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An agent model, plus two multimodal models that can see and operate computers. Nex-AGI releases Nex-N2.5 in mini, Pro, and Max sizes.📜 Apache 2.0. 🤖 🏆 Max leads the published BrowseComp comparison with 92.6. Pro ranks first on OSWorld-G with 87.4. 🖥️ The 35B mini and 397B Pro browse...

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ModelScope

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18,509 просмотров • 11 дней назад •via X (Twitter)

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Комментарии: 8

Фото профиля Ella Tech & Tool
Ella Tech & Tool10 дней назад

Nex-N2.5 Max Pro mini 🔥 92.6 on BrowseComp and Apache 2.0 too. Huge drop.

Фото профиля Noor Tech
Noor Tech10 дней назад

ModelScope: Nex-AGI releases Nex-N2.5 in Mini, Pro, Max. Apache 2.0. Max leads BrowseComp at 92.6, Pro tops OSWorld-G.

Фото профиля Chole Syntax Expert AI
Chole Syntax Expert AI11 дней назад

Nex N2.5 Max hitting 92.6 on BrowseComp and Apache 2.0 to boot This is a serious open release

Фото профиля Alice The Ai Expert
Alice The Ai Expert11 дней назад

Finally, agentic AI that actually sees, acts, and self-corrects.

Фото профиля Elise Tech Ai
Elise Tech Ai10 дней назад

Huge release — mini to 1.6T Max, all Apache 2.0. Congrats!

Фото профиля RAZA | AI EXPLORER
RAZA | AI EXPLORER10 дней назад

Strong lineup. The multimodal agent capabilities stand out.

Фото профиля ZenithAi
ZenithAi8 дней назад

Impressive lineup covering everything from lightweight handling to heavyweight reasoning

Фото профиля Sani Ai Tech
Sani Ai Tech10 дней назад

Impressive lineup pushing open agentic models toward practical autonomy

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Meet Nex-N2.5 family, our latest open source agentic models. Mini (35B) and Pro (397B) bring multimodal understanding and computer use. Max (1.6T) is a text-only MoE model built for complex reasoning, coding, and agent workflows. Strong benchmark results in workflow automation and computer use: ⚡ Max scores 50.2 on AutomationBench v1.0.6—just 0.1 points behind Claude Opus 5 ⚡ Pro scores 56.4 on OSWorld-2, ahead of Qwen3.8-Max at 46.7 With continuous action, visual feedback, and self-correction, NEX-N2.5 can work across Blender and CAD tools, handle expense reimbursements, and even play PC games—turning visual understanding into sustained, real-world execution. 🔗Hugging Face 🔗@modelscope 🔗 Github 🔗Website
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Meet Nex-N2.5 family, our latest open source agentic models. Mini (35B) and Pro (397B) bring multimodal understanding and computer use. Max (1.6T) is a text-only MoE model built for complex reasoning, coding, and agent workflows. Strong benchmark results in workflow automation and computer use: ⚡ Max scores 50.2 on AutomationBench v1.0.6—just 0.1 points behind Claude Opus 5 ⚡ Pro scores 56.4 on OSWorld-2, ahead of Qwen3.8-Max at 46.7 With continuous action, visual feedback, and self-correction, NEX-N2.5 can work across Blender and CAD tools, handle expense reimbursements, and even play PC games—turning visual understanding into sustained, real-world execution. 🔗Hugging Face 🔗@modelscope 🔗 Github 🔗Website

Nex

254,657 просмотров • 11 дней назад

Nex-N2.5 feels less like “another model launch” and more like a push toward agents that can actually finish real work. The part that stands out to me is Nex-N2.5 Pro: a 397B multimodal model built for Vision, Computer Use, and long-horizon interaction. Instead of only understanding a screen, it can keep reading changing UI states and continue operating software step by step. That means workflows like: → moving from an idea to a Blender scene → operating CAD and other professional tools → navigating browsers and software end to end → writing code, running it, checking the interface, fixing issues, and verifying again The brief even shows examples spanning games, Blender, FreeCAD, LabPlot, GeoGebra, and other real desktop workflows. Nex-N2.5 comes in Mini, Pro, and Max, with Mini and Pro focused on multimodal agent capabilities and Max using a 1.6T text-only MoE base for more complex agentic reasoning and coding. The direction is clear: AI agents are moving from “tell me what to do” toward “open the tool and do it.” Nex-N2.5 Mini and Pro are available on OpenRouter free for a limited time:
2:40

Nex-N2.5 feels less like “another model launch” and more like a push toward agents that can actually finish real work. The part that stands out to me is Nex-N2.5 Pro: a 397B multimodal model built for Vision, Computer Use, and long-horizon interaction. Instead of only understanding a screen, it can keep reading changing UI states and continue operating software step by step. That means workflows like: → moving from an idea to a Blender scene → operating CAD and other professional tools → navigating browsers and software end to end → writing code, running it, checking the interface, fixing issues, and verifying again The brief even shows examples spanning games, Blender, FreeCAD, LabPlot, GeoGebra, and other real desktop workflows. Nex-N2.5 comes in Mini, Pro, and Max, with Mini and Pro focused on multimodal agent capabilities and Max using a 1.6T text-only MoE base for more complex agentic reasoning and coding. The direction is clear: AI agents are moving from “tell me what to do” toward “open the tool and do it.” Nex-N2.5 Mini and Pro are available on OpenRouter free for a limited time:

Alamin

66,447 просмотров • 6 дней назад

AI agents are moving past the point where generating an answer is enough. The real question is whether an agent can stay inside a real environment long enough to actually finish the job. That’s what makes Nex-N2.5 interesting to me It is built around long-horizon computer use, with agents that can operate browsers and software, execute programs, see the results through visual feedback, and keep correcting their work instead of stopping at the first output. Nex-N2.5 Pro was put through exactly that kind of workflow: build a SQL interpreter, write tests, run it, identify what was missing, implement the next pieces, and keep iterating. The model progressively handled things like WHERE, JOIN, subqueries, qualified names and table aliases while testing its own implementation along the way. That is the difference I care about with agentic AI. The agent isn't just producing an artifact. It's operating inside a feedback loop: write → run → inspect the result → fix the problem → verify again. Nex-N2.5 is also released as a family of Mini, Pro and Max models, giving developers different options depending on the task and deployment requirements. The Nex-N2.5 Mini and Pro are both available through OpenRouter for free for a limited time: Try it out here: The real shift isn't just better AI outputs. It’s agents that can interact with an environment, respond to what happens, and keep working until the task is actually finished.
1:15

