#Chain
5400 related articles
·3 min
Complete Guide to LangChain 1.3: From Models and Agents to DeepAgent Architecture
A complete guide to LangChain 1.3: LLM invocation, Agent tool calling, Harness architecture, LangGraph, RAG, and DeepAgent — build a clear, modern Agent development knowledge base.
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Cross-Tokenizer Knowledge Distillation…
·2 min
Cross-Tokenizer Knowledge Distillation: Why 85% of Information Vanishes and How the Chain Rule Fixes It
Many-to-one token mapping in cross-tokenizer knowledge distillation silently drops 85% of teacher info, collapsing entropy from 2.09 to 0.32 bits. Learn the chain rule fix that restores retention to 83%+.
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Latent Reasoning: The Next-Generation …
·3 min
Latent Reasoning: The Next-Generation AI Reasoning Paradigm Beyond Chain of Thought
Is CoT really AI 'thinking'? This deep dive covers latent reasoning's rise — Coconut, HRM, BDH — and the core trade-offs between interpretability, efficiency, and governance in high-stakes AI.
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·3 min
AI Agents Are Reshaping Software Testing: A Practical Guide from Cursor to LangChain
A deep dive into AI-powered testing: Cursor Skills, Coze agents, and LangChain multi-agent systems for automated test case generation, BDD, and review workflows.
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·3 min
LangChain 1.3 in Practice: A Complete Learning Path from LLMs to Agent Development
A comprehensive guide to LangChain 1.3 — covering the full learning path from Models to Agent development, including Harness architecture, LangGraph, memory management, HITL, and Guardrails.
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·4 min
LangChain V1.3 in Practice: Master AI Engineering Thinking and Break Free from Tool Dependency
LangChain V1.3 course deep-dive: why engineering thinking beats tool-chasing. Covers RAG accuracy myths, Token cost control, and LangChain/LangGraph/Deep Agent breakdowns.
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·3 min
LangChain Practical Guide: From RAG Applications to Intelligent Agent Development
A comprehensive guide to LangChain: core concepts, RAG applications, Agent development, version selection (0.3/1.0), and career opportunities for Java/Python developers entering LLM development.
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·3 min
jscrambler npm Package Compromised: Supply Chain Attack Incident Response and Protection Guide
The jscrambler npm package v8.14.0 was hit by a supply chain attack, with malicious code able to steal environment variables, CI secrets, and deployment credentials. This guide covers self-inspection, credential rotation, and dependency hardening.
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·2 min
LangChain in Practice: Building the ChatDoc Document Chat Application with RAG
An in-depth explanation of RAG (Retrieval-Augmented Generation) principles, with a hands-on guide to loading PDF, Word, and other document formats in LangChain to build a complete ChatDoc Q&A app.
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·3 min
LangChain 1.3 in Practice: A Complete Technical Guide from LLMs to DeepAgent
An in-depth analysis of the core knowledge system of LangChain 1.3, covering the Harness architecture philosophy, DeepAgent positioning, LangGraph fundamentals, Agent memory, and human-in-the-loop.
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·3 min
LangChain V1.3 Hands-On Tutorial: A Complete Breakdown of RAG, Multi-Agents, and Engineering Mindset
An in-depth look at LangChain V1.3's core philosophy: from RAG to multi-agent workflows. Master LangGraph, Chain, and DeepAgent, learn token control and Human-in-the-loop, and become a true master of AI app development.
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·4 min
The Complete Roadmap from DevOps to MLOps: Skill Transfer and Toolchain Guide
How can DevOps engineers transition to MLOps? This guide explains the core differences between MLOps and DevOps, offers a phased learning path, tool recommendations (MLflow, DVC, Kubeflow), and practical project ideas.
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·4 min
Getting Started with LangChain: LLM Limitations, Agent Principles, and Environment Setup in Practice
An in-depth look at LangChain's core value: the three limitations of LLMs, unified model interfaces, modular architecture, configuring the DeepSeek API, and understanding the SystemMessage/HumanMessage/AIMessage/ToolMessage system to build a foundation for Agent development.
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·3 min
Getting Started with AI Agent Development: A Three-Step Practical Path with Python + LangChain
Learning AI Agent development is no longer daunting! This article outlines the simplest practical path: master just enough Python, grasp core LLM concepts, then build your first Agent with LangChain.
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·4 min
Baidu AI Automation System: Trigger Skill Chains with a Single Sentence, Say Goodbye to Manual Workflows
The Baidu AI Automation System uses departmentalized skill libraries and Agent-style orchestration to let managers trigger multi-skill workflows with one sentence—no tech background needed.
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·3 min
Langchain.js Agent Development: A Guide to Deconstructing the OpenClaw Engine Architecture
An in-depth analysis of AI agent development based on Langchain.js—comparing workflow agents and Agent Loops, deconstructing the TypeScript implementation path of an OpenClaw-like engine, covering structured output, MCP, and LangGraph.
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·3 min
Getting Started with LangChain: The Core Framework for LLM Application Development and Its Three Key Components
LangChain is the leading open-source framework for LLM application development, supporting GPT-4, GLM, and other mainstream models. This article dives into its three core concepts: Components, Chains, and Agents.
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·4 min
Four Major LangChain Updates: OpenWiki, Voice Agents, and Sub-Agent Orchestration Explained
LangChain releases four major updates: OpenWiki for auto-generating codebase docs, voice agent tutorials, Harbor evaluation integration, and deepagents programmable sub-agents.
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·5 min
Getting Started with LangChain: LLM Invocation, Agent Practice, and Environment Setup Guide
Starting from the three limitations of LLMs, this guide systematically explains LangChain's core positioning, environment setup, API key prep, model init, and the message system. Learn init_chat_model and AIMessage/HumanMessage/SystemMessage.
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·7 min
Decoding the Government Software Security Executive Order: New Supply Chain Compliance and SBOM Requirements
The new U.S. Executive Order requires government software suppliers to provide verifiable security assurances and mandates the SBOM system. This article analyzes the policy's core demands, real impact on suppliers, and the chain of challenges.
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