Building an AI application with a large language model involves much more than sending a prompt to an API and displaying the response. Modern AI applications often need to connect language models with documents, databases, APIs, search systems, tools, memory, and other sources of information.
This is where frameworks such as LlamaIndex and LangChain become useful. Both frameworks provide tools for building applications around large language models, but they approach the problem from somewhat different perspectives. LlamaIndex places strong emphasis on connecting LLMs with external data and building retrieval-based applications, while LangChain provides a broader framework for orchestrating models, prompts, tools, agents, and application workflows.
Understanding LlamaIndex vs LangChain is useful for developers deciding which framework fits a particular AI application.
LlamaIndex and LangChain are both open-source frameworks designed to help developers build applications powered by large language models.
LlamaIndex is particularly focused on connecting LLMs with external data. It provides components for ingesting documents, creating indexes, retrieving information, querying data, and building retrieval-augmented generation applications.
LangChain provides a broader application-development framework for working with language models, prompts, tools, agents, retrieval systems, and multi-step workflows. The choice between them depends on what you are building. If your primary challenge is connecting an LLM to a large collection of documents or specialized data, LlamaIndex can provide focused data-ingestion and retrieval capabilities. If you need to coordinate an LLM with multiple tools, prompts, APIs, agents, and application steps, LangChain provides a broader orchestration framework. The two frameworks are not mutually exclusive. Developers can also use them together when an application needs both sophisticated data retrieval and broader workflow orchestration.
What is LlamaIndex?
LlamaIndex is a framework for building applications that connect large language models with external data. An LLM may know a great deal about general information, but it does not automatically have access to an organization’s private documents, databases, internal knowledge bases, or constantly changing information. LlamaIndex provides components for bringing this information into an AI application.
A simplified example starts by loading documents:
from llama_index.core import SimpleDirectoryReader
documents = SimpleDirectoryReader(
“data”
).load_data()
print(len(documents))
The documents can then be indexed:
from llama_index.core import VectorStoreIndex
index = VectorStoreIndex.from_documents(
documents
)
A query engine can then be created:
query_engine = index.as_query_engine()
response = query_engine.query(
“What does the documentation say about the product?”
)
print(response)
The framework handles several parts of the retrieval process, allowing developers to build a question-answering application without manually implementing every retrieval operation.
LlamaIndex key features
LlamaIndex provides functionality for:
- Data ingestion
- Document parsing
- Data indexing
- Embeddings
- Vector retrieval
- Query engines
- RAG applications
- Metadata filtering
- Structured data access
- Data connectors
- Evaluation
One of its important strengths is its focus on making external data usable by LLM applications.
For example, an application can retrieve information from documents and then provide that information to an LLM as context. This makes LlamaIndex particularly relevant to applications such as document assistants, enterprise knowledge systems, research tools, and RAG pipelines.
What is LangChain?
LangChain is a framework for developing applications powered by language models.Rather than focusing primarily on one part of the AI stack, LangChain provides components that can be combined to create complete LLM application workflows.
A simple model interaction can look like:
from langchain_openai import ChatOpenAI
model = ChatOpenAI(
model=”gpt-4o-mini”
)
response = model.invoke(
“Explain retrieval augmented generation.”
)
print(response.content)
Developers can then build more structured workflows around the model. For example, prompts can be separated from application logic:
from langchain_core.prompts import ChatPromptTemplate
prompt = ChatPromptTemplate.from_template(
“Explain {topic} in simple terms.”
)
chain = prompt | model
response = chain.invoke({
“topic”: “vector databases”
})
print(response.content)
This composability is one of the important ideas behind LangChain.
LangChain key features
LangChain provides components for:
- Prompt templates
- Chat models
- Embedding models
- Document loaders
- Retrievers
- Chains
- Agents
- Tools
- Structured output
- Memory and state management
- Workflow orchestration
A developer can combine these components to create applications that perform multiple operations rather than simply generating one response.For example, an application could receive a question, search a database, call an external API, process the result, and then ask an LLM to formulate the final response.
LlamaIndex vs LangChain
The most important difference between LlamaIndex and LangChain is their primary emphasis. LlamaIndex is strongly centered around data and retrieval. It provides tools for turning external information into data structures that an LLM can query. LangChain is more broadly focused on LLM application orchestration. It provides components for connecting models with prompts, tools, agents, retrievers, APIs, and multi-step workflows.
This distinction becomes particularly useful when deciding what the central problem of an application is.
For example, imagine that you have 50,000 company documents and want employees to ask questions about them. LlamaIndex provides a natural set of components for loading, indexing, retrieving, and querying those documents.
Now imagine an AI assistant that needs to search company documents, check a CRM, call a weather API, calculate a value, and decide which tool to use depending on the question. LangChain’s broader orchestration capabilities can be useful for this type of workflow.
What is RAG?
Retrieval-Augmented Generation, or RAG, is an approach in which an AI system retrieves relevant external information before asking an LLM to generate an answer.
For example, documents can be loaded and converted into embeddings:
from llama_index.core import VectorStoreIndex
index = VectorStoreIndex.from_documents(
documents
)
query_engine = index.as_query_engine(
similarity_top_k=3
)
response = query_engine.query(
“What is the company’s remote work policy?”
)
print(response)
The retrieval step finds relevant information, while the language model uses that information to generate the response. Both LlamaIndex and LangChain can be used to build RAG applications. The difference is that LlamaIndex is particularly focused on the data and retrieval side of these applications, while LangChain can be used to orchestrate retrieval as part of a larger workflow.
What are the LangChain Key Components?
LangChain consists of several components that can be combined to create LLM applications.