AI agents are moving past the point where generating an answer is enough. The real question is whether an agent can stay inside a real environment long enough to actually finish the job. That’s what makes Nex-N2.5 interesting to me It is built around long-horizon computer use, with agents that can operate browsers and software, execute programs, see the results through visual feedback, and keep correcting their work instead of stopping at the first output. Nex-N2.5 Pro was put through exactly that kind of workflow: build a SQL interpreter, write tests, run it, identify what was missing, implement the next pieces, and keep iterating. The model progressively handled things like WHERE, JOIN, subqueries, qualified names and table aliases while testing its own implementation along the way. That is the difference I care about with agentic AI. The agent isn't just producing an artifact. It's operating inside a feedback loop: write → run → inspect the result → fix the problem → verify again. Nex-N2.5 is also released as a family of Mini, Pro and Max models, giving developers different options depending on the task and deployment requirements. The Nex-N2.5 Mini and Pro are both available through OpenRouter for free for a limited time: Try it out here: The real shift isn't just better AI outputs. It’s agents that can interact with an environment, respond to what happens, and keep working until the task is actually finished.

Mide

20,759 просмотров • 9 дней назад

Holy sh*t this is f*cking awesome China shipped a 397B model that can use a computer Nex-N2.5 Pro, open weights, Apache 2.0 Shanghai Chuangzhi Institute, out yesterday > 397B multimodal, vision + computer use > OSWorld-2: 56.4 vs Qwen3.8-Max at 46.7 > beat Pokémon Platinum to first gym badge > 468 actions, no human intervention most computer use agents fail after 5 clicks this one read dialogue, managed a party, fought battles switched members when HP got low, healed at the Pokémon Center on its own it also does Blender, CAD, vision-to-code watches your screen and builds a skill file from what you do Free on OpenRouter right now try it
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Holy sh*t this is f*cking awesome China shipped a 397B model that can use a computer Nex-N2.5 Pro, open weights, Apache 2.0 Shanghai Chuangzhi Institute, out yesterday > 397B multimodal, vision + computer use > OSWorld-2: 56.4 vs Qwen3.8-Max at 46.7 > beat Pokémon Platinum to first gym badge > 468 actions, no human intervention most computer use agents fail after 5 clicks this one read dialogue, managed a party, fought battles switched members when HP got low, healed at the Pokémon Center on its own it also does Blender, CAD, vision-to-code watches your screen and builds a skill file from what you do Free on OpenRouter right now try it

Rivon

17,079 просмотров • 9 дней назад

There’s a pattern with agent demos. You see the setup, then the result. The messy middle is usually missing. Nex-N2.5 Pro caught my attention because it leaves that part in. You can watch it read the screen, act, see what changed, and adjust. That’s where the 397B multimodal build comes in. Take a look at the Blender task below. Instead of just generating some code and calling it a day, it actually works through the whole thing. here’s what I noticed: It started by creating the scene, then checked the result instead of assuming everything went right. Something looked off? It went back in, changed the setup, rendered again and checked the new result. That little loop is what stood out to me. Because real computer work rarely goes: prompt → perfect result. There’s usually a lot of: “this doesn’t look right, let me fix that.” Seeing an AI handle that part inside Blender is probably the most interesting thing about this demo. You can try Nex-N2.5 Pro for free on OpenRouter.
1:15

There’s a pattern with agent demos. You see the setup, then the result. The messy middle is usually missing. Nex-N2.5 Pro caught my attention because it leaves that part in. You can watch it read the screen, act, see what changed, and adjust. That’s where the 397B multimodal build comes in. Take a look at the Blender task below. Instead of just generating some code and calling it a day, it actually works through the whole thing. here’s what I noticed: It started by creating the scene, then checked the result instead of assuming everything went right. Something looked off? It went back in, changed the setup, rendered again and checked the new result. That little loop is what stood out to me. Because real computer work rarely goes: prompt → perfect result. There’s usually a lot of: “this doesn’t look right, let me fix that.” Seeing an AI handle that part inside Blender is probably the most interesting thing about this demo. You can try Nex-N2.5 Pro for free on OpenRouter.

SayoClips

165,376 просмотров • 9 дней назад

This one is for builders who want an agent that can operate their computer. Today, we're releasing HoloDesktop CLI. Powered by Holo3 models, it brings H Agent directly into the agent harnesses you already use, including Claude R Perrin Code, Hermes Trismegistus, Cursor, and others. It runs locally on your device with low latency, full privacy, and no runtime cost. Most AI tooling assumes work happens through APIs. In reality, much of the world's work still happens in interfaces built for humans: browsers, desktop applications, spreadsheets, internal tools, and software with no API. HoloDesktop CLI gives agents the ability to see, understand, and act on these environments through the mouse and keyboard. From testing web applications through their GUI to navigating enterprise software, extracting information from documents, or interacting with internal tools, agents can now operate where work actually happens. We believe AI shouldn't just reason about work. It should be able to operate where work happens. #NVIDIA #ComputerUse #EnterpriseAI #DeveloperTools
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This one is for builders who want an agent that can operate their computer. Today, we're releasing HoloDesktop CLI. Powered by Holo3 models, it brings H Agent directly into the agent harnesses you already use, including Claude R Perrin Code, Hermes Trismegistus, Cursor, and others. It runs locally on your device with low latency, full privacy, and no runtime cost. Most AI tooling assumes work happens through APIs. In reality, much of the world's work still happens in interfaces built for humans: browsers, desktop applications, spreadsheets, internal tools, and software with no API. HoloDesktop CLI gives agents the ability to see, understand, and act on these environments through the mouse and keyboard. From testing web applications through their GUI to navigating enterprise software, extracting information from documents, or interacting with internal tools, agents can now operate where work actually happens. We believe AI shouldn't just reason about work. It should be able to operate where work happens. #NVIDIA #ComputerUse #EnterpriseAI #DeveloperTools

