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Our latest LangChain Academy course – Building Ambient Agents with LangGraph – is now available! Most agents today handle one request at a time through chat interfaces. But as models have improved, agents can now run in the background – and take on long-running, complex tasks. LangGraph is built...

33,192 просмотров • 1 год назад •via X (Twitter)

Комментарии: 8

Фото профиля LangChain
LangChain1 год назад

Enroll for free ➡️ Want to bring LangChain Academy training to your team? Nominate your team for a free, hands-on workshop – offered in person at your office or remotely. ➡️

Фото профиля LangChain
LangChain1 год назад

Special shoutout to LangChain community member @labdmitriy for all of the feedback they provided on the course!

Фото профиля Debug Dan
Debug Dan1 год назад

This development will likely advance scalable, efficient agent deployment workflows within production settings.

Фото профиля JeffK
JeffK1 год назад

This is great, I really love these LangChain academy courses. Please keep them coming. I also really appreciate Lance's videos on the youtube channel showing how to implement some of the latest techniques from papers in LangChain/LangGraph.

Фото профиля Eleven
Eleven1 год назад

This is amazing

Фото профиля Harsha Sai
Harsha Sai1 год назад

Is it for free?

Фото профиля Dhiraj Patra 🚴🏼
Dhiraj Patra 🚴🏼1 год назад

Thanks a lot

Фото профиля Richard Howes
Richard Howes1 год назад

Sick! I got platform when it was beta and I’m happy to pay for it now. Saves me time. I trust the builders of lang chain more then the next AI pop up shop. Thanks for keeping it easy to build

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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

131,850 просмотров • 1 год назад