Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

Introducing 🦀 CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents 🦀 CRAB provides an end-to-end and easy-to-use framework to build multimodal agents, operate environments, and create benchmarks to evaluate them, featuring three key components: - 🔀 Cross-environment support - agents can operate tasks in 📱 Android and 💻...

CamelAIOrg's profile picture

CAMEL-AI.org

8,634 subscribers

72,807 Aufrufe • vor 2 Jahren •via X (Twitter)

Wissenschaft & Technologie
LIVE

Anya Rossi• Live Now

Private livecam show

8 Kommentare

Profilbild von CAMEL-AI.org
CAMEL-AI.orgvor 2 Jahren

Of course, that wouldn't be possible thanks to the amazing contributors from our @CamelAIOrg community. @TianqiXu233 (KAUST), Linyao Chen (UTokyo), Dai-jie Wu (KAUST), Yanjun Chen (CMU), @zechengzh, Xiang Yao, @Zhiqiang_Xie (Stanford), @YongchaoC (Harvard), @atasteoff (Tsinghua), Bochen Qian (SUSTech), @philiptorr (Oxford), @BernardSGhanem (KAUST), @guohao_li (Oxford)

Profilbild von CAMEL-AI.org
CAMEL-AI.orgvor 2 Jahren

If you are interested in learning more, check out this blog post:

Profilbild von omni_georgio
omni_georgiovor 2 Jahren

🚀

Profilbild von Happy
Happyvor 2 Jahren

amazing

Profilbild von CAMEL-AI.org
CAMEL-AI.orgvor 2 Jahren

Thanks, more to come!

Profilbild von Tianqi Xu
Tianqi Xuvor 2 Jahren

Thank @omni_georgio for making this awesome video! We target to make CRAB the best LLM agent benchmark framework.

Profilbild von OP
OPvor 2 Jahren

hey @CamelAIOrg . That's promissing. Added CRAB to the new specialized directory for AI Agents and frameworks for building them. Now you can upvote it.

Profilbild von KALALA NZENIELE
KALALA NZENIELEvor 2 Jahren

@DFintelligence

Ähnliche Videos

Super excited to share 🧠MLGym 🦾 – the first Gym environment for AI Research Agents 🤖🔬 We introduce MLGym and MLGym-Bench, a new framework and benchmark for evaluating and developing LLM agents on AI research tasks. The key contributions of our work are: 🕹️ Enables the exploration of different training algorithms for AI Research Agents such as RL 🛠️ Provides a flexible evaluation framework that can accommodate different artifacts such as models, algorithms, or predictions 🤖 Allows researchers to evaluate any model without the need to develop a custom agentic harness 🎯 Introduces 13 diverse open-ended AI Research tasks for evaluating AI Research Agents on a wide range of domains such as computer vision, natural language processing, reinforcement learning, game theory, and logical reasoning. 📈 Proposes a new evaluation metric for AI Research Agents MLGym makes it easy to: 1) Add new tasks 2) Evaluate new models 3) Integrate new agents Check out a video of the MLGym Agent to see how it performs the full pipeline of idea generation💡, implementation 👩‍💻, experimentation 👩‍🔬, and iteration 🔄 to improve on ML tasks. Huge thanks to the exceptionally talented Deepak Nathani who led this work and to all the other amazing collaborators who made this possible 🙏🫶🚀
1:32

Super excited to share 🧠MLGym 🦾 – the first Gym environment for AI Research Agents 🤖🔬 We introduce MLGym and MLGym-Bench, a new framework and benchmark for evaluating and developing LLM agents on AI research tasks. The key contributions of our work are: 🕹️ Enables the exploration of different training algorithms for AI Research Agents such as RL 🛠️ Provides a flexible evaluation framework that can accommodate different artifacts such as models, algorithms, or predictions 🤖 Allows researchers to evaluate any model without the need to develop a custom agentic harness 🎯 Introduces 13 diverse open-ended AI Research tasks for evaluating AI Research Agents on a wide range of domains such as computer vision, natural language processing, reinforcement learning, game theory, and logical reasoning. 📈 Proposes a new evaluation metric for AI Research Agents MLGym makes it easy to: 1) Add new tasks 2) Evaluate new models 3) Integrate new agents Check out a video of the MLGym Agent to see how it performs the full pipeline of idea generation💡, implementation 👩‍💻, experimentation 👩‍🔬, and iteration 🔄 to improve on ML tasks. Huge thanks to the exceptionally talented Deepak Nathani who led this work and to all the other amazing collaborators who made this possible 🙏🫶🚀