H

29,953 просмотров • 2 месяцев назад

Introducing Dola Seed 2.0 Pro, referred to below as Seed 2.0 Pro We have launched Seed 2.0 Pro, our most capable model in the Dola Seed 2.0 series, engineered to power the next generation of autonomous AI agents. Enterprise AI is moving beyond models that simply analyze text or images. What businesses increasingly need are agents that can understand, reason, use tools, and execute tasks across complex workflows. That is exactly what Seed 2.0 Pro is built for. Seed 2.0 Pro combines strong reasoning with advanced image understanding and video understanding, giving enterprise agents the ability not only to interpret information, but also to take action. It is designed for high-value, multi-step enterprise workflows, with strong performance in: - tool calling - workflow execution across enterprise systems - agentic task completion - browser and computer use This makes Seed 2.0 Pro a powerful engine for a wide range of agent scenarios, from daily office automation and deep web research to in-depth report drafting, financial analysis, content moderation, physical inspection, and video creation workflows. It is also highly optimized for OpenClaw🦞 and ReAct architectures, helping enterprises build agents that can navigate digital interfaces, enter information, and complete tasks with high reliability. In short, Seed 2.0 Pro is not just built to generate insights. It is built to serve as the brain and execution engine for enterprise AI agents. And it brings these capabilities at a highly attractive price point, making advanced agent deployment more practical for enterprise teams. Try Seed 2.0 Pro for free: Or book a free consultation: #BytePlus #DolaSeed #EnterpriseAI #AIAgents #ImageUnderstanding #VideoUnderstanding #ReasoningModel #ModelArk #openclaw
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Introducing Dola Seed 2.0 Pro, referred to below as Seed 2.0 Pro We have launched Seed 2.0 Pro, our most capable model in the Dola Seed 2.0 series, engineered to power the next generation of autonomous AI agents. Enterprise AI is moving beyond models that simply analyze text or images. What businesses increasingly need are agents that can understand, reason, use tools, and execute tasks across complex workflows. That is exactly what Seed 2.0 Pro is built for. Seed 2.0 Pro combines strong reasoning with advanced image understanding and video understanding, giving enterprise agents the ability not only to interpret information, but also to take action. It is designed for high-value, multi-step enterprise workflows, with strong performance in: - tool calling - workflow execution across enterprise systems - agentic task completion - browser and computer use This makes Seed 2.0 Pro a powerful engine for a wide range of agent scenarios, from daily office automation and deep web research to in-depth report drafting, financial analysis, content moderation, physical inspection, and video creation workflows. It is also highly optimized for OpenClaw🦞 and ReAct architectures, helping enterprises build agents that can navigate digital interfaces, enter information, and complete tasks with high reliability. In short, Seed 2.0 Pro is not just built to generate insights. It is built to serve as the brain and execution engine for enterprise AI agents. And it brings these capabilities at a highly attractive price point, making advanced agent deployment more practical for enterprise teams. Try Seed 2.0 Pro for free: Or book a free consultation: #BytePlus #DolaSeed #EnterpriseAI #AIAgents #ImageUnderstanding #VideoUnderstanding #ReasoningModel #ModelArk #openclaw

BytePlus

96,329 просмотров • 5 месяцев назад

Anthropic's in trouble, again. The entire Claude experience is now available at 1/6th the price. Kimi now does everything Claude does, powered by K2.6, a 1-trillion-parameter MoE model that activates only 32B parameters per token. It covers all three features Claude has (Chat, Code, and Cowork): 1) Kimi Chat runs in four modes - Instant for fast responses - Thinking for deep reasoning - Agent for multi-step execution - and Agent Swarm for parallel workloads. There's a 262K context window across all of them. 2) Kimi Code is the open-source CLI coding agent with K2.6 as the default backend. K2.6 ranked #1 on OpenRouter's programming leaderboard by weekly usage. 3) Kimi Agent is the Cowork equivalent. It generates: - full websites with database and auth - presentation decks (editable PPTX output) - spreadsheets with formulas and charts - word docs and structured research reports. On top of this, Kimi K2.6 is also trained to decompose tasks into up to 300 parallel sub-agents. This helps it retain coherence even across 4,000+ tool calls in a single run, with sessions sustaining up to 13 hours. On SWE-Bench Pro: - Kimi K2.6 → 58.6 - GPT-5.4 xhigh → 57.7 - Gemini 3.1 Pro → 54.2 - Claude Opus 4.6 → 53.4 Kimi K2.6 model is open weights and self-hostable on 4x H100s in INT4. Find the link to the HuggingFace model page in the replies!
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Anthropic's in trouble, again. The entire Claude experience is now available at 1/6th the price. Kimi now does everything Claude does, powered by K2.6, a 1-trillion-parameter MoE model that activates only 32B parameters per token. It covers all three features Claude has (Chat, Code, and Cowork): 1) Kimi Chat runs in four modes - Instant for fast responses - Thinking for deep reasoning - Agent for multi-step execution - and Agent Swarm for parallel workloads. There's a 262K context window across all of them. 2) Kimi Code is the open-source CLI coding agent with K2.6 as the default backend. K2.6 ranked #1 on OpenRouter's programming leaderboard by weekly usage. 3) Kimi Agent is the Cowork equivalent. It generates: - full websites with database and auth - presentation decks (editable PPTX output) - spreadsheets with formulas and charts - word docs and structured research reports. On top of this, Kimi K2.6 is also trained to decompose tasks into up to 300 parallel sub-agents. This helps it retain coherence even across 4,000+ tool calls in a single run, with sessions sustaining up to 13 hours. On SWE-Bench Pro: - Kimi K2.6 → 58.6 - GPT-5.4 xhigh → 57.7 - Gemini 3.1 Pro → 54.2 - Claude Opus 4.6 → 53.4 Kimi K2.6 model is open weights and self-hostable on 4x H100s in INT4. Find the link to the HuggingFace model page in the replies!

