Master's graduate from Beijing Institute of Technology, published four top‑journal papers, previously worked as a developer at ByteDance and Alibaba. Currently researching large models at a major state‑owned enterprise. Committed to sharing concise, practical AI large‑model development experience, believing that AI large models will become as essential as PCs in the future. Let's start experimenting now!
This article walks through constructing a LangGraph graph that encapsulates three agents—task planning, web search, and report generation—into a DeepResearch application, then shows how to package and deploy the backend and frontend so users can interact with the system via a web UI.
This article walks through the design and implementation of a multi‑agent DeepResearch application using the Pipeline‑Agent pattern with LangGraph and LangChain, detailing three agents for task planning, web search via Tavily, and report generation, and provides complete Python code and test results.
This guide walks through Coze's large‑model configuration—including model selection, generation diversity, input/output parameters, and persona templates—demonstrates a complete e‑commerce chatbot setup, and details two plugin integration methods with three concise best‑practice rules for effective agent development.
This article walks beginners through three ways to create AI agents on the Coze platform—AI auto‑generation, template reuse, and standard workflow—showing step‑by‑step how to build, test, and publish a travel‑assistant bot in just five minutes.
This article provides a comprehensive overview of the Coze low‑code AI agent platform, detailing its free, multi‑model capabilities and six core functions—plugins, knowledge base, database, image flow, workflow, and multi‑agent collaboration—while illustrating how each feature lowers development barriers and enables sophisticated agent applications.
This article analyzes four mainstream low-code AI Agent development platforms—Coze, Dify, n8n, and LangFlow—detailing their features, deployment options, commercial models, strengths, and weaknesses, and provides a step‑by‑step learning path to help developers select the most suitable framework for 2025.
This article categorizes and compares twelve popular large‑model AI Agent development frameworks—low‑code platforms, basic programming paradigms, advanced code libraries, and multi‑agent systems—detailing their core features, typical use cases, and trade‑offs to help developers choose the most suitable solution.
This tutorial explains how LangGraph handles short‑term memory with InMemorySaver and thread‑isolated checkpoints, how to persist it using PostgresSaver, and how to build long‑term memory with InMemoryStore and PostgresStore, including semantic search and custom storage extensions.
The article introduces DeepSeek-V3.2, highlighting its new DeepSeek Sparse Attention (DSA) that boosts training and inference efficiency by up to 50%, cuts model usage costs dramatically, explains the updated API endpoints, and details the four‑stage post‑training pipeline that underpins the model’s performance improvements.
This tutorial shows how to use LangGraph’s low‑level API to create a multi‑turn chatbot, define structured state, add model and tool nodes, implement conditional branching for tool calls, and reproduce a ReACT graph agent with a weather‑assistant example.
