Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

Cloud agents and automations now support multi-repo environments. Configure a single environment with all the repos an agent needs, and reuse it across sessions.

cursor_ai's profile picture

Cursor

384,937 subscribers

46,988 Aufrufe • vor 2 Monaten •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

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.
2:17

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.

Warp

1,193,442 Aufrufe • vor 1 Monat

You can now create and manage automations in the same workspace as your agents. Automations are now available in the Agents Window. For the next 7 days, all agent runs for newly created automations are 50% off.
0:25

You can now create and manage automations in the same workspace as your agents. Automations are now available in the Agents Window. For the next 7 days, all agent runs for newly created automations are 50% off.

Cursor

194,454 Aufrufe • vor 1 Monat

Automations are now GA. You can now: • Set the model and reasoning level • Choose if runs happen in a worktree or existing branch • Reuse workflows with templates Automations are great for recurring tasks — daily repo briefings, issue triage, PR comment follow-up, and more.
0:38

Automations are now GA. You can now: • Set the model and reasoning level • Choose if runs happen in a worktree or existing branch • Reuse workflows with templates Automations are great for recurring tasks — daily repo briefings, issue triage, PR comment follow-up, and more.

OpenAI Developers

94,971 Aufrufe • vor 4 Monaten

OPENCLAW + OLLAMA CAN TURN YOUR LAPTOP INTO A LOCAL AI AGENT STACK WITH $0 QUERY COSTS. Research agents, content agents, support agents, and automations all running locally instead of paying for every prompt.
8:40

OPENCLAW + OLLAMA CAN TURN YOUR LAPTOP INTO A LOCAL AI AGENT STACK WITH $0 QUERY COSTS. Research agents, content agents, support agents, and automations all running locally instead of paying for every prompt.

0xMarioNawfal

83,732 Aufrufe • vor 4 Monaten

You told us you’re running multiple AI agents and wanted a better UX. We listened and shipped it! Here’s what’s new in the latest Visual Studio Code release: 🗂️ Unified agent sessions workspace for local, background, and cloud agents 💻 Claude and Codex support for local and cloud agents 🔀 Parallel subagents 🌐 Integrated browser And more...
1:09

You told us you’re running multiple AI agents and wanted a better UX. We listened and shipped it! Here’s what’s new in the latest Visual Studio Code release: 🗂️ Unified agent sessions workspace for local, background, and cloud agents 💻 Claude and Codex support for local and cloud agents 🔀 Parallel subagents 🌐 Integrated browser And more...

Visual Studio Code

587,497 Aufrufe • vor 5 Monaten

Starting today, you can run cloud agents inside fully configured development environments. Set them up the same way you'd set up a laptop for an engineer: cloned repos, installed dependencies, and toolchain credentials.
0:20

Starting today, you can run cloud agents inside fully configured development environments. Set them up the same way you'd set up a laptop for an engineer: cloned repos, installed dependencies, and toolchain credentials.

Cursor

287,639 Aufrufe • vor 2 Monaten

Introducing WhatsApp support for ElevenLabs Agents. Our omnichannel platform is now integrated with WhatsApp, letting teams design an agent once and deploy it across web, mobile, phone lines, and WhatsApp.
0:42

Introducing WhatsApp support for ElevenLabs Agents. Our omnichannel platform is now integrated with WhatsApp, letting teams design an agent once and deploy it across web, mobile, phone lines, and WhatsApp.

ElevenLabs

426,492 Aufrufe • vor 7 Monaten

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:

Andrew Ng

105,343 Aufrufe • vor 1 Jahr

Your customer support needs a voice agent built for the real world. Grok Voice Think Fast 1.0 handles complex workflows with speed and accuracy, even in hard-to-hear environments. From multi-step troubleshooting to high-volume tool calls, it keeps up.
0:34

Your customer support needs a voice agent built for the real world. Grok Voice Think Fast 1.0 handles complex workflows with speed and accuracy, even in hard-to-hear environments. From multi-step troubleshooting to high-volume tool calls, it keeps up.

SpaceXAI

77,036,645 Aufrufe • vor 2 Monaten

Wato (Wato) is building the collaboration layer for teams working with AI agents: shared knowledge, cloud agents, automations, and permissioned tools across the AI subscriptions companies already use. Congrats on the launch, Arihan & Rahul Rejeev!
2:07

Wato (Wato) is building the collaboration layer for teams working with AI agents: shared knowledge, cloud agents, automations, and permissioned tools across the AI subscriptions companies already use. Congrats on the launch, Arihan & Rahul Rejeev!

