Загрузка видео...

Не удалось загрузить видео

На главную

We just raised an $11M seed to help developers build complex agents across any model and any tool in 5 lines of code. Our round was led by Kindred Ventures (steve jang) and Saga (Max Altman, Ben Braverman, Thomson Nguyen). At Dedalus Labs, we believe the future of AI...

itsCathyDi's profile picture

Cathy Di

6,012 subscribers

1,054,358 просмотров • 1 год назад •via X (Twitter)

Наука и технологииОбразование
LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 53

Фото профиля Cathy Di
Cathy Di1 год назад

Give your agents wings at

Фото профиля Cathy Di
Cathy Di1 год назад

Thank you to all our angel investors and supporters! @KindredVentures, @stevejang, @TheYashKishore, @saga_ventures, @maxaltman, @braveben, @itsthomson, @e14fund, @c10n, @habibh, @emergencecap, @YazanElBaba, @gkunthara, @SunshineLakeVC, @donnystalter, @syou018, @transposevc, @karrwinn, @NikosLiodakis, @liquid2V, @MattMulvey17

Фото профиля Cathy Di
Cathy Di1 год назад

More investors & supporters: @fpvventures, @ouegner, @jayjreno, @twentytwovc, @AntWilson, @Thom_Wolf, @iamcal, @jakeklamka, @jlhenriod, @mwang420, @ooshma, @NateMatherson, @xlindadu, @jakemintz, @JdotJdotF, @richaberman, @andrewztan, @iEchoic, @DanWestgarth, @bhbryant, @ashtom, @TaylorGreene, @johnrlyman, @aminah_et_al, @mzsoliman, @jaimalhotra8, @JoeFahn_, @varadh, @kul, @forwarddeploy, @m_franceschetti, @Austen, @HazanPrinceton, @tri_dao

Фото профиля Cathy Di
Cathy Di1 год назад

@stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen More on what's next 👉

Фото профиля Jeff Henriod
Jeff Henriod1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen I had to go take a lap around my neighborhood after meeting @itsCathyDi because she brought so much energy. Gave me wings!!

Фото профиля Cathy Di
Cathy Di1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen 🏃🏻‍♂️🪽

Фото профиля 44ron
44ron1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen You had me at raised

Фото профиля Cathy Di
Cathy Di1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen 📈

Фото профиля Alex DeNuzzo
Alex DeNuzzo1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen FLY AGENTS FLY

Фото профиля Cathy Di
Cathy Di1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen 🤖🪽

Фото профиля Marcia Ong
Marcia Ong1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen Cool stuff, how different is it when compared to langchain ?

Фото профиля Cathy Di
Cathy Di1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen Thanks! We don't just route models; we support vendor-agnostic model handoffs. In 5 lines of code, you can also chain both local and hosted tools together and stream across providers, and anyone can deploy an MCP server with us in 3 clicks 🙂 Hope the difference is clear!

Фото профиля Marcia Ong
Marcia Ong1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen Hmm lang graph allow full customisation of agents in undirected graph. Will check out your sdk 🫡

Фото профиля Umesh Khanna 🇨🇦🇺🇸
Umesh Khanna 🇨🇦🇺🇸1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen Dedalus gives you wings!

Фото профиля Cathy Di
Cathy Di1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen You give US wings! 🪽

Фото профиля Ben Braverman
Ben Braverman1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @itsthomson @dedaluslabs @WindsorNguyen You guys are moving so fast it gives me real joy

Фото профиля Cathy Di
Cathy Di1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @itsthomson @dedaluslabs @WindsorNguyen 🚀🚀🚀

Фото профиля Joseph Guo ᯅ
Joseph Guo ᯅ1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen Yoooo congrats!! I knew you looked familiar and then I was like hold on I pitched next to you last weds 🥹

Фото профиля Cathy Di
Cathy Di1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen hahaha thanks for coming!

Фото профиля Archie McKenzie
Archie McKenzie1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen amazing!!!!

Фото профиля Michelle Marcelline
Michelle Marcelline1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen This is huge. Congrats @itsCathyDi @WindsorNguyen!

Фото профиля Cathy Di
Cathy Di1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen Thank you Michelle!

Фото профиля aryan mahajan
aryan mahajan1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen dedalus on 🔝

Фото профиля Cathy Di
Cathy Di1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen We flew there!

Фото профиля Malte Ubl
Malte Ubl1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen Looks amazing! Congratulations!