Avi Chawla

110,057 просмотров • 4 месяцев назад

Introducing PhoneLLM, an open model for voice agents. GPT 5.6 Terra performance on typical voice agent tasks at 1/3 the latency and 1/18 the cost. For voice agents, we need models that are both very low latency and very good at tool calling and instruction following. There's a trade-off here, and we often have to compromise on either latency or capability when building voice agents. With PhoneLLM (and the training and data stack that made this model possible) we're fixing this problem. For the last couple of years, most of the effort in frontier model development has gone towards leveraging test-time compute. Which is awesome! Models of all shapes and sizes are available that perform really, really well ... if you have "thinking" turned on for your model. But if you need your agent to respond at voice conversation speed, you can't use thinking models. PhoneLLM is a full-weights fine-tune of NVIDIA Nemotron Nano 30B. We trained on a wide range of real-world telephone and customer support use cases. The training focused on taking the excellent Nano 30B base capabilities and teaching the model to do typical voice agent tasks with thinking disabled. The results are really good: accurate tool calling and concise, on-topic responses in long conversations. And fast: TTFAT measured server-side is <100ms if you run PhoneLLM on a lightly loaded B200. :-) But seriously, when we characterize model latency, we do it with full, end-to-end, batched request simulations using real Pipecat voice agent pipelines. You can serve more than 80 concurrent agents on a single B200 with P95 end-to-end TTFAT <600ms. Including network overhead. That's an LLM cost-per-minute around $0.0025. (1/4 of a cent.) At a latency lower than any third-party API offers today. More details about this model, including weights on Hugging Face, how to spin it up with one click on Modal, and a starter project repo you can clone, are in the thread ...
3:28

Introducing PhoneLLM, an open model for voice agents. GPT 5.6 Terra performance on typical voice agent tasks at 1/3 the latency and 1/18 the cost. For voice agents, we need models that are both very low latency and very good at tool calling and instruction following. There's a trade-off here, and we often have to compromise on either latency or capability when building voice agents. With PhoneLLM (and the training and data stack that made this model possible) we're fixing this problem. For the last couple of years, most of the effort in frontier model development has gone towards leveraging test-time compute. Which is awesome! Models of all shapes and sizes are available that perform really, really well ... if you have "thinking" turned on for your model. But if you need your agent to respond at voice conversation speed, you can't use thinking models. PhoneLLM is a full-weights fine-tune of NVIDIA Nemotron Nano 30B. We trained on a wide range of real-world telephone and customer support use cases. The training focused on taking the excellent Nano 30B base capabilities and teaching the model to do typical voice agent tasks with thinking disabled. The results are really good: accurate tool calling and concise, on-topic responses in long conversations. And fast: TTFAT measured server-side is <100ms if you run PhoneLLM on a lightly loaded B200. :-) But seriously, when we characterize model latency, we do it with full, end-to-end, batched request simulations using real Pipecat voice agent pipelines. You can serve more than 80 concurrent agents on a single B200 with P95 end-to-end TTFAT <600ms. Including network overhead. That's an LLM cost-per-minute around $0.0025. (1/4 of a cent.) At a latency lower than any third-party API offers today. More details about this model, including weights on Hugging Face, how to spin it up with one click on Modal, and a starter project repo you can clone, are in the thread ...

kwindla

330,534 просмотров • 23 дней назад

Demis Hassabis reveals Google's three-stage stack rewrite: AI first, model first, agent first. Bolting agents onto normal software does not scale to billions. "We've got to, we've done a lot of engineering work under the hood in the last year, which you'll see, you see the surface of when we announced things like this." "But actually, you can almost think that whole stack had to be rewritten to be sort of AI first, model first, and now agent first." "So that's quite a big change from like normal software." "And obviously there's all this huge infrastructure that serves billions of users every day at lightning speed and that's all gonna work." "And I think we've made massive improvements on that over the past year. And that's why you can see all the goodness of the AI models flowing through into our product surfaces so quickly and in such compelling ways." Most companies are adding an agent interface to yesterday's software. Demis says Google changed the substrate itself. The moat may not be the model. It may be the infrastructure that lets the model reach billions of users every day without breaking. - Demis Hassabis (Demis Hassabis), CEO of Google DeepMind, with Rowan Cheung at Google I/O 2026
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Demis Hassabis reveals Google's three-stage stack rewrite: AI first, model first, agent first. Bolting agents onto normal software does not scale to billions. "We've got to, we've done a lot of engineering work under the hood in the last year, which you'll see, you see the surface of when we announced things like this." "But actually, you can almost think that whole stack had to be rewritten to be sort of AI first, model first, and now agent first." "So that's quite a big change from like normal software." "And obviously there's all this huge infrastructure that serves billions of users every day at lightning speed and that's all gonna work." "And I think we've made massive improvements on that over the past year. And that's why you can see all the goodness of the AI models flowing through into our product surfaces so quickly and in such compelling ways." Most companies are adding an agent interface to yesterday's software. Demis says Google changed the substrate itself. The moat may not be the model. It may be the infrastructure that lets the model reach billions of users every day without breaking. - Demis Hassabis (Demis Hassabis), CEO of Google DeepMind, with Rowan Cheung at Google I/O 2026

