Understanding LangChain Runnables

The Evolution from Chains to Modular Components

Introduction

When ChatGPT was released in November 2022, it marked the beginning of a new era in AI application development. As OpenAI opened their APIs to the public, developers worldwide began building LLM-based applications. However, they soon discovered that creating sophisticated AI applications required much more than just calling an LLM API.

This is where LangChain entered the scene, initially solving the problem of connecting to different LLM providers. But as the framework evolved, it encountered a fundamental challenge that led to the creation of one of its most important concepts: Runnables.

In this comprehensive guide, we’ll explore why Runnables exist, how they solve complex architectural problems, and how to implement them from scratch.

The Journey: From Components to Chains

The Initial Vision

The LangChain team had a brilliant insight: LLM applications follow common patterns. Whether you’re building a chatbot, PDF reader, or AI agent, you typically need similar components:

Core LangChain Components

LLM Models

Prompt Templates

Document Loaders

Text Splitters

Vector Stores