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March 06, 2024

LangChain Vs. LlamaIndex: Main Differences


Artur Haponik

CEO & Co-Founder

Reading time:

15 minutes

Large Language Models (LLMs) have continued to evolve and improve over the years, and so have the frameworks and platforms designed to support their development and implementation. Two such frameworks, LangChain and LlamaIndex, have emerged as leading options for those looking to improve the performance and functionality of these models.

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Both tools offer unique features, capabilities, and approaches when it comes to building robust applications with large language models.

This post will provide an in-depth review of the main differences between LangChain and LlamaIndex, highlighting their strengths and weaknesses to help you make an informed decision on which framework best suits your needs.

What is LangChain?

It’s basically an open-source and dynamic framework ideally designed to simplify the creation of data-aware and agentic applications by Large Language Models (LLMs). This framework provides a set of versatile features and functionalities that make it easy to work with LLMs such as OpenAI’s GPT-3, BERT, T5, and RoBERTa.[1] Whether you’re a beginner or a seasoned developer, LangChain is the ideal tool for creating LLM-powered applications and prototyping.

It is made up of six components, each with its own unique features and benefits.

These components include:

Key elements of LangChain


Schema basically refers to the fundamental data types, structure, and organization. The Schema defines the various types of data, their relationships, and how they’re represented across the codebase. It ensures consistent handling and efficient communication between components within the framework.


This component serves as the powerhouse of all AI-driven applications. LangChain models are divided into three main categories:

Large Language Models (LLMs)

Large Language Models (LLMs) are machine learning (ML) models trained on massive amounts of data to understand and generate human-like text. Within this framework, LLMs are tailored to operate seamlessly with textual data, serving as both input and output.

Read more about What is LangChain? Advancing Beyond Traditional Language Models

Chat Models

Whether provided by HuggingFace, OpenAI, Cohere, or any other AI research organization, chat models are quite similar to language models. [2] The only difference is that chat models work with message objects instead of textual data.

Chat models usually process a series of messages in order to produce message outputs, thus creating well-structured interactions among users.

Overall, there are three types of message objects, namely HumanMessage, SystemMessage, and AIMessage. Message objects are basically coverings around text messages that do not have any special effects on their own but help distinguish various entities in a conversation.

For best results, it’s highly recommended to use HumanMessage for text input by a human, AIMessage for texts generated by the chat model, and SystemMessage to offer context to a chat model on how it should respond to a given text in a conversation.

Embedding Models

Embedding models in LangChain are used to create vector representations for texts. These models accept text inputs and convert them into a vector of floating numbers, thus effectively converting human language into numeric values. The most common application of embedding models is in semantic search, where query embedding is usually compared to embeddings of various documents.


A prompt is simply an instruction for an LLM that elicits a desired response. In some cases, the generated response may be different depending on how the user phrased the prompt. The prompt component within this framework enables users to create tailored queries and prompts for large language models.

The overall simplicity in crafting prompts allows users to generate context-aware and informed responses. Whether you’re looking to extract specific information from a text, generate a creative text, or even engage in natural language conversations with a computer, LangChain’s prompt capabilities are vital.


LangChain’s indexes play a vital role in efficient information retrieval. This component is ideally designed to retrieve documents quickly and intelligently from a vast external knowledge base. Indexes are particularly important for LLM-powered applications that require real-time access to huge datasets, such as chatbots, search engines, and content recommendation systems.

The main items for building this framework’s index component include:

  • A tool for loading documents.
  • A tool for creating embedding vectors for those documents.
  • A tool that will keep track of those documents and vectors on the go.


Any reliable conversational system must have the ability to store and access historical messages, as it is vital for effective interactions. LangChain excels in this aspect by having an efficient memory component that ensures Large Language Models can store and retrieve chat history, thus resulting in more coherent and contextually-aware responses.

This component ensures that all incoming queries are not processed in isolation but are rather cross-referenced with prior information/interactions.

LangChain’s memory objects can either be passed around in chains or used alone to investigate the history of interactions, provide a summary, extract, and even show details from archived entities when mentioned in a new interaction.


The name of this framework is a fusion of ‘Lang’ and ‘Chain,’ chains are an important part of the LangChain framework. Chains essentially link multiple components and create something more effective. That said, the chain component represents the orchestration of complex workflows of LLM-powered applications within the framework.

With the help of this component, users can create sequences of instructions or interactions with language models, thus automating various processes. This is particularly beneficial for tasks that involve multiple steps, informed decision-making, and dynamic content generation.

Agents and tools

Regardless of how sophisticated or advanced they are, LLMs are often limited to the data they were trained on. For this reason, you cannot count on an LLM to provide you with accurate information regarding something like tomorrow’s weather forecast, recent breaking news, or a prediction for a football game scheduled to take place later this week.

