What Is LangChain?

Key takeaways

LangChain is a framework for developing applications driven by large language models (LLMs).

• The LangChain framework includes various components, including LLMs, agents, memory systems, and prompt templates.

• Some examples of LangChain’s capabilities include chatbots, data enhancements, and coding assistants.

• You can easily switch between various LLMs with LangChain.

Explore LangChain’s framework, discover its key features, and learn how to install and configure the tool for your projects. Afterward, if you’re ready to gain a deeper understanding of machine learning algorithms, enroll in the IBM Machine Learning Professional Certificate. You’ll also have the opportunity to learn how to train a neural network, build regression and classification models, create recommender systems in Python, and more.

What is LangChain used for?

LangChain simplifies the development, production, and deployment of LLM applications by enforcing standard interfaces. This framework allows developers working with artificial intelligence (AI) and machine learning (ML) applications to integrate LLMs with external frameworks. 

According to IBM, “Launched by Harrison Chase in October 2022, LangChain enjoyed a meteoric rise to prominence: as of June 2023, it was the single fastest-growing open source project on GitHub. Coinciding with the momentous launch of OpenAI’s ChatGPT, the following month, LangChain has played a significant role in making generative AI more accessible to enthusiasts in the wake of its widespread popularity [1].”

LangChain is available in the Python and JavaScript libraries and can aid in the development of applications such as chatbots and virtual agents.

What is the LangChain framework?

LangChain’s framework includes various components, including LLMs, agents, memory systems, and prompt templates.

• LLMs: Before using LangChain, you must develop a language model. You can use a publicly available language model, such as GPT-5, or train your own model. 

• Agents: Using LangChain, you can develop chains for complex applications, such as an agent. An agent is a chain that prompts the LLM to implement the optimal sequence to produce the best output in response to a user’s question or prompt. 

• Memory systems: LangChain enables you to implement various memory capabilities, including simple memory systems that can recall recent conversations with users and intricate memory structures that can remember and analyze historical data. 

• Prompt templates: Using LangChain to format queries for AI models and chatbots, you can develop templates. 

What is LangGraph in LangChain?

A low-level orchestration framework, LangGraph helps build, deploy, and manage long-running, stateful agents with both short-term and long-term working memory. You can use LangGraph on its own or integrate it seamlessly with other LangChain products. It’s also possible to utilize LangGraph without LangChain.

Key features of LangChain

LangChain provides seamless integration with popular language models and continuous data retrieval. 

Integration with language models

LangChain seamlessly integrates with various language models, including OpenAI, Google Generative AI, and Amazon Web Services (AWS), among others. Since LangChain is not a standalone tool, it must integrate with an LLM to fulfill its intended function. For instance, ChatGPT is a chatbot application that utilizes one of the GPT language models. The GPT model processes the input and generates a natural language response, and the application provides the user experience (UX) interface for the user to interact with the chatbot.

Data retrieval 

Each LLM is trained on a set of data. For instance, GPT’s training data consists of the entirety of the internet. LangChain implements retrieval-augmented generation (RAG), an application design that compares input data to more recently retrieved data, enabling the LLM to provide a more accurate answer to a given query. This data might come from different sources, necessitating a conversion into a consistent format to continue processing it. LangChain converts the disparate data into this format internally, enabling data to pass between various chains seamlessly.

Who uses LangChain?

Various organizations and professionals utilize LangChain:

• Microsoft: Developers from Microsoft utilize LangChain to build AI applications, aid in natural language experiences, and enhance existing features within their product offering. 

• Elastic: Elastic, a widely used search analytics company, utilizes LangChain to build AI chatbot applications like the Elastic AI Assistant. They use this chatbot to optimize security-related workflows. 

• Ally Financial: Ally Financial, the US’s largest digital bank, utilizes LangChain to implement a coding module called the PII Masking module, which safeguards personally identifiable information (PII). 

Pros and cons of using LangChain

This framework offers a powerful tool with a rich community and ample resources. Still, it also poses some potential challenges. Explore both in more detail.

Some advantages of using LangChain include:

• Open-source access: LangChain is an open-source framework that enables collaboration with peers and ready access to tutorials, resources, and documentation from the LangChain Community. 

• Optimizes workflows: Developers can easily switch between various LLMs, minimizing integration complexity and streamlining workflows.

Some disadvantages of using LangChain include:

• Abstraction design: Since LangChain integrates multiple emerging technologies like AI and LLMs, designing abstractions can introduce additional layers of complexity, which may require more effort to interpret.

• Steep learning curve: Beginners may struggle with understanding and debugging LangChain due to its complexity.

Getting started with LangChain

Learn how to install and set up LangChain and discover various capabilities within the tool, including chatbots, data enhancements, and coding assistants.

Installation and setup

LangChain’s website provides a tutorial on how to set up and install LangChain to build LLM-powered applications. It also explains how to use language models, prompt templates, and debug and trace your application with LangChain.

LangChain’s website also provides a tutorial on how to build an LLM-driven chatbot. Users can create a chatbot that streamlines data from external data sources (conversational RAG), build actionable chatbots that can complete various actions (agents), or develop chatbots that utilize LLMs to converse with the user. If you’re interested in learning more about multiple aspects of chatbot development, LangChain’s site provides tutorials on managing and adding message history and more.

Basic examples and implementation

Some examples of LangChain’s capabilities include chatbots, data enhancements, and coding assistants. 

• Chatbots: LangChain could help you develop chatbots that can process complex questions and have sophisticated user interactions, similar to ChatGPT. 

• Data enhancement: LangChain can generate new data based on the data it’s been trained on, which can contribute to ML training and data set development. 

• Coding assistants: The tool helps users create coding assistants utilizing LangChain and OpenAI’s API. Developers can use these assistants to augment their coding skills and enhance productivity. 

Read more: What Is an API? (+ How Do They Work?)

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