Kilo Code
Open-source AI coding assistant for VS Code and JetBrains that plans, writes, and debugs code with multi-model support.
🛡️ AgentReady threat assessment
MAESTRO 7-layer threat model + OWASP AIVSS risk score for Kilo Code, derived from its capabilities.
These scores are auto-generated from public information (the agent's own listing, docs, and repository) using the canonical OWASP AIVSS formula and the MAESTRO framework — an estimate for guidance, not a penetration test, audit, or certification. See the scoring methodology — every score is re-derived by the same automated method as an agent's public evidence changes.
Overview
Kilo Code is an open-source AI coding assistant designed to run inside VS Code, JetBrains IDEs, and web-based editors. Built as a full coding agent, it can plan features, generate new code from natural language, refactor and debug existing projects, run terminal commands, and even automate the browser with user approval. Kilo Code combines ideas from tools like Cline and Roo into a unified workflow with dedicated modes such as Architect, Coder, and Debugger. It connects to 400+ frontier and open models through OpenRouter and similar providers, offers optional hosted credits so developers can start without managing their own API keys, and supports an MCP-style server marketplace for extending the agent with custom tools. This makes it a flexible choice for individual developers and teams who want powerful AI assistance while keeping configuration transparent and community-driven.
Key features and capabilities
- VS Code extension
- JetBrains plugin
- open-source coding assistant
- code generation
- refactoring
- debugging
- terminal automation
- browser automation
- MCP server marketplace
- multi-model support
Use cases
- Planning new features and project architecture with an AI Architect mode that understands your codebase.
- Generating boilerplate, components, tests, and scripts from natural language requests.
- Refactoring legacy code, improving readability, and enforcing consistent patterns across large projects.
- Debugging issues by having the agent read logs, run commands, and propose targeted fixes.
- Automating repetitive development workflows such as running test suites, scaffolding projects, or editing multiple files.
- Experimenting with and comparing different LLMs for coding tasks through a single IDE integration.