Roberta Raileanu

105,252 Aufrufe • vor 1 Jahr

Introducing Giga's browser agent. Giga can now log in and completes tasks end-to-end, just like your human agents. It used to take months to get support APIs deployed for AI agents in the enterprise. Now these integrations can be built instantly.
1:16

Introducing Giga's browser agent. Giga can now log in and completes tasks end-to-end, just like your human agents. It used to take months to get support APIs deployed for AI agents in the enterprise. Now these integrations can be built instantly.

Esha

1,482,656 Aufrufe • vor 9 Monaten

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.
0:35

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.

zzz

14,690 Aufrufe • vor 16 Tagen

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.
1:57

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

344,019 Aufrufe • vor 2 Jahren

🤔Can we assess agents across various apps & OS w.o. crafting new envs? OSWorld🖥️: A unified, real computer env for multimodal agents to evaluate open-ended computer tasks with arbitrary apps and interfaces on Ubuntu, Windows, & macOS. + annotated 369 real-world computer tasks 👇
1:00

🤔Can we assess agents across various apps & OS w.o. crafting new envs? OSWorld🖥️: A unified, real computer env for multimodal agents to evaluate open-ended computer tasks with arbitrary apps and interfaces on Ubuntu, Windows, & macOS. + annotated 369 real-world computer tasks 👇

Tianbao Xie

68,746 Aufrufe • vor 2 Jahren

That's a lot of crab eggs! This guy is going to help this crab by removing the barnicles. 🦀 🦀
3:18

That's a lot of crab eggs! This guy is going to help this crab by removing the barnicles. 🦀 🦀

@LynneK

172,935 Aufrufe • vor 7 Monaten

Reasoning models tailored for diverse AI tasks. 🚀 Meet Amazon Nova 2 foundation models, supporting fast, cost-effective reasoning to multimodal capabilities. Power versatile tasks like AI agents, code-generation, and Conversational AI. Choose the perfect match for your workload.
0:29

Reasoning models tailored for diverse AI tasks. 🚀 Meet Amazon Nova 2 foundation models, supporting fast, cost-effective reasoning to multimodal capabilities. Power versatile tasks like AI agents, code-generation, and Conversational AI. Choose the perfect match for your workload.

Amazon Web Services

2,074,736 Aufrufe • vor 10 Monaten

🎮 Computer Use Agent Arena is LIVE! 🚀 🔥 Easiest way to test computer-use agents in the wild without any setup 🌟 Compare top VLMs: OpenAI Operator, Claude 3.7, Gemini 2.5 Pro, Qwen 2.5 vl and more 🕹️ Test agents on 100+ real apps & webs with one-click config 🔒 Safe & free access on cloud-hosted machines Page: Leaderboard (tentative): Blog: Data & Code (coming soon): ⭐️Why Computer Agent Arena? 1️⃣Beyond Static Benchmarks: We use computers to perform enormous tasks and workflows every day, and AI agents have the potential to automate these tasks. However, existing benchmarks are very limited (e.g., only 369 tasks in OSWorld and 812 tasks in WebArena). To better measure their capabilities, we introduce Computer Agent Arena for users to easily compare & test AI agents on all kinds of crowdsourced real-world computer use tasks. 2️⃣Cloud Testing, Simplified: As agents like OpenAI’s Operator and Claude 3.7 sonnet release, users face configuration challenges and privacy hurdles to deploy on their own computers. Our platform integrates these agents with cloud-hosted machines, providing users with quick and secure access. 3️⃣Unified Embodied Digital Environment: Unlike Chatbot Arena, we provide users with a real embodied environment—computers—where all agents are grounded in real computer tasks and environments. Led by @XLANG_Lab [1/🧵]
0:32

