agent-gateway-krakend-operator

Agent Gateway KrakenD Operator

The Agent Gateway KrakenD Operator is a specialized Kubernetes operator that provides a KrakenD-based Agent gateway implementation for the Agent Runtime Operator ecosystem. It manages AgentGateway resources and automatically generates KrakenD configurations to expose Agent workloads through a unified gateway interface.

This operator works as a companion to the Agent Runtime Operator and implements the AgentGatewayClass controller pattern to provide centralized gateway management for agentic workloads.

Table of Contents

• Prerequisites
• Getting Started
• Configuration
• End-to-End (E2E) Testing
• Testing Tools and Configuration
• Sample Data
• Contribution

Prerequisites

IMPORTANT: The Agent Runtime Operator must be installed before deploying this operator.

This operator depends on the Agent Runtime Operator to provide Agent, AgentGateway, and AgentGatewayClass Custom Resource Definitions (CRDs). Please install and configure the Agent Runtime Operator first by following the instructions in the Agent Runtime Operator repository.

Before working with this project, ensure you have the following tools installed on your system:

• Go: version 1.24.0 or higher
• Docker: version 20.10+ (or a compatible alternative like Podman)
• kubectl: The Kubernetes command-line tool
• kind: For running Kubernetes locally in Docker
• make: The build automation tool

Getting Started

📖 For detailed setup instructions, see our Getting Started guide in the documentation.

Quick Start:

Note: This operator requires the Agent Runtime Operator to be installed first, as it provides the required CRDs (AgentGateway and AgentGatewayClass).

# Create local cluster and install cert-manager
kind create cluster
kubectl apply -f https://github.com/cert-manager/cert-manager/releases/download/v1.19.1/cert-manager.yaml

# Install the Agent Runtime Operator (provides CRDs)
kubectl apply -f https://github.com/agentic-layer/agent-runtime-operator/releases/download/v0.13.0/install.yaml

# Install the Agent Gateway operator
kubectl apply -f  https://github.com/agentic-layer/agent-gateway-krakend-operator/releases/download/v0.3.0/install.yaml

Development

Follow the prerequisites above to set up your local environment. Then follow these steps to build and deploy the operator locally:

# Install CRDs into the cluster
make install
# Build docker image
make docker-build
# Load image into kind cluster (not needed if using local registry)
make kind-load
# Deploy the operator to the cluster
make deploy

After a successful start, you should see the controller manager pod running in the agent-gateway-krakend-operator-system namespace.

kubectl get pods -n agent-gateway-krakend-operator-system

Configuration

AgentGateway Configuration

To create a KrakenD-based gateway for your agents, define an AgentGateway resource:

apiVersion: runtime.agentic-layer.ai/v1alpha1
kind: AgentGateway
metadata:
  labels:
    app.kubernetes.io/name: agent-gateway-krakend-operator
    app.kubernetes.io/managed-by: kustomize
  name: my-agent-gateway
spec:
  agentGatewayClassName: krakend  # Optional: specify controller responsibility
  replicas: 2  # Number of gateway replicas (optional, default: 1)
  timeout: "30000ms"  # Request timeout (optional, default: 60000ms)

Agent Integration

The gateway automatically discovers and configures endpoints for Agent resources that have exposed: true:

apiVersion: runtime.agentic-layer.ai/v1alpha1
kind: Agent
metadata:
  name: weather-agent
spec:
  framework: google-adk
  image: eu.gcr.io/agentic-layer/weather-agent:0.3.0
  exposed: true  # This agent will be included in the gateway configuration
  protocols:
    - type: A2A

The operator will:

1. Discover all exposed agents across all namespaces
2. Generate KrakenD endpoint configurations for each agent
3. Create a ConfigMap with the complete KrakenD configuration
4. Deploy a KrakenD deployment that serves the unified gateway
5. Create a ServiceAccount for the gateway deployment

Endpoint Routing:

Each exposed agent gets endpoints in two formats:

• Namespaced (always): /{namespace}/{agent-name} and /{namespace}/{agent-name}/.well-known/agent-card.json
• Shorthand (when unique): /{agent-name} and /{agent-name}/.well-known/agent-card.json

Shorthand endpoints are only created when the agent name is unique across all namespaces. This prevents conflicts while providing convenience for uniquely-named agents.

The gateway also provides OpenAI-compatible endpoints:

• GET /models - List all exposed agents
• POST /chat/completions - Chat with agents using the model parameter for routing

Default AgentGatewayClass

The operator installs a AgentGatewayClass resource:

apiVersion: runtime.agentic-layer.ai/v1alpha1
kind: AgentGatewayClass
metadata:
  name: krakend
spec:
  controller: runtime.agentic-layer.ai/agent-gateway-controller

End-to-End (E2E) Testing

Prerequisites for E2E Tests

• kind must be installed and available in PATH
• Docker running and accessible
• kubectl configured and working

Running E2E Tests

The E2E tests automatically create an isolated Kind cluster, deploy the operator, run comprehensive tests, and clean up afterwards.

# Run complete E2E test suite
make test-e2e

The E2E test suite includes:

• Operator deployment verification
• CRD installation testing
• Webhook functionality testing
• Certificate management verification

Manual E2E Test Setup

If you need to run E2E tests manually or inspect the test environment:

# Set up test cluster (will create 'ai-gateway-litellm-test-e2e' cluster)
make setup-test-e2e

# Run E2E tests against the existing cluster
KIND_CLUSTER=ai-gateway-litellm-test-e2e go test ./test/e2e/ -v -ginkgo.v

# Clean up test cluster when done
make cleanup-test-e2e

E2E Test Coverage

E2E Test Features:

• OpenAI API /models and /chat/completions endpoints
• Unique agent routing
• Namespace conflict resolution
• Agent gateway deployment verification
• KrakenD configuration generation

Testing Tools and Configuration

The project includes comprehensive test coverage:

• Unit Tests: Complete test suite for the controller with AgentGatewayClass responsibility checks
• Integration Tests: Tests for Agent discovery and KrakenD configuration generation
• E2E Tests: End-to-end tests for OpenAI API flows and namespace conflict resolution
• Ginkgo/Gomega: BDD-style testing framework
• EnvTest: Kubernetes API server testing environment

Run tests with:

# Run unit and integration tests
make test

# Run E2E tests in kind cluster
make test-e2e

Sample Data

The project includes sample manifests to help you get started.

• Where to find sample data? Sample manifests are located in the config/samples/ directory.

• How to deploy sample resources? You can deploy sample AgentGateway resources with:

  kubectl apply -k config/samples/
  

• How to verify the sample gateway? After applying the sample, check the created resources:

  # Check the gateway status
  kubectl get agentgateways -o yaml
  
  # Check the KrakenD deployment
  kubectl get deployments -l provider=krakend
  
  # Check the generated configuration
  kubectl get configmaps -l provider=krakend
  

Contribution

See Contribution Guide for details on contribution, and the process for submitting pull requests.