This is unless you integrate the LLM with another tool like the National Weather Service (NWS) API, which can read the available data and subsequently generate a response for you. [3]

This is where the agents and tools component comes in. Agents are basically software entities that interact with LangChain and its components. They often represent external knowledge bases, users, and other AI models needed to facilitate effective communication and data exchange within the LangChain framework. Unlike chains that assume all tools within LangChain must be used, agents decide the most relevant tools for each query and only use those tools for as long as they require them.

Notably, agents and tools have a wide variety of functionalities that help in building and executing high-quality LLM-powered applications. These functionalities include pre-processing data for efficient LLM consumption, managing conversations, connecting LangChain with APIs or external databases, triggering workflows within LangChain, performing query transformations, maintaining context across interactions, and post-processing outputs to ensure they meet task goals.

Generally, agents and tools within the LangChain framework exist to enable users fine-tune their interactions with LLMs and help developers create diverse LLM-powered applications with ease.

What is LlamaIndex?

Previously known as GPT Index, LlamaIndex is a data framework specifically designed to support and enhance the capabilities of LLMs. This framework primarily focuses on ingesting, structuring, and accessing private or domain-specific data, thus offering a simple interface for indexing and retrieval of relevant information from huge textual datasets.

Additionally, LlamaIndex offers a wide variety of tools and user-friendly features that facilitate the seamless integration of private or domain-specific data into LLMs.

How LlamaIndex works, workflow

This framework usually excels in use cases where precise queries and high-quality responses are vital. As a result, LlamaIndex is the ideal tool for text-based search, as well as for situations where generating accurate and contextually aware responses is important.

Overall, this framework’s main goal is to improve document management in organizations with the help of advanced technology, thus providing a simple and efficient way of searching, organizing, and summarizing documents using LLMs and advanced indexing techniques.

Here is a detailed breakdown of the various components that make up LlamaIndex:


Within the LlamaIndex framework, querying is all about how a user requests information from the system. Generally, it focuses on optimizing the execution of queries by providing desired results within the shortest time possible. These capabilities make the framework invaluable in various LLM-powered applications where fast information retrieval is paramount, like search engines and real-time chatbots.

That said, querying an index graph within this framework involves two major tasks. First, a collection of nodes relevant to the query are fetched. Second, a response_synthesis module is completed, using the collection of nodes and the original user query to generate an accurate and coherent response. Notably, the relevance of a particular node is usually based on the index type.

Relevant nodes can be retrieved in two different configurations, namely, list index and vector index. List index querying basically uses all the nodes in the list to generate the desired response. On the other hand, vector index querying relies on the similarity between the query vector and the indexed vectors to generate a response. In vector index querying, only the nodes that have surpassed a certain relevance threshold to the user query are retrieved and sent to the response_synthesis model.

Response synthesis

Response synthesis simply refers to the manner in which the LlamaIndex framework generates and presents data or responses to user queries. This process is optimized to generate concise, coherent, and contextually relevant responses. Therefore, all the responses generated by this process are accurate and presented in a way that is easy for all users to comprehend. The main goal of response synthesis is to ensure users receive accurate responses without unnecessary jargon.


One of the best things about LlamaIndex is its ability to compose an index from other indexes. Composability within this framework refers to utilizing modular and reusable components to create complex queries and workflows. Thanks to this feature, users can create complex queries by simply splitting them into smaller, manageable parts.

Composability is particularly useful when you need to search and summarize multiple diverse data sources. Instead of going over every data source individually, you can simply create a separate index over every data source and subsequently create a list index over the multiple indexes. This will help create concise and informative summaries in the shortest time possible.

Data connectors

It’s possible that the data you’re using to build an LLM-powered application is not contained in a simple text file. You may have the data stored in various sources, including confluence pages, PDF documents on your USB flash drive, or even Google Workspace documents on the cloud. Loading data from all these forces will take a lot of time and effort. Fortunately, LlamaIndex is here to assist.

LlamaIndex offers a variety of data connectors and loaders available on LlamaHub, a registry of open-source data connectors. These data connectors allow you to access and ingest data from its native source and format, thus eliminating the need for time-consuming and tedious data conversion processes.

With the help of these data connectors, you can load data from all types of sources, including external databases, APIs, SQL databases, PDFs, and other datasets. This facilitates seamless integration of data, which is vital for developing data-intensive LLM-powered applications. Moreover, data connectors within the LlamaIndex framework offer other benefits like enhancing data quality, improved data performance via caching, and enhanced data security through encryption. [4]

Query transformations

Query transformation refers to the ability to modify a user’s questions on the go to help generate more accurate answers. This is a great feature when it comes to handling complex queries. The main idea behind query transformation is to rephrase the user’s question into simpler terms or break down the question into smaller, manageable parts.

This LlamaIndex component allows you to adapt and refine your question as required during runtime. As a result, you can easily adjust your question to meet changing needs without actually reconfiguring the entire system. This level of flexibility is important in scenarios where the query requirements are subject to constant change.

Node postprocessors

In LlamaIndex, node postprocessors always come after data retrieval and before response_synthesis to help filter the set of selected nodes. They allow users to adjust and refine responses to their queries. This makes the LlamaIndex component crucial when dealing with data that needs transformation, structuring, or further processing after retrieval.


