sets

The merge operation aligns token‑bucket usage with feed metrics based on a common identifier. In most deployments, client_id from OpenClaw maps to feed_id in Moltbook. Below is a Python script that performs the join, calculates a combined efficiency score, and writes the result to merged_report.csv.

Python Code Example

import json
import pandas as pd

# Load token‑bucket JSON
with open('token_bucket.json') as f:
    token_data = json.load(f)

token_df = pd.DataFrame(token_data)
token_df.rename(columns={'client_id': 'feed_id'}, inplace=True)

# Load Moltbook CSV
molt_df = pd.read_csv('moltbook_metrics.csv')

# Merge on feed_id
merged = pd.merge(token_df, molt_df, on='feed_id', how='inner')

# Compute a simple efficiency metric:
#   efficiency = (tokens_used / impressions) * 1000
merged['efficiency'] = (merged['tokens_used'] / merged['impressions']) * 1000

# Reorder columns for readability
final_cols = ['feed_id', 'bucket_id', 'tokens_used', 'impressions',
              'clicks', 'ctr', 'efficiency', 'reset_time', 'date']
merged = merged[final_cols]

# Export to CSV
merged.to_csv('merged_report.csv', index=False)
print('✅ Merged report saved as merged_report.csv')

The script follows a MECE‑compatible flow:

1. Load each source into a pandas DataFrame.
2. Rename the key column to enable a clean inner join.
3. Calculate a custom efficiency metric that normalizes token consumption by impressions.
4. Export the enriched dataset for downstream visualization.

Run the script with python merge_token_molt.py. The output file will look like this:

feed_id,bucket_id,tokens_used,impressions,clicks,ctr,efficiency,reset_time,date
feed_01,bkt_001,1245,15000,375,0.025,83.0,2026-04-01T00:00:00Z,2026-03-20
feed_02,bkt_002,987,8200,164,0.020,120.4,2026-04-01T00:00:00Z,2026-03-20

Visualizing the Combined Data

Two popular options are Plotly (for ad‑hoc notebooks) and Grafana (for persistent dashboards). Choose the one that matches your team’s workflow.

Plotly Interactive Chart

The snippet below creates a scatter plot where each point represents a feed. The X‑axis shows tokens_used, the Y‑axis shows impressions, and the point size encodes the efficiency score.

import pandas as pd
import plotly.express as px

df = pd.read_csv('merged_report.csv')

fig = px.scatter(
    df,
    x='tokens_used',
    y='impressions',
    size='efficiency',
    color='feed_id',
    hover_data=['clicks', 'ctr'],
    title='Token Usage vs. Impressions (Efficiency Highlighted)'
)

fig.update_layout(template='plotly_dark')
fig.show()

Save the figure as an HTML file for embedding in internal reports:

fig.write_html('token_vs_impressions.html')

Grafana Dashboard (CSV Data Source)

If you prefer a live dashboard, follow these steps:

1. Install the CSV Plugin in Grafana.
2. Configure a new data source pointing to merged_report.csv.
3. Create a Time series panel using the date field.
4. Add a Bar gauge panel for the efficiency metric.

Grafana will automatically refresh the panel whenever the CSV file is updated by the Python script.

Publishing the Article on ubos.tech

UBOS uses a Markdown‑to‑HTML pipeline, but you can directly paste the HTML you see here into the editor. Follow these best‑practice steps:

• Copy the entire <body> block into the Content field of the UBOS blog editor.
• Set the Meta Title to “Export OpenClaw Token‑Bucket Data, Merge Moltbook Metrics, Visualize Results”.
• Insert the internal link to the OpenClaw hosting page (OpenClaw hosting on UBOS) where you first mention the OpenClaw instance.
• Choose the appropriate UBOS pricing plans tag if the article is part of a paid‑feature series.
• Enable Schema.org Article markup in the settings to boost GEO visibility.

After publishing, share the URL on LinkedIn, X, and relevant developer forums to attract the target audience.

Conclusion and Next Steps

We’ve demonstrated a complete end‑to‑end workflow:

• Export token‑bucket usage via the OpenClaw Rating API.
• Pull Moltbook feed metrics with the UBOS CLI.
• Merge and enrich the data using a lightweight Python script.
• Visualize the results with Plotly or Grafana.
• Publish a developer‑friendly guide on ubos.tech.

Future enhancements could include:

1. Automating the pipeline with Workflow automation studio.
2. Adding anomaly detection using the Chroma DB integration for vector‑based similarity checks.
3. Embedding the visualizations directly into a custom Web app editor on UBOS dashboard.

Happy coding, and may your data pipelines be as smooth as a well‑tuned token bucket!