How to Build a Real-Time Dashboard with OpenClaw Sub-Agents and PostgreSQL

Static dashboards age badly. You build one, requirements change, APIs rate-limit you, and the data is stale by the time you open the page.

A better approach is to treat your dashboard as a living agent workflow: multiple sub-agents fetch data in parallel, write time-stamped metrics to PostgreSQL, and a coordinator composes a clean status report every few minutes. When something crosses a threshold, you get an alert immediately.

This guide shows you how to implement the dynamic dashboard use case from the OpenClaw community: a real-time dashboard with parallel sub-agents, PostgreSQL storage for historical trends, and alerting rules you can tune in plain English.

Internal links you will want handy:

• Use case library: /blog/openclaw-use-cases
• Skills overview: /blog/openclaw-skills-guide

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The Problem: Visibility Across Too Many Systems

If you ship software, you probably care about a mix of:

• Product signals: signups, activations, churn, feedback
• Engineering signals: CI status, deploy frequency, open issues
• Community signals: GitHub stars, Discord activity, social mentions
• Infrastructure signals: CPU, memory, disk, service uptime

Traditional dashboards force you to pick one world (Grafana for infra, spreadsheets for growth, GitHub notifications for OSS). But what you actually need is a single place that answers:

• What changed since the last update?
• Is anything breaking right now?
• What deserves my attention today?

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The Solution: A Parallel, Agent-Driven Dashboard

The dynamic dashboard pattern is simple:

1. A coordinator runs on a schedule (cron).
2. It spawns sub-agents in parallel (one per data source).
3. Each sub-agent fetches data and writes atomic metrics into PostgreSQL.
4. The coordinator aggregates metrics into a formatted status update.
5. Alert rules are evaluated. If triggered, you get a ping.

Conceptually:

cron -> coordinator
   -> spawn github-agent
   -> spawn social-agent
   -> spawn system-agent
   -> spawn market-agent

sub-agents -> write metrics -> PostgreSQL
coordinator -> format dashboard -> Discord/Telegram
coordinator -> evaluate alerts -> DM/ping

Why this works:

• Parallel fetch avoids slow sequential polling.
• Each agent has a small scope, which keeps context tight.
• PostgreSQL gives you history, deltas, and trends.

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Skills and Components You Need

To build this, you typically need:

• Sub-agent spawning (sessions_spawn / sessions_send) for parallel execution
• GitHub access via gh CLI or GitHub API
• Social monitoring (Bird skill for X/Twitter, or another integration)
• Web access (web_search / web_fetch) for external sources
• PostgreSQL to store metrics
• A delivery channel (Discord, Telegram, Slack)
• Cron scheduling to run the job every N minutes

If you are deploying quickly, ClawRapid is the simplest path because it is set up to run OpenClaw workflows with persistent storage and scheduling.

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Step 1: Create Your Metrics Schema (PostgreSQL)

Start with a schema that is flexible. You want to store lots of small, timestamped values.

CREATE TABLE metrics (
  id SERIAL PRIMARY KEY,
  source TEXT NOT NULL,
  metric_name TEXT NOT NULL,
  metric_value NUMERIC,
  meta JSONB,
  timestamp TIMESTAMPTZ DEFAULT NOW()
);

CREATE TABLE alerts (
  id SERIAL PRIMARY KEY,
  source TEXT NOT NULL,
  metric_name TEXT NOT NULL,
  condition TEXT NOT NULL,     -- e.g. 'gt', 'lt', 'delta_gt'
  threshold NUMERIC NOT NULL,
  cooldown_minutes INT DEFAULT 60,
  last_triggered TIMESTAMPTZ
);

Notes:

• meta is optional but useful (store repo name, link, units).
• Keep the schema generic so adding new metrics is trivial.

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Step 2: Define the Data Sources (What to Track)

Write down your initial sources and keep it small. A solid v1 includes:

GitHub (one repo)

• Stars (current)
• Stars delta (since last run or last hour)
• Open issues
• Commits in last 24 hours

Social (one handle or keyword)

• Mentions count
• Top mention link
• Sentiment ratio (basic)

System health (one machine)

• CPU percent
• Memory percent
• Disk percent
• Service status (up/down)

Then expand over time.

