Video yükleniyor...

Video Yüklenemedi

Bu video yüklenirken bir sorun oluştu. Bu geçici bir ağ sorunundan kaynaklanıyor olabilir veya video kullanılamıyor olabilir.

Ana Sayfaya Dön

🔮 Using our agent framework, users can easily create agents with native access to our suite of proprietary tools including: 🔁 Self Reflection 🧠 Memory 👍 Human-in-the-loop 📁 File Sharing & Creation 🔂 Recurring Tasks 🔌 Integrations 🫂 Multi-agent Collaboration And more!

OpenServ

16,568 subscribers

77,919 görüntüleme • 1 yıl önce •via X (Twitter)

Bilim & Teknoloji Eğitim

Anya Rossi• Live Now

Private livecam show

11 Yorum

Infinite Liquidity🔮 profil fotoğrafı

Infinite Liquidity🔮1 yıl önce

This is the best product I have seen this year in all of crypto. Something with an actual use case for anyone, not just people inside the crypto space.. Will be a huge flex for any dev to have their agents on there

Rickyticky Bobbywobbin profil fotoğrafı

Rickyticky Bobbywobbin1 yıl önce

INTRODUCING: Agentic Security - LLM Security Scanner! 🔍 🔑 Features: Scans for prompt injections, jailbreaking & more. Provides detailed reports & options to customize attack rules. 🔗access the GitHub Link ↓

Samitoshi profil fotoğrafı

Samitoshi1 yıl önce

Beautiful and very professional user interface. Most promising Ai Agent platform in both web 2 and web 3. Good work @openservai 👏 $SERV

Mikael Z profil fotoğrafı

Mikael Z1 yıl önce

Wow, this is a piece of art of UI. People will be all over it once it is live!!! $SERV

lolaksppbura profil fotoğrafı

lolaksppbura1 yıl önce

A little Christmas gift from $SERV Team

BONTESA profil fotoğrafı

BONTESA1 yıl önce

This project is so professional. best AI agent in the space and only a few really know it YET. i feel tier1 listings are coming for this one

Lamentis;) profil fotoğrafı

Lamentis;)1 yıl önce

The user friendly interface for agent collaboration and task execution is impressive, setting a new standard for ease of use in AI development.

Semp profil fotoğrafı

Semp1 yıl önce

Love the recurring tasks! This will be epic! $SERV

Don Massimo (Periodontist) profil fotoğrafı

Don Massimo (Periodontist)1 yıl önce

That’s a huge milestone for humanity and crypto $Serv @milesdeutscher @cryptomanran

Lingua Franca profil fotoğrafı

Lingua Franca1 yıl önce

here we go

Rutger van der Tas profil fotoğrafı

Rutger van der Tas1 yıl önce

I’d love to create agents for my art projects.

Benzer Videolar

🚀 Say hello to the new & improved phidata 🚀 Build, ship, and monitor Agents with blazing-fast memory, knowledge, tools & reasoning 🔥 ⚡️ 70% faster memory & knowledge 🛠 100+ tools 🧠 Reasoning Agents 🤝 Multi-agent collaboration 📊 Built-in monitoring and Agent UI

🚀 Say hello to the new & improved phidata 🚀 Build, ship, and monitor Agents with blazing-fast memory, knowledge, tools & reasoning 🔥 ⚡️ 70% faster memory & knowledge 🛠 100+ tools 🧠 Reasoning Agents 🤝 Multi-agent collaboration 📊 Built-in monitoring and Agent UI

Ashpreet Bedi

114,527 görüntüleme • 1 yıl önce

Tencent announces AppAgent Multimodal Agents as Smartphone Users paper page: Recent advancements in large language models (LLMs) have led to the creation of intelligent agents capable of performing complex tasks. This paper introduces a novel LLM-based multimodal agent framework designed to operate smartphone applications. Our framework enables the agent to operate smartphone applications through a simplified action space, mimicking human-like interactions such as tapping and swiping. This novel approach bypasses the need for system back-end access, thereby broadening its applicability across diverse apps. Central to our agent's functionality is its innovative learning method. The agent learns to navigate and use new apps either through autonomous exploration or by observing human demonstrations. This process generates a knowledge base that the agent refers to for executing complex tasks across different applications. To demonstrate the practicality of our agent, we conducted extensive testing over 50 tasks in 10 different applications, including social media, email, maps, shopping, and sophisticated image editing tools. The results affirm our agent's proficiency in handling a diverse array of high-level tasks.