Y Combinator

17,004 Aufrufe • vor 1 Monat

OpenClaw and Hermes Agent can now clone themselves. They can literally undergo mitosis, creating hundreds of exact replicas of themselves, including their computer environment, all of their files, and all of their Obsidian vaults. Everything that you took the time to craft into an environment for these agents can be instantly cloned through a single click on Orgo. This will forever change the world of agents. Agent mitosis is here.
1:23

OpenClaw and Hermes Agent can now clone themselves. They can literally undergo mitosis, creating hundreds of exact replicas of themselves, including their computer environment, all of their files, and all of their Obsidian vaults. Everything that you took the time to craft into an environment for these agents can be instantly cloned through a single click on Orgo. This will forever change the world of agents. Agent mitosis is here.

nick vasilescu

20,453 Aufrufe • vor 3 Monaten

Agentic systems aren’t just for enterprises. As Humayun (Humayun) puts it: agents represent processes, software, even people, but the key shift is autonomy. Not just automation, but agents that can make decisions using context, history, and intent. The real unlock? Multi-agent systems, where different agents interact across environments, orgs, and ecosystems. That’s where it scales.
2:48

Agentic systems aren’t just for enterprises. As Humayun (Humayun) puts it: agents represent processes, software, even people, but the key shift is autonomy. Not just automation, but agents that can make decisions using context, history, and intent. The real unlock? Multi-agent systems, where different agents interact across environments, orgs, and ecosystems. That’s where it scales.

Fetch.ai

17,636 Aufrufe • vor 6 Monaten

Introducing Notte CLI Control browser automations from your terminal. Start sessions, navigate pages, run AI agents, deploy functions, all from the CLI. Want to see your agents working? “notte sessions viewer” opens the Console live. Terminal + Console. Build and ship browser agents & automations from anywhere.
4:26

Introducing Notte CLI Control browser automations from your terminal. Start sessions, navigate pages, run AI agents, deploy functions, all from the CLI. Want to see your agents working? “notte sessions viewer” opens the Console live. Terminal + Console. Build and ship browser agents & automations from anywhere.

Notte

429,966 Aufrufe • vor 4 Monaten

The Multi-Repo AI Agent by Zencoder is a clear evolution in AI tooling. Automation adapts to complex environments by understanding distributed code, integrating with existing workflows, and supporting continuous delivery. More > Partnership zencoderai
1:32

The Multi-Repo AI Agent by Zencoder is a clear evolution in AI tooling. Automation adapts to complex environments by understanding distributed code, integrating with existing workflows, and supporting continuous delivery. More > Partnership zencoderai

Antonio Grasso

12,887 Aufrufe • vor 11 Monaten

Inside the Agent Console, the 𝗔𝗣𝗜 𝗘𝘅𝗽𝗹𝗼𝗿𝗲𝗿 offers a direct way to interact with Agents. Explore commands, configure parameters, and view responses instantly. See it in action ↓
0:58

Inside the Agent Console, the 𝗔𝗣𝗜 𝗘𝘅𝗽𝗹𝗼𝗿𝗲𝗿 offers a direct way to interact with Agents. Explore commands, configure parameters, and view responses instantly. See it in action ↓

Teneo Protocol

35,336 Aufrufe • vor 4 Monaten

5 huge AI agents, MCP, and LLM updates today: 1. China just dropped an opensource Deep Research multi-agent framework with MCP support. It connects AI Agents with specialized tools for tasks like web search, crawling, and Python code execution. 100% opensource.
1:16

5 huge AI agents, MCP, and LLM updates today: 1. China just dropped an opensource Deep Research multi-agent framework with MCP support. It connects AI Agents with specialized tools for tasks like web search, crawling, and Python code execution. 100% opensource.

Unwind AI

79,540 Aufrufe • vor 1 Jahr

🤖 Agents SDK—our new open-source SDK for orchestrating multi-agent workflows, improving upon Swarm. Configure agents with built-in tools, hand off tasks, add safety guardrails, and visualize execution traces for debugging and optimizing performance.
0:32

🤖 Agents SDK—our new open-source SDK for orchestrating multi-agent workflows, improving upon Swarm. Configure agents with built-in tools, hand off tasks, add safety guardrails, and visualize execution traces for debugging and optimizing performance.

