Incident Response System
Multi-agent system that detects production incidents, diagnoses root causes, suggests fixes, and coordinates team response in real-time.
🎯 Buy once, deploy on any framework
Includes implementations for CrewAI. One purchase — all platforms.
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- ✓ All 1 platform implementations
- ✓ Full source code & documentation
- ✓ Commercial license included
- ✓ 30-day money-back guarantee
- ✓ Free updates for 1 year
- ✓ 30-day email support
Choose Your Platform
One purchase includes all 1 implementations. Deploy on whichever framework fits your stack.
CrewAI
CrewAI crew with 4 specialized agents and production-ready tools.
Included in CrewAI version
- ✓crew.py with 4 agents
- ✓Custom tools
- ✓Config templates
- ✓Deployment guide
⚡ Why OpenClaw?
One-click install, automatic orchestration, built-in cron scheduling, and memory integration. Other platforms require manual setup — OpenClaw gets you to production in minutes.
Code Preview — CrewAI
from crewai import Agent, Crew
alert = Agent(role='Alert Analyst', goal='Filter noise, find real incidents', tools=[alert_processor])
root_cause = Agent(role='Root Cause Investigator', goal='Diagnose incidents', tools=[log_analyzer, metrics])Agent Architecture
How the 4 agents work together
Your data, triggers, or requests
Alert Analyzer
Alert Processing
Filters noise, identifies real incidents, classifies severity.
Root Cause Agent
Root Cause Investigation
Investigates logs, metrics, and traces to find root cause.
Fix Suggester
Solution Recommendation
Proposes fixes based on similar past incidents.
Coordinator
Response Coordination
Manages notifications, escalations, and post-mortems.
Structured results, reports, and actions
What's Included
Everything you get with this template
The Problem
Engineers scramble through logs at 3 AM, spend 30 minutes understanding what's wrong, and alert fatigue means real incidents get lost.
The Solution
A 4-agent system that processes alerts, investigates root causes, suggests fixes, and coordinates response with proper timelines.
Tools You'll Need
Everything required to build this 4-agent system — click any tool for details
Agent orchestration
LLM for log analysis and diagnosis
Error tracking and stack traces
Incident notifications
Metrics and APM data
Incident history and runbooks
Diagnostic decision tracing
Implementation Guide
8 steps to build this system • 3-4 hours estimated
📋 Prerequisites
Map alert sources and severity taxonomy
Catalog all monitoring alerts and define trigger criteria.
Build alert processing pipeline
Connect monitoring webhooks with noise filtering and correlation.
Configure log and metrics analysis
Wire Root Cause Agent to logging and metrics systems.
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Code Preview
Sample agent setup — see platform-specific previews above
from crewai import Agent, Crew
alert = Agent(role='Alert Analyst', goal='Filter noise, find real incidents', tools=[alert_processor])
root_cause = Agent(role='Root Cause Investigator', goal='Diagnose incidents', tools=[log_analyzer, metrics])Example Input & Output
See what goes in and what comes out
{"alert": "HTTP 500 spike on /api/checkout", "severity": "high"}{"root_cause": "DB connection pool exhausted", "fix": "Increase pool_size to 50", "mttr_estimate": "15 min"}Requirements
Reviews
What builders are saying
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Frequently Asked Questions
Do I get all platform implementations?+
Yes — one purchase includes all platform implementations.
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