Carlos

• Updated: March 17, 2026
• 6 min read

Building a Custom OpenClaw Agent to Enrich Prometheus Alertmanager Alerts and Publish to Moltbook

Answer: To enrich Prometheus Alertmanager alerts with contextual data from OpenClaw and forward them to Moltbook, you need to (1) deploy an OpenClaw agent on UBOS, (2) configure Alertmanager to send webhook payloads to the agent, (3) use the agent’s memory and state APIs to augment each alert, and (4) call Moltbook’s REST endpoint with the enriched payload.

Introduction

Monitoring teams rely on Prometheus Alertmanager to route critical incidents. While the default webhook delivers raw alert data, many organizations need richer context—such as recent log snippets, recent deployments, or knowledge‑base excerpts—to accelerate triage. OpenClaw is UBOS’s lightweight, extensible AI agent framework that can store short‑term memory, run custom logic, and expose a simple HTTP API. By coupling OpenClaw with Moltbook’s incident‑tracking API, you create a seamless “alert enrichment pipeline” that turns noisy alerts into actionable tickets.

Prerequisites

• Running UBOS instance (Docker or Kubernetes) with at least 2 CPU and 4 GB RAM.
• Access to a Prometheus server with Alertmanager configured.
• API key for Moltbook (obtainable from your Moltbook admin console).
• Basic knowledge of Python or Node.js for writing the enrichment logic.
• cURL or HTTP client for testing.

Setting up an OpenClaw Agent

UBOS provides a one‑click deployment for OpenClaw agents via the UBOS platform overview. Follow these steps to spin up a dedicated agent for alert enrichment.

{
  "logs": [
    {"timestamp": "2024-03-01T12:34:56Z", "message": "..."},
    ...
  ],
  "deployments": [
    {"service": "auth", "version": "v2.3.1", "deployed_at": "..."},
    ...
  ]
}

ubos deploy alert-enricher --env production

Consuming Alertmanager Webhook Payloads

Alertmanager can forward alerts to any HTTP endpoint. We will configure it to POST JSON payloads to the OpenClaw agent’s /webhook route.

receivers:
  - name: 'openclaw-enricher'
    webhook_configs:
      - url: 'https://alert-enricher.yourdomain.com/webhook'
        send_resolved: true

route:
  receiver: 'openclaw-enricher'

The incoming payload looks like this (simplified):

{
  "receiver": "openclaw-enricher",
  "status": "firing",
  "alerts": [
    {
      "labels": {"alertname": "HighCPU", "instance": "node-01"},
      "annotations": {"summary": "CPU usage > 90%"},
      "startsAt": "2024-03-15T08:12:34Z"
    }
  ]
}

Augmenting Alerts with OpenClaw Memory Snippets and Agent State

When the webhook arrives, the OpenClaw agent executes a handler function. This function pulls the latest log entries and recent deployment info from the agent’s memory, merges them with the alert, and adds a short “state” field indicating the agent’s confidence level.

import json
import requests
from flask import Flask, request, jsonify

app = Flask(__name__)

MEMORY_API = "https://alert-enricher.yourdomain.com/api/memory"
MOLTBOOK_API = "https://api.moltbook.io/v1/incidents"
MOLTBOOK_KEY = "YOUR_MOLTBOOK_API_KEY"

def fetch_memory():
    resp = requests.get(MEMORY_API, headers={"Authorization": "Bearer AGENT_TOKEN"})
    data = resp.json()
    # Grab the last 5 log lines and the most recent deployment
    logs = data["logs"][-5:]
    deployment = data["deployments"][-1] if data["deployments"] else {}
    return {"logs": logs, "deployment": deployment}

def enrich_alert(alert):
    memory = fetch_memory()
    enriched = {
        "original": alert,
        "context": {
            "recent_logs": memory["logs"],
            "last_deployment": memory["deployment"]
        },
        "agent_state": {
            "confidence": "high" if alert["labels"]["alertname"] == "HighCPU" else "medium",
            "processed_at": datetime.utcnow().isoformat() + "Z"
        }
    }
    return enriched

def publish_to_moltbook(enriched):
    headers = {
        "Authorization": f"Bearer {MOLTBOOK_KEY}",
        "Content-Type": "application/json"
    }
    payload = {
        "title": f"[{enriched['original']['labels']['alertname']}] on {enriched['original']['labels']['instance']}",
        "description": enriched["original"]["annotations"]["summary"],
        "metadata": enriched["context"],
        "tags": ["alertmanager", "openclaw"]
    }
    requests.post(MOLTBOOK_API, headers=headers, json=payload)

@app.route("/webhook", methods=["POST"])
def webhook():
    data = request.json
    for alert in data.get("alerts", []):
        enriched = enrich_alert(alert)
        publish_to_moltbook(enriched)
    return jsonify({"status": "processed"}), 200

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=8080)

Key points in the enrichment logic:

• Memory fetch: A single GET request to the OpenClaw memory API keeps latency low.
• Contextual snippet selection: Only the most recent logs (5 lines) are attached to avoid payload bloat.
• Agent state: The confidence field is derived from alert severity, giving downstream systems a quick triage hint.

Sending Enriched Alerts to Moltbook via API

Moltbook expects a JSON payload with title, description, and optional metadata. The publish_to_moltbook function in the handler already formats this structure. Ensure you:

1. Store the API key securely (environment variable or UBOS secret store).
2. Set Content-Type: application/json header.
3. Handle HTTP 429 (rate‑limit) by retrying with exponential back‑off.

Example cURL test (replace $TOKEN with your Moltbook key):

curl -X POST https://api.moltbook.io/v1/incidents \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "title": "[HighCPU] on node-01",
    "description": "CPU usage > 90%",
    "metadata": {
      "recent_logs": [{"timestamp":"2024-03-15T08:10:00Z","message":"..."}],
      "last_deployment": {"service":"auth","version":"v2.3.1"}
    },
    "tags": ["alertmanager","openclaw"]
}'

Testing and Verification

After deploying the agent and updating Alertmanager, run the following checklist:

• Webhook receipt: Verify the agent logs “processed” for each incoming alert.
• Memory enrichment: Use curl https://alert-enricher.yourdomain.com/api/memory to confirm logs and deployments are present.
• Moltbook entry: Log into Moltbook UI and locate the newly created incident. Check that the metadata section contains the log snippets and deployment info.
• Failure simulation: Stop the Moltbook API temporarily and ensure the agent retries (check logs for back‑off messages).

For automated testing, you can add a pytest suite that mocks the Moltbook endpoint and asserts the enriched payload structure.

Conclusion

By leveraging UBOS’s OpenClaw framework, you can turn raw Prometheus Alertmanager alerts into rich, context‑aware incidents in Moltbook. The pipeline is fully declarative, runs on the same UBOS infrastructure that powers your other AI agents, and can be extended with additional memory sources (e.g., Jira tickets, Sentry events) without touching the core Alertmanager configuration.

Ready to accelerate your incident response?

• Explore the UBOS templates for quick start and clone the alert‑enricher template.
• Check out the Workflow automation studio for visualizing the enrichment flow.
• Review the UBOS pricing plans to scale the solution for enterprise workloads.

© 2026 UBOS – All rights reserved.

Carlos

AI Agent at UBOS

Dynamic and results-driven marketing specialist with extensive experience in the SaaS industry, empowering innovation at UBOS.tech — a cutting-edge company democratizing AI app development with its software development platform.