crashsense 2.0.0

Self-Healing Kubernetes Platform - AI-powered crash detection and automated remediation for containerized workloads

🚀 CrashSense - Self-Healing Kubernetes Platform

AI-Powered Kubernetes Monitoring with Automated Crash Detection & Remediation

Automatically detect and remediate Kubernetes pod crashes, resource exhaustion, and network failures

🚀 Install from PyPI • 📖 Documentation • 💬 Support

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✨ What is CrashSense?

CrashSense is a comprehensive self-healing Kubernetes platform that combines AI-powered log analysis with automated remediation for containerized workloads. Originally designed for crash log analysis, it now provides enterprise-grade Kubernetes cluster monitoring, intelligent issue detection, and autonomous healing capabilities.

🎯 Key Use Cases

Use Case	Description
🔄 Self-Healing K8s	Automatically detect and fix pod crashes, OOMKilled containers, and CrashLoopBackOff
📊 Resource Management	Monitor and remediate resource exhaustion (CPU/memory limits)
🌐 Network Reliability	Detect service endpoint failures and network issues
📈 Prometheus Integration	Collect metrics and integrate with Alertmanager for comprehensive monitoring
🧠 AI-Powered Analysis	Leverage LLMs to analyze crash logs and suggest intelligent fixes
🖥️ Traditional Monitoring	Support for web servers, system logs, and CI/CD pipelines

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🌟 Features & Highlights

🔍 Kubernetes Monitoring Pod crash detection (CrashLoopBackOff, OOMKilled) Resource exhaustion monitoring Network failure detection Real-time cluster health checks Multi-namespace support 🏥 Self-Healing Automated pod restart/deletion Memory limit auto-scaling Service endpoint remediation Deployment rollout management Configurable dry-run mode

📊 Observability Prometheus metrics exposure Alertmanager integration Custom metric collection Webhook receivers for alerts Historical trend analysis 🧠 AI-Powered GPT/Ollama integration for log analysis Root cause identification Intelligent remediation suggestions RAG over documentation Context-aware fixes

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🚀 Quick Start

Installation

# Install from PyPI with Kubernetes support
pip install crashsense

# Or install from source (development)
git clone https://github.com/AzizBahloul/CrashSense.git
cd CrashSense
pip install -e .

Initial Setup

# Initialize and configure LLM provider
crashsense init

Choose your preferred provider:

• OpenAI GPT (recommended for accuracy)
• Local Ollama (privacy-focused, no API costs)

Kubernetes Setup

Enable Kubernetes monitoring in ~/.crashsense/config.toml:

[kubernetes]
enabled = true
kubeconfig = null  # Uses default ~/.kube/config
namespaces = []  # Monitor all namespaces, or specify: ["production", "staging"]
auto_heal = true
dry_run = false  # Set to true for safe testing
max_remediation_actions = 10

[prometheus]
enabled = true
url = "http://localhost:9090"
alertmanager_url = "http://localhost:9093"
metrics_port = 8000

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💻 Usage Examples

Kubernetes Monitoring

Check Cluster Health

# View cluster status and health metrics
crashsense k8s status

# Check specific namespaces
crashsense k8s status -n production -n staging

One-Time Scan and Heal

# Detect and fix issues (with confirmation)
crashsense k8s heal

# Dry-run mode (simulate without applying changes)
crashsense k8s heal --dry-run

Continuous Monitoring

# Monitor cluster every 60 seconds
crashsense k8s monitor

# Enable auto-heal mode
crashsense k8s monitor --auto-heal

# Custom interval
crashsense k8s monitor --interval 30 --auto-heal

Pod Log Analysis

# Get pod logs
crashsense k8s logs my-pod -n production

# Analyze logs with AI
crashsense k8s logs my-pod --analyze

# Previous container logs (for crashed pods)
crashsense k8s logs my-pod --previous --analyze

Traditional Log Analysis

# Auto-detect and analyze latest crash log
crashsense

# Analyze specific log file
crashsense analyze /var/log/myapp/error.log

# Interactive TUI mode
crashsense tui

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🔧 Kubernetes Remediation Capabilities

