LangChain Introduction

LangChain is a Python framework for building large language model (LLM) applications.

LangChain provides a unified interface to connect various AI models and supports building intelligent Agents that can automatically call tools, retrieve knowledge, and remember context.

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What is LangChain?

Simply put, LangChain solves a core problem:Enabling large language models to interact with the external world.。

Native LLMs can only generate text based on training data. But in real-world applications, we need AI to query databases, call APIs, search documents, and send emails.

LangChain provides a set of standardized components to chain these capabilities together.

Component	Role	Core Function	Common Uses
Models	Connect large language models	Unified model interface Support multi-model switching Call GPT / Claude / Gemini, etc.	Chatbots Text generation AI Q&A
Prompts (Prompt Templates)	Manage prompt templates	Prompt parameterization Dynamic variable replacement Template reuse	AI conversation Content generation Structured output
Document Loader	Read external document data	Load PDF / TXT / DOCX Read web pages and databases Unify document formats	Knowledge base RAG systems Document Q&A
Text Splitter	Split long text	Text chunk splitting Control token length Optimize vector retrieval	RAG Vector database Long text processing
Memory	Implement contextual memory	Save chat history Long-term memory Dialogue state management	Chatbots AI assistants Agent
Retriever	Retrieve relevant knowledge content	Vector search Semantic search RAG data recall	Enterprise knowledge base AI search Document Q&A
Tools	Call external tools and APIs	Search the internet Database queries Execute code	AI Agent Automation tasks Data analysis
Output Parser	Parse model output results	Structured output JSON parsing Format validation	API responses Automation systems Data processing
Chains	Combine multiple components to form workflows	Multi-step execution Process orchestration Component chaining	Complex AI applications RAG workflows Agent systems

From a technical perspective, LangChain is a modular LLM application development framework that includes three layers:

Layer	Description	Package name
Core abstraction layer	Defines basic interfaces for models, tools, messages, etc.	langchain-core
User interface layer	Provides high-level APIs such as init_chat_model, create_agent	langchain
Integration layer	Connects third-party services such as OpenAI, Anthropic, Ollama	langchain-openai, etc.

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Why Choose LangChain?

If you only need to call a model API once, a direct HTTP request is sufficient. But when you need to build a complete AI application, LangChain offers the following advantages:

Capability	Description	Applicable scenarios
Unified model interface	Switch between OpenAI / Anthropic / DeepSeek and other models with one set of code	Multi-model comparison testing, cost optimization
Agent architecture	The model automatically decides when to call tools, forming a thought-action loop	Automation tasks, intelligent customer service
Middleware system	Insert custom logic before and after model calls (retry, caching, filtering)	Reliability assurance in production environments
Structured output	Have the model return JSON in a specified format for easy program parsing	Data extraction, form filling
Memory and persistence	Built-in conversation memory and cross-session storage capabilities	Multi-turn conversations, user preference memory
Rich ecosystem	Hundreds of third-party integrations covering mainstream models and tools	Quickly integrate various services

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What Can LangChain Do?

The following are the most typical application scenarios for LangChain:

Intelligent Chatbots

A chat assistant with multi-turn conversation memory that can call external tools (check weather, check orders, send emails).

RAG Knowledge Base Q&A

Vectorize and store private documents (PDFs, web pages, databases), allowing the model to answer questions based on these documents with cited sources.

Agent Automation Assistant

The model autonomously plans task steps, calls different tools as needed, and completes complex multi-step operations, such as "Help me organize last week's sales data and generate a report."

Data Extraction and Analysis

Extract structured information from unstructured text (such as extracting key fields from scanned contract documents), or have the model generate data analysis conclusions.

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Relationship Between LangChain and LangGraph

Many beginners are confused about the difference between these two libraries. Simply put:

Comparison dimension	LangChain	LangGraph
Positioning	High-level Agent framework, ready to use out of the box	Low-level workflow engine, fine-grained control
Ease of learning	Low, create an Agent in 10 lines of code	Medium to high, requires understanding the concept of graphs
Applicable scenarios	Standard Agent applications, rapid prototyping	Complex multi-step workflows, multi-Agent collaboration
Relationship	LangChain's create_agent() is built on top of LangGraph

This tutorial focuses on LangChain. If you are just starting out, beginning with LangChain is the right choice. When you need finer process control, then dive deeper into LangGraph.

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Preparation

Before you start learning, you only need to have the following foundation:

• Basic Python syntax (functions, classes, type annotations)
• Able to install Python packages with pip
• Familiar with basic command-line operations
• Have a large model API Key (OpenAI, Anthropic, etc. all acceptable)

The code examples in this tutorial have been tested in a Python 3.10+ environment.

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