Karl Mehta

36,402 просмотров • 2 месяцев назад

🚀 Introducing AgentCPM-Explore: The First Open-Source 4B-Agent Model to Conquer GAIA & Complex Real-World Tasks! 🤗 Hugging Face: 🔗 GitHub: ✨ Key Highlights: ✅ SOTA Agentic Performance: Sets a new benchmark for 4B-scale agent models—outperforming all peers, surpassing 8B models, and rivaling select 30B+ and closed-source LLMs. 🧠 Deep Research Capability: Excels at long-horizon reasoning, supports 100+ turns of autonomous interaction with multi-source cross-validation, human-like self-correction, and dynamic tool use + strategy adaptation—just like a real researcher! 🔓 Full-Stack Open Source: We’re open-sourcing the entire end-to-end agent stack—not just the model! Empower your own innovations with - AgentRL: Asynchronous reinforcement learning framework - AgentDock: Secure, extensible tool sandbox - AgentToLeaP: An one-click evaluation platform for agent tool-learning capabilitie - Full training data pipeline & reproducible workflows #AgentCPM #OpenSourceAI #AgenticAI #AI #GAIA #LLM #OpenBMB #AIAgents #HuggingFace
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🚀 Introducing AgentCPM-Explore: The First Open-Source 4B-Agent Model to Conquer GAIA & Complex Real-World Tasks! 🤗 Hugging Face: 🔗 GitHub: ✨ Key Highlights: ✅ SOTA Agentic Performance: Sets a new benchmark for 4B-scale agent models—outperforming all peers, surpassing 8B models, and rivaling select 30B+ and closed-source LLMs. 🧠 Deep Research Capability: Excels at long-horizon reasoning, supports 100+ turns of autonomous interaction with multi-source cross-validation, human-like self-correction, and dynamic tool use + strategy adaptation—just like a real researcher! 🔓 Full-Stack Open Source: We’re open-sourcing the entire end-to-end agent stack—not just the model! Empower your own innovations with - AgentRL: Asynchronous reinforcement learning framework - AgentDock: Secure, extensible tool sandbox - AgentToLeaP: An one-click evaluation platform for agent tool-learning capabilitie - Full training data pipeline & reproducible workflows #AgentCPM #OpenSourceAI #AgenticAI #AI #GAIA #LLM #OpenBMB #AIAgents #HuggingFace

OpenBMB

13,996 просмотров • 8 месяцев назад

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.
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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

185,401 просмотров • 7 месяцев назад

Progress in open models is keeping Big AI labs up at night, and I'm here for it! We have a brand new open-weight multimodal model optimized for long-horizon tasks. This model is really good at something: it can work on tasks that keep evolving over time. • 280B total parameters, but only 16B active • 512K context window • Understands text, images, and audio • Strong reasoning, coding, and tool use But the best of all: the model learns and adapts to new information! Imagine you start running an agent today to solve a problem, and while it's working, you get new information that changes the initial conditions, or you change your mind. The agents you run today don't have issues with short tasks and goals that don't change, but reality is messy, and that makes it hard for long-horizon agents to succeed. The new dots3-note Preview model introduces TEMPO. TEMPO is a new reinforcement learning technique that lets the model periodically pause and critique its own progress. Basically, from time to time, the agent asks itself: "Am I getting closer to the goal, or am I wasting my time?" The same model switches between actor and critic. The actor works on the problem. The critic looks at the current state, reasons about how much progress it has made, and determines what should happen next. TEMPO gives the model feedback along the way. This is huge for any agent that can work on long-horizon tasks without wasting its time.
4:05

Progress in open models is keeping Big AI labs up at night, and I'm here for it! We have a brand new open-weight multimodal model optimized for long-horizon tasks. This model is really good at something: it can work on tasks that keep evolving over time. • 280B total parameters, but only 16B active • 512K context window • Understands text, images, and audio • Strong reasoning, coding, and tool use But the best of all: the model learns and adapts to new information! Imagine you start running an agent today to solve a problem, and while it's working, you get new information that changes the initial conditions, or you change your mind. The agents you run today don't have issues with short tasks and goals that don't change, but reality is messy, and that makes it hard for long-horizon agents to succeed. The new dots3-note Preview model introduces TEMPO. TEMPO is a new reinforcement learning technique that lets the model periodically pause and critique its own progress. Basically, from time to time, the agent asks itself: "Am I getting closer to the goal, or am I wasting my time?" The same model switches between actor and critic. The actor works on the problem. The critic looks at the current state, reasons about how much progress it has made, and determines what should happen next. TEMPO gives the model feedback along the way. This is huge for any agent that can work on long-horizon tasks without wasting its time.

Santiago

80,792 просмотров • 1 месяц назад

The same kinds of productivity gains we've seen in coding with AI agents are heading to the rest of knowledge work. This is the jump when you go from having a chatbot to being able to actually have an agent go off and do work for minutes or even hours and come back with a complete work output that you then review. Here's an example of the new Box Agent filling out an RFP response from an existing knowledge base. This process would normally take hours to fill out, and requires the full attention of the user doing the work. Now, you provide the Box Agent with the RFP questions, and it will go off, make a plan, extract all the relevant questions, read through existing source material to come up with an answer, and then generate a new word document as the final output. All while you're doing something else. The key to this architecture is that the agent is able to use all of the same tools in the background that a user uses to get work done. The agent can search for documents, read entire files, run scripts and tools in the background, and even be able to write code on the fly to automate tasks it hasn't seen before. And best of all, the Box Agent will (soon) work from the Box MCP and CLI so you can invoke it in any agentic system as a step in a process. This kind of agent complexity would have been impossible even 6 months ago. Models consistently failed at tracking long running tasks or using the right tools at the right moment for the task. But this is all now possible because of models like GPT-5.4, Opus 4.6, and Gemini 3, and is only getting better by the month. Just as we moved from engineers writing code and using AI as an assistant to answer questions, in many areas of knowledge work -like legal, finance, consulting, sales, marketing, and more- when we have a problem we'll just kick off the AI agent to just go work on it for us in the background.
2:14

The same kinds of productivity gains we've seen in coding with AI agents are heading to the rest of knowledge work. This is the jump when you go from having a chatbot to being able to actually have an agent go off and do work for minutes or even hours and come back with a complete work output that you then review. Here's an example of the new Box Agent filling out an RFP response from an existing knowledge base. This process would normally take hours to fill out, and requires the full attention of the user doing the work. Now, you provide the Box Agent with the RFP questions, and it will go off, make a plan, extract all the relevant questions, read through existing source material to come up with an answer, and then generate a new word document as the final output. All while you're doing something else. The key to this architecture is that the agent is able to use all of the same tools in the background that a user uses to get work done. The agent can search for documents, read entire files, run scripts and tools in the background, and even be able to write code on the fly to automate tasks it hasn't seen before. And best of all, the Box Agent will (soon) work from the Box MCP and CLI so you can invoke it in any agentic system as a step in a process. This kind of agent complexity would have been impossible even 6 months ago. Models consistently failed at tracking long running tasks or using the right tools at the right moment for the task. But this is all now possible because of models like GPT-5.4, Opus 4.6, and Gemini 3, and is only getting better by the month. Just as we moved from engineers writing code and using AI as an assistant to answer questions, in many areas of knowledge work -like legal, finance, consulting, sales, marketing, and more- when we have a problem we'll just kick off the AI agent to just go work on it for us in the background.

