vibectl 0.3.0

A vibes-based alternative to kubectl

vibectl

A vibes-based alternative to kubectl for interacting with Kubernetes clusters. Make your cluster management more intuitive and fun!

Features

• 🌟 Vibes-based interaction with Kubernetes clusters
• 🧠 Memory-aware autonomous Kubernetes operations
• 🚀 Intuitive commands that just feel right
• 🎯 Simplified cluster management
• 🔍 Smart context awareness
• ✨ AI-powered summaries of cluster state
• 🧠 Natural language resource queries
• 🎨 Theme support with multiple visual styles
• 📊 Intelligent output formatting for different resource types

Requirements

• Python 3.10+
• kubectl command-line tool installed and in your PATH
• API key for your chosen LLM provider:
  • Anthropic API key (for Claude models, default)
  • OpenAI API key (for GPT models)
  • Ollama (for local models, no API key required)

Installation

Option 1: Standard Pip Installation (Non-NixOS users)

1. Install using pip:

   pip install vibectl
   

2. Install the LLM provider for your chosen model:

   # For Anthropic (Claude) models (default)
   pip install llm-anthropic
   llm install llm-anthropic
   
   # For OpenAI models
   pip install llm-openai
   llm install llm-openai
   
   # For Ollama (local models)
   pip install llm-ollama
   llm install llm-ollama
   

3. Configure your API key (using one of these methods):

   # For Anthropic (default model)
   export VIBECTL_ANTHROPIC_API_KEY=your-api-key
   
   # For OpenAI
   export VIBECTL_OPENAI_API_KEY=your-api-key
   
   # Using config (more permanent)
   vibectl config set model_keys.anthropic your-api-key
   
   # Using key files (more secure)
   echo "your-api-key" > ~/.anthropic-key
   chmod 600 ~/.anthropic-key
   vibectl config set model_key_files.anthropic ~/.anthropic-key
   

See Model API Key Management for more detailed configuration options.

Option 2: Development Installation with Flake (NixOS users)

1. Install Flake
2. Clone and set up:

   git clone https://github.com/othercriteria/vibectl.git
   cd vibectl
   flake develop
   

3. Configure your API key for your chosen model (see above)

The development environment will automatically:

• Create and activate a Python virtual environment
• Install all dependencies including development tools
• Set up the Anthropic LLM provider

Usage

Autonomous Mode with vibectl vibe

The vibectl vibe command is a powerful, memory-aware tool that can autonomously plan and execute Kubernetes operations:

# Use with a specific request
vibectl vibe "create a deployment for our frontend app"

# Use without arguments - autonomous mode based on memory context
vibectl vibe

# Continue working on a previous task
vibectl vibe "continue setting up the database system"

The vibe command works by:

1. Understanding your cluster context from memory
2. Planning appropriate actions
3. Executing kubectl commands with your confirmation
4. Updating memory with results
5. Planning next steps

Example Flow with Memory

Memory: "We are working in `foo` namespace. We have created deployment `bar`.
We need to create a service for `bar`."

Command: vibectl vibe "keep working on the bar system"

Planning: Need to create a service for the bar deployment
Action: kubectl create service clusterip bar-service --tcp=80:8080
Confirmation: [Y/n]

Updated Memory: "We are working in the `foo` namespace. We have created
deployment `bar` with service `bar-service`. We don't know if it is alive yet."

No-Argument Mode

When run without arguments, vibectl vibe uses memory context to determine what to do next. If no memory exists, it begins with discovery commands:

Command: vibectl vibe

Planning: Need to understand the cluster context first
Action: kubectl cluster-info
Confirmation: [Y/n]

Updated Memory: "We are working with a Kubernetes cluster running version 1.25.4
with control plane at https://cluster.example.com. Next, we should understand
what namespaces and workloads are available."

Other Common Commands

# Display version and configuration
vibectl version
vibectl config show

# Basic operations with AI-powered summaries
vibectl get pods                                  # List pods with summary
vibectl describe deployment my-app                # Get detailed info
vibectl logs pod/my-pod                          # Get pod logs
vibectl scale deployment/nginx --replicas=3      # Scale a deployment

# Natural language commands
vibectl get vibe show me pods with high restarts
vibectl create vibe an nginx pod with 3 replicas
vibectl delete vibe remove all failed pods
vibectl describe vibe what's wrong with the database

# Direct kubectl access
vibectl just get pods                            # Pass directly to kubectl

Memory

vibectl maintains context between command invocations with its memory feature:

# View current memory
vibectl memory show

# Manually set memory content
vibectl memory set "Running backend deployment in staging namespace"

# Edit memory in your preferred editor
vibectl memory set --edit

# Clear memory content
vibectl memory clear

# Control memory updates
vibectl memory disable      # Stop updating memory
vibectl memory enable       # Resume memory updates

Memory helps vibectl understand context from previous commands, enabling references like "the namespace I mentioned earlier" without repeating information. This is especially powerful with the autonomous vibectl vibe command.

Configuration

# Set a custom kubeconfig file
vibectl config set kubeconfig /path/to/kubeconfig

# Use a different LLM model (default: claude-3.7-sonnet)
vibectl config set model claude-3.7-sonnet  # Default
vibectl config set model gpt-4              # OpenAI
vibectl config set model ollama:llama3      # Local Ollama

# Configure API keys (multiple methods available)
vibectl config set model_keys.anthropic your-api-key
vibectl config set model_key_files.openai /path/to/key-file

# Control output display
vibectl config set show_raw_output true    # Always show raw kubectl output
vibectl config set show_kubectl true       # Show kubectl commands being executed

# Set visual theme
vibectl theme set dark

For detailed API key management options, see Model API Key Management.

Custom Instructions

You can customize how vibectl generates responses by setting custom instructions that will be included in all vibe prompts:

# Set custom instructions
vibectl instructions set "Use a ton of emojis! 😁"

# View current instructions
vibectl instructions show

# Clear instructions
vibectl instructions clear

Typical use cases for custom instructions:

• Style preferences: "Use a ton of emojis! 😁"
• Security requirements: "Redact the last 3 octets of IPs."
• Focus areas: "Focus on security issues."
• Output customization: "Be extremely concise."

Output Formatting

Commands provide AI-powered summaries using rich text formatting:

• Resource names and counts in bold
• Healthy/good status in green
• Warnings in yellow
• Errors in red
• Kubernetes concepts in blue
• Timing information in italics

Example:

[bold]3 pods[/bold] in [blue]default namespace[/blue], all [green]Running[/green]
[bold]nginx-pod[/bold] [italic]running for 2 days[/italic]
[yellow]Warning: 2 pods have high restart counts[/yellow]

Project Structure

For a comprehensive overview of the project's structure and organization, please see STRUCTURE.md. This documentation is maintained according to our project structure rules to ensure it stays up-to-date and accurate.

Development

This project uses Flake for development environment management. The environment is automatically set up when you run flake develop.

Running Tests

pytest

Code Quality

The project uses pre-commit hooks for code quality, configured in .pre-commit-config.yaml. These run automatically on commit and include:

• Ruff format for code formatting (replaces Black)
• Ruff check for linting and error detection (replaces Flake8)
• Ruff check --fix for import sorting (replaces isort)
• MyPy for type checking

Configuration for Ruff is managed in the pyproject.toml file under the [tool.ruff] section.

Cursor Rules

The project uses Cursor rules (.mdc files in .cursor/rules/) to maintain consistent development practices. For details on these rules, including their purpose and implementation, see RULES.md.

License

This project is licensed under the MIT License - see the LICENSE file for details.