Engio knowledge base

1 Deployment steps

Leverage modern container technology capabilities to quickly deploy Engio ALM on your Linux or Windows server.

Docker setup

Ubuntu/Debian

sudo -s
apt-get update && apt-get -y install docker.io docker-compose
systemctl enable docker --now

CentOS/RedHat

sudo -s
yum -y install curl docker-compose git && (curl -sSL https://get.docker.com | sh)
systemctl enable docker --now

Engio ALM setup

mkdir -p /var/www/devprom/logs /var/www/devprom/update /var/www/devprom/backup /home/devprom
cd /home/devprom

wget --no-check-certificate -O devprom.zip https://engio.team/download
unzip -q -a devprom.zip  
mv devprom /var/www/devprom/htdocs
chown -R 33:33 /var/www/devprom

git clone https://github.com/devprom-dev/docker.git
cd docker

Change the default values in the .env file

vi .env

Install and start the containers:

docker-compose up -d

Open a browser, navigate to the application. Provide the MySQL user password, which is specified in the MYSQL_PASSWORD variable of the .env file (default: devprom_pass).

Click the Install button and wait for the installation to complete.

Backup

Automatically generated daily backup copies will be available on the host in the /var/www/devprom/backup directory. Organize their backup to a separate storage location.

Use of AI features

The ALM functionality includes built‑in AI features that enhance team productivity when working with project artifacts. To use them, install the additional components using the following command:

docker-compose -f aitools.yml up -d
Component Description
mcp

An MCP service that provides AI agents with a clear, extended API description for working with project artifacts (reading, creating, modifying), semantic search (RAG), and so on. To use it in an AI agent, connect the MCP service:

{
  "mcpServers": {
    "mcp-alm": {
      "url": "http://<адрес сервера>:9345/mcp",
      "headers": {
        "Devprom-Auth-Key": "***",
        "Devprom-Base-Url": "http://<адрес сервера>"
      }
    }
  }
}

Devprom-Auth-Key: The user’s API key under which operations will be performed in the system when the AI agent is running.

Devprom-Base-Url: The server address where the ALM application is accessible.
chromadb A vector database management system (vector DBMS) for storing embeddings (vectorized representations of user data). Vectors are generated and cached when data is created or modified, then used by AI functions without consuming computational resources of language models.
ollama

An open AI model management service that allows connecting paid and free language models, as well as other specialized AI models. This service is designed for local use of AI functions (without internet access), but requires dedicated computing resources (e.g., GPU) to ensure acceptable model performance and quality. If external LLMs are used, this component is not required.


The external model usage settings are located in the Administration → Settings → Application section.
ollama_models A temporary container that installs a free model for generating vector representations (embeddings) for evaluation purposes. An internet connection is required to download the model into the Ollama service.

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