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