Introduction
LangChain is one of the most popular frameworks for building applications with Large Language Models (LLMs). From intelligent chatbots to document summarizers and retrieval-based assistants, LangChain gives developers the building blocks they need to turn LLMs into functioning products.
key features of langchain
- LangChain is highly modular, allowing developers to mix and match components to build custom workflows.
- It supports integration with various LLMs, including OpenAI’s GPT, Hugging Face models, and others.
- It provides tools for managing and optimizing prompts, including templating and dynamic prompt generation.
- It provides tools for indexing and retrieving documents, making it easier to build retrieval-augmented generation (RAG) systems.
- It supports agents, which are systems that use LLMs to decide what actions to take and in what order.
- It supports memory to store and retrieve context across interactions.
- LangChain allows you to create chains of operations where the output of one step is fed as input to the next.
Core Components of LangChain
Unlike monolithic libraries, LangChain is built around modular abstractions. This means you can pick and choose only the parts you need — ideal for scaling from small prototypes to production-grade systems.
Each of the core LangChain components plays a specific role: some wrap LLMs, others handle memory, tools, or data retrieval. You can combine them in flexible ways to match your app’s requirements.
This modularity also encourages experimentation. You can prototype quickly with just a chain and prompt, then incrementally add memory, tools, and agents as your application matures
