🤖 Rundeck Plugin - Ollama Workflow step

A workflow step plugin that integrates Ollama AI models with Rundeck for intelligent automation and analysis. This plugin is community supported

🌟 Features

• 🔌 Connect to local or remote Ollama instances
• 🤖 Support for custom models
• 💬 Configure custom prompts
• 📋 Receive responses in JSON format
• ⚙️ Customize model parameters (temperature, max tokens, etc.)

📋 Table of Contents

• Installation
  • Installing Ollama
  • Installing the Plugin
    • UI Installation
    • Manual Installation
• Configuration
• Usage
• Available Models
• Parameters

🔧 Installation

Installing Ollama

1. Install Ollama on your Linux system:

curl -fsSL https://ollama.com/install.sh | sh

2. Start the Ollama service:

systemctl start ollama

3. Verify the installation:

ollama --version

4. Pull your first model:

ollama pull llama3

For more information, visit the Ollama documentation.

Installing the Plugin

UI Installation

1. Log in to your Rundeck instance as an admin
2. Navigate to System Menu (gear icon) → System Configuration → Plugins
3. Click on the "Upload Plugin" button
4. Either:
   • Drag and drop the .jar file into the upload area
   • Click "Choose File" and select the .jar file
5. Click "Upload" to install the plugin
6. Restart Rundeck when prompted

Manual Installation

1. Download the latest release from the releases page
2. Copy the .jar file to your Rundeck plugins directory:

sudo cp ollama-ai-0.1.0.jar /var/lib/rundeck/libext/

3. Restart Rundeck:

sudo systemctl restart rundeckd

⚙️ Configuration

Ollama Server Setup

By default, the plugin connects to http://localhost:11434. You can configure a different server URL in the workflow step configuration.

Requirements

• Rundeck 3.x or later
• Java 11 or later
• A running Ollama instance

🚀 Usage

Basic Usage

1. Add the "OllamaAI - AI Text Generation" step to your workflow
2. Configure the following:
   • Choose your model (or select "custom-model")
   • Enter your prompt
   • Adjust additional parameters as needed

Sample Job Definition

[ {
  "defaultTab" : "nodes",
  "description" : "",
  "executionEnabled" : true,
  "id" : "e219e8ef-167f-4905-b0d5-50f0aa73dbb1",
  "loglevel" : "INFO",
  "name" : "Testing Local LLM (Ollama) Simplification of Logs",
  "nodeFilterEditable" : false,
  "plugins" : {
    "ExecutionLifecycle" : { }
  },
  "scheduleEnabled" : true,
  "schedules" : [ ],
  "sequence" : {
    "commands" : [ {
      "autoSecureInput" : "false",
      "passSecureInput" : "false",
      "plugins" : {
        "LogFilter" : [ {
          "config" : {
            "captureMultipleKeysValues" : "false",
            "hideOutput" : "false",
            "logData" : "false",
            "name" : "logoutput",
            "regex" : "(.*)"
          },
          "type" : "key-value-data-multilines"
        } ]
      },
      "script" : "#!/bin/bash\n\n# Basic health check script for AWS machine\n# Save this as health_check.sh and make it executable with: chmod +x health_check.sh\n\nLOG_FILE=\"system_health.log\"\nDATE=$(date '+%Y-%m-%d %H:%M:%S')\nHOSTNAME=$(hostname)\nIP_ADDRESS=$(hostname -I | awk '{print $1}')\n\necho \"==================================================\" >> $LOG_FILE\necho \"System Health Check: $DATE\" >> $LOG_FILE\necho \"Hostname: $HOSTNAME\" >> $LOG_FILE\necho \"IP Address: $IP_ADDRESS\" >> $LOG_FILE\necho \"==================================================\" >> $LOG_FILE\n\n# Check uptime\necho -e \"\\n--- System Uptime ---\" >> $LOG_FILE\nuptime >> $LOG_FILE\n\n# Check memory usage\necho -e \"\\n--- Memory Usage ---\" >> $LOG_FILE\nfree -h >> $LOG_FILE\n\n# Check disk usage\necho -e \"\\n--- Disk Usage ---\" >> $LOG_FILE\ndf -h | grep -v \"tmpfs\" >> $LOG_FILE\n\n# Check CPU load\necho -e \"\\n--- CPU Load ---\" >> $LOG_FILE\ntop -bn1 | head -n 20 >> $LOG_FILE\n\n# Check for high CPU processes\necho -e \"\\n--- Top 5 CPU-consuming processes ---\" >> $LOG_FILE\nps aux --sort=-%cpu | head -n 6 >> $LOG_FILE\n\n# Check for high memory processes\necho -e \"\\n--- Top 5 Memory-consuming processes ---\" >> $LOG_FILE\nps aux --sort=-%mem | head -n 6 >> $LOG_FILE\n\n# Check network connections\necho -e \"\\n--- Active Network Connections ---\" >> $LOG_FILE\nnetstat -tuln | head -n 20 >> $LOG_FILE\n"
    }, {
      "configuration" : {
        "customModelName" : "phi",
        "logFilterOutput" : "${data.logoutput}",
        "maxTokens" : "1000",
        "ollamaModel" : "phi3",
        "ollamaServerUrl" : "http://localhost:11434",
        "prompt" : "Simplify this\n\nCategorize as problematic, needs configuration or running well. Use emojis\n\nif something is wrong, please highlight resolution steps",
        "temperature" : "0.7",
        "topP" : "1.0"
      },
      "description" : "Simplify with Ollama",
      "nodeStep" : true,
      "type" : "ollamaai-plugin"
    } ],
    "keepgoing" : false,
    "strategy" : "node-first"
  },
  "uuid" : "e219e8ef-167f-4905-b0d5-50f0aa73dbb1"
} ]

Response Format

{
  "response": "The AI-generated response text",
  "metadata": {...},
  "model": "llama3"
}

Access in subsequent steps:

${data.ollamaai.response}
${data.ollamaai.metadata}
${data.ollamaai.model}

🤖 Available Models

Model Category	Available Models
LLaMA Family	llama3, llama3:8b, llama3:70b, llama2, llama2:13b, llama2:70b
Mistral Series	mistral, mixtral
Specialized	phi3, gemma, codellama
Compact	orca-mini
Other	vicuna, wizard-vicuna, stable-diffusion

💡 Use any model from your Ollama instance by selecting "custom-model"

⚙️ Parameters

Parameter	Description	Default
Ollama Model	The Ollama model to use	llama3
Custom Model Name	Name of custom model	-
Ollama Server URL	Server URL	http://localhost:11434
Prompt	AI prompt	-
Temperature	Randomness (0.0-1.0)	0.7
Max Tokens	Token generation limit	1000
Top P	Nucleus sampling control	1.0

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📝 License: MIT License