EVA Agent Installation
The EVA Agent is the core component responsible for intelligent inference and data processing within the EVA service. Please follow the steps below to proceed with the configuration.
🛠️ Prerequisites
Before starting the installation, ensure that the following tools are ready:
- Kubernetes Cluster: A cluster where EVA will be deployed.
- kubectl: Go to Installation Guide
- Helm: Go to Installation Guide
- AWS CLI Configuration: AWS credentials are required for ECR image access. Run the following command in your terminal to configure:
aws configure
# Enter your AWS Access Key ID, Secret Access Key, etc., as prompted.
🚀 Installing EVA Agent
Step 1: Register and Update Helm Repository
Add and update the repository to fetch the latest version of the charts.
helm repo add eva-agent https://mellerikat.github.io/eva-agent
helm repo update
Step 2: Download Configuration (values) Templates
Download the necessary template files for your environment. Choose the files that match your setup (AWS or K3s).
- Common values template: https://github.com/mellerikat/eva-agent/blob/chartmuseum/release/2.3-a3.0/1/eva-agent/values.yaml
- Common secret values template: https://github.com/mellerikat/eva-agent/blob/chartmuseum/release/2.3-a3.0/1/eva-agent/secret-values.yaml.tpl
- K3s specific values template: https://github.com/mellerikat/eva-agent/blob/chartmuseum/release/2.3-a3.0/1/eva-agent/values-k3s.yaml
- AWS specific values template: https://github.com/mellerikat/eva-agent/blob/chartmuseum/release/2.3-a3.0/1/eva-agent/values-aws.yaml
# 1. Download common basic settings and secret templates
curl -L "https://raw.githubusercontent.com/mellerikat/eva-agent/chartmuseum/release/2.3-a3.0/1/eva-agent/values.yaml" -o values.yaml
curl -L "https://raw.githubusercontent.com/mellerikat/eva-agent/chartmuseum/release/2.3-a3.0/1/eva-agent/secret-values.yaml.tpl" -o values-secret.yaml
# 2. For Cloud (AWS) environment
curl -L "https://raw.githubusercontent.com/mellerikat/eva-agent/chartmuseum/release/2.3-a3.0/1/eva-agent/values-aws.yaml" -o values-aws.yaml
# 3. For On-premise (K3s) environment
curl -L "https://raw.githubusercontent.com/mellerikat/eva-agent/chartmuseum/release/2.3-a3.0/1/eva-agent/values-k3s.yaml" -o values-k3s.yaml
Step 3: Update Settings for Your Environment
Open the values.yaml and values-secret.yaml files to modify the key environment variables.
| Category | Name | Description | On-premise (K3s) | Cloud (AWS) |
|---|---|---|---|---|
| Runtime | image.tag | Image tag | "2.3-a3.0" | "2.3-a3.0" |
| Runtime | replicaCount | Number of replicas | 1 | 1 |
| Runtime | nodeSelector | Node selector | eks.amazonaws.com/nodegroup: ng-an2-eva-agent | |
| Env Var | env.MODEL_CATALOG_FILE | Path to the available model list config | config/model_catalog.json | config/model_catalog.json |
| Env Var | env.VLLM_BASE_URL | vLLM endpoint (Leave blank if not used) | http://eva-agent-vllm-service/v1 | http://eva-agent-vllm-service/v1 |
| Env Var | env.LLM_PROVIDER | Provider for the LLM service | vllm | vllm |
| Env Var | env.LLM_MODEL | Local model to be served | qwen3-vl-8b-awq | qwen3-vl-8b-awq |
| Env Var | env.QDRANT_HOST | Qdrant DB server address | http://eva-agent-qdrant | http://eva-agent-qdrant |
| Env Var | env.QDRANT_PORT | Qdrant DB port number | "6333" | "6333" |
| Env Var | env.OPENAI_API_TYPE | OpenAI API type | azure | azure |
| Env Var | env.OPENAI_API_VERSION | OpenAI API version | 2025-04-01-preview | 2025-04-01-preview |
| Env Var | env.AZURE_OPENAI_ENDPOINT | Azure OpenAI API endpoint | https://xxxx | https://xxxx |
| Env Var | env.DEPLOYMENT_NAME | Azure model deployment name | gpt-5-chat-2025-08-07 | gpt-5-chat-2025-08-07 |
| Env Var | env.AZURE_MANAGEMENT_BASE_URL | Azure Management Endpoint | https://management.azure.com | https://management.azure.com |
| Secret | secretEnv.OPENAI_API_KEY | OpenAI API Key (Dummy value required if unused) | ||
| Secret | secretEnv.AZURE_OPENAI_API_KEY | Azure OpenAI API Key | ||
| Secret | secretEnv.AZURE_CLIENT_SECRET | Azure App Registration Client Secret | ||
| Secret | secretEnv.CLOVA_API_KEY | Naver Hyper Clova API Key |
⚠️ Warning: For Secret items such as
OPENAI_API_KEY, you must provide a dummy value even if they are not in use to avoid errors. (Do not comment them out).
Step 4: Run EVA Agent Installation
Once all configuration files are ready, proceed with the installation using the command below. The Helm installation begins after the ECR login.
# 1. Login to AWS ECR (To secure Image Pull permissions)
NS=eva-agent
ACCOUNT_ID=339713051385
AWS_ECR_REGION=ap-northeast-2
AWS_ECR_HOST=${ACCOUNT_ID}.dkr.ecr.${AWS_ECR_REGION}.amazonaws.com
aws ecr get-login-password --profile default | docker login --username AWS --password-stdin $AWS_ECR_HOST
# 2. Execute Helm Install (Match the yaml files for your environment)
docker_config_file="$HOME/.docker/config.json"
values_file="$docker_config_file-values.yaml"
cat > "$values_file" << EOF
dockerConfig:
json: $(cat "$docker_config_file" | base64 -w0)
EOF
# --version is the chart version (not the image tag)
helm install eva-agent eva-agent/eva-agent --version=2.1.2 -n eva-agent \
-f values.yaml \
-f values-k3s.yaml \
-f values-secret.yaml \
-f $values_file
rm -f "$values_file"
(Note: If you are in a AWS environment, use values-aws.yaml instead of values-k3s.yaml.)
Step 5: Verify Installation Status
After the installation is complete, verify that all services are up and running.
kubectl get all -n eva-agent