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Step 3: Prompt the Coordinator to Spawn Sub-Agents

Use a single prompt that describes:

• Schedule
• Sub-agent responsibilities
• Output format
• Where to store metrics

Template prompt:

You are my dynamic dashboard manager.

Every 15 minutes, run a cron job to:

1) Spawn sub-agents in parallel:
- GitHub agent: fetch stars, forks, open issues, commits (24h) for repo my-org/my-repo
- Social agent: fetch mention count and top mention for keyword "MyProduct"
- System agent: get CPU, memory, disk, and service status via shell commands

2) Each sub-agent writes metrics rows into PostgreSQL table metrics.
Include source, metric_name, metric_value, meta, and timestamp.

3) The coordinator queries latest metrics, calculates deltas, formats a dashboard update,
and posts it to Discord #dashboard.

4) Evaluate alert rules:
- if cpu > 90% for 2 consecutive runs -> alert
- if disk > 85% -> warn
- if stars delta > 50 in 1 hour -> alert

5) If an alert triggers, DM me with the details and a suggested next action.

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Step 4: Design the Dashboard Output (Readable in 20 Seconds)

A dashboard that needs careful reading will be ignored. Use a stable layout.

Example format:

Dashboard Update - 2026-03-02 09:15

GitHub
- Stars: 12,340 (+82 / 24h)
- Open issues: 41
- Commits (24h): 9

Social
- Mentions: 17 (top: link)
- Sentiment: 70% positive, 20% neutral, 10% negative

System
- CPU: 38%
- Memory: 62%
- Disk: 71%
- Services: api=UP, worker=UP

Alerts
- none

Notes
- Next check in 15 minutes

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Step 5: Alerting Rules That Do Not Become Noise

Most alert systems fail because they are too chatty. A few practical rules:

• Add cooldowns (do not re-alert for the same condition for 60 minutes).
• Alert on sustained issues (two consecutive runs) instead of spikes.
• Include context and a suggested next action.

Example alert message:

ALERT: CPU is above 90% (92%, 94%) for 2 consecutive runs.
Likely cause: worker backlog or runaway process.
Suggested next action: check top processes, review recent deploy.

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Advanced: Historical Trends and Natural Language Queries

Once you have a week of data, your dashboard becomes a simple analytics system.

Useful SQL:

-- last value per metric
SELECT DISTINCT ON (source, metric_name)
  source, metric_name, metric_value, timestamp
FROM metrics
ORDER BY source, metric_name, timestamp DESC;

Ask OpenClaw:

• "Show star growth for the last 30 days"
• "When did CPU spikes start?"
• "What happened the last time disk exceeded 85%?"

Because metrics are in PostgreSQL, you can always backtrace.

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Troubleshooting and Reliability Checklist

Make metric writes idempotent

Retries can create duplicates. Pick one approach:

• Store a run_id for each cron execution and include it in every metric row.
• Or store a rounded time bucket and enforce uniqueness per metric per bucket.

Add timeouts and fallbacks per source

If social fetch fails, the dashboard should still render. Mark the source as degraded and use last known values.

Store raw payloads briefly

For debugging, keep raw API payloads (JSON) in meta or a separate table with short retention.

Stagger schedules

Not all sources need the same cadence. System metrics every 5 minutes, social every 30, GitHub every 15 is common.

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FAQ

Do I need to build a frontend?

No. Start with text dashboards in Discord or Telegram. If you want visuals later, you can render an HTML dashboard with Canvas.

Why PostgreSQL instead of just keeping the last value in memory?

History is what lets you compute deltas, trends, and anomalies. PostgreSQL also makes the system resilient across restarts.

How expensive is this to run?

Most runs are cheap because you are fetching small payloads. Use smaller models for data collection and reserve larger models for synthesis or incident analysis.

Can the agent auto-fix issues?

Yes, but start with alert-only. Once trusted, add safe remediation: restart a service, clear logs, scale a worker.

What if one sub-agent fails?

The coordinator should still post a dashboard and show the failed source as down or degraded.

Can I add more sources later?

Yes. The pattern scales linearly: add a new sub-agent, define metrics, update the aggregator format.

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Getting Started with ClawRapid

Setting up sub-agents, PostgreSQL connectivity, scheduling, and safe alerting from scratch takes time. ClawRapid is designed to make these OpenClaw workflows deployable quickly with the right defaults.

Deploy your real-time dashboard in minutes, not days.