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Introducing AgentHQ 🤖🏢 Multi-agent orchestration shouldn’t feel boring or dull, buried under terminals, JSON, and dashboards. AgentHQ makes agent orchestration fun, simple, and reliable. Hire Claude and Codex agents, assign tasks, and watch your team collaborate in a retro 2D office. Learn more ⬇️ 🧵

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Kye Gomez (swarms)

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52,025 görüntüleme • 4 gün önce •via X (Twitter)

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Introducing multi-agent orchestration in Oz, with support for Claude Code, Codex and the Warp Agent. Use /orchestrate to delegate complex tasks across a team of agents, locally or in the cloud.
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Introducing multi-agent orchestration in Oz, with support for Claude Code, Codex and the Warp Agent. Use /orchestrate to delegate complex tasks across a team of agents, locally or in the cloud.

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Today, we're launching the Traycer Desktop App. Free. Open source. Built with AI orchestration in mind. What’s new: • Bring your existing subscription (Claude, Codex, Opencode, etc.) • Agent-to-Agent communication and Loops • Persistent workspaces with tabs and sub-tabs • Share tasks and collaborate with teammates Capability stopped being the hard part. The environment is the new frontier, and that’s where Traycer is.

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Introducing Agent Orchestration in Notion 🎼 Imagine a Decagon ticket routes to your coding agent, which proposes a fix and loops your team in to approve. That’s no longer a dream. Now, anyone can work with agents in Notion, not just engineers.
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Introducing Agent Orchestration in Notion 🎼 Imagine a Decagon ticket routes to your coding agent, which proposes a fix and loops your team in to approve. That’s no longer a dream. Now, anyone can work with agents in Notion, not just engineers.

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Introducing Claude agents in Notion. Your task board is Claude’s to-do list. Anthropic runs the model and the agent harness. Notion is the orchestration layer: context, UI, and a shared place for teams to review and ship, and kick off Claude workflows. Join the waitlist:
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Introducing Claude agents in Notion. Your task board is Claude’s to-do list. Anthropic runs the model and the agent harness. Notion is the orchestration layer: context, UI, and a shared place for teams to review and ship, and kick off Claude workflows. Join the waitlist:

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🤖 Introducing Taskade's AI Teams with Multi-Agent Collaboration, now in beta! Assemble your team of AI Agents for collaboration, intelligent responses, and dynamic interactions. They plan, execute, and manage tasks—all together! Reply 'AI Teams' for early access! ✨
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🤖 Introducing Taskade's AI Teams with Multi-Agent Collaboration, now in beta! Assemble your team of AI Agents for collaboration, intelligent responses, and dynamic interactions. They plan, execute, and manage tasks—all together! Reply 'AI Teams' for early access! ✨

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🤯 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 👇

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New agentic short course! Multi AI Agent Systems with crewAI, built with CrewAI's founder and CEO João Moura. In this course, you'll learn how to break down complex tasks into subtasks for multiple AI agents, each playing a specialized role, to execute. For example, to generate a research report, you might have researcher, writer, and quality assurance agents collaborate. You'll define the roles, expectations, and interactions between the agents—like a manager organizing a team. You'll work with key agentic AI techniques like role-playing, tool use, memory, guardrails, and cross-agent collaboration. And you'll build your own multi-agent systems that can tackle complex tasks. I think you'll find it both productive and fun to design agents and watch them collaborate to get things done. Let me know what you think! I believe multi-agent architectures will drive significant progress in AI systems. Please sign up here!

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Why have one AI agent handle everything when you can build a team? 🤝 Multi-agent architecture allows a main agent to spin up specialized sub-agents and handle complex tasks in parallel - making workflows faster and more efficient inside Google AI Studio. Check out the full I/O session: 🧵👇

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Introducing CRABWISE 🦀 Build trust with your agents and set rules they can't break. Local-first agent monitoring, audit trail, and policy enforcement with multi-agent, multi-provider support → live agent monitoring → local policy enforcement (commandments) → a hash-chained audit trail → terminal UIs for status, watch, and audit → support for OpenClaw, Claude Code, Codex CLI (more to come) + pairs nicely with Crabwalk🦀 Follow me Luciano Castillo to stay updated + RT!

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New course: Nvidia's NeMo Agent Toolkit: Making Agents Reliable, taught by Brian McBrayer 🐬 from NVIDIA. Many teams struggle to turn agent demos into reliable systems that are ready for production. This short course teaches you to harden agentic workflows into reliable systems using Nvidia's open-source NeMo Agent Toolkit (NAT). Whether you built your agent in raw Python or using a framework like LangGraph, or CrewAI, NAT provides building blocks for observability, evaluation, and deployment that turn proofs-of-concept into production-ready systems. NAT makes it easy to troubleshoot and optimize agent performance with execution traces, systematic evaluations, and CI/CD integration. Skills you'll gain: - Build configuration-driven agent workflows with REST APIs and minimal code - Add observability with tracing to visualize agent reasoning and debug performance bottlenecks - Create systematic evaluations using gold-standard datasets to measure and improve agent reliability - Deploy multi-agent systems with authentication, rate limiting, and professional web interfaces - Orchestrate agents from different frameworks to collaborate on complex tasks Join and learn how to turn agent demos into reliable systems!