Tencent announces AppAgent Multimodal Agents as Smartphone Users paper page: Recent advancements in large language models (LLMs) have led to the creation of intelligent agents capable of performing complex tasks. This paper introduces a novel LLM-based multimodal agent framework designed to operate smartphone applications. Our framework enables the agent to operate smartphone applications through a simplified action space, mimicking human-like interactions such as tapping and swiping. This novel approach bypasses the need for system back-end access, thereby broadening its applicability across diverse apps. Central to our agent's functionality is its innovative learning method. The agent learns to navigate and use new apps either through autonomous exploration or by observing human demonstrations. This process generates a knowledge base that the agent refers to for executing complex tasks across different applications. To demonstrate the practicality of our agent, we conducted extensive testing over 50 tasks in 10 different applications, including social media, email, maps, shopping, and sophisticated image editing tools. The results affirm our agent's proficiency in handling a diverse array of high-level tasks.

AK

343,816 görüntüleme • 2 yıl önce

🚨 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

🚨 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

Abacus.AI

36,949,828 görüntüleme • 17 gün önce

I wanted to share a quick demo of what we've been working on with our ai agent cloud. This enables fast deployment of agents that have access to a suite of tools, and was designed with agent interoperability in mind. This demo shows how you can go from nothing to an AI twitter agent in a couple minutes. This is what we are using internally to manage deployments, so we will consistently upgrading its capabilities. The next goals are to enable simple TEE deployments for agents, and focus on building out feature for agent interoperability to simplify agent to agent collaboration.

I wanted to share a quick demo of what we've been working on with our ai agent cloud. This enables fast deployment of agents that have access to a suite of tools, and was designed with agent interoperability in mind. This demo shows how you can go from nothing to an AI twitter agent in a couple minutes. This is what we are using internally to manage deployments, so we will consistently upgrading its capabilities. The next goals are to enable simple TEE deployments for agents, and focus on building out feature for agent interoperability to simplify agent to agent collaboration.

Johnny

26,738 görüntüleme • 1 yıl önce

Chakra AI Agent Chat & Integrations.🧠 Seamlessly chat with your agent inside the Chakra dashboard, across Discord, Telegram, and more. Create and integrate your agent effortlessly — with Chakra.

Chakra AI Agent Chat & Integrations.🧠 Seamlessly chat with your agent inside the Chakra dashboard, across Discord, Telegram, and more. Create and integrate your agent effortlessly — with Chakra.

Chakra Agents

10,271 görüntüleme • 1 yıl önce

🤝 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:

🤝 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:

AITECH

43,206 görüntüleme • 9 ay önce

Playground is now live! With Playground, anyone can experiment with AI agent creation using our in-house AI agent framework—no token launch required. Soon, you’ll be able to connect your AI agent directly to Twitter and let it run autonomously. Additional tools (e.g., DeFi, gaming, on-chain transactions, L1 launcher) will roll out with the upcoming Agent Intelligence Protocol (AIP). This is your chance to get familiar with agent creation ahead of our bonding curve launch next week.

Playground is now live! With Playground, anyone can experiment with AI agent creation using our in-house AI agent framework—no token launch required. Soon, you’ll be able to connect your AI agent directly to Twitter and let it run autonomously. Additional tools (e.g., DeFi, gaming, on-chain transactions, L1 launcher) will roll out with the upcoming Agent Intelligence Protocol (AIP). This is your chance to get familiar with agent creation ahead of our bonding curve launch next week.

delaunch

259,079 görüntüleme • 1 yıl önce

LangChain Academy is live! Our first course — Introduction to LangGraph — teaches you the in-and-outs of building a reliable AI agent. In this course, you’ll learn how to: 🛠️ Build agents with LangGraph's graph-based workflows 🔄 Use memory + human-in-the-loop for smarter, self-corrective agents 📚 Create your own AI assistant that can perform knowledge tasks Enroll now for free ➡️ Bring LangChain Academy to your company ➡️

LangChain Academy is live! Our first course — Introduction to LangGraph — teaches you the in-and-outs of building a reliable AI agent. In this course, you’ll learn how to: 🛠️ Build agents with LangGraph's graph-based workflows 🔄 Use memory + human-in-the-loop for smarter, self-corrective agents 📚 Create your own AI assistant that can perform knowledge tasks Enroll now for free ➡️ Bring LangChain Academy to your company ➡️

LangChain

105,415 görüntüleme • 1 yıl önce

Portal Studio - Sneak Peek Here's an exclusive sneak peek into our first product as part of our strategy with AI-native tools, unlocking game creation. With Portal Studio - connect your agents, create workspaces and collaborate with your agents. A virtual workspace for agent orchestration all in one place. 📽️👇

Portal Studio - Sneak Peek Here's an exclusive sneak peek into our first product as part of our strategy with AI-native tools, unlocking game creation. With Portal Studio - connect your agents, create workspaces and collaborate with your agents. A virtual workspace for agent orchestration all in one place. 📽️👇

Portal

453,994 görüntüleme • 24 gün önce

2/ And with our Rubber Duck agent you get a multi-model reflection loop as a reviewer. GPT-5.5 can review the output of another model or vice versa!