Фото профиля jia
jia1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen LFG CONGRATS!!

Фото профиля Cathy Di
Cathy Di1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen THANK YOU

Фото профиля Olivia Benjamin
Olivia Benjamin1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen Congrats @itsCathyDi! Only the beginning 🚀

Фото профиля Cathy Di
Cathy Di1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen Psyched to be partnering with Operator!

Фото профиля arlan
arlan1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen nicee

Фото профиля jpark
jpark1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen damn 11 is so aggressive yall cooked

Фото профиля Anny Zhou
Anny Zhou1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen 🪽Go Dedalus!

Фото профиля steve jang
steve jang1 год назад

@KindredVentures @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen 🫰🏽🫰🏽🫰🏽

Фото профиля Cathy Di
Cathy Di1 год назад

@KindredVentures @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen 🪽🪽🪽

Фото профиля Nick Evans
Nick Evans1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen This is so cool. Beautiful video too. Who shot your video?

Фото профиля Cathy Di
Cathy Di1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen @findingjaden

Фото профиля Lily yang
Lily yang1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen Congratulations!!!!

Фото профиля Cathy Di
Cathy Di1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen Thanks Lily!

Фото профиля alissa (nyc!)
alissa (nyc!)1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen Congrats Cathy!!! So excited :)

Фото профиля Nano
Nano1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen bullish bullish bullish.

Фото профиля Cathy Di
Cathy Di1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen Giving bulls wings

Фото профиля Cathy Di
Cathy Di1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen 🐄🪽

Фото профиля Ernest McCarter
Ernest McCarter1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen congrats!!!!

Фото профиля Megan Wang
Megan Wang1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen bullish!! 🪽🪽

Фото профиля Mr. Bai
Mr. Bai1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen congrats Cathy for raising an avocado seed round :)

Фото профиля Megan Wang
Megan Wang1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen building wings to fly 🪽

Фото профиля David
David1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen Yup, if a VC want to throw money my way, please don't be shy!

Фото профиля Hugo Davies
Hugo Davies1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen 🚀🚀🚀🚀Lfg Cathy!!!

Фото профиля ellen livia ᯅ
ellen livia ᯅ1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen i saw greek @xdenuzzo in the background😂 Congrats dedalus!

Фото профиля Cathy Di
Cathy Di1 год назад

@ellen_x_ai @KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen @xdenuzzo hahaha you might be the first one

Фото профиля Alex
Alex1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen Congrats! Excited for you and the team

Фото профиля Joyce Zhang
Joyce Zhang1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen huge congrats!

Фото профиля First_Name Last_Name
First_Name Last_Name1 год назад

@KindredVentures @stevejang @Saga_Ventures @maxaltman @braveben @itsthomson @dedaluslabs @WindsorNguyen Just for clarity on what 11 mill gets you. lol

Похожие видео

Introducing Sarvam Voice Agents MCP. Describe the voice agent you want, and your coding tool builds it. The MCP connects your coding tool to Sarvam, so you can create agents, run campaigns, and analyse calls from the environment you already code in. Watch Sarvam Voice Agents MCP build a voice agent, call a list of people and report back. Build your first voice agent with the MCP:
2:48

Introducing Sarvam Voice Agents MCP. Describe the voice agent you want, and your coding tool builds it. The MCP connects your coding tool to Sarvam, so you can create agents, run campaigns, and analyse calls from the environment you already code in. Watch Sarvam Voice Agents MCP build a voice agent, call a list of people and report back. Build your first voice agent with the MCP:

Sarvam

20,218 просмотров • 22 дней назад

The Agent SDK is now available. Define agents as code, with prompts in Markdown and tools in TypeScript. Deploy them on our hosted infrastructure, or run them on your own. Built-in evals let you measure every change and make your agents better over time.
0:28

The Agent SDK is now available. Define agents as code, with prompts in Markdown and tools in TypeScript. Deploy them on our hosted infrastructure, or run them on your own. Built-in evals let you measure every change and make your agents better over time.

