The AI community is abuzz with the official launch of LangGraph, a new tool aimed at enhancing large language model (LLM) applications. LangGraph, introduced last week and now more formally presented through a blog and YouTube series, promises to simplify the integration of LLMs with various tools, potentially revolutionizing how agent architectures are built. It offers features like a Pregel graph structure, channels, topics, and nodes, and it supports the creation of robust agent architectures. The tool is designed to be user-friendly, enabling LLMs to call tools with varying levels of autonomy, and has been made available in both JavaScript and Python versions. The AI community, including experts from @heathercmiller's CMU Composable Systems Lab and AI professionals, have praised LangGraph for its potential to streamline the prototyping process for LLM applications, exemplified by the LangChain AgentExecutor and a chat agent executor aimed at chat models. Additionally, new resources have been developed to minimize LLM hallucinations, leveraging @pydantic for validation.
Ever wondered how LLMs go from lines of code to mind-blowing conversations? It's all about teamwork between two key layers: Application Layer and infrastructure layer. Learn more about LLMs: https://t.co/2L7TlzJOaL https://t.co/Eb8iCdYsEx
🦜🕸️LangGraph JS The JavaScript version of LangGraph is now live! Same interface and concepts as the python version Repo: https://t.co/8vJXDIW9TH Blog: https://t.co/QSvdocqjz9 YouTube: https://t.co/PHz6GaqELt
🦜🕸️LangGraph JS Released LangGraph makes it easy to modify agent runtimes with @LangChainAI. So far in JS we have a couple examples to help you get started: 🤖Example of replicating the LangChain AgentExecutor 📷A chat agent executor (aimed specifically at chat models) Super… https://t.co/LOZQK40hsN
New awesome article on minimising LLM hallucinations with @pydantic by @jxnlco. https://t.co/cnl5q3fbB9 The coolest thing about LLMs and validation is that all the same principles, know-how and best practices from any other application development apply!
Revamped tool usage docs are in @LangChainAI JS/TS too 💪! Little known fact: you can use tools with chains as well as agents, depending on how much autonomy you want to give to the LLM. https://t.co/S2tP7yQL0e https://t.co/9HMJoJlOqL
🔧Tool Usage w/ LangChain A big use case for LangChain is making it easy for LLMs to call tools. This can range from putting a simple natural-language-interface on top of a function, to agents We've revamped our docs to highlight this use case more. New guides include:… https://t.co/tAMFfrX3s3
Testing out LLM apps fast is key to know what works well. Haven't fully figured all the parts... ...but to me it seems like the 𝗕𝗘𝗦𝗧 𝗟𝗟𝗠 𝗣𝗥𝗢𝗧𝗢𝗧𝗬𝗣𝗜𝗡𝗚 process for teams simply leverages the #langchain ecosystem: 🧵
LangGraph 🕸️ might just be the next shift to enable much more robust agent architectures through the use of a - Pregel graph structure - channels - topics - nodes - more! Check out our deep dive teaser video to understand LangGraph from the code up - longer video coming soon https://t.co/a1fJnttRnV https://t.co/U8Y9pTn8jw
We sat down with AI experts @IgorPogany,@TheAIAdvantage and @besanushi to get the inside scoop on effective prompt engineering frameworks to maximize LLM output. https://t.co/ZaiEsYq0n5
This is an incredible resource for people seeking to navigate the space of tools for creating LLM-based systems right now. @heathercmiller and the team really did a deep dive here! https://t.co/WCvgs2G66Y
We previewed LangGraph last week, but excited to dive a lot more into why we're building this, the details of what it looks like, and some more examples Blog: https://t.co/AqBfFrnw8I YouTube series: https://t.co/NHXrpgaO5h We've got a lot more exciting examples coming https://t.co/YI5MCcQj5k
🦜🕸️LangGraph We introduced LangGraph last week, but are excited to launch it more officially today (blog and YouTube series). It includes: 🤖Example of replicating the LangChain AgentExecutor 💬A chat agent executor (aimed specifically at chat models) LangGraph makes it easy… https://t.co/KrI3gzKLfy
We spent a long time thinking about the many LLM frameworks and how they differ. Article below with @heathercmiller's CMU Composable Systems Lab (@Lambda_freak, Haoze He) is the best place to understand this emerging **stack**. Agents, optimizers, chains, schemas, prompts?⤵️ https://t.co/KlwatjMH5E https://t.co/MeDPORh5zS