Andrii Bidochko

• Updated: March 23, 2026
• 5 min read

Step‑by‑step guide: Building a real‑time Grafana dashboard for OpenClaw ROI metrics

To build a real‑time Grafana dashboard that visualizes OpenClaw ROI metrics such as open rates, conversion rates, and cost‑per‑lead, you need to (1) collect the metrics from OpenClaw, (2) store them in a time‑series database, (3) configure Grafana data sources, (4) design panels with live refresh, and (5) optionally enrich the view with AI‑agent insights.

Introduction

OpenClaw rebranding and AI‑agent hype

Earlier this year, the marketing automation platform formerly known as OpenClaw announced a name transition to better reflect its AI‑first strategy. The rebrand aligns the product with the exploding industry buzz around AI agents that can automatically optimize campaigns, predict lead quality, and generate copy on the fly. By leveraging the new OpenClaw APIs, developers can now pull granular ROI data and feed it into any analytics stack.

Why real‑time ROI dashboards matter

Marketing teams need instant feedback to allocate spend, iterate creatives, and justify budgets. A real‑time Grafana dashboard provides:

• Immediate visibility into open and click‑through rates.
• Live conversion tracking to spot bottlenecks.
• Cost‑per‑lead (CPL) trends that trigger automated bidding adjustments.
• AI‑driven alerts when metrics deviate from predicted baselines.

Prerequisites

Before you start, make sure you have the following:

1. A registered UBOS homepage account with access to the OpenClaw hosting environment.
2. Grafana installed locally or on a server (Grafana OSS 9+ is recommended).
3. Read‑only API credentials for the newly branded OpenClaw platform.
4. A time‑series database – either Chroma DB integration for vector‑based storage or InfluxDB/Prometheus for classic metrics.
5. Basic familiarity with Docker, curl, and JSON.

Setting up data collection

Exporting OpenClaw metrics

OpenClaw exposes a REST endpoint that returns ROI data in JSON. Below is a minimal curl request that pulls the last 5 minutes of metrics:

curl -X GET "https://api.openclaw.io/v1/metrics?interval=5m" \
     -H "Authorization: Bearer YOUR_API_TOKEN" \
     -H "Accept: application/json"

The response includes fields such as open_rate, conversion_rate, and cpl. Store this JSON in a variable for the next step.

Ingesting into InfluxDB

Assuming you run InfluxDB in a Docker container, you can write the metrics using the line protocol. The following Bash snippet parses the JSON and pushes it to InfluxDB:

#!/usr/bin/env bash
API_RESPONSE=$(curl -s -X GET "https://api.openclaw.io/v1/metrics?interval=1m" \
  -H "Authorization: Bearer $OPENCLAW_TOKEN")
OPEN_RATE=$(echo $API_RESPONSE | jq -r '.open_rate')
CONV_RATE=$(echo $API_RESPONSE | jq -r '.conversion_rate')
CPL=$(echo $API_RESPONSE | jq -r '.cpl')

cat <<EOF | curl -i -XPOST "http://localhost:8086/api/v2/write?org=my-org&bucket=openclaw_metrics&precision=s" \
  -H "Authorization: Token $INFLUX_TOKEN" \
  --data-binary @-
openclaw_metrics open_rate=$OPEN_RATE,conversion_rate=$CONV_RATE,cpl=$CPL $(date +%s)
EOF

Schedule this script with cron or a Kubernetes CronJob to ensure continuous ingestion.

Building the Grafana dashboard

Creating data sources

Log into Grafana, navigate to Configuration → Data Sources**, and add a new InfluxDB source:

• URL: http://localhost:8086
• Organization: my-org
• Bucket: openclaw_metrics
• Authentication: Token (use the same token you used in the ingestion script).

Designing panels for each metric

For a clean, MECE‑structured view, create three separate rows—one per KPI.

Each panel should use the Refresh interval set to 5s for true real‑time monitoring.

Real‑time refresh settings

In the dashboard settings, enable Auto‑Refresh and choose 5 seconds. Also, turn on Live‑Tail for the panels that support it, so new points appear without a full page reload.

Enhancing with AI‑agent insights

Grafana can call external APIs via the Workflow automation studio. Use this feature to invoke an AI model that predicts next‑hour trends based on the last 24 hours of data.

Step‑by‑step AI integration

1. Create a new Webhook data source in Grafana pointing to a UBOS‑hosted endpoint.
2. Deploy a tiny Flask app on UBOS that forwards the payload to the OpenAI ChatGPT integration and returns a forecast.
3. Map the forecasted predicted_cpl to a new panel titled “AI‑Predicted CPL”.

Because the AI model runs on the same UBOS infrastructure, latency stays under 200 ms, preserving the dashboard’s real‑time feel.

Publishing the article on UBOS blog

When you publish this guide on the UBOS blog, embed the internal link to the OpenClaw hosting page exactly once (already done above). Use the following markdown‑compatible snippet to ensure the link is indexed correctly:

[OpenClaw hosting on UBOS](https://ubos.tech/host-openclaw/)

Make sure the article’s meta title contains the primary keyword “OpenClaw Grafana dashboard” and the meta description mentions “real‑time ROI metrics”.

Conclusion and next steps

By following this hands‑on guide you now have a live Grafana dashboard that:

• Shows OpenClaw’s core ROI metrics in seconds.
• Refreshes automatically without manual reloads.
• Leverages AI agents to forecast future performance.

Next, consider expanding the dashboard with additional dimensions such as UBOS templates for quick start or integrating the AI Video Generator to create automated performance recap videos.

Ready to host your own OpenClaw instance and keep the data pipeline running 24/7? Explore the UBOS pricing plans and spin up a managed environment in minutes.

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.