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Run with IDPBuilder 🏗️ 💻

IDPBuilder is a tool for creating local Internal Developer Platform environments using KIND clusters. It provides a fast way to deploy and test platform components including ArgoCD, Vault, Backstage, and AI Platform Engineering agents.

Prerequisites​

Architecture Overview​

IDPBuilder creates a KIND cluster and deploys platform components via ArgoCD. The CAIPE (Community AI Platform Engineering) stack adds authentication, secret management, and multi-agent AI capabilities:

Component Flow​

  1. IDPBuilder Initialization: Creates KIND cluster and deploys ArgoCD + Gitea as foundation
  2. Platform Deployment: ArgoCD deploys all platform components from Git repositories
  3. Authentication Setup: Keycloak provides SSO for Backstage and other platform services
  4. Secret Management: Vault stores secrets, External Secrets distributes to applications
  5. Developer Access: NGINX Ingress routes traffic, Backstage provides developer portal
  6. AI Integration: Agent-Forge plugin in Backstage connects to CAIPE MAS Agent
  7. Multi-Agent System: Orchestrator manages individual agents for different platform domains

Section Outline​

  1. Create KIND Cluster with IDPBuilder
  2. Access ArgoCD and Monitor Deployments
  3. Configure Vault Secrets
  4. Access Backstage Portal
  5. Use CAIPE MAS Agent

Create KIND Cluster with IDPBuilder​

Step 1: Create the Platform​

# Create cluster with reference implementation + lightweight AI stack
idpbuilder create \
  --use-path-routing \
  --package https://github.com/cnoe-io/stacks//ref-implementation \
  --package https://github.com/suwhang-cisco/stacks//ai-platform-engineering

This command will:

  • Create a KIND cluster
  • Install core platform components
  • Deploy ArgoCD, Vault, and Backstage
  • Configure ingress with path-based routing

This takes around 5-10 minutes. Feel free to grab a coffee while it's deploying ☕

Step 2: Verify Cluster​

# Check cluster status
kubectl get nodes

# Verify all pods are running
kubectl get pods --all-namespaces

# Check ingress configuration
kubectl get ingress --all-namespaces

Access ArgoCD and Monitor Deployments​

Once the cluster is created, IDPBuilder outputs the ArgoCD URL.

Step 1: Get ArgoCD Credentials​

# Get admin password
idpbuilder get secrets -p argocd

Step 2: Access ArgoCD​

Open https://cnoe.localtest.me:8443/argocd/ and login with:

  • Username: admin
  • Password: From the command above

Monitor application sync status. Initial synchronization takes 3-5 minutes.

Configure Vault Secrets​

After Vault application syncs on ArgoCD successfully:

ArgoCD Vault Sync

Step 1: Get Vault Token​

# Extract root token
kubectl get secret vault-root-token -n vault -o jsonpath="{.data}" | \
  jq -r 'to_entries[] | "\(.key): \(.value | @base64d)"'

Step 2: Access Vault​

Open https://vault.cnoe.localtest.me:8443/. When you are asked to log in to the Vault UI, use the root token from the previous step.

Step 3: Update Secrets​

  1. Navigate to secrets/ai-platform-engineering in Vault UI: https://vault.cnoe.localtest.me:8443/ui/vault/secrets/secret/kv/list/ai-platform-engineering/

  2. Configure Global LLM Settings: Vault Secrets The global secret is required and contains LLM provider configuration shared across all agents:

    • LLM_PROVIDER: Defaults to azure-openai. Available options are: azure-openai, openai, aws-bedrock
    • Configure only the fields for your chosen provider:

    For AWS Bedrock (aws-bedrock):

    LLM_PROVIDER: "aws-bedrock"
    AWS_ACCESS_KEY_ID: <your-access-key>
    AWS_SECRET_ACCESS_KEY: <your-secret-key>
    AWS_REGION: <your-region>
    AWS_BEDROCK_MODEL_ID: <your-model-id>
    AWS_BEDROCK_PROVIDER: <your-provider>

    For OpenAI (openai):

    LLM_PROVIDER: "openai"
    OPENAI_API_KEY: <your-api-key>
    OPENAI_ENDPOINT: <your-endpoint>
    OPENAI_MODEL_NAME: <your-model-name>

    For Azure OpenAI (azure-openai):

    LLM_PROVIDER: "azure-openai"
    AZURE_OPENAI_API_KEY: <your-api-key>
    AZURE_OPENAI_ENDPOINT: <your-endpoint>
    AZURE_OPENAI_API_VERSION: <your-api-version>

  3. Configure Agent-Specific Secrets: For each agent you plan to use, populate all required fields in their respective secrets (e.g., github-secret, pagerduty-secret, jira-secret). All fields are required for the agent to function properly.

  4. Force secret refresh:

# Refresh secrets
kubectl delete secret --all -n ai-platform-engineering
# Once new secrets are created, delete pods to pick up new secrets
kubectl delete pod --all -n ai-platform-engineering

Access Backstage Portal​

Step 1: Get Backstage Credentials​

# Get user1 password
idpbuilder get secrets | grep USER_PASSWORD | sed 's/.*USER_PASSWORD=\([^,]*\).*/\1/'

Step 2: Login to Backstage​

Open https://cnoe.localtest.me:8443/ and login with:

  • Username: user1
  • Password: From Step 1 above

Use CAIPE (Community AI Platform Engineering) MAS Agent​

Once logged into Backstage:

  1. Look for the agent icon in the bottom-right corner
  2. Click to open the AI assistant
  3. Start chatting with the platform engineering agent

Useful Addresses for IDPBuilder Cluster​

Cleanup​

# Destroy the cluster and all resources
kind delete cluster --name localdev