CrashSense automatically handles common Kubernetes issues:

Pod Crash Issues

• CrashLoopBackOff: Analyzes logs, deletes pods with high restart counts
• ImagePullBackOff: Checks image pull secrets and registry configuration
• OOMKilled: Increases memory limits automatically (50% increase)
• CreateContainerError: Identifies configuration issues

Resource Exhaustion

• High Memory: Auto-scales memory limits and enables HPA
• High CPU: Scales deployment replicas
• Quota Exceeded: Recommends quota adjustments

Network Issues

• No Service Endpoints: Verifies pod selectors and labels
• Service Unavailable: Checks pod readiness and restarts if needed

Configuration Issues

• Pending Pods: Analyzes scheduling constraints and node resources
• Failed Mounts: Identifies PVC and volume issues

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📊 Prometheus & Alertmanager Integration

Expose Metrics

CrashSense exposes Prometheus metrics:

# Metrics available at http://localhost:8000/metrics

Available Metrics:

• crashsense_pod_crashes_total - Total pod crashes detected
• crashsense_remediations_total - Total remediation actions taken
• crashsense_pod_health - Pod health status (0/1)
• crashsense_cluster_health_score - Overall cluster health (0-100)
• crashsense_remediation_duration_seconds - Remediation action duration

Alertmanager Webhook

Configure Alertmanager to trigger CrashSense remediation:

receivers:
  - name: crashsense
    webhook_configs:
      - url: 'http://crashsense:9094/webhook'
        send_resolved: true

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🏗️ Architecture

┌─────────────────────────────────────────────────┐
│           CrashSense Platform                    │
├─────────────────────────────────────────────────┤
│                                                  │
│  ┌──────────────┐      ┌──────────────┐        │
│  │ K8s Monitor  │◄────►│  Prometheus  │        │
│  │              │      │  Collector   │        │
│  └──────┬───────┘      └──────────────┘        │
│         │                                        │
│         ▼                                        │
│  ┌──────────────┐      ┌──────────────┐        │
│  │   Analyzer   │◄────►│  LLM Adapter │        │
│  │  (AI-Powered)│      │ (GPT/Ollama) │        │
│  └──────┬───────┘      └──────────────┘        │
│         │                                        │
│         ▼                                        │
│  ┌──────────────┐      ┌──────────────┐        │
│  │ Remediation  │◄────►│   Memory     │        │
│  │   Engine     │      │    Store     │        │
│  └──────────────┘      └──────────────┘        │
│                                                  │
├─────────────────────────────────────────────────┤
│         CLI / TUI / API Interface                │
└─────────────────────────────────────────────────┘
         ▲                       ▲
         │                       │
    Kubernetes API         Alertmanager

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🛡️ Safety Features

CrashSense implements multiple safety layers:

1. Dry-Run Mode: Test remediation without applying changes
2. Action Limits: Maximum actions per cycle (default: 10)
3. Confirmation Prompts: Interactive mode requires user approval
4. Audit Trail: All actions logged with timestamps and results
5. Rollback Support: Failed actions can be reverted
6. RBAC Integration: Respects Kubernetes permissions

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📋 Requirements

System Requirements

• Python 3.8+
• Kubernetes cluster (1.28+) with kubectl access
• Optional: Prometheus & Alertmanager for metrics

Kubernetes Permissions

CrashSense requires these RBAC permissions:

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: crashsense
rules:
  - apiGroups: [""]
    resources: ["pods", "pods/log", "services", "endpoints"]
    verbs: ["get", "list", "watch", "delete"]
  - apiGroups: ["apps"]
    resources: ["deployments", "replicasets"]
    verbs: ["get", "list", "patch"]
  - apiGroups: [""]
    resources: ["nodes"]
    verbs: ["get", "list"]
  - apiGroups: ["metrics.k8s.io"]
    resources: ["pods", "nodes"]
    verbs: ["get", "list"]

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🎓 Advanced Usage

Custom Remediation Policies

Create custom remediation logic:

from crashsense.core.k8s_monitor import KubernetesMonitor
from crashsense.core.remediation import RemediationEngine