2/ And with our Rubber Duck agent you get a multi-model reflection loop as a reviewer. GPT-5.5 can review the output of another model or vice versa!

Satya Nadella

39,725 görüntüleme • 1 ay önce

Our latest LangChain Academy course – Building Ambient Agents with LangGraph – is now available! Most agents today handle one request at a time through chat interfaces. But as models have improved, agents can now run in the background – and take on long-running, complex tasks. LangGraph is built for these “ambient agents,” with support for human-in-the-loop workflows and memory. LangGraph Platform provides the infrastructure to run these agents at scale, and LangSmith helps you observe, evaluate, and improve your agents. Together, they make it easier to build reliable, production-ready agents. In this course, you’ll: 📩 Learn the fundamentals of LangGraph and how to build your own email agent 📊 Evaluate your agent using LangSmith ✅ Add human-in-the-loop for reviews, and memory so your agent adapts over time 🚀 Deploy your agent with LangGraph Platform and connect it to Gmail

Our latest LangChain Academy course – Building Ambient Agents with LangGraph – is now available! Most agents today handle one request at a time through chat interfaces. But as models have improved, agents can now run in the background – and take on long-running, complex tasks. LangGraph is built for these “ambient agents,” with support for human-in-the-loop workflows and memory. LangGraph Platform provides the infrastructure to run these agents at scale, and LangSmith helps you observe, evaluate, and improve your agents. Together, they make it easier to build reliable, production-ready agents. In this course, you’ll: 📩 Learn the fundamentals of LangGraph and how to build your own email agent 📊 Evaluate your agent using LangSmith ✅ Add human-in-the-loop for reviews, and memory so your agent adapts over time 🚀 Deploy your agent with LangGraph Platform and connect it to Gmail

LangChain

33,192 görüntüleme • 11 ay önce

🚨 Agent Swarms Help Create Billion Dollar Companies Use SOTA AI models including - Opus 4.7 and Sonnet 4.6 - GPT 5.5 - Nano Banan Pro - SeeDance 20 to create multi-agent architectures with swarms of agents Thousands of people are using these multi-agent systems to run entire businesses

🚨 Agent Swarms Help Create Billion Dollar Companies Use SOTA AI models including - Opus 4.7 and Sonnet 4.6 - GPT 5.5 - Nano Banan Pro - SeeDance 20 to create multi-agent architectures with swarms of agents Thousands of people are using these multi-agent systems to run entire businesses

Abacus.AI

62,489,563 görüntüleme • 24 gün önce

is now in beta! is our agent launchpad, create and tokenise agents right on the platform, and for a limited time, all the agents will have free and unlimited access to posting tweets and images on X! >All agents powered by Moemate skills framework >Trade agents directly >X access and profile control >Image and text tweets based on agent personality Upcoming features include discord and telegram access, livestreaming, skills marketplace for on-chain and off-chain skills, desktop companions, on-chain wallet management and trading, AR/VR capabilities and multi-agent content creation.

HeyOz

208,732 görüntüleme • 1 yıl önce

Agent mode is now available in public preview for all users in Visual Studio 17.14. Agent mode in Visual Studio allows you to define tasks using natural language, with Copilot autonomously planning, editing your codebase, invoking tools, and iterating to resolve issues. Unlike Copilot Chat or Edits, agent mode doesn’t stop at one suggestion or file edit. Read more about it in our blog. 📖 #visualstudionews #githubcopilot #agent

Agent mode is now available in public preview for all users in Visual Studio 17.14. Agent mode in Visual Studio allows you to define tasks using natural language, with Copilot autonomously planning, editing your codebase, invoking tools, and iterating to resolve issues. Unlike Copilot Chat or Edits, agent mode doesn’t stop at one suggestion or file edit. Read more about it in our blog. 📖 #visualstudionews #githubcopilot #agent

Visual Studio

93,262 görüntüleme • 1 yıl önce

ADK FTW! Learn how to go from >prompt_to agent in this Agent Development Kit (ADK) demo. Our new #OpenSource framework simplifies the process of building agents and sophisticated multi-agent systems while maintaining precise control over agent behavior ↓

ADK FTW! Learn how to go from >prompt_to agent in this Agent Development Kit (ADK) demo. Our new #OpenSource framework simplifies the process of building agents and sophisticated multi-agent systems while maintaining precise control over agent behavior ↓

Google Cloud Tech

10,428 görüntüleme • 1 yıl önce

Build smarter AI agents, faster. ⚡️ The open-source Agent Development Kit makes it easy to create modular, multi-agent systems using the models and tools you prefer. Join the ADK Hackathon →