Aaron Levie

24,728 просмотров • 5 месяцев назад

🚨 Breaking News OpenAI just announced the launch of its groundbreaking ChatGPT agent. It can navigate calendars, generate editable presentations, execute code, and more. ⁠ What ChatGPT Agent Can Do: Task Automation: Easily manage your calendar, craft professional presentations and slideshows, and run code automatically. Deep Web Integration: Combines functionalities from OpenAI’s earlier tools like Operator (for web interactions) and Deep Research (for synthesizing extensive web-based data into clear reports). Natural Interaction: Simply prompt ChatGPT using natural language to get tasks done. App Connectivity: Supports connectors to popular apps like Gmail and GitHub, and can directly use APIs to extend functionality even further. ⁠ 📈 Impressive Benchmarks: Humanity’s Last Exam: Scores 41.6%, nearly double the previous models (OpenAI o3 and o4-mini). FrontierMath: Achieves a remarkable 27.4% with tool assistance, significantly surpassing previous leading scores (6.3% from o4-mini). ⁠ Built with Safety in Mind: OpenAI emphasizes safety due to the agent’s enhanced capabilities, ensuring responsible usage while exploring new frontiers of AI interactions. ⁠ Now available for ChatGPT Pro, Plus, and Team subscribers. Just activate “agent mode” from the ChatGPT menu and explore a smarter, more productive future!
0:42

🚨 Breaking News OpenAI just announced the launch of its groundbreaking ChatGPT agent. It can navigate calendars, generate editable presentations, execute code, and more. ⁠ What ChatGPT Agent Can Do: Task Automation: Easily manage your calendar, craft professional presentations and slideshows, and run code automatically. Deep Web Integration: Combines functionalities from OpenAI’s earlier tools like Operator (for web interactions) and Deep Research (for synthesizing extensive web-based data into clear reports). Natural Interaction: Simply prompt ChatGPT using natural language to get tasks done. App Connectivity: Supports connectors to popular apps like Gmail and GitHub, and can directly use APIs to extend functionality even further. ⁠ 📈 Impressive Benchmarks: Humanity’s Last Exam: Scores 41.6%, nearly double the previous models (OpenAI o3 and o4-mini). FrontierMath: Achieves a remarkable 27.4% with tool assistance, significantly surpassing previous leading scores (6.3% from o4-mini). ⁠ Built with Safety in Mind: OpenAI emphasizes safety due to the agent’s enhanced capabilities, ensuring responsible usage while exploring new frontiers of AI interactions. ⁠ Now available for ChatGPT Pro, Plus, and Team subscribers. Just activate “agent mode” from the ChatGPT menu and explore a smarter, more productive future!

There's An AI For That

32,716 просмотров • 1 год назад

I have been testing DeepSeek-V4-Pro with the Pi coding agent. I am mindblown by how well it works out of the box. A few notes: I spent a few hours building an LLM wiki with an agent powered entirely by DeepSeek-V4-Pro on Fireworks inference. This is the first time I feel like there is an open-weight model that can reason at the level of Claude and Codex. And it does this in a cost-effective way with support for 1M context length. To be clear, I am using DeepSeek-V4-Pro inside of Pi without any special configuration. It works out of the box. It's exciting that there is a model that can just be plugged into a basic harness like Pi, and it just works. I've never seen that before. Most models require lots of configuration and setup. DeepSeek's DeepSeek-V4-Pro is clearly good at agentic coding (probably the best from the open-weight models), but the model is also great on knowledge-intensive tasks where reasoning matters. The agent pulled agentic engineering best practices from different company docs (Anthropic, OpenAI, Google, Stripe, Meta, Modal, DeepSeek, Mistral, Cohere), searched and digested Reddit and HN threads, summarized arxiv papers, and surfaced trending GitHub repos. Then it distilled everything into actionable tips across categories. I love the Wiki it built. The quality is really good. Here is a snapshot of what the wiki looks like: DeepSeek-V4-Pro handled the task without breaking stride. Multi-step research queries, code generation for scaffolding, context-heavy reasoning across disparate sources. For coding specifically, this is the first open-weight model that genuinely feels like a Codex or Claude Code experience. It compares in capability and actual multi-turn agentic work. What made the loop feel so responsive was Fireworks' inference speed (the fastest in the market) and the fact that they actually validate models at the systems level before shipping. No corrupted reasoning traces. Just fast, reliable iteration. The hybrid CSA and HCA attention design cuts KV cache to just 10% and inference FLOPs by nearly 4x at 1M-token context. This is what makes the agent loop actually fast and cheap enough to run in practice. For devs who've been watching open-weight models close the gap but haven't found one that actually delivers in practice, this is the closest I've seen. Try it here:
0:31

I have been testing DeepSeek-V4-Pro with the Pi coding agent. I am mindblown by how well it works out of the box. A few notes: I spent a few hours building an LLM wiki with an agent powered entirely by DeepSeek-V4-Pro on Fireworks inference. This is the first time I feel like there is an open-weight model that can reason at the level of Claude and Codex. And it does this in a cost-effective way with support for 1M context length. To be clear, I am using DeepSeek-V4-Pro inside of Pi without any special configuration. It works out of the box. It's exciting that there is a model that can just be plugged into a basic harness like Pi, and it just works. I've never seen that before. Most models require lots of configuration and setup. DeepSeek's DeepSeek-V4-Pro is clearly good at agentic coding (probably the best from the open-weight models), but the model is also great on knowledge-intensive tasks where reasoning matters. The agent pulled agentic engineering best practices from different company docs (Anthropic, OpenAI, Google, Stripe, Meta, Modal, DeepSeek, Mistral, Cohere), searched and digested Reddit and HN threads, summarized arxiv papers, and surfaced trending GitHub repos. Then it distilled everything into actionable tips across categories. I love the Wiki it built. The quality is really good. Here is a snapshot of what the wiki looks like: DeepSeek-V4-Pro handled the task without breaking stride. Multi-step research queries, code generation for scaffolding, context-heavy reasoning across disparate sources. For coding specifically, this is the first open-weight model that genuinely feels like a Codex or Claude Code experience. It compares in capability and actual multi-turn agentic work. What made the loop feel so responsive was Fireworks' inference speed (the fastest in the market) and the fact that they actually validate models at the systems level before shipping. No corrupted reasoning traces. Just fast, reliable iteration. The hybrid CSA and HCA attention design cuts KV cache to just 10% and inference FLOPs by nearly 4x at 1M-token context. This is what makes the agent loop actually fast and cheap enough to run in practice. For devs who've been watching open-weight models close the gap but haven't found one that actually delivers in practice, this is the closest I've seen. Try it here:

