ai-agent-k8sgpt

To create an AI agent in Python that interacts with k8sGPT, you'll need to integrate your Python code with Kubernetes and have it communicate with the k8sGPT service. k8sGPT is a tool designed to simplify troubleshooting and diagnostics in Kubernetes clusters by leveraging GPT-based AI models to analyze and interpret Kubernetes states and logs.

Here's a step-by-step guide to building an AI agent that connects with k8sGPT:

Prerequisites

1. Kubernetes Cluster: You need access to a Kubernetes cluster where k8sGPT is deployed.
2. Kubernetes Client for Python: Install the kubernetes Python package to interact with the Kubernetes API.
3. k8sGPT Deployment: k8sGPT should be deployed and running in your Kubernetes cluster.

Step 1: Install Required Python Packages

First, you need to install the necessary Python packages.

pip install kubernetes requests

Step 2: Set Up the Kubernetes Client

The Kubernetes client allows your Python script to interact with the Kubernetes API.

from kubernetes import client, config

# Load Kubernetes configuration
config.load_kube_config()

# Create a Kubernetes API client
v1 = client.CoreV1Api()

Step 3: Interact with k8sGPT

Assuming k8sGPT is exposed via a service, you can interact with it using HTTP requests from your Python agent. For example, you might query k8sGPT for insights or recommendations based on the current state of the cluster.

import requests

class K8sGPTAgent:
    def __init__(self, service_url):
        self.service_url = service_url

    def get_insights(self):
        response = requests.get(f"{self.service_url}/api/v1/insights")
        if response.status_code == 200:
            return response.json()
        else:
            return {"error": "Failed to fetch insights from k8sGPT"}

    def analyze_namespace(self, namespace):
        response = requests.get(f"{self.service_url}/api/v1/analyze/{namespace}")
        if response.status_code == 200:
            return response.json()
        else:
            return {"error": f"Failed to analyze namespace {namespace}"}

# Initialize the agent with the k8sGPT service URL
k8sgpt_agent = K8sGPTAgent(service_url="http://k8sgpt-service.default.svc.cluster.local")

# Fetch and print insights
insights = k8sgpt_agent.get_insights()
print(insights)

# Analyze a specific namespace
namespace_analysis = k8sgpt_agent.analyze_namespace("default")
print(namespace_analysis)

Step 4: Example of Full Agent

Here's the full code that brings everything together:

from kubernetes import client, config
import requests

# Load Kubernetes configuration
config.load_kube_config()

# Create a Kubernetes API client
v1 = client.CoreV1Api()

class K8sGPTAgent:
    def __init__(self, service_url):
        self.service_url = service_url

    def get_insights(self):
        response = requests.get(f"{self.service_url}/api/v1/insights")
        if response.status_code == 200:
            return response.json()
        else:
            return {"error": "Failed to fetch insights from k8sGPT"}

    def analyze_namespace(self, namespace):
        response = requests.get(f"{self.service_url}/api/v1/analyze/{namespace}")
        if response.status_code == 200:
            return response.json()
        else:
            return {"error": f"Failed to analyze namespace {namespace}"}

# Initialize the agent with the k8sGPT service URL
k8sgpt_agent = K8sGPTAgent(service_url="http://k8sgpt-service.default.svc.cluster.local")

# Fetch and print general insights
insights = k8sgpt_agent.get_insights()
print("Cluster Insights:", insights)

# Analyze a specific namespace, e.g., "default"
namespace_analysis = k8sgpt_agent.analyze_namespace("default")
print(f"Namespace 'default' Analysis:", namespace_analysis)

Step 5: Deploy and Run Your Agent

You can run this Python script from within your cluster (e.g., in a Kubernetes Job or Pod) or from an external system with access to the cluster's API server.

Explanation:

• K8s Client: The Kubernetes client is used to interact with your Kubernetes cluster (optional, but useful if you need to fetch cluster data).
• HTTP Requests: The agent sends HTTP requests to the k8sGPT service to retrieve insights or perform analysis.
• Service URL: The service URL should point to where k8sGPT is exposed (e.g., via a Kubernetes Service).

Conclusion:

This Python AI agent interacts with k8sGPT to gather insights and perform analyses on your Kubernetes cluster. It can be expanded to automate troubleshooting, make decisions based on k8sGPT's insights, or trigger alerts and actions depending on the findings.