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

105,434 次观看 • 1 年前 •via X (Twitter)

10 条评论

Juan Sensio 的头像
Juan Sensio1 年前

Just what I was looking for, starting right now 🤗

TarkWong 的头像
TarkWong1 年前

Wow,great!

nicker narayana 的头像
nicker narayana1 年前

@unkallben @threecheers99 spread it to any one who wanna dive in to LLM and Generative AI this is best place to. begin with on building backend Apps using GenAI. any one planning to dive in to programming, this gives chance to program and also build new gen products

Danish Khan 的头像
Danish Khan1 年前

Signed up, looks amazing!

Wendy Carlosa 的头像
Wendy Carlosa1 年前

on mute I'm certain I should be negotiating the release of these hostages but then with sound I give up

Aditya 的头像
Aditya1 年前

Had a first look at the course, looks very practical.

GPT.Biz 的头像
GPT.Biz1 年前

This course looks super helpful for anyone interested in AI development! I recommend checking it out if you're keen to learn how to build intelligent agents.

William Gray 的头像
William Gray1 年前

I have started the course, TBH I love what I have seen so far. I have the course running on my virtual server and accessing it via visual studio code. When will studio be available for Linux, if at all.

Nick Dimitrov 的头像
Nick Dimitrov1 年前

This is all nice and sweet with OpenAI. How does it work with HF + local Meta Llama 3.1? I can't make it work in past two days. Different errors occur, from missing chat templates to missing eos/ bos tokens, to inability to bind tools with ChatHuggingFace.bind_tools. any guides?

The Martian 的头像
The Martian1 年前

Finally

相关视频

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

131,640 次观看 • 1 年前