The AIxiv column is a section published by Machine Heart that reports academic and technical content. Over the past few years, the AIxiv column has reported on more than 2000 pieces of content, covering top laboratories from major universities and companies worldwide, effectively promoting academic exchange and dissemination. If you have excellent work to share, … Read more
Abstract: MetaGPT is an innovative metaprogramming framework that encodes standard operating procedures into prompt sequences, enabling agents with human domain expertise to validate intermediate results and reduce errors for a smoother workflow. MetaGPT utilizes an assembly line paradigm to assign different roles to various agents, efficiently breaking down complex tasks into subtasks that involve collaboration … Read more
Author: Wang Lei, Senior Architect of MindSpore, AI Engineering Expert Organizer: Qingke AI >Join us! NLP Paper Submission, LLM Communication, Live Paper Discussion Group Introduction to MetaGPT In recent years, large language models have become a hot topic in the AI field due to their powerful natural language processing capabilities. They can not only generate … Read more
AI Agents are considered by many to be the future direction of large model development. Previously, Lilian Weng, head of OpenAI’s safety team, published a detailed blog introducing AI autonomous agent robots, which attracted a lot of attention. Released in July, MetaGPT is a brand new AI Agent project that provides an automated agent framework … Read more
Using Pydantic model as state Previously, we might have used TypedDict the most, but Pydantic model is also a great choice. Let’s see how this type can serve as the graph state. Input Validation from langgraph.graph import StateGraph, START, END from typing_extensions import TypedDict from pydantic import BaseModel # Overall state of the graph (this … Read more
LangGraph allows you to easily manage conversation memory in graphs. These operational guides demonstrate how to implement various strategies for this. Managing Conversation History One of the most common use cases for persistence is using it to track conversation history. It makes continuing conversations easier. However, as conversations get longer, this conversation history accumulates and … Read more
LLMs are not perfect when calling tools. Models may attempt to call non-existent tools or fail to return parameters that match the requested schema. Strategies such as keeping the schema simple, reducing the number of tools passed at once, and using good names and descriptions can help mitigate this risk, but they are not foolproof. … Read more
Combining control flow (edges) and state updates (nodes) can be very useful. For instance, you might want to execute state updates and decide which node to transition to next within the same node. LangGraph provides a way to achieve this by returning an object from the Command node function: def my_node(state: State) -> Command[Literal["my_other_node"]]: return … Read more
In this tutorial, we will learn how to build an intelligent dialogue agent with memory capabilities using LangGraph and LangChain. This agent can use search tools to answer questions and maintain the continuity of conversations. 1. Obtain API Key for Search Tool Log in to the search tool website: <span>https://tavily.com/</span>, generate an API key as … Read more
How to Pass Config to the Tools At runtime, we may need to pass values to the tools, such as user ID. For security reasons, this value should be set by application logic rather than controlled by the LLM. The LLM should only manage its expected parameters. The LangChain tools use the Runnable interface, where … Read more