Storage is an important aspect of developing LLM-powered applications. Developers need sufficient storage for vectors, nodes, and the index itself. Storage across LlamaIndex primarily focuses on efficient data storage and quick retrieval. The framework’s storage component is responsible for data management and ensuring relevant information can be retrieved easily.

The main differences between LangChain and LlamaIndex

Although both LangChain and LlamaIndex share some overlap and can be used together in building robust and versatile LLM-powered applications, they’re quite different. Here are some of the key differences between the two platforms:

The main differences between LangChain and LlamaIndex

Core functionality

LangChain is a multi-purpose framework that provides developers with a set of tools, functionalities, and features needed to create and deploy a wide variety of LLM-powered applications. The framework’s main emphasis is to streamline the development process for developers of all skill levels. [5]

On the other hand, LlamaIndex is specifically designed to create search and retrieval applications. It provides a simple interface for indexing and retrieval of relevant documents, thus maintaining its core focus of ensuring efficient data storage and access for LLMs.

Use cases

LangChain is an adaptable and versatile framework ideally designed for creating LLM-powered applications that require advanced AI capabilities. Plus, the platform’s excellent memory management and chain capabilities make it best suited for maintaining long and contextually relevant conversations. Some of the most common applications of LangChain include text generation, language translation, text summarization, and text classification.

In contrast, LlamaIndex is well-suited for scenarios where text search and high-quality responses are the top priorities. The most common use cases of LlamaIndex include content generation, document search and retrieval, LLM augmentation chatbots and virtual assistants.

Pricing and availability

If you’re looking for a cost-effective platform for building LLM-driven applications between LangChain and LlamaIndex, you should know the former is an open-source and free tool everyone can use. Most importantly, LangChain’s source code is available for download on GitHub. LlamaIndex, on the other hand, is a commercial product whose pricing is determined by usage.

Final thoughts

Any LLM-powered application can harness the benefits of both LangChain and LlamaIndex, depending on requirements. That said, your choice between the platforms will mainly depend on your specific needs and the objectives of your LLM project. LangChain excels at offering flexibility, versatility, and advanced customization, making it suitable for context-aware applications.

On the other hand, LlamaIndex is good at fast data retrieval and generating concise responses. This makes it ideal for knowledge-driven applications such as chatbots and virtual assistants, content-based recommendation systems, and question-answering systems. Plus, you can never go wrong by leveraging the strengths of both LangChain and LlamaIndex to develop sophisticated LLM-driven applications.

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FAQ: LangChain vs. LlamaIndex

  • What is LangChain?

    LangChain is an open-source framework designed to simplify the creation of data-aware and agentic applications with Large Language Models (LLMs). It offers versatile features for working with models like GPT-3, BERT, T5, and RoBERTa, making it ideal for both beginners and seasoned developers.
  • What unique features does LangChain offer?

    LangChain offers six main components including Schema for data organization, Models for AI applications, Prompts for tailored queries, Indexes for efficient information retrieval, Memory for storing chat history, and Chains for orchestrating complex workflows.
  • What is LlamaIndex?

    Previously known as GPT Index, LlamaIndex is a data framework focused on ingesting, structuring, and accessing private or domain-specific data for LLMs. It simplifies indexing and retrieval of information, making it perfect for text-based search and generating accurate responses.
  • What are the core components of LlamaIndex?

    LlamaIndex’s components include Querying for optimized information requests, Response Synthesis for generating coherent responses, Composability for creating complex queries, Data Connectors for seamless data integration, Query Transformations, Node Postprocessors for refining responses, and efficient Storage solutions.
  • How do LangChain and LlamaIndex differ?

    LangChain is a multi-purpose framework with a wide range of tools for various LLM-powered applications, focusing on flexibility and advanced AI capabilities. LlamaIndex, however, is specialized for search and retrieval applications, emphasizing fast data retrieval and concise response generation.
  • What are typical use cases for LangChain and LlamaIndex?

    LangChain is suitable for text generation, language translation, text summarization, and classification. LlamaIndex excels in content generation, document search and retrieval, chatbots, and virtual assistants.
  • Are LangChain and LlamaIndex free to use?

    LangChain is an open-source and free tool available on GitHub, making it accessible for anyone to use. LlamaIndex is a commercial product, with pricing based on usage.
  • Can LangChain and LlamaIndex be used together?

    Yes, depending on your LLM project’s specific needs and objectives, leveraging the strengths of both LangChain and LlamaIndex can lead to the development of sophisticated LLM-driven applications.

This article is an updated version of the publication from Dec. 20, 2023.


[1] The Best LLMs. URL: Accessed on December 18, 2023
[2] What are the Top AI Research Organizations? URL: Accessed on December 19, 2023
[3] Weather.Gov. API Web Service. URL: Accessed on December 19, 2023
[4] Encryption. URL: Accessed on December 19, 2023
[5] Software Developer Levels. URL: Accessed on December 19, 2023


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