Version: main 🚧

Helm

Deploy AI Platform Engineering on any Kubernetes cluster using the official Helm chart published to GitHub Container Registry.

Prerequisites

Helm 3
kubectl configured against your cluster
A running cluster (Minikube, Kind, EKS, GKE, AKS, etc.)

Chart registry

The chart is published as an OCI artifact:

oci://ghcr.io/cnoe-io/charts/ai-platform-engineering

Browse available versions (stable releases only, no RCs): 👉 ghcr.io/cnoe-io/charts/ai-platform-engineering

Chart structure

ai-platform-engineering/        # Parent chart
├── README.md                   # Full parameter reference
└── charts/
    ├── supervisor-agent/       # Orchestrator / LangGraph supervisor
    │   └── README.md
    ├── agent/                  # Generic agent subchart (aliased per integration)
    ├── langgraph-redis/        # Redis Stack for persistence (optional)
    │   └── README.md
    ├── caipe-ui/               # CAIPE web UI (optional)
    ├── rag-stack/              # RAG pipeline (optional)
    └── slim/ slim-control-plane/  # AGNTCY SLIM dataplane (optional)

Full parameter tables for each chart (auto-generated — regenerate with make docs-helm-charts):

ai-platform-engineering — parent chart (global values, agent selection)
supervisor-agent — orchestrator values, persistence, LLM secrets
agent — individual agent subchart
caipe-ui — UI subchart values
caipe-ui-mongodb — MongoDB for UI persistence
dynamic-agents — dynamic agent builder service
langgraph-redis — Redis Stack subchart values
slack-bot — Slack bot integration
rag-stack — RAG knowledge base (parent chart)
rag-server — RAG server
rag-redis — Redis for RAG vector store
rag-ingestors — data ingestors
agent-ontology — Graph RAG ontology agent

Step 1 — Get example values files

Pull the chart locally to access the bundled example values files:

helm pull oci://ghcr.io/cnoe-io/charts/ai-platform-engineering --version <VERSION> --untar

Replace <VERSION> with the latest stable version from the registry page.

Step 2 — Configure secrets

Choose one of three approaches to provide API keys and agent credentials.

Option A — Direct values file (development)

cp ai-platform-engineering/values-secrets.yaml.example values-secrets.yaml
# Edit values-secrets.yaml with your LLM keys and agent credentials

Never commit values-secrets.yaml.

Option B — Existing Kubernetes Secrets

If your secrets are already in the cluster:

# values.yaml
agent-argocd:
  secrets:
    secretName: "my-existing-secret"

Option C — External Secrets Operator (recommended for production)

cp ai-platform-engineering/values-external-secrets.yaml.example values-external-secrets.yaml
# Edit to point at your Vault / AWS Secrets Manager / GCP Secret Manager store

Step 3 — Choose agents

Agents are enabled via Helm tags. Common profiles:

Tag	Agents included
`basic`	argocd, backstage, github
`complete`	all agents
Individual	`agent-argocd`, `agent-github`, `agent-jira`, `agent-slack`, …

Step 4 — Install

# Minimal (in-memory, no persistence)
helm install ai-platform-engineering \
  oci://ghcr.io/cnoe-io/charts/ai-platform-engineering \
  --version <VERSION> \
  --values values-secrets.yaml

# With basic agents
helm install ai-platform-engineering \
  oci://ghcr.io/cnoe-io/charts/ai-platform-engineering \
  --version <VERSION> \
  --values values-secrets.yaml \
  --set tags.basic=true

# With External Secrets Operator
helm install ai-platform-engineering \
  oci://ghcr.io/cnoe-io/charts/ai-platform-engineering \
  --version <VERSION> \
  --values values-external-secrets.yaml

Step 5 — Verify

kubectl get pods
kubectl get services

Wait for pods to reach Running / 1/1 Ready.