Build smarter AI agents, faster. ⚡️ The open-source Agent Development Kit makes it easy to create modular, multi-agent systems using the models and tools you prefer. Join the ADK Hackathon →

Google for Developers

11,240 görüntüleme • 1 yıl önce

Today we’re launching the first and only human-like AI agents in the world. Super Agents™ are the first agents with human‑level skills – they DM you, take @ mentions, send emails, manage docs, tasks, and more. Not just tools or API calls, but real skills fine‑tuned for how teams actually work. The first agents with 100% context – fully native in ClickUp and fully synced from other apps. Super Agents see your work the same way that humans do: tasks, docs, schedules, and conversations all in one place. The first agents that learn from human interactions automatically, without any setup or configuration – when you give feedback, they listen and improve how they work. The first agents with human‑level memory for custom agents – historical memory for every interaction, short-term working memory, and even long‑term memory stored in docs you can literally open, inspect, and edit. The first agents that are literally the same as users – our agentic user model is the same as our user data model. This gives you permissions and capabilities that you and your systems are already familiar with. The first infinite agent catalog – where anyone can create and customize agents in minutes, for literally any type of work imaginable. It's the most intuitive way to build agents on the planet. 95% of companies are failing in AI adoption. The reality is that AI isn't meant to be adopted, it's meant to be adapted – to you. Super Agents are automatically personalized to you and your company using proprietary state-of-the-art agent architecture, orchestration, and tooling. Today is the largest step forward we've ever made towards our mission of making people more productive. Maximize human productivity, with ClickUp Super Agents. Available NOW. For everyone.

Today we’re launching the first and only human-like AI agents in the world. Super Agents™ are the first agents with human‑level skills – they DM you, take @ mentions, send emails, manage docs, tasks, and more. Not just tools or API calls, but real skills fine‑tuned for how teams actually work. The first agents with 100% context – fully native in ClickUp and fully synced from other apps. Super Agents see your work the same way that humans do: tasks, docs, schedules, and conversations all in one place. The first agents that learn from human interactions automatically, without any setup or configuration – when you give feedback, they listen and improve how they work. The first agents with human‑level memory for custom agents – historical memory for every interaction, short-term working memory, and even long‑term memory stored in docs you can literally open, inspect, and edit. The first agents that are literally the same as users – our agentic user model is the same as our user data model. This gives you permissions and capabilities that you and your systems are already familiar with. The first infinite agent catalog – where anyone can create and customize agents in minutes, for literally any type of work imaginable. It's the most intuitive way to build agents on the planet. 95% of companies are failing in AI adoption. The reality is that AI isn't meant to be adopted, it's meant to be adapted – to you. Super Agents are automatically personalized to you and your company using proprietary state-of-the-art agent architecture, orchestration, and tooling. Today is the largest step forward we've ever made towards our mission of making people more productive. Maximize human productivity, with ClickUp Super Agents. Available NOW. For everyone.

Zeb Evans

320,284 görüntüleme • 5 ay önce

🚀Excited to Introduce Coagents🚀: An innovative framework for building human-in-the-loop agents into applications - powered by LangGraph & CopilotKit. Coagents enable: - End-users to correct agent steps (in-app)🛤️ - Stream intermediate agent state 🪢 - Expose LangGraph agents as specialized skills for an app's copilot 🧠 --- Human-in-the-loop is key to taking agents from cool demos to actually useful tools. Coagents, in combination with LangGraph are a big step towards making in-app, human-in-the-loop agents trivially easy to develop. Check out the first ever demo of Coagents, an AI Children's book generator 👇 Coagents will be entirely open-sourced. Interested in contributing/early access? CC: , shadcn, Harrison Chase, Lance Martin, Ariel Weinberger

🚀Excited to Introduce Coagents🚀: An innovative framework for building human-in-the-loop agents into applications - powered by LangGraph & CopilotKit. Coagents enable: - End-users to correct agent steps (in-app)🛤️ - Stream intermediate agent state 🪢 - Expose LangGraph agents as specialized skills for an app's copilot 🧠 --- Human-in-the-loop is key to taking agents from cool demos to actually useful tools. Coagents, in combination with LangGraph are a big step towards making in-app, human-in-the-loop agents trivially easy to develop. Check out the first ever demo of Coagents, an AI Children's book generator 👇 Coagents will be entirely open-sourced. Interested in contributing/early access? CC: , shadcn, Harrison Chase, Lance Martin, Ariel Weinberger

CopilotKit🪁

69,301 görüntüleme • 1 yıl önce

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.

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

184,938 görüntüleme • 4 ay önce