I open-sourced Orkas — a local-first desktop agent where a lead agent directs a team of sub-agents (MIT, BYO keys)

Four things have been driving me nuts with AI agents lately: 1. Context bloat — one agent juggling search, code, writing, and formatting in a single context window, and it falls apart halfway through, especially for handling complex tasks. 2. Switching models mid-task means starting over. 3. Copy-pasting context between different chat windows for different specialists. 4. Subscriptions. So many subscriptions. So I built Orkas and open-sourced it. **What it does:** It's a desktop agent app (macOS / Windows) — not a local LLM, just to be clear. You bring your own keys (OpenAI / Anthropic / Gemini / Kimi / GLM / MiniMax). The idea is simple: a **lead agent** owns the conversation and pulls in **specialized sub-agents** for different parts of the task. Each sub-agent has its own context boundary, so the lead agent stays clean. * Say "research X and write a report" → research sub-agent + writing sub-agent collaborate in the same chat * Build a code review sub-agent once, reuse it anytime * Everything stays on your machine — configs, history, files. No cloud, no server, no middleman watching your prompts. * Agents improve themselves the more you use them MIT licensed. Early but usable. Would love honest feedback — break it, tell me what sucks.