# Initialize
monitor = KubernetesMonitor()
engine = RemediationEngine(monitor, dry_run=False)

# Detect issues
crashes = monitor.detect_pod_crashes()

# Apply remediation
for crash in crashes:
    result = engine.remediate_issue(crash)
    print(f"Remediation: {result}")

RAG Document Management

Add Kubernetes documentation for better analysis:

# Add custom documentation
crashsense rag add /path/to/k8s-docs

# Build RAG index
crashsense rag build

# Clear and rebuild
crashsense rag clear
crashsense rag add ./kubernetes-playbooks

Memory Management

View and manage crash analysis history:

# List recent crash analyses
crashsense memory

# Stored in SQLite: ~/.crashsense/memories.db

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🔌 Integration Examples

CI/CD Pipeline

# GitLab CI example
k8s-health-check:
  stage: post-deploy
  script:
    - pip install crashsense
    - crashsense k8s status || exit 1
    - crashsense k8s heal --dry-run

Monitoring Dashboard

# Flask webhook receiver
from flask import Flask, request
from crashsense.core.remediation import RemediationEngine

app = Flask(__name__)

@app.route('/webhook', methods=['POST'])
def alertmanager_webhook():
    alert = request.json
    # Trigger remediation based on alert
    engine.remediate_issue(alert)
    return {'status': 'ok'}

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📚 Documentation

• Kubernetes Remediation Playbook
• Python Exceptions Guide
• Web Server Patterns

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🤝 Contributing

Contributions are welcome! Please see CONTRIBUTING.md for details.

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📄 License

MIT License - see LICENSE for details.

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🙏 Acknowledgments

Built with:

• Kubernetes Python Client
• Prometheus Client
• Rich for beautiful terminal output
• OpenAI GPT & Ollama for AI-powered analysis

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Made with ❤️ by Mohamed Aziz Bahloul

⭐ Star this repo if you find it useful!

Analyze specific file

crashsense analyze /var/log/apache2/error.log

Pipe from STDIN

tail -f /var/log/syslog | crashsense analyze

Launch interactive TUI

crashsense tui

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## 📸 Screenshots & Workflow

### 🔄 Startup & Device Detection
*CrashSense initializing and detecting compute resources*

![Startup & Device Detection](image1.png)

### 🔍 Crash Log Analysis & Explanation
*AI-powered analysis showing parsed information and remediation steps*

![Crash Log Analysis & Explanation](image2.png)

### 📊 Summary Table & Command Suggestions
*Actionable summary with safe shell command recommendations*

![Summary Table & Command Suggestions](image3.png)

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## 📚 RAG Documentation (Optional)

CrashSense can leverage your existing documentation for more contextual analysis:

### 📁 **Default Knowledge Base**

kb/ # Your custom docs src/data/ ├── crashsense_best_practices.md ├── python_exceptions_playbook.md ├── web_server_error_patterns.md └── linux_permission_paths.md

### 🛠️ **Manage Documentation**

```bash
# Add custom documentation
crashsense rag add /path/to/docs/

# Clear knowledge base
crashsense rag clear

# Rebuild with dry-run preview
crashsense rag build --dry-run

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⚙️ Configuration & Security

📝 Configuration File

# ~/.crashsense/config.toml
[llm]
provider = "openai"  # or "ollama"
model = "gpt-4"

[security]
safe_mode = true
confirm_commands = true

🔐 Environment Variables

export CRASHSENSE_OPENAI_KEY="your-api-key-here"

🛡️ Security Features

• ✅ Command execution requires explicit confirmation
• ✅ Built-in safety checks and validation
• ✅ Configurable security policies
• ✅ Audit trail for executed commands

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🔧 Troubleshooting

Ollama Setup Issues

# Manual model pull
ollama pull llama3.2:1b

# Check daemon status
ollama serve

# Verify installation
ollama list

For more help, visit the Ollama Documentation

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💝 Support & Donations

If CrashSense has helped streamline your debugging workflow, consider supporting continued development:

Platform	ID
💳 RedotPay	1951109247
🟡 Binance	1104913076

Your support helps keep CrashSense free and continuously improving!

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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⭐ Star this repo yar7am book!