# Kilo Code Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/kilo-code
> Markdown URL: https://aitinkerers.org/technologies/kilo-code.md
> Technology record last updated: 2026-03-01T00:13:23Z
> Generated: 2026-09-21T22:55:24Z

Kilo Code is the open-source, agentic engineering platform that accelerates development: It orchestrates 500+ LLMs to plan, build, and ship code across VS Code, JetBrains, and the CLI.

Kilo Code delivers high-velocity, agentic AI for the entire software development lifecycle. This open-source platform replaces fragmented toolchains with specialized modes (Architect, Code, Debug, Orchestrator) that manage complex, multi-step tasks from concept to deployment. It processes six trillion tokens a month, offering unparalleled model freedom: developers access over 500 LLMs with transparent, consumption-based pricing and Bring Your Own Key (BYOK) support. Integration is seamless across major environments—VS Code, JetBrains, and the CLI—allowing engineers to maintain context and achieve 'Kilo Speed' without friction.

- Official technology site: https://kilo.ai
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Parallelizing Agents with Git Worktrees](https://asuncion.aitinkerers.org/talks/rsvp_F0jjunc-SJE)

In this demo, I’ll start with a brief introduction to Git worktrees, explaining what they are, how they work, and why they’re useful when working with a single repository across multiple tasks. This quick overview will provide the foundation needed to understand the rest of the demo. From there, I’ll show how Git worktrees can be used to run multiple agents in parallel, each operating in its own isolated working tree. This allows you to work on multiple features, experiments, or fixes at the same time without worrying about conflicting file changes, frequent branch switching, or stepping on each other’s work. I’ll demonstrate how this setup enables true parallel development: each agent can independently make changes, run tests, and iterate, while still sharing the same underlying Git history. This approach is particularly effective for coordinating autonomous or semi-autonomous agents, as it minimizes merge friction, reduces context switching, and scales naturally as the number of concurrent tasks grows. By the end of the demo, you’ll have a clear, practical understanding of both Git worktrees and how to use them to parallelize agent workflows, helping you move faster while keeping your development process clean and conflict-free.

- Event context: Último Encuentro del Año: AI Tinkerers Asunción — 2025-12-18 — Asunción
- Public talk page: https://asuncion.aitinkerers.org/talks/rsvp_F0jjunc-SJE

## Related Technologies

- [Claude Code](https://aitinkerers.org/technologies/claude-code) ([Markdown](https://aitinkerers.org/technologies/claude-code.md)) — 215 public demos
- [Gemini CLI](https://aitinkerers.org/technologies/gemini-cli) ([Markdown](https://aitinkerers.org/technologies/gemini-cli.md)) — 16 public demos
- [git](https://aitinkerers.org/technologies/git) ([Markdown](https://aitinkerers.org/technologies/git.md)) — 24 public demos