dotta 📎

Hermes Agent now has multi-agent via the Kanban, new in v0.12.0. Agents claim tasks from a board, work in parallel, and hand off when blocked. You watch progress and unblock from one easy view instead of juggling terminals. We asked it to plan and make this video about itself:

Over the last few days, I explored 4 AI agent orchestrator apps: - DP Code - Emdash - Letta Code - Superset Most of them position themselves as a "harness aggregator" or an Agentic Development Environment, a layer on top of your existing AI tools that tries to unify workflows and coordinate multiple agents. I connected my Claude Code, Codex, Cursor, Minimax API, and OpenCode across these platforms and spent a few hours with each to understand how they actually perform in real usage. Here’s what I found: DP Code > Started off as a T3 Chat–style clone, but has gone through multiple revamps, currently in alpha and now looks very close to the Codex app > Lets you connect Gemini, Claude, OpenAI, and OpenCode > You can split work across multiple chats and run multiple agents in parallel > Switching providers mid-task is supported, which is a really nice touch Emdash > YC-backed and focused on running multiple coding agents in parallel > Supports 20+ AI providers in a single project with separate Git workflows > Has UI-based plugin, MCP, and skills installation On Windows it felt a bit sluggish under the hood it was opening separate terminals per agent and lagged a bit (might be smoother on macOS) Letta Code > A memory-first coding agent similar to hermes agent i guess > Automatically detects already logged-in AI providers and lets you use them directly which didn't work with others > Feels quite similar to OpenCode, but with stronger memory handling > Also supports cron jobs, which is useful for automation workflows Superset > Very similar to Emdash (or vice versa) in terms of positioning > Currently macOS-only, I tried running it via an Electron wrapper, but it didn’t work well > Focuses on monitoring multiple agents from a single interface > Each agent’s changes are isolated, which is helpful for managing parallel workflows Overall, all of them are pushing in the same direction of multi-agent workflows and provider abstraction, it will be interesting to see which stays in the long term game.