What is LangChain?
LangChain is a Python (and JavaScript) framework for building applications powered by large language models. It gives you a standard interface for connecting LLMs to external data, tools, memory, and other LLMs — so you are not reinventing the plumbing every time.
At its core, LangChain is a set of composable building blocks. You snap them together to create chains, agents, and pipelines that would otherwise take hundreds of lines of boilerplate.
One-line version: LangChain is the glue between your LLM and everything else — documents, APIs, tools, memory, databases.
Why use LangChain?
Without LangChain
You manually handle prompt formatting, API calls, output parsing, tool definitions, memory management, and retrieval logic — every project from scratch.
With LangChain
All of that is pre-built. You compose components using a clean, consistent interface — chain a prompt to a model to a parser in three lines.
Prerequisites
- Python 3.10+ and basic Python knowledge
- Familiarity with what an LLM is (GPT-4, Claude, etc.)
- An OpenAI API key — used for all hands-on examples
- Completed the AI Agents series or similar background is helpful but not required
Note: Pages 1–6 are conceptual with short code snippets. You only need a full Python environment for Pages 7 and 8.
What you will learn
- How LangChain's core abstractions work (models, prompts, chains, agents)
- How to write and compose prompts with PromptTemplate and LCEL
- How to add memory to a conversation chain
- How to give an agent tools and let it reason with them
- How to build a full RAG pipeline over your own documents
- How to ship a working document Q&A chatbot end-to-end
Series overview
Introduction ← You are here
What LangChain is, why it exists, prerequisites, and series overview.
Core Concepts
LLM vs Chat Models, Chains vs Agents, LCEL & Runnable interface, LangGraph intro.
Prompt Templates
PromptTemplate, ChatPromptTemplate, few-shot prompts, structured outputs, guardrails.
Chains & LCEL
LLMChain, SequentialChain, and the modern LCEL pipe syntax in depth.
Memory
Conversation memory, summary memory, token limits, and when NOT to use memory.
Tools & Agents
Tool vs Agent distinction, ReAct flow, web search and calculator tool examples.
RAG Pipeline
Chunking, embeddings, FAISS, similarity search, retrieval strategies, common mistakes.
Build a RAG Chatbot
Hands-on: full document Q&A chatbot — load, chunk, embed, store, retrieve, answer.
Embedding Models
OpenAIEmbeddings, BedrockEmbeddings, OllamaEmbeddings — embed_query, embed_documents, async variants, CacheBackedEmbeddings.
Vector Stores
Chroma, Pinecone, Qdrant, FAISS, AstraDB, ElasticSearch — add, search, MMR, delete, hybrid search.
Document Loaders
WebBaseLoader, PyPDFLoader, GitHubLoader, YouTubeLoader, Apify — load(), load_and_split(), Document class.
Text Splitters
RecursiveCharacterTextSplitter, MarkdownHeaderTextSplitter, SpacyTextSplitter, language-aware splitting.
LangGraph Basics
StateGraph, nodes, edges, compile(), invoke(), stream() — build your first stateful graph.
LangGraph Agents
create_agent(), AgentState, tool-calling agents, streaming modes, MemorySaver checkpointer.
Human-in-the-Loop
interrupt(), Command(resume=...), multiple interrupts, LangSmith Fleet, state forking.
Multi-Agent Systems
Supervisor pattern, SubAgent, AsyncSubAgent, A2A protocol, deep agents architecture.
Middleware
before_model / after_model hooks, prompt caching, content moderation, logging, retry middleware.
Advanced RAG
HyDE, Hybrid RAG (vector + BM25 + reranker), Agentic RAG, Ragatouille, RAG evaluation.
Tracing & Evaluation
LangSmith tracing, @traceable, RunTree, evaluate(), trajectory evaluators, LLM-as-judge.
Deployment & Production
langgraph deploy, LangGraph SDK client, MCP adapters, sandbox environments, auth & PII.
Quick summary
- LangChain connects LLMs to data, tools, memory, and other LLMs
- It provides composable building blocks — no boilerplate per project
- 20 comprehensive chapters covering LangChain basics, advanced patterns, production deployment, and more
- Start with Page 2 to understand the core abstractions
LangChain Introduction FAQ
What is LangChain used for?
LangChain is a framework for building applications powered by large language models. It connects LLMs to external data, tools, memory, and other models so you can build chatbots, RAG pipelines, agents, and document Q&A systems without writing the plumbing from scratch.
Is LangChain free to use?
Yes. LangChain is open-source and free to install. You only pay for the underlying services you call through it, such as an OpenAI or Anthropic API key for the language model and any hosted vector database you choose.
Do I need to know Python to learn LangChain?
Basic Python is enough to follow this series. You should know functions, imports, and how to run a script. LangChain also has a JavaScript version, but all hands-on examples here use Python 3.10+ and an OpenAI API key.
What can you build with LangChain?
You can build RAG chatbots over your own documents, autonomous agents that call tools, conversational assistants with memory, summarizers, and structured-output extractors. This series ends by shipping a working document Q&A chatbot end-to-end.
How long does it take to learn LangChain?
Most developers grasp the core abstractions in a few hours. This 20-page series is designed to take you from zero to a working RAG chatbot, with the first six pages being conceptual and the rest hands-on.