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LangChain is a Python framework for building Large Language Model (LLM) applications. LangChain provides a unified interface to connect to various AI models and supports building intelligent Agents that can automatically call tools, retrieve knowledge, and remember context. !(#) * * * ## 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 practical applications, we need AI to be able to query databases, call APIs, search documents, and send emails. LangChain provides a set of standardized components to chain these capabilities together. !(#) | Component | Purpose | Core Features | Common Use Cases | | --- | --- | --- | --- | | **Models** | Connect to large language models | * Unified model interface * Support for multi-model switching * Call GPT / Claude / Gemini, etc. | * Chatbots * Text generation * AI Q&A | | **Prompts** | Manage Prompt templates | * Prompt parameterization * Dynamic variable replacement * Template reuse | * AI dialogue * Content generation * Structured output | | **Document Loader** | Read external document data | * Load PDF / TXT / DOCX * Read web pages and databases * Unified document format | * Knowledge base * RAG systems * Document Q&A | | **Text Splitter** | Split long texts | * Text Chunk splitting * Control Token length * Optimize vector retrieval | * RAG * Vector databases * Long text processing | | **Memory** | Implement context memory | * Save chat history * Long-term memory * Conversation state management | * Chatbots * AI assistants * Agents | | **Retriever** | Retrieve related knowledge content | * Vector search * Semantic retrieval * 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 Agents * Automated tasks * Data analysis | | **Output Parser** | Parse model output results | * Structured output * JSON parsing * Format validation | * API returns * Automated systems * Data processing | | **Chains** | Combine multiple components into a workflow | * 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 consists of 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 like init_chat_model, create_agent | langchain | | Integration Layer | Connects to third-party services like OpenAI, Anthropic, Ollama, etc. | langchain-openai, etc. | * * * ## Why Choose LangChain If you are just calling a model API once, a direct HTTP request is enough. But when you need to build a complete AI application, LangChain provides 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 | Automated tasks, intelligent customer service | | Middleware System | Insert custom logic before and after model calls (retry, cache, filter) | Reliability guarantee in production environments | | Structured Output | Make the model return JSON in a specified format for easy program parsing | Data extraction, form filling | | Memory & Persistence | Built-in conversation memory and cross-session storage capabilities | Multi-turn dialogue, user preference memory | | Rich Ecosystem | Hundreds of third-party integrations covering mainstream models and tools | Quick access to various services | * * * ## What Can LangChain Do Here are the most typical application scenarios for LangChain: ### Intelligent Chatbots Chat assistants with multi-turn dialogue 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) so that the model can answer questions based on these documents, with cited sources attached. ### Agent Automated Assistants 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 a scanned contract), or have the model generate data analysis conclusions. * * * ## The 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 | | Learning Curve | Low, create an Agent in 10 lines of code | Medium to High, requires understanding Graph concepts | | 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 to learn, starting with LangChain is the right choice. When you need more fine-grained process control, you can then dive into LangGraph. * * * ## Prerequisites Before you start learning, you only need to have the following basics: * Python basic syntax (functions, classes, type annotations) * Ability to use pip to install Python packages * Understanding of basic command-line operations * Have a large model API Key (OpenAI, Anthropic, etc. are all fine) > The code examples in this tutorial have been tested in a Python 3.10+ environment.