Cursor

412,803 просмотров • 11 дней назад

Deploy Your Agents Across Multiple Networks ⚙️ Build with Zero Code: Our no-code agent builder lets you create and deploy powerful agents in just minutes. Automate Smarter, Faster, and Easier. Join Sentio today and unlock the future of Web3 automation! #AI #SEN #AGENTS
0:20

Deploy Your Agents Across Multiple Networks ⚙️ Build with Zero Code: Our no-code agent builder lets you create and deploy powerful agents in just minutes. Automate Smarter, Faster, and Easier. Join Sentio today and unlock the future of Web3 automation! #AI #SEN #AGENTS

Sentio

45,479 просмотров • 1 год назад

Agents & MCP are great... but how can you DEPLOY an agent with MCP tools? Here's the fastest way I've found to build and deploy with the OpenAI Agents SDK in <3 minutes.
3:46

Agents & MCP are great... but how can you DEPLOY an agent with MCP tools? Here's the fastest way I've found to build and deploy with the OpenAI Agents SDK in <3 minutes.

matt palmer

64,690 просмотров • 1 год назад

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.
0:43

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.

Aaron Levie

17,543 просмотров • 7 месяцев назад

AI power users are here and the case for agent-friendly software is materializing. Aaron Levie on agents outnumbering people 1000x: "If you start to imagine that we all have to build software for agents… we spend as much time now thinking about the agent interface to our tool as we do the human interface." "If you have a hundred or a thousand times more agents than people, then your software has to be built for agents… it's going to be through an API or a CLI or MCP." "What if you give a coding agent access to your SaaS tools and a coding agent access to your knowledge work, workflows and context?" "It can actually code its way or use APIs through whatever task it's trying to achieve." "That appears to be like a paradigm that is starting to compound… and I actually think it kind of makes sense as the ultimate manifestation of this stuff." Aaron Levie
1:23

AI power users are here and the case for agent-friendly software is materializing. Aaron Levie on agents outnumbering people 1000x: "If you start to imagine that we all have to build software for agents… we spend as much time now thinking about the agent interface to our tool as we do the human interface." "If you have a hundred or a thousand times more agents than people, then your software has to be built for agents… it's going to be through an API or a CLI or MCP." "What if you give a coding agent access to your SaaS tools and a coding agent access to your knowledge work, workflows and context?" "It can actually code its way or use APIs through whatever task it's trying to achieve." "That appears to be like a paradigm that is starting to compound… and I actually think it kind of makes sense as the ultimate manifestation of this stuff." Aaron Levie

a16z

42,152 просмотров • 2 месяцев назад

At DataInsta , we just rebranded InstaAgents to Daiko. In Japanese, Daiko means acting on your behalf. We chose this name because our AI builder creates autonomous agents that take ownership of complex tasks and do the heavy lifting for your business. Most AI projects fail because teams lack the tools, the talent, and a reliable way to evaluate agents. Testing is hard, especially for multiple agent setups and long workflows. We built something different. Daiko is battle tested and evaluated across many industries. With Daiko you can: ◾ Build and deploy agents that automate real workflows ◾ Describe your task and goals so our AI generator can build full multiple agent flows ◾ Choose from over 60 templates and use cases or create your own ◾ Connect your data and APIs, deploy anywhere, and set up evaluations and guardrails ◾ Host the platform on your own servers with white label options We designed this for real use cases in healthcare, deep tech, manufacturing, logistics, retail, legal, finance, and operations. We can handle everything for you. We manage the entire process from strategy to full scale deployment in just a few days. We charge nothing until you are happy with the value we create, and there are no upfront fees. Or, you can do it yourself. You get immediate access to our AI builder and the DataInsta marketplace. Just sign up, post your project, and hire from thousands of vetted AI experts by the hour or per project to lead the build on your terms. More Details :
1:11

At DataInsta , we just rebranded InstaAgents to Daiko. In Japanese, Daiko means acting on your behalf. We chose this name because our AI builder creates autonomous agents that take ownership of complex tasks and do the heavy lifting for your business. Most AI projects fail because teams lack the tools, the talent, and a reliable way to evaluate agents. Testing is hard, especially for multiple agent setups and long workflows. We built something different. Daiko is battle tested and evaluated across many industries. With Daiko you can: ◾ Build and deploy agents that automate real workflows ◾ Describe your task and goals so our AI generator can build full multiple agent flows ◾ Choose from over 60 templates and use cases or create your own ◾ Connect your data and APIs, deploy anywhere, and set up evaluations and guardrails ◾ Host the platform on your own servers with white label options We designed this for real use cases in healthcare, deep tech, manufacturing, logistics, retail, legal, finance, and operations. We can handle everything for you. We manage the entire process from strategy to full scale deployment in just a few days. We charge nothing until you are happy with the value we create, and there are no upfront fees. Or, you can do it yourself. You get immediate access to our AI builder and the DataInsta marketplace. Just sign up, post your project, and hire from thousands of vetted AI experts by the hour or per project to lead the build on your terms. More Details :