OpenAI Developers

164,359 Aufrufe • vor 1 Jahr

Veil SDK/CLI v0.0.5 is live. Fully revamped for both humans and agents - Cleaner human-readable output - Stable JSON output for automations - Updated agent skill file Point your agent at the GitHub repo and get private on Base in seconds.
0:29

Veil SDK/CLI v0.0.5 is live. Fully revamped for both humans and agents - Cleaner human-readable output - Stable JSON output for automations - Updated agent skill file Point your agent at the GitHub repo and get private on Base in seconds.

Veil.Cash

16,178 Aufrufe • vor 3 Monaten

Introducing LobeHub: Agent teammates that grow with you. LobeHub is the ultimate space for work and life: to find, build, and collaborate with agent teammates that grow with you. We’re building the world’s first and largest human–agent co-evolving network. Two years ago, we built LobeChat, an open-source interface for using different AI models. Today, LobeChat has 70k+ GitHub stars and serves 6M+ users worldwide. How to fully unlock the power of models has always been a shared mission between us and the community. We started with interaction — a fundamentally new, agent-first experience. Agents are no longer passive tools invoked in a single conversation. They should be proactive, always-on units of work. Treating agents as the minimal atomic unit is also the core of our agent harness infra. Today’s agents are mostly one-off executors. Even with memory, it’s often global — and hallucinates. We build long-term agent teammates that evolve with users. Each agent has its own dedicated memory space, editable by users, allowing humans and agents to co-evolve over time. This, in turn, allows us to design clearer rewards for reinforcement learning and create cleaner environments for continual learning. Agent teammates can work in groups. Through a multi-agent system, agent groups operate faster, more cost-effective, and go beyond what single-agent systems can achieve. For example, a single agent often requires heavy user involvement to proceed step by step, whereas LobeHub can execute the same work from a single instruction, with a supervisor orchestrating agents that run in parallel or debate to produce better results. We are building the collaboration network among agent teammates — and between humans and agent teammates as well. Ease of use matters. AI intelligence and shared human intelligence are equally important. With simple instructions and tool selection, you can effortlessly build and team up with agent coworkers to deliver complex, systematic work — even assembling a quant team to execute trades. Through the LobeHub community, anyone can discover, reuse, and remix agents and agent groups, customizing them to fit their own workflows, preferences, and needs. Last but not least, our vision started with LobeChat: multi-model support is the most efficient approach for users. We believe different models excel in different scenarios. By routing across multiple models, LobeHub improves cost efficiency and unlocks capabilities that a single-model setup cannot easily support.
2:41

Introducing LobeHub: Agent teammates that grow with you. LobeHub is the ultimate space for work and life: to find, build, and collaborate with agent teammates that grow with you. We’re building the world’s first and largest human–agent co-evolving network. Two years ago, we built LobeChat, an open-source interface for using different AI models. Today, LobeChat has 70k+ GitHub stars and serves 6M+ users worldwide. How to fully unlock the power of models has always been a shared mission between us and the community. We started with interaction — a fundamentally new, agent-first experience. Agents are no longer passive tools invoked in a single conversation. They should be proactive, always-on units of work. Treating agents as the minimal atomic unit is also the core of our agent harness infra. Today’s agents are mostly one-off executors. Even with memory, it’s often global — and hallucinates. We build long-term agent teammates that evolve with users. Each agent has its own dedicated memory space, editable by users, allowing humans and agents to co-evolve over time. This, in turn, allows us to design clearer rewards for reinforcement learning and create cleaner environments for continual learning. Agent teammates can work in groups. Through a multi-agent system, agent groups operate faster, more cost-effective, and go beyond what single-agent systems can achieve. For example, a single agent often requires heavy user involvement to proceed step by step, whereas LobeHub can execute the same work from a single instruction, with a supervisor orchestrating agents that run in parallel or debate to produce better results. We are building the collaboration network among agent teammates — and between humans and agent teammates as well. Ease of use matters. AI intelligence and shared human intelligence are equally important. With simple instructions and tool selection, you can effortlessly build and team up with agent coworkers to deliver complex, systematic work — even assembling a quant team to execute trades. Through the LobeHub community, anyone can discover, reuse, and remix agents and agent groups, customizing them to fit their own workflows, preferences, and needs. Last but not least, our vision started with LobeChat: multi-model support is the most efficient approach for users. We believe different models excel in different scenarios. By routing across multiple models, LobeHub improves cost efficiency and unlocks capabilities that a single-model setup cannot easily support.

LobeHub

185,161 Aufrufe • vor 5 Monaten