🎮 Computer Use Agent Arena is LIVE! 🚀 🔥 Easiest way to test computer-use agents in the wild without any setup 🌟 Compare top VLMs: OpenAI Operator, Claude 3.7, Gemini 2.5 Pro, Qwen 2.5 vl and more 🕹️ Test agents on 100+ real apps & webs with one-click config 🔒 Safe & free access on cloud-hosted machines Page: Leaderboard (tentative): Blog: Data & Code (coming soon): ⭐️Why Computer Agent Arena? 1️⃣Beyond Static Benchmarks: We use computers to perform enormous tasks and workflows every day, and AI agents have the potential to automate these tasks. However, existing benchmarks are very limited (e.g., only 369 tasks in OSWorld and 812 tasks in WebArena). To better measure their capabilities, we introduce Computer Agent Arena for users to easily compare & test AI agents on all kinds of crowdsourced real-world computer use tasks. 2️⃣Cloud Testing, Simplified: As agents like OpenAI’s Operator and Claude 3.7 sonnet release, users face configuration challenges and privacy hurdles to deploy on their own computers. Our platform integrates these agents with cloud-hosted machines, providing users with quick and secure access. 3️⃣Unified Embodied Digital Environment: Unlike Chatbot Arena, we provide users with a real embodied environment—computers—where all agents are grounded in real computer tasks and environments. Led by @XLANG_Lab [1/🧵]

Bowen Wang

93,330 Aufrufe • vor 1 Jahr

Introducing: ✨Nexus✨ Nexus is a tool to create, run and monitor AI agents. You can connect these agents to computers, phones and even robots. Our vision-first AI agents interact with environments much like humans do, enabling them to perform tasks that were previously impossible for any other agent. Nexus lets anyone control an army of AI agents through pure natural language. ‼️
1:43

Introducing: ✨Nexus✨ Nexus is a tool to create, run and monitor AI agents. You can connect these agents to computers, phones and even robots. Our vision-first AI agents interact with environments much like humans do, enabling them to perform tasks that were previously impossible for any other agent. Nexus lets anyone control an army of AI agents through pure natural language. ‼️

Jan Schnyder

27,901 Aufrufe • vor 1 Jahr

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!
1:46

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!

elvis

45,690 Aufrufe • vor 1 Jahr

"I really don't want to manage an AI team." Cathryn found a solution: Paperclip, the open-source project What she showed me: • Paperclip leads her agents using its project management setup • Humans on her team use it to assign tasks to agents • Agents delegate tasks to humans or other agents • Paperclip turns your goals into agent tasks • It turns an SEO audit doc (for example) into agent tasks • It organizes OpenClaw agents OR even creates its own agents Also: fast-forward to 9min25sec to see a 3-minute Paperclip setup. (YouTube version in first comment.)
12:48

"I really don't want to manage an AI team." Cathryn found a solution: Paperclip, the open-source project What she showed me: • Paperclip leads her agents using its project management setup • Humans on her team use it to assign tasks to agents • Agents delegate tasks to humans or other agents • Paperclip turns your goals into agent tasks • It turns an SEO audit doc (for example) into agent tasks • It organizes OpenClaw agents OR even creates its own agents Also: fast-forward to 9min25sec to see a 3-minute Paperclip setup. (YouTube version in first comment.)