Abu

28,610 просмотров • 7 месяцев назад

eve now supports the Link agent wallet. You can swiftly give your agent payment capabilities in just a few lines of code – the same tools that power Muse, Instinct, Grok Bot, and all your favorite personal agents. Let’s take a look at an Eve and Link powered shopping agent ➡️
4:25

eve now supports the Link agent wallet. You can swiftly give your agent payment capabilities in just a few lines of code – the same tools that power Muse, Instinct, Grok Bot, and all your favorite personal agents. Let’s take a look at an Eve and Link powered shopping agent ➡️

Steve Kaliski

16,283 просмотров • 1 месяц назад

Opus 5.5 makes this AI stack look f…cking illegal 10 GitHub repos for building everything around the model 01 zen-mcp-server ▸ → your agent debates Gemini, GPT and local models mid-task 02 claude-context ▸ → semantic code search so the agent stops grepping blind 03 wshobson/agents ▸ → a roster of subagents, each with one narrow job 04 mcp-agent ▸ → workflow patterns built around MCP tools, not around chat 05 Backlog.md ▸ → tasks live in git, so the agent and you read the same board 06 Claude Code Development Kit ▸ → hooks, subagents and MCP wired for context at scale 07 agent-rules ▸ → shared guardrails the agent inherits in every repo 08 Agent Skills ▸ → one file that teaches the agent a job, portable across tools 09 agent-scan ▸ → scans your agents, MCP servers and skills for what leaks 10 awesome-claude-code-toolkit ▸ → the map: agents, skills, commands, hooks, MCP configs the architecture: rules → context → subagents → tools → state → review → scan I'd split the stack like this: context: claude-context → Development Kit work: wshobson/agents → mcp-agent → zen-mcp-server state: Backlog.md safety: agent-rules → Agent Skills → agent-scan everyone posts the SDK and the CLI the layer that decides whether your agent survives a week is this one the model is one folder in the stack ⭣
0:24

Opus 5.5 makes this AI stack look f…cking illegal 10 GitHub repos for building everything around the model 01 zen-mcp-server ▸ → your agent debates Gemini, GPT and local models mid-task 02 claude-context ▸ → semantic code search so the agent stops grepping blind 03 wshobson/agents ▸ → a roster of subagents, each with one narrow job 04 mcp-agent ▸ → workflow patterns built around MCP tools, not around chat 05 Backlog.md ▸ → tasks live in git, so the agent and you read the same board 06 Claude Code Development Kit ▸ → hooks, subagents and MCP wired for context at scale 07 agent-rules ▸ → shared guardrails the agent inherits in every repo 08 Agent Skills ▸ → one file that teaches the agent a job, portable across tools 09 agent-scan ▸ → scans your agents, MCP servers and skills for what leaks 10 awesome-claude-code-toolkit ▸ → the map: agents, skills, commands, hooks, MCP configs the architecture: rules → context → subagents → tools → state → review → scan I'd split the stack like this: context: claude-context → Development Kit work: wshobson/agents → mcp-agent → zen-mcp-server state: Backlog.md safety: agent-rules → Agent Skills → agent-scan everyone posts the SDK and the CLI the layer that decides whether your agent survives a week is this one the model is one folder in the stack ⭣

rody

18,089 просмотров • 1 месяц назад

Thrilled to see Amazon Web Services making a major contribution to the open source AI community with the launch of the Strands Agents, an open source AI agents SDK! The core of Strands is the simple agentic loop that connects the model and tools together, like the two strands of DNA. This model-driven approach to agent building eliminates the need for complex agent orchestration by embracing the capabilities of state-of-the-art models to plan, chain thoughts, call tools, and reflect. Providing open source tools and interoperability with open source protocols is an important part of our strategy to enable an agentic future. Can't wait to see what you build with Strands!
1:33

Thrilled to see Amazon Web Services making a major contribution to the open source AI community with the launch of the Strands Agents, an open source AI agents SDK! The core of Strands is the simple agentic loop that connects the model and tools together, like the two strands of DNA. This model-driven approach to agent building eliminates the need for complex agent orchestration by embracing the capabilities of state-of-the-art models to plan, chain thoughts, call tools, and reflect. Providing open source tools and interoperability with open source protocols is an important part of our strategy to enable an agentic future. Can't wait to see what you build with Strands!

