🚀 Understanding Runnable in LangChain (With Practical Examples + Code)
While learning LangChain and LangGraph recently, one concept that initially confused me was Runnable.
I kept seeing it everywhere:
Even after writing some code, I still needed conceptual clarity. So I decided to break it down through experiments and examples.
Today I want to share a simple explanation that helped me understand it.
What is Runnable in LangChain?
In simple terms:
👉 A Runnable is a standard executable unit in LangChain.
It represents one step of work in an AI pipeline.
That step could be:
All of these can behave the same way because they follow the Runnable interface.
Common methods include:
This unified interface is what makes LangChain workflows composable.
Why Runnable is Powerful
Before Runnables, AI pipelines were messy.
Now everything can be connected like building blocks:
Prompt → Model → Parser → Logic → Next Prompt
LangChain calls this approach LCEL (LangChain Expression Language).
Example:
chain = prompt | model | parser
This creates a RunnableSequence automatically.
Example: Parallel Runnable Pipeline
Here is a simple example where the same input generates different outputs.
from langchain_core.prompts import PromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnableParallel, RunnableLambda
from langchain_openai import ChatOpenAI
model = ChatOpenAI(model="gpt-4o-mini")
parser = StrOutputParser()
tweet_prompt = PromptTemplate.from_template("Write a tweet on {topic}")
linkedin_prompt = PromptTemplate.from_template("Write a LinkedIn post on {topic}")
tweet_chain = tweet_prompt | model | parser
linkedin_chain = linkedin_prompt | model | parser
final_chain = RunnableParallel({
"tweet": tweet_chain,
"linkedin": linkedin_chain,
"tweet_length": tweet_chain | RunnableLambda(lambda x: len(x.split()))
})
print(final_chain.invoke({"topic": "AI in telecom"}))
This pipeline:
• Generates a Tweet • Generates a LinkedIn post • Calculates tweet word count
All from the same input.
Key Runnable Types to Know
1️⃣ RunnableSequence
Runs steps one after another
Prompt → LLM → Parser → Next Step
2️⃣ RunnableParallel
Runs multiple tasks simultaneously
Useful when generating different outputs from the same input.
3️⃣ RunnableLambda
Wraps custom Python logic inside a chain.
Example use cases:
4️⃣ RunnablePassthrough
Passes input unchanged through a pipeline.
Helpful when you want to keep the original input alongside transformed outputs.
5️⃣ RunnableBranch
Allows conditional routing inside chains.
Example:
My Practice Code (GitHub)
To understand this better, I created small runnable experiments covering:
• RunnableSequence • RunnableParallel • RunnableLambda • RunnablePassthrough • RunnableBranch
GitHub repo:
Interview Questions You May Get
If you're preparing for AI / LLM / LangChain roles, expect questions like:
Understanding Runnable well helps a lot when working with LangChain agents, LangGraph workflows, and production AI pipelines.
My Key Learning
The biggest mindset shift was this:
👉 Everything in LangChain is a Runnable.
Prompts, models, parsers, chains — all behave like composable building blocks.
Once this clicks, LangChain architecture becomes much easier to design.
If you're also exploring LangGraph, agent workflows, or production LLM systems, I'd love to connect and discuss.
#AIEngineering #LangChain #LangGraph #GenerativeAI #LLM #MLOps #Python #AI