Author of the rpcx microservice framework, original book author, and chair of Baidu's Go CMC committee.
This article walks through setting up MCP servers in both stdio and SSE modes, shows how to load their tools into LangChain agents using the new create_agent API, compares the two communication methods, and provides complete runnable Python examples.
This guide explains LangChainGo’s Chain abstraction, detailing the core Chain interface, the LLMChain implementation, various chain types such as SequentialChain and RetrievalQA, and provides a complete walkthrough of setting up prompts, creating chains, and executing them with example Go code.
The article examines Go 1.25’s experimental Green Tea garbage collector, explains why the traditional mark‑sweep approach hurts modern CPUs, details the page‑oriented redesign and its AVX‑512 vector acceleration, and shows how these changes can cut GC‑related CPU usage by 10‑40%.
LangChain’s stateless LLMs require a memory component to retain conversation context, and this article explains the importance of memory, compares built‑in memory types, and walks through two practical examples—custom buffer memory and basic message history—showing how to implement and use them with code.
LangGraph extends LangChain by letting developers define stateful, multi‑agent LLM workflows as graphs with nodes, edges, and shared state, and the article walks through core concepts, typical use cases, and a detailed example that shows how to define state, nodes, edges, compile and run the graph.
This guide explains what LangChain toolkits are, why they simplify building domain‑specific AI agents, lists common built‑in toolkits, and walks through the step‑by‑step process of instantiating a toolkit, retrieving its tools, and creating an OpenAI‑powered agent, illustrated with a SQL database example.
This guide explains how LangChain’s Expression Language (LCEL) lets you declaratively connect prompts, models, and output parsers into chains, walks through environment setup, dependency installation, and detailed code examples ranging from a basic joke generator to retrieval‑augmented generation and memory‑enabled agents.
Learn how LangChain enables multimodal interactions by preparing inputs, constructing prompts, invoking models like GPT‑4o, and processing responses, with a complete example that demonstrates image‑question answering, code walkthrough, environment setup, and key considerations for API keys and image URLs.
This guide explains what LangChain tools are, why clear descriptions matter, and walks through three ways to create them—using the @tool decorator, StructuredTool with Pydantic models, and custom BaseTool subclasses—plus examples of built‑in tools and reference links.
This article explains the core concept of LangChain agents—combining an LLM, a set of tools, and a reasoning‑action loop—to enable dynamic decision‑making, tool invocation, and iterative observation for solving complex, multi‑step tasks.