elvis

60,281 просмотров • 4 месяцев назад

OpenClaw meets RL! OpenClaw Agents adapt through memory files and skills, but the base model weights never actually change. OpenClaw-RL solves this! It wraps a self-hosted model as an OpenAI-compatible API, intercepts live conversations from OpenClaw, and trains the policy in the background using RL. The architecture is fully async. This means serving, reward scoring, and training all run in parallel. Once done, weights get hot-swapped after every batch while the agent keeps responding. Currently, it has two training modes: - Binary RL (GRPO): A process reward model scores each turn as good, bad, or neutral. That scalar reward drives policy updates via a PPO-style clipped objective. - On-Policy Distillation: When concrete corrections come in like "you should have checked that file first," it uses that feedback as a richer, directional training signal at the token level. When to use OpenClaw-RL? To be fair, a lot of agent behavior can already be improved through better memory and skill design. OpenClaw's existing skill ecosystem and community-built self-improvement skills handle a wide range of use cases without touching model weights at all. If the agent keeps forgetting preferences, that's a memory problem. And if it doesn't know how to handle a specific workflow, that's a skill problem. Both are solvable at the prompt and context layer. Where RL becomes interesting is when the failure pattern lives deeper in the model's reasoning itself. Things like consistently poor tool selection order, weak multi-step planning, or failing to interpret ambiguous instructions the way a specific user intends. Research on agentic RL (like ARTIST and Agent-R1) has shown that these behavioral patterns hit a ceiling with prompt-based approaches alone, especially in complex multi-turn tasks where the model needs to recover from tool failures or adapt its strategy mid-execution. That's the layer OpenClaw-RL targets, and it's a meaningful distinction from what OpenClaw offers. I have shared the repo in the replies!
0:35

OpenClaw meets RL! OpenClaw Agents adapt through memory files and skills, but the base model weights never actually change. OpenClaw-RL solves this! It wraps a self-hosted model as an OpenAI-compatible API, intercepts live conversations from OpenClaw, and trains the policy in the background using RL. The architecture is fully async. This means serving, reward scoring, and training all run in parallel. Once done, weights get hot-swapped after every batch while the agent keeps responding. Currently, it has two training modes: - Binary RL (GRPO): A process reward model scores each turn as good, bad, or neutral. That scalar reward drives policy updates via a PPO-style clipped objective. - On-Policy Distillation: When concrete corrections come in like "you should have checked that file first," it uses that feedback as a richer, directional training signal at the token level. When to use OpenClaw-RL? To be fair, a lot of agent behavior can already be improved through better memory and skill design. OpenClaw's existing skill ecosystem and community-built self-improvement skills handle a wide range of use cases without touching model weights at all. If the agent keeps forgetting preferences, that's a memory problem. And if it doesn't know how to handle a specific workflow, that's a skill problem. Both are solvable at the prompt and context layer. Where RL becomes interesting is when the failure pattern lives deeper in the model's reasoning itself. Things like consistently poor tool selection order, weak multi-step planning, or failing to interpret ambiguous instructions the way a specific user intends. Research on agentic RL (like ARTIST and Agent-R1) has shown that these behavioral patterns hit a ceiling with prompt-based approaches alone, especially in complex multi-turn tasks where the model needs to recover from tool failures or adapt its strategy mid-execution. That's the layer OpenClaw-RL targets, and it's a meaningful distinction from what OpenClaw offers. I have shared the repo in the replies!

Avi Chawla

138,769 просмотров • 6 месяцев назад

I’ve been testing Hy4 preview in WorkBuddy, and the most interesting part is not simply the model size, it’s how much practical work it can handle with a relatively focused active parameter count. Hy4 preview brings together stronger code understanding, generation, and editing; improved document and information processing; workflow automation; web and game development; cross-tool collaboration; and more reliable completion of complex, multi-step tasks. In other words, it is designed for work that requires planning, tool use, iteration, and follow-through, not just a quick answer in a chat window. Compared with its initial release, the current Hy4 preview is noticeably faster and better-performing in practical workflows. Following an upgrade released yesterday, it can complete tasks with fewer conversation rounds and lower token usage, while reasoning more quickly and making the overall user experience feel smoother from the first instruction to the final result. For my test, I gave it a demanding Three.js game-prototyping task with a 770B-parameter model and 49B active parameters. The result was more revealing than a simple first-look demo: Hy4 preview handled the core logic, edge cases, and follow-up changes while maintaining the broader context of the project. That combination of capability, speed, context, and active compute is what makes its cost-effectiveness worth examining. A fair evaluation should use the same prompt and environment configuration across models, changing only the model itself. That makes it easier to assess task completion, planning quality, tool-calling stability, reasoning speed, token efficiency, and performance over longer workflows without confusing the result with different settings. If you want to test the model yourself, access Hy4 preview through WorkBuddy and see how it performs on a real coding, document, automation, or creative task: Tencent Hy Tencent AI WorkBuddy
1:02