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🏆 Introducing TARS 1/ Your AI Executive Assistant that Turns Natural Language into Action. -> With TARS, you can seamlessly orchestrate multiple AI agents to gather insights, automate tasks, and take actions across platforms — all in one place. -> Build upon the 1st productized multi-agent orchestration (MAO) ecosystem designed for AI Agentic workflow. 🏠 Explore more:

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🚨 INTRODUCING THE ABACUS AI SUPERCOMPUTER - literally build ANY cloud service with a prompt - host open-source LLMs & always-on agents - bring your own Codex, Claude or Anti-gravity - multi-agent architectures with Opus 4.8 and GPT 5.5 - run 3D games or any other server at scale - connect git and collaborate with teams Build a billion dollar business by simply prompting in english!!

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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:
2:49

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:

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Introducing: BuxFather 🤖 Spin up AI agents without leaving Telegram. No signups. No payments. > Runs 24/7, fully autonomous and self-improving > Includes stealth browsers w/ persistent auth > Use your own Claude Code / Codex subscription A computer, agent, and browser in a few taps. Get started now ↓🔗

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Sharing our latest short course: Building and Evaluating Data Agents, created in collaboration with Snowflake and taught by Anupam Datta (Anupam Datta) and Josh Reini (Josh Reini). A data agent extracts data from sources such as files or databases, analyzes it, and provides insights and visualizes its findings. But most data agents struggle with reliability or can't handle multi-step reasoning. In this course, you'll learn to build, trace, and evaluate a multi-agent workflow that plans tasks, pulls context from structured and unstructured data, performs web search, and summarizes or visualizes the final results. Learn more and enroll for free!

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Introducing Figma export in MagicPath. Design and build with our native agent, or your favorite external one (Codex, Claude Code, etc.). Explore tons of different directions, then bring your work into Figma as fully editable designs. As simple as a copy-paste.
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Introducing Figma export in MagicPath. Design and build with our native agent, or your favorite external one (Codex, Claude Code, etc.). Explore tons of different directions, then bring your work into Figma as fully editable designs. As simple as a copy-paste.

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My top open source repos: 1. Mission Control self-hosted AI agent orchestration dashboard tasks, multi-agent workflows, spend, governance 5.7k stars 2. awesome-hermes-agent curated skills, tools, integrations, and resources for Hermes Agent 4.6k stars 3. council-of-high-intelligence 18 AI personas deliberate hard decisions across multiple LLM providers 3.4k stars 4. xint X intelligence CLI for search, monitoring, analysis, and engagement 5. marketing-dashboard open-source marketing ops control center for AI agent teams 6. awesome-agent-cortex sovereign agent stack for memory, identity, and agent ownership the thesis is simple: agents need infrastructure, not more demos. Follow nyk moved → @nykdotdev for the daily build in public.
1:30

My top open source repos: 1. Mission Control self-hosted AI agent orchestration dashboard tasks, multi-agent workflows, spend, governance 5.7k stars 2. awesome-hermes-agent curated skills, tools, integrations, and resources for Hermes Agent 4.6k stars 3. council-of-high-intelligence 18 AI personas deliberate hard decisions across multiple LLM providers 3.4k stars 4. xint X intelligence CLI for search, monitoring, analysis, and engagement 5. marketing-dashboard open-source marketing ops control center for AI agent teams 6. awesome-agent-cortex sovereign agent stack for memory, identity, and agent ownership the thesis is simple: agents need infrastructure, not more demos. Follow nyk moved → @nykdotdev for the daily build in public.

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Introducing the context course: a free course on doing ML with agent context. You will learn how to train models, optimize inferences, and build datasets, all by defining harness context with`SKILLS.md`, Plugins, MCP, Subagents, and Hooks. The course includes: - Weekly live AMA on YouTube - Weekly practical projects for ML with context - Instructions in Pi, Codex, Claude, and Opencode - Tutorials and guides on fundamentals - Interactive Quizzes Learn to give AI agents the right knowledge, tools, and structure to actually get work done. Skills, MCP servers, plugins, multi-agent workflows, and building an agent from scratch. Join here:
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Introducing the context course: a free course on doing ML with agent context. You will learn how to train models, optimize inferences, and build datasets, all by defining harness context with`SKILLS.md`, Plugins, MCP, Subagents, and Hooks. The course includes: - Weekly live AMA on YouTube - Weekly practical projects for ML with context - Instructions in Pi, Codex, Claude, and Opencode - Tutorials and guides on fundamentals - Interactive Quizzes Learn to give AI agents the right knowledge, tools, and structure to actually get work done. Skills, MCP servers, plugins, multi-agent workflows, and building an agent from scratch. Join here:

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NEW: Zapier Agents now work like a team. Roles, handoffs, and closing the loop. What's new: Agent-to-agent calling: Your AI agents now pass work like a relay team, each handling the step they’re best at (no human handoffs needed) Copilot: Describe your workflow in plain language, and Copilot maps the workflow and builds your full agent team, start to finish. Live knowledge sources: Your agents now pull live data straight from Box, Dropbox & Google Drive, always staying up-to-date. Template sharing: Turn your best agents into Zapier community templates. Share wins, showcase use cases, and turn your best builds into content. These specialized agents now collaborate to run full workflows (sales, support, marketing, and more), not just isolated tasks.
3:41

NEW: Zapier Agents now work like a team. Roles, handoffs, and closing the loop. What's new: Agent-to-agent calling: Your AI agents now pass work like a relay team, each handling the step they’re best at (no human handoffs needed) Copilot: Describe your workflow in plain language, and Copilot maps the workflow and builds your full agent team, start to finish. Live knowledge sources: Your agents now pull live data straight from Box, Dropbox & Google Drive, always staying up-to-date. Template sharing: Turn your best agents into Zapier community templates. Share wins, showcase use cases, and turn your best builds into content. These specialized agents now collaborate to run full workflows (sales, support, marketing, and more), not just isolated tasks.

Wade Foster

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