Andrew Warner

54,178 Aufrufe • vor 7 Monaten

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
3:02

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 Aufrufe • vor 1 Jahr

The town hall for your agents 🦀
0:30

The town hall for your agents 🦀

moltx

34,980 Aufrufe • vor 8 Monaten

I built a framework for co-ordinating AFK coding agents. It's called Sandcastle. Watch me use it to pick tasks, parallelize N coding agents, and merge the code - all AFK:
5:06

I built a framework for co-ordinating AFK coding agents. It's called Sandcastle. Watch me use it to pick tasks, parallelize N coding agents, and merge the code - all AFK:

Matt Pocock

60,298 Aufrufe • vor 6 Monaten

🦀 Crab Tie 🦀 Don’t wait to find out the true potential of the ever-popular Moon Tie! With emphasis on the exposure and vulnerability of the model, these variations on the floor and in suspension are perfect for semenawa. ➡️
0:27

🦀 Crab Tie 🦀 Don’t wait to find out the true potential of the ever-popular Moon Tie! With emphasis on the exposure and vulnerability of the model, these variations on the floor and in suspension are perfect for semenawa. ➡️

Shibari Study

52,952 Aufrufe • vor 3 Jahren

Agents are getting complex to manage: models, skills, automations, permissions, cloud environments, orchestration patterns... So, we built the Terraform for agents: one repository that configures all of your agents as code. The Warp Factories configuration spec defines: - Agents and orchestration: how work gets routed and which models agents use - Automations: the events and schedules that kick off work - Access and environments: repositories, secrets, MCP servers, and runners - Measurement: scorers and benchmarks for understanding and improving performance Because it’s all code, both engineers and agents can propose changes to the factory itself. That makes it possible to test new configurations, benchmark them, and even have your factory propose improvements as PRs.
0:21

Agents are getting complex to manage: models, skills, automations, permissions, cloud environments, orchestration patterns... So, we built the Terraform for agents: one repository that configures all of your agents as code. The Warp Factories configuration spec defines: - Agents and orchestration: how work gets routed and which models agents use - Automations: the events and schedules that kick off work - Access and environments: repositories, secrets, MCP servers, and runners - Measurement: scorers and benchmarks for understanding and improving performance Because it’s all code, both engineers and agents can propose changes to the factory itself. That makes it possible to test new configurations, benchmark them, and even have your factory propose improvements as PRs.

Warp

756,814 Aufrufe • vor 26 Tagen

How capable are web agents at solving knowledge work tasks? 🤔 Are LLMs up to the challenge? 🤖 Introducing WorkArena: a benchmark where agents meet the world 𝘸𝘪𝘭𝘥 web of enterprise software 🌐🖥️ Paper: Website: 🧵 1/7
0:25

How capable are web agents at solving knowledge work tasks? 🤔 Are LLMs up to the challenge? 🤖 Introducing WorkArena: a benchmark where agents meet the world 𝘸𝘪𝘭𝘥 web of enterprise software 🌐🖥️ Paper: Website: 🧵 1/7

Alexandre Lacoste

24,550 Aufrufe • vor 2 Jahren

Pouring Mud into a Crab Hole to Catch Crab! 🦀🪣😱
0:51

Pouring Mud into a Crab Hole to Catch Crab! 🦀🪣😱

Sangeetha

551,409 Aufrufe • vor 2 Monaten

🚨 Announcing AutoBots - Recursively Self-Improving Agents (RSI) Super excited to launch our multi-LLM-based recursively self-improving agents We use DeepSeek Flash 4 for easy tasks and Fable 5 for super hard ones. The agents learn over time, becoming MORE efficient and smarter with each run We use these RSI agents to run our company! It's amazingly good with little to no human intervention required Examples include bug-fixing agents, viral tweet- or video-makers, growth-hacking agents, and so on. AutoBots work in the background to help increase your revenue.
1:11

🚨 Announcing AutoBots - Recursively Self-Improving Agents (RSI) Super excited to launch our multi-LLM-based recursively self-improving agents We use DeepSeek Flash 4 for easy tasks and Fable 5 for super hard ones. The agents learn over time, becoming MORE efficient and smarter with each run We use these RSI agents to run our company! It's amazingly good with little to no human intervention required Examples include bug-fixing agents, viral tweet- or video-makers, growth-hacking agents, and so on. AutoBots work in the background to help increase your revenue.

Bindu Reddy

2,504,428 Aufrufe • vor 2 Monaten