ubermorgen/qwen3-devops

Ollama DevOps Agent

A lightweight AI-powered DevOps automation tool using a fine-tuned Qwen3-1.7B model with Ollama and SmolAgents. Specialized for Docker and Kubernetes workflows with sequential tool execution and structured reasoning.

Features

• Sequential Tool Execution: Calls ONE tool at a time, waits for results, then proceeds
• Structured Reasoning: Uses <think> and <plan> tags to show thought process
• Validation-Aware: Checks command outputs for errors before proceeding
• Multi-Step Tasks: Handles complex workflows requiring multiple tool calls
• Approval Mode: User confirmation before executing each tool call for enhanced safety (enabled by default)
• Resource Efficient: Optimized for local development (1GB GGUF model)
• Fast: Completes typical DevOps tasks in ~10 seconds

What's Special About This Model?

This model is fine-tuned specifically for DevOps automation with improved reasoning capabilities:

• Docker & Kubernetes Expert: Trained on 300+ Docker and Kubernetes workflows (90% of training data)
• One tool at a time: Unlike base models that try to call all tools at once, this model executes sequentially
• Explicit planning: Shows reasoning with <think> and <plan> before acting
• Uses actual values: Extracts and uses real values from tool responses in subsequent calls
• Error handling: Validates each step and tries alternative approaches on failure

Training Data Focus

The model has been trained on:

• Docker workflows: Building images, containers, Docker Compose, optimization
• Kubernetes operations: Pods, deployments, services, configurations
• General DevOps: File operations, system commands, basic troubleshooting

⚠️ Note: The model has limited training on cloud-specific CLIs (gcloud, AWS CLI, Azure CLI). For best results, use it for Docker and Kubernetes tasks.

Example Output

Task: Get all pods in default namespace

Step 1: Execute kubectl command
<tool_call>
{"name": "bash", "arguments": {"command": "kubectl get pods -n default"}}
</tool_call>

[Receives pod list]

Step 2: Provide summary
<tool_call>
{"name": "final_answer", "arguments": {"answer": "Successfully retrieved 10 pods in default namespace..."}}
</tool_call>

Quick Start

🎯 Recommended: Native Installation

For the best experience with full DevOps capabilities:

curl -fsSL https://raw.githubusercontent.com/ubermorgenland/devops-agent/main/install.sh | bash

This will automatically:

• Install Ollama (if not present)
• Install Python dependencies
• Download the model from Hugging Face
• Create the Ollama model
• Set up the devops-agent CLI command

Why native installation?

• ✅ Full system access - manage real infrastructure
• ✅ No credential mounting - works with your existing setup
• ✅ Better performance - no container overhead
• ✅ Simpler usage - just run devops-agent

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