This article explains how Baidu Map’s AI navigation system converts spoken queries into accurate map instructions by combining speech recognition, intent parsing, large‑language‑model reasoning, tool calling, and memory‑reflection techniques, showcasing the underlying technologies that enable instant, context‑aware responses.
This tutorial shows how to set up a Python environment, install LangGraph, and use its high‑level prebuilt graph API—specifically create_react_agent—to quickly create a weather‑assistant AI agent with just two lines of code, illustrating the full tool‑calling workflow and ReACT loop.
This article explores the practical integration of AI agents with Model Context Protocol (MCP) to build a smart reporting assistant and automate batch task creation, detailing the technical workflow, tool‑calling capabilities, implementation steps, challenges faced, and the benefits of combining agents with traditional engineering systems.
This article walks through integrating RAG‑based tool calling into a FastGPT workflow to enable AI‑driven customer service that can fetch real‑time train ticket information by invoking external APIs, configuring HTTP nodes, and handling parameters step by step.
The article presents the 12‑Factor Agents framework, adapting the classic 12‑Factor App methodology to large‑language‑model agents and detailing twelve concrete engineering principles—ranging from prompt control and context engineering to human‑in‑the‑loop and stateless design—that together enable production‑grade, observable, and maintainable AI agents.
This article walks through constructing a stateful ReAct‑style LLM agent using LangGraph, detailing the core components—State, Nodes, Edges—defining a weather‑lookup tool with Open‑Meteo, configuring the graph’s nodes and conditional edges, and executing the workflow with streaming to observe each step in real time.
This article explains fundamental AI concepts—including models, prompts, prompt templates, embeddings, tokens, structured output, data integration, RAG, and tool calling—and shows how Spring AI simplifies their use for Java developers building intelligent applications.
Spring AI 1.0 GA introduces a comprehensive suite of AI capabilities for Java developers, including a ChatClient supporting 20 models, vector‑store integrations, RAG pipelines, advanced chat memory, @Tool function calling, model evaluation, observability, Model Context Protocol, and autonomous agents, with examples for major cloud providers.
Spring AI 1.0, the first production‑grade Java AI framework, introduces ready‑to‑use APIs, seamless model integration, enterprise‑level RAG engine, smart tool calling, and three development modes, empowering developers to rapidly build, customize, and fully control AI applications with major model providers like OpenAI, Anthropic, DeepSeek.
Learn how to quickly set up Alibaba Cloud’s Bailei platform to call the open-source Qwen3 large language model, explore its cost‑effective performance, dual‑mode reasoning, multilingual support, and enhanced agent capabilities, and follow step‑by‑step instructions for API key configuration, Cherry Studio integration, and tool‑calling setup.
Discover the Model Context Protocol (MCP)—an open standard from Anthropic that unifies AI model access to external data sources, enabling real‑time search, tool integration, and bidirectional communication, with practical examples, setup guides, and code snippets for developers to quickly build AI‑driven applications.
This article explains how the Model Context Protocol (MCP) enables large language models to retrieve up‑to‑date external information through standardized tool calls, illustrating the complete end‑to‑end workflow with Python code for the MCP server, client, and host, and discussing its advantages for building AI agents.
This article explains the Model Context Protocol (MCP) – an open, consensus‑based standard that lets large language models seamlessly interact with external tools and data, describes its architecture, why it’s needed, how models choose tools, and provides a step‑by‑step Python server implementation with code examples.
This article explains the concept of tool-specific tokens for large language models, detailing how they enable efficient, reliable tool calls, the implementation steps, advantages over JSON, practical advice, comparisons, challenges, and future directions for AI agents.
This article explains the evolution from Spring AI FunctionCall to the newer ToolCalling, introduces the Model Context Protocol (MCP), and provides step‑by‑step code examples for building MCP servers and clients in Java, demonstrating how to integrate large language models with backend services.
This article explains how Spring AI's tool‑calling feature extends large language models by integrating custom Java tools for information retrieval and operations, and provides step‑by‑step Spring Boot 3 examples—including date‑time and weather tools—complete with configuration, code snippets, and execution results.
The article presents Anthropic’s open‑source Model Context Protocol (MCP) – a client‑server framework that standardizes how large language models securely access resources, prompts, and tools (the “HTTP of AI”) – and demonstrates its use through a hot‑fix scraper and a dynamic chatbot that discovers and invokes tools via JSON‑formatted calls.
The article details how the author discovered a bug in Cursor’s closed‑source IDE where correctly registered MCP services, such as a weather query tool, are omitted from the LLM’s tool list, preventing calls, and explains the debugging steps that compared Cursor with the open‑source Cline implementation to pinpoint the registration flaw.
Baidu officially launched the Wenxin 4.5 and X1 large language models, showcasing native multimodal foundations, advanced attention masks, heterogeneous expert extensions, and tool‑calling capabilities, while offering low‑cost API access on the Qianfan platform and outlining the underlying technical innovations that drive their performance gains.
This article explains how Spring AI Alibaba enables AI agents to leverage Retrieval‑Augmented Generation and Tool Calling by providing over twenty ready‑made RAG data source connectors and more than twenty function‑calling interfaces, along with practical code examples for integrating document readers and weather services.
This article presents ModelScope‑Agent, an open‑source multi‑modal multi‑agent framework built on the ModelScope community, explains its underlying agent concepts, outlines its architecture and key features, showcases several real‑world applications such as ModelScope GPT, Story‑Agent and Facechain‑Agent, and includes a detailed Q&A on future directions and challenges.
This article reviews the open‑source 9‑billion‑parameter GLM‑4‑9B model, covering installation, quick‑start inference code, quirky Chinese riddles that highlight its strengths over GPT‑4, extensive benchmark tables for dialogue, multilingual, tool‑calling and multimodal tasks, and its broader impact on the Chinese AI ecosystem.
