LangChain

What is LangChain?

LangChain is a library that enables developers to build sophisticated LLM applications by providing abstractions for chains, agents, memory, and document processing. It simplifies integration with various LLMs, vector stores, and external tools.

Core Components

• LLMs & Chat Models: Unified interface across different models
• Prompts: Template management and dynamic prompt construction
• Chains: Sequential combinations of components
• Memory: Conversation history and context management
• Agents: Autonomous decision-making with tool use
• Document Loaders: Integration with various data sources
• Vector Stores: RAG (Retrieval Augmented Generation) support

Typical Use Cases

• RAG Systems

  Retrieval-augmented generation combining LLMs with proprietary documents and data.

• Conversational AI

  Building chatbots and assistants with multi-turn conversations and memory.

• Task Automation

  Autonomous agents performing complex workflows with tool integration.

• Data Analysis

  LLM-powered analysis and insights generation from structured and unstructured data.

Mental Model

Think of LangChain as building blocks for LLM applications. You compose primitive pieces (models, prompts, memory, tools) into chains that flow data through multiple processing steps. Each link adds capability—prompt formatting, model inference, tool calling, context retrieval.

Architecture Overview

[Input]
   ↓
[Prompt Template]
   ↓
[Language Model]
   ↓
[Output Parser]
   ↓
[Memory / History] ← → [Vector Store / RAG]
   ↓
[Tool / Function Calls]
   ↓
[Result]
      

LangChain chains connect LLMs with memory, document processing, and external tools. Each component is composable, allowing flexible architectures from simple chains to complex agent loops.

Key Concepts Glossary

• Chain: Sequence of components where output flows to next input
• Prompt: Template for formatting input to language models
• Agent: Loop using LLM to decide tool calls and reason about results
• Memory: Persistent context from previous conversations
• RAG (Retrieval Augmented Generation): Combining LLM with retrieved documents
• Vector Store: Database for semantic search of documents

When to Use LangChain

Choose LangChain if you need:

• Flexible composition of LLM components for custom architectures
• RAG systems combining LLMs with proprietary documents
• Support for multiple LLM providers and vector databases

Consider alternatives if:

• You need team-based orchestration with role assignment (try CrewAI)
• You prefer higher-level abstractions for specific domains

Getting Started

Install LangChain and create a simple chain:

pip install langchain langchain-openai
python -c "from langchain_openai import ChatOpenAI; model = ChatOpenAI()"

→ LangChain Documentation

Resources for Further Learning

• Official Documentation - Comprehensive guides and API docs
• GitHub Repository - Source code and examples
• Tutorials - Guided projects for common patterns
• Discord Community - Developer support and discussions