Loading video...

Video Failed to Load

Go Home

🤯 Hermes agent just got a Kanban task board, and it changes everything. Watch multiple AI agents collaborate in real time, each with a defined role, completing tasks with way more depth than your typical sub-agents. This is the future of multi-agent workflows 👇

12,854 views • 6 months ago •via X (Twitter)

8 Comments

Teknium 🪽's profile picture
Teknium 🪽6 months ago

Should I see about letting hermes/orchestrator profile get the ability to make profiles? ^_^

Boxmining's profile picture
Boxmining6 months ago

Full video: Subscribe for more

[完全版]就活対策まとめ's profile picture
[完全版]就活対策まとめ6 months ago

Built roach-pi docs as a hosted site on docsbook-io with translations in 50+ languages, each indexed separately. Ok if I send the link?

Nathan Esquenazi's profile picture
Nathan Esquenazi6 months ago

Check out our full open source Kanban support for Hermes on mobile and macOS too

RevaloEDIT's profile picture
RevaloEDIT6 months ago

You are a very smart person, I would add to the video moments where your icon gets bigger when you are simply explaining something?

SEVEN's profile picture
SEVEN6 months ago

hermes kanban setup making multi agents feel real now

Rin Cyberkoi's profile picture
Rin Cyberkoi6 months ago

seeing this everywhere, wow a huge update....

hugh madden's profile picture
hugh madden6 months ago

Tricky part is what settings to share and what not (discord token for example) it unfortunately leads to profile groups. As simple as possible but no more so 🤷

Related Videos

The more agents and projects you have running, the harder it gets to keep track of everything through chat alone. This is where Hermes Kanban comes in. Here's how Hermes Kanban works, why it's worth using, and how I'd recommend setting it up: Chat is where you work with an agent. Kanban is where you manage the work across agents. Hermes Kanban gives you a visual dashboard for tracking tasks across your agents and projects. But it's much more than a task board. Here's what you can do with it: • Create tasks and assign them to specific Hermes agents. • Track what's ready, running, blocked, or completed. • Set dependencies so tasks wait for other work to finish. • Coordinate handoffs between specialized agents. • Review progress and see where something needs your attention. And with the dispatcher, Hermes can pick up tasks that are ready to run and launch the appropriate agent to handle them. Now, here's something I'd recommend: **Create a separate Kanban board for each major project.** For example: 📌 Content production Research → Drafting → Review → Assets → Done 📌 Software development Bugs → Features → Testing → Review → Done 📌 Business operations Research → Waiting → In Progress → Blocked → Done Hermes supports multiple isolated boards, so each project can have its own tasks, workspaces, logs, and agent workflows. You can also connect a board to a Hermes Project to keep the work tied to the right project context. The more work you hand off to Hermes, the more valuable this becomes. Instead of digging through conversations trying to figure out what every agent is doing, give each project its own board and manage the work from one place. 🎥 Video credit: Tom

Hermes Release Watch

27,640 views • 1 month ago

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:

Andrew Ng

105,894 views • 1 year ago

how to build a 1-person AI company that: - runs locally - is 100% open source - has no human employees, only ai agents - lets every agent collaborate through email most people already know that multiple ai agents can work together. frameworks for that have existed for a while. the harder part is figuring out how to organize those agents in a way that actually makes sense. instead of building complicated workflows with dozens of connected nodes, this project treats your ai system like a real company. every agent has a specific job. every job reports to a manager. instead of talking to every agent yourself, you only communicate with the manager, who delegates tasks to the right agents and brings the results back to you. just like a real business. that's exactly how alook works. each agent runs in its own claude code, codex, or opencode session. every agent has a clearly defined role and its own email inbox. they communicate with each other through email while everything runs locally on your own machine. your data stays on your computer. nothing is sent to someone else's servers. here's a simple example. imagine building an ai sales team. one agent becomes the sales manager. another researches companies and finds qualified leads. another writes outreach emails, handles replies, follows up with prospects, and reports the results. you don't need to coordinate any of this yourself. the manager agent handles the communication between the team and sends you one clear update. it's a much more natural way to build multi-agent systems because you're organizing ai the same way people organize companies. the entire project is open source. github link:

MIKE

19,938 views • 4 months ago