#LangChain
164 related articles
Product Reviews2025 Comparison of Six Major Agent Development Frameworks: AutoGen/LangChain/LangGraph Selection Guide
In-depth comparison of six AI Agent frameworks—AutoGen, LangChain, LangGraph, Google ADK, OpenAI Agents, AgentScope—covering architecture, ecosystem maturity, and practical selection advice.
TutorialsLangChain 1.3 Event Stream in Practice: Real-Time Agent Monitoring from 4 Perspectives
Master LangChain 1.3 Event Stream V3 with 4 monitoring perspectives: run.messages, tool_cause, and more for real-time Agent debugging, streaming output, tool tracking, and token cost control.
Tutorials2025 LLM Career Transition Roadmap: A Three-Phase Plan from Foundations → Frameworks → Real-World Projects
A systematic 2025 LLM career transition roadmap covering Python, Transformers, LangChain, LlamaIndex, RAG, Agent development, and fine-tuning across three phases achievable in 2-3 months.
TutorialsImplementing LangGraph in TypeScript: Deconstructing the Core Principles of AI Agent Frameworks from Scratch
Learn how to implement LangGraph's core design from scratch in TypeScript, covering state-graph-driven architecture, node-based orchestration, and the ReAct pattern for AI Agent development.
TutorialsEnterprise Deep Research Agent Implementation Guide: Architecture Design and Core Pain Points
A deep dive into enterprise Deep Research Agent deployment: architecture design, LangChain framework, and solutions to the top 10 pain points including tool chaos, context contamination, and cost control.
TutorialsLangChain Multimodal in Practice: Image Recognition with ChatPromptTemplate
Learn how to use LangChain's ChatPromptTemplate for multimodal image recognition with URL, Base64, and local path inputs, plus PySide6 desktop app integration.
TutorialsAI Agent Learning Roadmap: A Complete Guide from LangChain to Enterprise Deployment
In-depth analysis of the popular GitHub project ai-agents-from-zero, covering LangChain, LangGraph, RAG, MCP and more, with a complete learning path from beginner to enterprise AI Agent development.
Product ReviewsAix-DB Open-Source Framework: A Multi-Agent Approach to Natural Language Database Querying
Deep dive into Aix-DB: an open-source multi-agent framework using LangChain/LangGraph and MCP protocol for natural language to SQL, data analysis, and visualization.
TutorialsComplete Guide to Agentic RAG: Principles, Architecture, and Hands-On Implementation
Deep dive into Agentic RAG principles and implementation. Compare traditional RAG limitations, learn tool-based retrieval with ReAct Agent loops, with LangChain/LangGraph code examples.
TutorialsAgentic RAG Practical Guide: The Complete Evolution from Traditional RAG to Agent-Based Retrieval
Deep dive into Agentic RAG vs traditional RAG, with ChatPDF case study and LangChain code walkthrough covering tool design, multi-turn iteration, and autonomous decision-making for LLM engineers.
TutorialsA Practical Guide for QA Engineers: Building 18 AI Agents with 5 Essential Tools
Learn how QA engineers can build 18 AI Agents using Coze, LangChain, Dify, Cursor, and Skills — covering requirements analysis, test case generation, script writing, and regression testing with up to 10x efficiency gains.
Product ReviewsHow to Choose an AI Agent Framework in 2026? Pros and Cons of Four Leading Solutions
Compare four leading AI Agent frameworks in 2026: Coze, AutoGen, CrewAI, LangChain, and AutoGen Studio — covering coding requirements, private deployment, and commercialization.
Product ReviewsAI Agent Framework Selection in 2025: Pros and Cons of Coze, AutoGPT, LangChain, and AutoGen
2025 comparison of four major AI Agent frameworks: Coze for beginners, AutoGPT/LangChain/MetaGPT for developers, Microsoft AutoGen for enterprise self-hosted deployment. A practical selection guide.
Deep DivesThe Complete Guide to Agentic RAG: Principles and Code Implementation
Deep dive into Agentic RAG vs traditional RAG, covering tool calling, multi-step iteration, query rewriting, with LangChain and LangGraph code examples for building intelligent retrieval systems.
TutorialsLangChain Agent in Practice: Building a Production-Grade AI Agent from Scratch
A comprehensive guide to building production-grade AI Agents with LangChain, covering architecture design, tools, memory systems, ReAct strategy, and enterprise deployment.
TutorialsLangChain Deep Agents in Practice: Ten Agent Development Pain Points and Their Solutions
Deep dive into LangChain Deep Agents paradigm, analyzing ten Agent development pain points including tool sprawl and context pollution, with practical enterprise solutions using Deep Research.
TutorialsAgentic RAG in Practice: Architecture Deep Dive and LangChain Implementation Guide
Compare traditional RAG vs Agentic RAG architectures, explore planning, tool use, and multi-step iteration capabilities, with full LangChain/LangGraph ReAct Agent code and ChatBoss project examples.
TutorialsAgentic RAG in Practice: Architecture Deep Dive and LangChain Implementation
Deep dive into Agentic RAG vs traditional RAG, covering planning, tool calling, and multi-step iteration capabilities with complete LangChain and LangGraph code implementation.
Deep DivesAgent, MCP, and Function Calling: A Complete Guide to Their Differences and Relationships
A deep dive into the relationships between AI Agent, MCP protocol, Function Calling, and Prompt. From basics to full architecture, build a clear cognitive framework for AI app development.
Tech FrontiersDeepAgents: LangChain's Deep Agent Development Framework Built on LangGraph
DeepAgents is LangChain's open-source Agent framework built on LangGraph, supporting multi-step reasoning, state management, and multi-Agent collaboration for production-grade AI development.