Swami Sivasubramanian

32,314 просмотров • 1 год назад

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:
2:49

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:

Andrew Ng

105,894 просмотров • 1 год назад

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.
4:16

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

27,179 просмотров • 1 год назад

🔗 New LangChain Academy Course: Introduction to LangChain (Python) 🔗 Learn how to build with LangChain – our open source framework that makes it easy to start building agents with any model provider. In this course, you’ll create agents that can reason, use tools, and take action, and learn how to debug their behavior with LangSmith. Along the way, you’ll: - Build an agent with the `create_agent` abstraction - Use LangChain’s core building blocks: Models, Messages, Memory, and Tools - Customize your agent with middleware - Debug your agent with LangSmith Observability & Studio By the end of the course, you’ll have assembled a full team of personal assistants. Enroll for free ➡️
2:25

🔗 New LangChain Academy Course: Introduction to LangChain (Python) 🔗 Learn how to build with LangChain – our open source framework that makes it easy to start building agents with any model provider. In this course, you’ll create agents that can reason, use tools, and take action, and learn how to debug their behavior with LangSmith. Along the way, you’ll: - Build an agent with the `create_agent` abstraction - Use LangChain’s core building blocks: Models, Messages, Memory, and Tools - Customize your agent with middleware - Debug your agent with LangSmith Observability & Studio By the end of the course, you’ll have assembled a full team of personal assistants. Enroll for free ➡️

LangChain

41,241 просмотров • 10 месяцев назад

🧬 Excited to open-source Biomni! With just a few lines of code, you can now automate biomedical research with AI agent! We are releasing Biomni A1 (agent) + E1 (env) with 150 specialized tools, 59 databases, and 105 software. E1 is our first attempt at curating the bio-agent environment, but it only scratches the surface: Call to help build Biomni-E2 - an open environment for bio-agent, built with and for the community! We welcome new tools, benchmarks, datasets, agents, and beyond to build the environment together. Significant contributors will be invited as co-authors on our upcoming E2 manuscript. More to come! 💻 🌐 Biomni
0:37

🧬 Excited to open-source Biomni! With just a few lines of code, you can now automate biomedical research with AI agent! We are releasing Biomni A1 (agent) + E1 (env) with 150 specialized tools, 59 databases, and 105 software. E1 is our first attempt at curating the bio-agent environment, but it only scratches the surface: Call to help build Biomni-E2 - an open environment for bio-agent, built with and for the community! We welcome new tools, benchmarks, datasets, agents, and beyond to build the environment together. Significant contributors will be invited as co-authors on our upcoming E2 manuscript. More to come! 💻 🌐 Biomni

Kexin Huang

34,860 просмотров • 1 год назад

🚀 LangSmith Agent Builder is now available in Public Beta Now anyone can create production ready agents without writing code, just chat. Agent Builder guides you from initial idea to deployed agent, creating detailed prompts, selecting required tools, and even creating subagents. Our Beta release also includes: 🧰 Bring your own tools via MCP server 🧩 Browse, copy, and customize agents across a shared workspace 🧠 Use your preferred model Try Agent Builder free: Read more on the launch:
0:56

🚀 LangSmith Agent Builder is now available in Public Beta Now anyone can create production ready agents without writing code, just chat. Agent Builder guides you from initial idea to deployed agent, creating detailed prompts, selecting required tools, and even creating subagents. Our Beta release also includes: 🧰 Bring your own tools via MCP server 🧩 Browse, copy, and customize agents across a shared workspace 🧠 Use your preferred model Try Agent Builder free: Read more on the launch:

LangChain

37,887 просмотров • 11 месяцев назад

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!
2:43

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

Andrew Ng

142,256 просмотров • 1 год назад

Today we're launching Airbyte Agents. It’s a platform meant to solve what we call the discovery gap. Agents in demos are successful because they know the fields and sources they have to call to solve their task. Agents in production fail because they don’t have that knowledge assembled beforehand. Airbyte Agents, powered by the Context Store, fixes that with one unified layer of pre-materialized context across all of your business systems. Real results. 80% fewer tokens. 40% fewer tool calls. Get more out of your AI tool’s plan limit. Three ways to build: MCP, SDK, or no-code Automations 💡 We're early, and we want to build with our users. Try Airbyte Agents today.
2:51

Today we're launching Airbyte Agents. It’s a platform meant to solve what we call the discovery gap. Agents in demos are successful because they know the fields and sources they have to call to solve their task. Agents in production fail because they don’t have that knowledge assembled beforehand. Airbyte Agents, powered by the Context Store, fixes that with one unified layer of pre-materialized context across all of your business systems. Real results. 80% fewer tokens. 40% fewer tool calls. Get more out of your AI tool’s plan limit. Three ways to build: MCP, SDK, or no-code Automations 💡 We're early, and we want to build with our users. Try Airbyte Agents today.

