Component architecture

LangChain’s power comes from how its components work together to create sophisticated AI applications. This page provides diagrams showcasing the relationships between different components.

Core component ecosystem

The diagram below shows how LangChain’s major components connect to form complete AI applications:

How components connect

Each component layer builds on the previous ones:

1. Input processing – Transform raw data into structured documents
2. Embedding & storage – Convert text into searchable vector representations
3. Retrieval – Find relevant information based on user queries
4. Generation – Use AI models to create responses, optionally with tools
5. Orchestration – Coordinate everything through agents and memory systems

Component categories

LangChain organizes components into these main categories:

Category	Purpose	Key Components	Use Cases
Models	AI reasoning and generation	Chat models, LLMs, Embedding models	Text generation, reasoning, semantic understanding
Tools	External capabilities	APIs, databases, etc.	Web search, data access, computations
Agents	Orchestration and reasoning	ReAct agents, tool calling agents	Nondeterministic workflows, decision making
Memory	Context preservation	Message history, custom state	Conversations, stateful interactions
Retrievers	Information access	Vector retrievers, web retrievers	RAG, knowledge base search
Document processing	Data ingestion	Loaders, splitters, transformers	PDF processing, web scraping
Vector Stores	Semantic search	Chroma, Pinecone, FAISS	Similarity search, embeddings storage

Common patterns

RAG (Retrieval-Augmented generation)

Agent with tools

Multi-agent system

Learn more

Now that you understand how components relate to each other, explore specific areas:

• Building your first RAG system
• Creating agents
• Working with tools
• Setting up memory
• Browse integrations

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