A Beginner's Guide to Getting Started with Runnables in Langchain.

As the use of large language models (LLMs) continues to expand, AI engineers and enthusiasts face a growing need to build applications that are not only powerful but also modular, reusable, and easy to maintain. Whether you are designing a chatbot, a retrieval-augmented generation (RAG) system, or a multi-step AI workflow, one key challenge is managing how data flows from one stage to another in a clean and consistent way.

This is where LangChain comes in. LangChain is an open-source framework that helps developers build context-aware applications with language models by offering tools for chaining together various components like prompts, models, tools, and memory.

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At the heart of LangChain lies a simple yet powerful concept called Runnables. Runnables are standardized building blocks that define how each step in an AI pipeline receives input, processes it, and produces output. Think of them as the glue that connects different parts of your workflow into a smooth and testable process.

Understanding Runnables is essential if you want to build flexible and scalable AI systems. In this guide, we’ll look at what Runnables are, how they work in LangChain, and how you can use them to structure your AI applications more efficiently.

What is a Runnable?

In LangChain, a Runnable is any component that takes an input, does something with it, and returns an output. It’s a simple concept with big benefits.

Runnable is like a building block in a pipeline. It could be a prompt template, a language model, a function, or even a sequence of tasks. What matters is that it follows a consistent pattern: it receives data, processes it, and passes it along.

LangChain uses Runnables to make AI workflows more modular and predictable. Instead of writing one long block of code, you break your logic into smaller steps that can be tested, reused, or rearranged as needed.

For example, you might have one Runnable that formats a prompt, another that sends it to an LLM, and a third that parses the response. Each one does its job and hands off the result to the next.

By using Runnables, you gain clarity, flexibility, and control over how your AI applications run.

Core Types of Runnables

LangChain offers several types of Runnables. Each one helps you handle different parts of an AI workflow, while keeping things organized and easy to follow. Let’s walk through the most common ones:

1. RunnableLambda

This lets you wrap a simple function as a Runnable.

For example, you can create a small function that formats input or cleans text, then plug it into your chain.

import { RunnableLambda } from "langchain/schema/runnable";

const formatInput = new RunnableLambda((input) => input.toUpperCase());

2. RunnableSequence

This runs tasks in order, one after the other.

It’s useful when you want to connect multiple Runnables into a pipeline.

const chain = RunnableSequence.from([formatter, llm, parser]);

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3. RunnableMap

This runs multiple tasks in parallel and collects their results.

Great for situations where you need to process different things at the same time.

const parallelTasks = new RunnableMap({
  summary: summarizer,
  sentiment: sentimentAnalyzer,
});

4. RunnableBranch

This helps you create conditional logic.

Think of it like an if-else statement inside your chain.

const decision = RunnableBranch.from([
  [(input) => input.type === "text", textHandler],
  [(input) => input.type === "image", imageHandler],
]);

5. RunnablePassthrough

This does nothing.

Yes, nothing. But it’s helpful for testing or placeholder steps in a chain.

import { RunnablePassthrough } from "langchain/schema/runnable";

const passthrough = new RunnablePassthrough();

These types are the core tools for shaping how data flows in your LangChain applications. You can mix and match them to create chains that are both powerful and easy to maintain.

How to Use Runnables in a Chain

Now that you know the core types of Runnables, let’s see how to actually use them in a chain.

At the core, Runnables use two main methods to run:

• .invoke() – for a single input and output
• .pipe() – to connect multiple Runnables into a flow

Let’s walk through a simple example. Suppose you want to:

1. Format a user input
2. Send it to a language model
3. Parse the result

Here’s how you could do it using Runnables:

import { RunnableLambda, RunnableSequence } from "langchain/schema/runnable";
import { ChatOpenAI } from "langchain/chat_models/openai";
import { PromptTemplate } from "@langchain/core/prompts";

// Step 1: Format input
const formatInput = new RunnableLambda((input) => ({ topic: input }));

// Step 2: Create prompt
const prompt = PromptTemplate.fromTemplate("Write a short blog post about {topic}");

// Step 3: Add language model
const model = new ChatOpenAI({ temperature: 0.7 });

// Step 4: Chain everything
const chain = RunnableSequence.from([
  formatInput,
  prompt,
  model,
]);

// Run the chain
const result = await chain.invoke("runnables in LangChain");

console.log(result);

What’s Happening Here?

• The input string "runnables in LangChain" is turned into a { topic: ... } object.
• That object fills the prompt template.
• The prompt is passed to the model.
• The model generates the final response.

By chaining Runnables like this, your logic stays clean and each part does just one job.

Importance of Runnables

Runnables aren’t just about structure, they unlock real power in how you build with LangChain. Here’s why they matter:

1. Modularity

Each Runnable focuses on a single task. This makes your code easier to read, debug, and improve. You can swap in a new model, prompt, or function without touching the rest of your pipeline.

2. Reusability

Once you create a Runnable, you can reuse it across different chains or projects. This saves time and keeps your logic consistent.

3. Composability

You can combine Runnables like puzzle pieces. Whether it’s a straight sequence, a branch with conditions, or a map that runs in parallel, you’re in control of how your AI workflow behaves.

4. Testability

Since each Runnable is self-contained, you can test it on its own. This makes it easier to catch bugs before they affect the entire chain.

5. Clean Data Flow

With .pipe(), .invoke(), and .stream(), you can control how data moves through your system in a smooth and predictable way. No more tangled logic.

In short, Runnables help you build smarter and cleaner. Whether you're experimenting or scaling, they give you the flexibility you need without the mess.

Best Practices for Using Runnables

Once you start working with Runnables, a few good habits can make your experience even better. These best practices will help you write cleaner, more reliable chains as your projects grow.

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1. Keep Each Runnable Focused

Let each Runnable do one thing only. Whether it’s formatting input, calling a model, or parsing output, keep the responsibility clear. This makes debugging and reusing components much easier.

2. Name Your Steps Clearly

Give meaningful names to your functions and chains. You’ll thank yourself later when you come back to the code or share it with teammates.

3. Test in Isolation

Test each Runnable on its own before combining them. This helps you catch errors early and understand where things might break in the chain.

4. Use .pipe() for Clarity

When chaining multiple steps, use .pipe() or RunnableSequence.from() to make the flow easy to read. It mirrors how data moves, step by step.

5. Don’t Over-Chain

It’s tempting to pack everything into one long chain. But if it gets too long, break it into smaller pieces. Combine only what makes sense together.

6. Log and Debug as You Go

Add simple logging inside RunnableLambda functions or after key steps to see what’s happening at each stage. It helps especially when the output isn’t what you expected.

Practicing these small tips can make a big difference in how fast and smoothly you build with LangChain.

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Runnables are one of the most important concepts to master when working with LangChain. They help you break complex logic into simple, reusable pieces and give you full control over how data flows in your application.

Whether you’re building a chatbot, a content generator, or a question-answering tool, Runnables make it easier to think clearly, test confidently, and scale smoothly.

Start small. Try chaining a prompt and a model together. Add a parser. Maybe a branch. With each step, you’ll see how these building blocks open up creative and powerful ways to work with AI.

If you're serious about building with language models, learning how to use Runnables isn’t optional—it’s the foundation.