Airbyte

6,762,384 просмотров • 6 месяцев назад

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!
2:17

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!

Andrew Ng

127,724 просмотров • 1 год назад

We raised $125M to build the platform for agent engineering. Thank you to our investors (IVP, Sequoia Capital, Benchmark, Amplify Partners, Sapphire Ventures, CapitalG, and more) for their belief in us, and to our customers like Replit ⠕, Clay, Vanta, Cloudflare, Rippling, Cisco, Workday, and many more for their trust. Read more here about what this means for the future of building agents:
1:14

We raised $125M to build the platform for agent engineering. Thank you to our investors (IVP, Sequoia Capital, Benchmark, Amplify Partners, Sapphire Ventures, CapitalG, and more) for their belief in us, and to our customers like Replit ⠕, Clay, Vanta, Cloudflare, Rippling, Cisco, Workday, and many more for their trust. Read more here about what this means for the future of building agents:

LangChain

159,859 просмотров • 1 год назад

We are excited to announce a powerful step for the future of FOMO! Taking a page out of Virtuals book on BASE, FOMO will be releasing the ability for future projects to be paired in $FOMO in the coming weeks. This is the biggest release we have ever announced. Launch your AI Agent Token + $FOMO trading pair Every individual agent token is paired with the $FOMO token in its liquidity pool. When launching an agent on you will need $FOMO tokens, which are used to create the liquidity pool. This process creates deflationary pressure for FOMO and the entire agent ecosystem. When creating your agent and token, you will have the option to pair your launch with FOMO or SOL, as our goal is not to alienate any project, but rather invite the best communities, CTO’s and builders to launch with us. If you decide to pair your project with FOMO you in turn get full marketing and dev support, once your project graduates the bonding curve and reaches Raydium. Further, as an added incentive, as our revenue grows we will be using part of the funds to support projects that have paired in FOMO. And Devs who launch tokens paired in FOMO will earn fees from their AI Agent token launch. Building the most robust agents using our framework will catapult us as one of the most prominent standards of the Solana ecosystem. Not only have we developed our own core infrastructure, but we also pull from some of the best repo’s and developer talent in all of AI, not just blockchain. Our team is comprised of 9 world class artificial intelligence engineers, PHDs in mathematics and engineering from the top companies on the cutting edge of AI. The future of AI Agents will be on Solana and we will help lead the way.
0:39

We are excited to announce a powerful step for the future of FOMO! Taking a page out of Virtuals book on BASE, FOMO will be releasing the ability for future projects to be paired in $FOMO in the coming weeks. This is the biggest release we have ever announced. Launch your AI Agent Token + $FOMO trading pair Every individual agent token is paired with the $FOMO token in its liquidity pool. When launching an agent on you will need $FOMO tokens, which are used to create the liquidity pool. This process creates deflationary pressure for FOMO and the entire agent ecosystem. When creating your agent and token, you will have the option to pair your launch with FOMO or SOL, as our goal is not to alienate any project, but rather invite the best communities, CTO’s and builders to launch with us. If you decide to pair your project with FOMO you in turn get full marketing and dev support, once your project graduates the bonding curve and reaches Raydium. Further, as an added incentive, as our revenue grows we will be using part of the funds to support projects that have paired in FOMO. And Devs who launch tokens paired in FOMO will earn fees from their AI Agent token launch. Building the most robust agents using our framework will catapult us as one of the most prominent standards of the Solana ecosystem. Not only have we developed our own core infrastructure, but we also pull from some of the best repo’s and developer talent in all of AI, not just blockchain. Our team is comprised of 9 world class artificial intelligence engineers, PHDs in mathematics and engineering from the top companies on the cutting edge of AI. The future of AI Agents will be on Solana and we will help lead the way.

FOMO

129,867 просмотров • 1 год назад