#LangChain Agent development
1026 related articles
·8 min
LangChain Crash Course: A Complete Roadmap from Zero to Agent Development
A systematic roadmap from LangChain and LangGraph to multi-agent development, covering RAG, Tool Calling, MCP, and more, helping developers break into AI app development.
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·3 min
AI Development for Java Engineers: A Complete Guide to LangChain4j Agents
Java developers can build AI apps too! Learn LangChain4j fundamentals including RAG, Agents, Function Calling, and hands-on projects — no Python required.
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·3 min
LangChain + MCP + Agent: A Complete Guide to Enterprise-Grade AI Agent Development
A deep dive into LangChain, LangGraph, MCP, and enterprise AI Agent development: covering Streamable HTTP updates, DeepSeek R1 Function Calling limits, and Qwen3 agent capabilities.
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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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·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
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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·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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·7 min
LangChain Hands-On Guide: From LLM Calls to Agent Development
Master LangChain from scratch: the three limitations of LLMs, init_chat_model unified interface config, the Message type system, and the path from LLM calls to Agent development.
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·6 min
Getting Started with LangChain: Unified Interfaces and Agent Development Explained
A beginner's guide to the LangChain open-source framework: explaining how to use the init_chat_model unified interface, tips for disabling DeepSeek's thinking mode, and core essentials of Agent development.
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·7 min
Andrew Ng Teams Up with LangChain: Core Principles of LangGraph Agent Development
Andrew Ng and LangChain CEO Harrison Chase present AI Agents in LangGraph, covering five core agent design patterns and LangGraph's graph-based framework for building cyclical agentic workflows.
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·7 min
Andrew Ng Teams Up with LangChain: Core Concepts of LangGraph Agent Development
Andrew Ng and LangChain CEO Harrison Chase's AI Agents in LangGraph course covers five agent design patterns and LangGraph's graph-based framework for building cyclical AI workflows.
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·2 min
LangChain + MCP Agent Development: A Practical Guide to LLM Selection and Common Pitfalls
A deep dive into LLM selection for LangChain and MCP agent development, comparing DeepSeek V3/R1 vs Qwen3 on Function Calling and MCP support with practical tips.
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·3 min
LangChain from Beginner to Agent Development: A Complete Learning Path for LLM Application Development
A systematic guide to LangChain LLM application development, covering environment setup, core components (RAG, Chain, Memory), and Agent development to help developers master LLM app building.
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Industry Insights·3 min
Deep Dive into LangChain's Full-Lifecycle Toolchain for Agent Development
LangChain unveils a full-lifecycle toolchain for Agent development at Interrupt, covering Deep Agents 0.6, SmithDB, Context Hub, LLM Gateway, and LangSmith Engine.
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Tutorials·3 min
Getting Started with LangChain: Core Concepts and Hands-On Guide for LLM and Agent Development
A systematic guide to LangChain's core features, covering LLM vs. Agent concepts, unified interface design, multi-provider support, environment setup, and hands-on code examples for AI app development.
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Tutorials·2 min
LangGraph vs LangChain: How to Choose Your Agent Development Framework
In-depth comparison of LangGraph vs LangChain: controllability, extensibility, and FastAPI-powered performance. Covers storage, enterprise private deployment, and migration guidance for agent developers.
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Tutorials·2 min
LangChain Unified Interface in Practice: Complete Guide from Model Invocation to Agent Development
A detailed guide to LangChain's unified interface, comparing model invocation methods, explaining LLM vs Agent differences, with complete Python walkthrough from setup to agent development.
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Tutorials·1 min
LangChain Agent Development Tutorial: RAG and Agent Hands-On Learning Path
A systematic learning path for LangChain Agent development covering RAG, autonomous Agent decision-making, and tool calling—from zero to production-ready projects.
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Tutorials·2 min
LangChain Beginner's Guide: A Complete Guide to LLM and Agent Development
A deep dive into LangChain framework essentials, clarifying the key differences between LLMs and Agents, covering Agent development, DeepAgent advanced topics, and learning paths.
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Product Reviews·3 min
2025 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.
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