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🚀 LAUNCHING: Genspark Multi-Agent Orchestration! One lead agent coordinates multiple sub-agents to tackle complex tasks. Watch the demo: Create 10 agents simultaneously - 10 professional slide decks in the time it takes to make one. Experience it: Blog:

Genspark

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23,144 views • 1 year ago •via X (Twitter)

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It’s getting insanely easy to build complex multi-agent systems in n8n. You can now build supervisor agents that delegates tasks to specialized sub agents. Just add the sub agents via AI Agent Tool node. All in one place and a lot easier to debug! Zero code!

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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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🚨 Agent Swarms - Multi-Agents Delegate Complex Prompts To Sub-Agents Use Gemini 3.5 Flash, Opus 4.7 and GPT 5.5 xHIgh to create complex multi-agent system A master agent can orchestrate several worker agents to just do things Build full-stack apps, mobile apps and automate complex scheduled tasks Each agent can do different tasks including Q/A, monitoring and software development

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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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🤝 AI Agents That Work Together — Collaboration Made Simple! In this demo, John explores one of Agent Forge’s most powerful features: agent-to-agent collaboration. With Agent Forge, you can create agents with different roles, connect them, and watch them collaborate to solve complex tasks. Modular, scalable, and fully automated. 🔗 Try here:

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AITECH CLOUD NETWORK

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Communities can now build with a coordinated team of AI agents. Agent execution is live on connecting community decisions to the agents that carry them out. $ZZZ brings the full workflow into one workspace: Discussion → decisions → tasks → execution. 1. Members chat and vote. An agent creates tasks from those discussions and records the decisions. 2. The lead agent prioritises tasks, clarifies the details with members and presents interactive mockups for approval. 3. The lead agent coordinates a swarm of agents to complete the tasks. Watch a community discussion become a task, then see the agents get to work.

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this is pure f*cking treasure 10 GitHub repos with 165K+ combined stars to build your own AI agent team that works 24/7 most people still use one AI chat for everything. these repos turn Claude into a whole team: one agent researches, one codes, one reviews, and a lead runs them all one resource per step, from your first 3 agents to a full swarm running Claude and Codex together (1/10) CrewAI the easiest way to assemble a team of agents: give them roles and tasks, then hit kickoff ▸ (2/10) LangGraph connect agents into a graph with checkpoints, so if something fails it resumes instead of starting over ▸ (3/10) wshobson/agents ready-made agents for Claude Code (backend, frontend, security, tests, devops) plus an orchestrator to run them ▸ (4/10) oh-my-team 12 specialist agents for Claude Code that split the work and merge the results ▸ (5/10) agent-swarm your own self-hosted AI company: one lead agent hires workers and runs them 24/7 ▸ (6/10) Open-Agents spawns many Claude agents in parallel, so 6 tasks run at once instead of one after another ▸ (7/10) swarms an enterprise-grade framework for when your team grows from 5 agents to 50 ▸ (8/10) Microsoft Agent Framework Microsoft's new framework, the AutoGen successor, for workflows, agents and runtime in one place ▸ (9/10) CLI Agent Orchestrator runs Claude, Codex and other CLIs side by side as one team, with a supervisor watching all of them ▸ (10/10) ensemble launches Codex and Claude together, so they build and ship as one ▸ the loop: assemble → plug in → run → scale → orchestrate pick one, click, let it run, and while everyone else is still typing prompts, your agents are already shipping ↓

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create-llama is now also the easiest way to get started with multi-agents using LlamaIndex 🦙 v0.2 adds multi-agents using LlamaIndex Workflows. Create a multi-agent chat UI like the one below in a single command line! 🖥️ $ npx create-llama 📄

create-llama is now also the easiest way to get started with multi-agents using LlamaIndex 🦙 v0.2 adds multi-agents using LlamaIndex Workflows. Create a multi-agent chat UI like the one below in a single command line! 🖥️ $ npx create-llama 📄

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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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Google just released a free 2-hour course On going from one prompt to a complete multi-agent graph: 17:44 - Build your first AI agent 39:30 - Create self-improving agent loops 1:12:38 - Connect those loops into graphs 1:34:26 - Make agents regulate themselves 1:55:05 - Orchestrate the full multi-agent system Most people are still building one agent and stopping there Google is teaching the entire stack: Prompt → Agents → Loops → Graphs → Multi-Agent Systems Single agents are the old workflow Self-regulating agent graphs are the new one This free 2-hour course is worth more than most paid agent engineering programs Bookmark and watch it today Then read the full architecture below ↓

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About a year ago, we launched the Agent-to-Agent protocol because AI agents shouldn’t work in silos. Today, developers are using A2A to coordinate specialized agents across enterprise data systems, commerce workflows, DevOps environments, and scientific research. FoldRun is one example, simplifying petabyte-scale protein modeling through agent-to-agent collaboration. Learn how to build with it in the blog:

About a year ago, we launched the Agent-to-Agent protocol because AI agents shouldn’t work in silos. Today, developers are using A2A to coordinate specialized agents across enterprise data systems, commerce workflows, DevOps environments, and scientific research. FoldRun is one example, simplifying petabyte-scale protein modeling through agent-to-agent collaboration. Learn how to build with it in the blog:

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Preview of our DeepResearchSwarm™ It's incomprehensibly fast as it leverages a multi-agent architecture instead of just one agent. It utilizes a joint hierarchical-parallel architecture that creates a plan and then concurrently executes the plan with multiple search agents. Workflow: Task → Research Director → multiple search agents → final summary by summarization agent. Stay tuned for the release ↓

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Big release here! AG-UI 🤝 Google A2A Embed A2A multi-agent meshes in your frontend, and build fullstack multi-agent apps with A2A and AG-UI. 🔍How it works: Simply drop-in A2A agent endpoints, and an A2A AG-UI orchestrator agent will bi-directionally stream events between your Agents & application. Includes: - UI for A2A Interactions - Generative UI for Agent Tool Calls - Human-in-the Loop Approvals - State Synchronization (agent mesh application state) - Frontend Tool Calls for A2A Agents Watch the demo below > LangGraph & Google ADK agents working together simultaneously inside an app Demo GitHub, docs, and more 👇

Big release here! AG-UI 🤝 Google A2A Embed A2A multi-agent meshes in your frontend, and build fullstack multi-agent apps with A2A and AG-UI. 🔍How it works: Simply drop-in A2A agent endpoints, and an A2A AG-UI orchestrator agent will bi-directionally stream events between your Agents & application. Includes: - UI for A2A Interactions - Generative UI for Agent Tool Calls - Human-in-the Loop Approvals - State Synchronization (agent mesh application state) - Frontend Tool Calls for A2A Agents Watch the demo below > LangGraph & Google ADK agents working together simultaneously inside an app Demo GitHub, docs, and more 👇

CopilotKit🪁

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Hermes Agent + Mixture of Agents is insane one agent is good. multiple agents working together is a different game watch this

Hermes Agent + Mixture of Agents is insane one agent is good. multiple agents working together is a different game watch this

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51,606 views • 4 months ago