I’ve been testing Hy4 preview in WorkBuddy, and the most interesting part is not simply the model size, it’s how much practical work it can handle with a relatively focused active parameter count. Hy4 preview brings together stronger code understanding, generation, and editing; improved document and information processing; workflow automation; web and game development; cross-tool collaboration; and more reliable completion of complex, multi-step tasks. In other words, it is designed for work that requires planning, tool use, iteration, and follow-through, not just a quick answer in a chat window. Compared with its initial release, the current Hy4 preview is noticeably faster and better-performing in practical workflows. Following an upgrade released yesterday, it can complete tasks with fewer conversation rounds and lower token usage, while reasoning more quickly and making the overall user experience feel smoother from the first instruction to the final result. For my test, I gave it a demanding Three.js game-prototyping task with a 770B-parameter model and 49B active parameters. The result was more revealing than a simple first-look demo: Hy4 preview handled the core logic, edge cases, and follow-up changes while maintaining the broader context of the project. That combination of capability, speed, context, and active compute is what makes its cost-effectiveness worth examining. A fair evaluation should use the same prompt and environment configuration across models, changing only the model itself. That makes it easier to assess task completion, planning quality, tool-calling stability, reasoning speed, token efficiency, and performance over longer workflows without confusing the result with different settings. If you want to test the model yourself, access Hy4 preview through WorkBuddy and see how it performs on a real coding, document, automation, or creative task: Tencent Hy Tencent AI WorkBuddy

Tyler Wayne

56,152 просмотров • 9 дней назад

🚀New Amazon Q Developer agent for software development is available to customers: This agent is based on a new agent architecture that has exciting results coming from the SWE-bench scores (on the full and verified benchmarks) representing AI models’ ability to resolve real-world coding problems. Interesting aspect of Q Agent is that with these newest updates, Q drove nearly 50% more successful coding tasks completed. What makes Q Dev Agent remarkable? The agent architecture is not just about using the best LLMs (which we do), but also giving the agent the ability to constantly explore multiple paths to find the best way to resolve a particular problem (and back tracking when it has reached dead end like a developer would do). Needless to say, we are just getting started on the developer agent and we are constantly pushing to advance our AI capabilities while maintaining quality, security, privacy, and reliability to keep Amazon Q Developer an innovative and trusted option available to our customers using agents for software development. We highlighted the results of our first SWE-bench submission of Amazon Q Developer back in June blog post; with these updates, our new agent resolves 51% more coding tasks than its previous iteration on the SWE-bench verified dataset, and 43% more on the full dataset. That’s the difference a few months make, and I can’t wait to share what our teams will deliver at re:Invent this December. Here's a quick demo showcasing our new Agent in action:
1:28

🚀New Amazon Q Developer agent for software development is available to customers: This agent is based on a new agent architecture that has exciting results coming from the SWE-bench scores (on the full and verified benchmarks) representing AI models’ ability to resolve real-world coding problems. Interesting aspect of Q Agent is that with these newest updates, Q drove nearly 50% more successful coding tasks completed. What makes Q Dev Agent remarkable? The agent architecture is not just about using the best LLMs (which we do), but also giving the agent the ability to constantly explore multiple paths to find the best way to resolve a particular problem (and back tracking when it has reached dead end like a developer would do). Needless to say, we are just getting started on the developer agent and we are constantly pushing to advance our AI capabilities while maintaining quality, security, privacy, and reliability to keep Amazon Q Developer an innovative and trusted option available to our customers using agents for software development. We highlighted the results of our first SWE-bench submission of Amazon Q Developer back in June blog post; with these updates, our new agent resolves 51% more coding tasks than its previous iteration on the SWE-bench verified dataset, and 43% more on the full dataset. That’s the difference a few months make, and I can’t wait to share what our teams will deliver at re:Invent this December. Here's a quick demo showcasing our new Agent in action:

Swami Sivasubramanian

29,013 просмотров • 2 лет назад

New short course: Practical Multi AI Agents and Advanced Use Cases with crewAI. Learn to build and deploy advanced agent-based systems in real applications in this course, created with CrewAI and taught by its founder, João Moura! (Disclosure: I've made a small seed investment in CrewAI.) In this course, you’ll learn how to create advanced agent-based apps that use external tools, do performance testing, can be trained with human feedback, and perform multiple tasks with different large language models. You will build several practical agentic apps that provide real business value, such as an automated project planning system, lead scoring and engagement pipeline, customer support data analysis, and a robust content creation system. In detail, you will learn how to: - Create these multi-agent systems with the building blocks of tasks, agents, and crews, along with the different things that make them work, such as caching, memory, and guardrails. - Integrate your multi-agent application with internal and external systems. - Connect multiple agents in complex setups, including parallel, sequential, and hybrid configurations, and create flows involving multiple agentic applications working together. - Test your agentic workflow and train it using human feedback to optimize its performance for better and more consistent results. - Work with multiple LLMs in your multi-agent system, using the appropriate model sizes and providers to fit each agent’s specific task. - Start a project from scratch in your environment and prepare it for deployment. You’ll also learn from an interview between João and Jacob Wilson, the Commercial GenAI Principal at PwC , in which they discuss deploying agentic workflows in real industry use cases. By the end of this course, you will be equipped to start building custom multi-agentic systems for your work. Please sign up here!
2:52

New short course: Practical Multi AI Agents and Advanced Use Cases with crewAI. Learn to build and deploy advanced agent-based systems in real applications in this course, created with CrewAI and taught by its founder, João Moura! (Disclosure: I've made a small seed investment in CrewAI.) In this course, you’ll learn how to create advanced agent-based apps that use external tools, do performance testing, can be trained with human feedback, and perform multiple tasks with different large language models. You will build several practical agentic apps that provide real business value, such as an automated project planning system, lead scoring and engagement pipeline, customer support data analysis, and a robust content creation system. In detail, you will learn how to: - Create these multi-agent systems with the building blocks of tasks, agents, and crews, along with the different things that make them work, such as caching, memory, and guardrails. - Integrate your multi-agent application with internal and external systems. - Connect multiple agents in complex setups, including parallel, sequential, and hybrid configurations, and create flows involving multiple agentic applications working together. - Test your agentic workflow and train it using human feedback to optimize its performance for better and more consistent results. - Work with multiple LLMs in your multi-agent system, using the appropriate model sizes and providers to fit each agent’s specific task. - Start a project from scratch in your environment and prepare it for deployment. You’ll also learn from an interview between João and Jacob Wilson, the Commercial GenAI Principal at PwC , in which they discuss deploying agentic workflows in real industry use cases. By the end of this course, you will be equipped to start building custom multi-agentic systems for your work. Please sign up here!

Andrew Ng

341,204 просмотров • 1 год назад