Overview of Articles in the Official Account If you have any questions, feel free to add WeChat: jasper_8017. Looking forward to discussing and progressing together with like-minded friends! Follow 👇 the official account and learn AI technology together! In the article 【The Future of AI – AI Agent Series】【MetaGPT】2. Implementing Your First Agent, we have … Read more
Hello everyone, I am Student Xiao Zhang, continuously learning C++ advanced knowledge and practical cases of AI large models. I will keep sharing, and welcome everyone to like + follow for mutual learning and progress. This article will explore a practical case of MetaGPT – the Werewolf game. The source code for this case can … Read more
Hello everyone, I am 【Student Xiao Zhang】. Continuous learning and continuous output of practical knowledge, follow me, and let’s learn AI large model technology together! Overview of Articles in Official Account If you have any questions, feel free to add me on WeChat: jasper_8017. Looking forward to discussing and progressing together with like-minded friends! Previously, … 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
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
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
How to Handle a Large Number of Tools The subset of tools available for invocation is typically determined by the model (although many providers also allow users to specify or limit the selection of tools). As the number of available tools increases, you may want to limit the LLM’s selection to reduce token consumption and … Read more
LangGraph has built-in first-class stream support. Streaming output graph .stream and .astream are synchronous and asynchronous methods for streaming outputs back from the graph run. When calling these methods, several different modes can be specified (for example, `graph.stream(…, mode=”…”)`): values: This will transmit the full value of the state after each step of the graph. … Read more
Sometimes, we want the LLM that calls the tools to fill in a subset of parameters for the tool functions, while providing other values for the remaining parameters at runtime. If you are using LangChain-style tools, a simple way to handle this situation is to annotate the function parameters with InjectedArg. This annotation excludes the … 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