Step 6 — Access

Port-forward (quickest)

kubectl port-forward service/ai-platform-engineering-supervisor-agent 8000:8000

Then connect with the agent chat CLI:

uvx --no-cache git+https://github.com/cnoe-io/agent-chat-cli.git a2a --host localhost --port 8000

Ingress (domain access)

# Enable ingress on Minikube
minikube addons enable ingress

# Deploy with ingress
helm upgrade ai-platform-engineering \
  oci://ghcr.io/cnoe-io/charts/ai-platform-engineering \
  --version <VERSION> \
  --values values-secrets.yaml \
  --values ai-platform-engineering/values-ingress.yaml.example

# Add Minikube IP to /etc/hosts
echo "$(minikube ip) supervisor-agent.local" | sudo tee -a /etc/hosts

Upgrade and uninstall

# Upgrade to a new version
helm upgrade ai-platform-engineering \
  oci://ghcr.io/cnoe-io/charts/ai-platform-engineering \
  --version <NEW_VERSION> \
  --values values-secrets.yaml

# Uninstall
helm uninstall ai-platform-engineering

Prompt configuration

Value	Description
`promptConfigType: default`	Balanced orchestrator, general use (default)
`promptConfigType: deep_agent`	Strict zero-hallucination mode for production
`promptConfig: \|`	Provide a fully custom prompt config inline

helm install ai-platform-engineering \
  oci://ghcr.io/cnoe-io/charts/ai-platform-engineering \
  --version <VERSION> \
  --values values-secrets.yaml \
  --set promptConfigType=deep_agent

Persistence

See the Persistence page for full options (in-memory default, Redis, Postgres, MongoDB, fact extraction).

Quick start — Redis persistence

# values-persistence.yaml
global:
  langgraphRedis:
    enabled: true

supervisor-agent:
  checkpointPersistence:
    type: redis
    redis:
      autoDiscoverService: langgraph-redis

  memoryPersistence:
    type: redis
    redis:
      autoDiscoverService: langgraph-redis
    enableFactExtraction: true
    maxMemories: 50
    maxSummaries: 10

helm install ai-platform-engineering \
  oci://ghcr.io/cnoe-io/charts/ai-platform-engineering \
  --version <VERSION> \
  --values values-secrets.yaml \
  --values values-persistence.yaml

Or as --set flags:

helm install ai-platform-engineering \
  oci://ghcr.io/cnoe-io/charts/ai-platform-engineering \
  --version <VERSION> \
  --values values-secrets.yaml \
  --set global.langgraphRedis.enabled=true \
  --set supervisor-agent.checkpointPersistence.type=redis \
  --set supervisor-agent.checkpointPersistence.redis.autoDiscoverService=langgraph-redis \
  --set supervisor-agent.memoryPersistence.type=redis \
  --set supervisor-agent.memoryPersistence.redis.autoDiscoverService=langgraph-redis \
  --set supervisor-agent.memoryPersistence.enableFactExtraction=true

Optional components

Component	Helm flag	Notes
RAG stack	`--set tags.rag-stack=true`	Milvus, Langfuse, embedding server
CAIPE UI	`--set tags.caipe-ui=true`	Web chat UI
SLIM dataplane	`--set global.slim.enabled=true`	AGNTCY SLIM transport
Redis persistence	`--set global.langgraphRedis.enabled=true`	See Persistence
Slack bot	`--set tags.slack-bot=true`	Slack client (not an agent)

Security notes

Use Kubernetes Secrets or the External Secrets Operator for credentials — never inline plaintext in committed files.
Rotate LLM API keys regularly.
Use HTTPS in production (configure TLS in ingress.tls).

Prerequisites​

Chart registry​

Chart structure​

Step 1 — Get example values files​

Step 2 — Configure secrets​

Option A — Direct values file (development)​

Option B — Existing Kubernetes Secrets​

Option C — External Secrets Operator (recommended for production)​

Step 3 — Choose agents​

Step 4 — Install​

Step 5 — Verify​

Step 6 — Access​

Port-forward (quickest)​

Ingress (domain access)​

Upgrade and uninstall​

Prompt configuration​

Persistence​

Quick start — Redis persistence​

Optional components​

Security notes​