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

OpenServ

22,380 subscribers

78,016 просмотров • 1 год назад •via X (Twitter)

Наука и технологии Образование

Anya Rossi• Live Now

Private livecam show

Комментарии: 11

Фото профиля Infinite Liquidity🔮

Infinite Liquidity🔮1 год назад

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

Фото профиля Rickyticky Bobbywobbin

Rickyticky Bobbywobbin1 год назад

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

Фото профиля Samitoshi

Samitoshi1 год назад

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

Фото профиля Mikael Z

Mikael Z1 год назад

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

Фото профиля lolaksppbura

lolaksppbura1 год назад

A little Christmas gift from $SERV Team

Фото профиля BONTESA

BONTESA1 год назад

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

Фото профиля Lamentis;)

Lamentis;)1 год назад

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

Фото профиля Semp

Semp1 год назад

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

Фото профиля Don Massimo (Periodontist)

Don Massimo (Periodontist)1 год назад

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

Фото профиля Lingua Franca

Lingua Franca1 год назад

here we go

Фото профиля Rutger van der Tas

Rutger van der Tas1 год назад

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

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81,696 просмотров • 10 месяцев назад

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13,322 просмотров • 9 месяцев назад

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19,388 просмотров • 10 дней 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 ➡️

🔗 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

41,016 просмотров • 7 месяцев назад

New short course: Long-Term Agentic Memory with LangGraph. Learn to build an agent with long-term memory in this course developed in collaboration with taught by its Co-Founder and CEO, Harrison Chase! Personal assistance and productivity tasks have become important use cases for agents. An important feature of an AI assistant, such as a coding or calendar assistant, is its ability to keep improving over time from its experience. Agent memory is the key capability that enables this. To add memory to an agent, you must first figure out what to store and what to retrieve when it is time to use the information. Additionally, you’ll have to decide when to update the stored information. For example, you might update in each iteration loop of the agent or perform updates in the background, with a helper agent. In this course, you will learn a mental framework to build agents with long-term memory. You'll create a useful email assistant that can respond, ignore, and notify using writing, scheduling, and memory-management tools. You’ll develop your agent's memory by adding facts to its memory store, provide examples to learn the user's preferences, and optimize system prompts to evolve instructions based on previous responses. In detail, you’ll: - Learn how the three types of memory--semantic, episodic, and procedural–and the two update mechanisms–via hot path and in the background–apply to your agents. - Build an email agent with writing, scheduling, and availability tools, along with a router that triages incoming email and handles it accordingly by ignoring, responding, or notifying the user. - Add tools to your email agent that allow it to operate on semantic memory by learning facts about the user, storing them in a long-term memory store, and searching over them in future interactions. - Incorporate episodic memory, in the form of few-shot examples, in the triage step of your agents to help them learn and update user preferences. - Add procedural memory as system prompts, optimized with feedback to improve the instructions the agent follows. Learn how to approach memory in agents, and start building agents with long-term memory with LangGraph! Please sign up here:

New short course: Long-Term Agentic Memory with LangGraph. Learn to build an agent with long-term memory in this course developed in collaboration with taught by its Co-Founder and CEO, Harrison Chase! Personal assistance and productivity tasks have become important use cases for agents. An important feature of an AI assistant, such as a coding or calendar assistant, is its ability to keep improving over time from its experience. Agent memory is the key capability that enables this. To add memory to an agent, you must first figure out what to store and what to retrieve when it is time to use the information. Additionally, you’ll have to decide when to update the stored information. For example, you might update in each iteration loop of the agent or perform updates in the background, with a helper agent. In this course, you will learn a mental framework to build agents with long-term memory. You'll create a useful email assistant that can respond, ignore, and notify using writing, scheduling, and memory-management tools. You’ll develop your agent's memory by adding facts to its memory store, provide examples to learn the user's preferences, and optimize system prompts to evolve instructions based on previous responses. In detail, you’ll: - Learn how the three types of memory--semantic, episodic, and procedural–and the two update mechanisms–via hot path and in the background–apply to your agents. - Build an email agent with writing, scheduling, and availability tools, along with a router that triages incoming email and handles it accordingly by ignoring, responding, or notifying the user. - Add tools to your email agent that allow it to operate on semantic memory by learning facts about the user, storing them in a long-term memory store, and searching over them in future interactions. - Incorporate episodic memory, in the form of few-shot examples, in the triage step of your agents to help them learn and update user preferences. - Add procedural memory as system prompts, optimized with feedback to improve the instructions the agent follows. Learn how to approach memory in agents, and start building agents with long-term memory with LangGraph! Please sign up here:

Andrew Ng

131,779 просмотров • 1 год назад