Kilo

What is Kilo?

Kilo (also known as Kilo Code) is an advanced AI coding agent designed to function as an autonomous junior developer rather than a simple chatbot. Integrated into VS Code, JetBrains IDEs, and the command line, it uses a "Plan-Act-Observe-Fix" loop to execute complex multi-step tasks, edit files, and run commands. The platform supports a wide range of AI models, including those from OpenAI, Anthropic, and Google, allowing users to bring their own API keys (BYOK) or use local models. Kilo emphasizes transparency and control, offering features like a persistent "Memory Bank" to understand project context and automated code reviews to streamline collaboration.

How to use Kilo?

To use Kilo, install the extension for Visual Studio Code or JetBrains, or set up the CLI tool for terminal access. After installation, authenticate using a free Kilo account or configure your own API keys for preferred models like Claude or GPT. Open the Kilo panel in your editor, select a specific mode (e.g., Architect, Coder, or Debugger), and describe your task in natural language. The agent will then analyze your codebase, propose a plan, and autonomously execute edits and commands while you review and approve its actions.

Kilo's Core Features

• Autonomous agentic workflow that plans, executes, and verifies coding tasks

• Support for 500+ AI models including Claude, GPT-4, and local LLMs

• Deep integration with VS Code, JetBrains IDEs, and terminal environments

• Persistent 'Memory Bank' that retains project context and user preferences

• Specialized modes like Architect, Coder, Debugger, and Ask for targeted assistance

• Automated Code Review agent that provides feedback on Pull Requests

• Terminal and browser automation capabilities for end-to-end task execution

• Open-source foundation ensuring transparency and no vendor lock-in

• Flexible billing with a free tier and Bring Your Own Key (BYOK) options

• Model Context Protocol (MCP) support for extending functionality with custom tools

Kilo's Use Cases

• #1

  Generating complex features from natural language prompts

• #2

  Refactoring legacy codebases for better performance and readability

• #3

  Debugging errors by analyzing logs and automatically applying fixes

• #4

  Automating repetitive tasks like project scaffolding and dependency installation

• #5

  Conducting automated code reviews on GitHub Pull Requests

• #6

  Running terminal commands and scripts autonomously

• #7

  executing browser-based testing and automation

• #8

  Switching between different AI models to optimize for cost or capability

• #9

  Maintaining project context across sessions using the Memory Bank