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Artificial Intelligence

LLM

Python

Understanding Runnables in LangChain

Tech Lead, CTO AXI Technologies

Romain DE LA SOUCHÈRE

Published on 15 mai 2025 · 10 min of reading

Table of content

What is a Runnable?

Basic Usage of a Runnable

Mapping and branching with RunnableMap and RunnableLambda

Streaming, batch, and asynchronicity

Why Adopt Runnables?

Conclusion

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LangChain is a powerful Python library that facilitates the creation of complex AI applications by chaining various components: LLMs, prompts, tools, knowledge bases, etc. With the introduction of Runnables, LangChain offers a unified abstraction to compose, execute, and debug processing chains in a more flexible, readable, and maintainable way.

In this article, we will explore what a <code>Runnable is, how it works, and how to use it effectively in your AI projects with LangChain.

What is a Runnable?

A <code class="inline-code"><code>Runnable is a composable processing unit in LangChain. It is an interface that defines a method <code>invoke(input) </code>to execute a process on an input and return an output. All standard components of LangChain (such as <code>PromptTemplates, <code>ChatModels, <code>Retrievers, etc.) implement this interface.

Key Advantages of Runnables:

Smooth Composition with operators like | (pipe), RunnableSequence, RunnableMap, etc.

Synchronous or Asynchronous Executions, in batch or streaming.

Increased Traceability and Observability thanks to native support for debugging and callbacks.

Interoperability with the primitives of the LangChain Core library.

Basic Usage of a Runnable

Here is a simple example of using Runnables with a <code>PromptTemplate and a <code>ChatOpenAI:

python

To create more complex flows, you can use <code>RunnableSequence:

Mapping and branching with RunnableMap and RunnableLambda

LangChain also provides tools to parallelize or decompose processes:

RunnableMap allows processing multiple keys in parallel.
RunnableLambda enables creating custom functions within a flow.

python

Streaming, batch, and asynchronicity

The Runnables support the following operations:

invoke(input): simple execution.

batch(inputs): processing a list of inputs.

stream(input): processing with a response stream (useful for LLMs).

ainvoke(input): asynchronous version.

This allows for great flexibility in web contexts, backend development, or production deployment.

Why Adopt Runnables?

Runnables offer a declarative, composable, and introspectable API. Their adoption facilitates:

unit testing of each component,
visualization of chains (notably through LangSmith),
evolving your code without side effects,
parallelization or performance optimization.

Conclusion

LangChain has revolutionized the creation of modular AI applications. With Runnables, the library takes a leap: it becomes a true framework for building clear, robust, and scalable AI pipelines.

To learn more, check out the official documentation on Runnables or explore composition examples.

If you are still unsure or need specific support, our team is here to help

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Romain DE LA SOUCHÈRE

Tech Lead, CTO AXI Technologies

Expert Data Engineering et Cloud, Romain affiche plus de 11 ans d'expérience, dont plusieurs années comme Lead Developer sur des solutions Smart Building haute performance. Il y a conçu et mis en production des moteurs de traitement capables d'absorber des centaines de milliers de données de capteurs par minute, ainsi que des bases clusterisées gérant plus de 10 millions de données dynamiques. Certifié Microsoft Azure DevOps Engineer Expert, il maîtrise aussi bien le développement back-end (Python, C#) que le DevOps (Docker, Kubernetes, Terraform) et les agents LLM. Formateur en Python, cloud, DevOps et IA générative appliquée, il forme avec une obsession : Amener chaque apprenant à concevoir et déployer des architectures réellement scalables en production.

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