1. Prompts
Prompts define the instructions and context given to a model.LangChain’s prompt templates allow dynamic values to be inserted into reusable prompts.
from langchain_core.prompts import ChatPromptTemplate
prompt = ChatPromptTemplate.from_messages([
(
“system”,
“You are a helpful AI assistant.”
),
(
“human”,
“Explain {concept} with an example.”
)
])
messages = prompt.invoke({
“concept”: “vector embeddings”
})
print(messages)
This is more maintainable than constructing every prompt manually inside application code.
2. Models
LangChain provides standardized interfaces for interacting with different language models.
For example:
from langchain_openai import ChatOpenAI
model = ChatOpenAI(
model=”gpt-4o-mini”,
temperature=0
)
response = model.invoke(
“What is semantic search?”
)
print(response.content)
The application can use model abstractions without tightly coupling every part of the application to one provider.
3. Memory
AI applications sometimes need to maintain information about previous interactions. Modern LangChain applications often handle conversation state explicitly rather than relying on an unrestricted memory buffer. A simple message history can be represented as:
from langchain_core.messages import HumanMessage, AIMessage
history = [
HumanMessage(
content=”What is RAG?”
),
AIMessage(
content=”RAG combines retrieval with generation.”
)
]
for message in history:
print(message.content)
This history can then be incorporated into subsequent model calls.
4. Chains
Chains connect multiple operations together. For example, a prompt can be connected directly to a model:
chain = prompt | model
response = chain.invoke({
“concept”: “RAG”
})
print(response.content)
More complex applications can connect several processing steps. A chain could retrieve information, format the retrieved documents, construct a prompt, call a model, and process the final output.
5. Agents
Agents allow an LLM to determine which tools or actions should be used to complete a task. For example, an assistant might have access to a calculator and a search tool. A simplified tool definition could look like:
from langchain_core.tools import tool
@tool
def calculate_tax(amount: float, rate: float) -> float:
“””Calculate tax on an amount.”””
return amount * rate / 100
print(
calculate_tax.invoke({
“amount”: 50000,
“rate”: 18
})
)
An agent can then be configured to decide when a tool should be used.
LangChain agents and toolkits
Tools allow an AI application to interact with systems outside the language model.
These can include:
- APIs
- Databases
- Search engines
- Calculators
- File systems
- Business applications
A toolkit groups related tools that can be used by an agent. This makes LangChain useful for applications where an LLM needs to perform actions rather than simply answer questions.
What are the LangChain Integrations: LangSmith and LangServe?
LangChain applications often involve more than the core framework.
1. LangSmith
LangSmith is designed for developing, tracing, evaluating, and monitoring LLM applications. For example, developers can instrument an application to track model calls and inspect execution behavior. A basic tracing configuration can be enabled through environment variables:
import os
os.environ[“LANGSMITH_TRACING”] = “true”
os.environ[“LANGSMITH_API_KEY”] = “your-api-key”
This can help developers investigate issues such as unexpected model outputs, inefficient workflows, or poor retrieval results. Evaluation is particularly important for RAG and agent applications because a technically functioning application can still produce poor responses.
2. LangServe
LangServe was designed to help developers expose LangChain runnables and chains as APIs. For example, an application might create a chain:
chain = prompt | model
and expose that chain through a web service. This allows other applications to interact with the AI workflow through an API rather than directly importing the underlying Python code. For newer production architectures, developers should consider the current LangChain ecosystem and deployment recommendations rather than treating LangServe as the only deployment approach.
What are the LlamaIndex Key Components?
LlamaIndex provides several components focused on connecting data with LLM applications.
1. LlamaIndex Typical Workflow
A typical workflow starts with loading data.
from llama_index.core import SimpleDirectoryReader
documents = SimpleDirectoryReader(
“documents”
).load_data()
The documents can then be indexed:
from llama_index.core import VectorStoreIndex
index = VectorStoreIndex.from_documents(
documents
)
A query engine can then retrieve relevant information:
query_engine = index.as_query_engine(
similarity_top_k=5
)
response = query_engine.query(
“Summarize the main points in these documents.”
)
print(response)
This workflow can be expanded with custom embedding models, vector databases, metadata filters, reranking, structured data, and evaluation. LlamaIndex also supports different approaches to data ingestion and indexing, allowing developers to build systems around many different sources.
2. LlamaHub
LlamaHub provides a collection of integrations and data connectors for bringing external information into LlamaIndex applications. Instead of writing a separate document loader for every possible source, developers can use available connectors where appropriate. For example, an application may need to ingest information from files, databases, cloud storage, or other external systems. Once the information has been loaded, it can be transformed into documents or nodes and incorporated into the indexing and retrieval pipeline. A simplified conceptual example is:
from llama_index.core import Document
documents = [
Document(
text=”LlamaIndex connects LLMs with external data.”
),
Document(
text=”RAG retrieves relevant information before generation.”
)
]
index = VectorStoreIndex.from_documents(
documents
)
engine = index.as_query_engine()
response = engine.query(
“How does LlamaIndex work with external data?”
)
print(response)
The key idea is that LlamaIndex treats external data as a central part of the AI application rather than as an afterthought.
Conclusion
LlamaIndex and LangChain both provide powerful building blocks for developing applications around large language models, but their areas of emphasis are different. LlamaIndex is particularly useful when the core challenge involves connecting an LLM with external data, building indexes, retrieving relevant information, and developing RAG or knowledge-based applications. LangChain provides a broader framework for composing models, prompts, tools, agents, retrieval systems, and multi-step application workflows.
The choice therefore depends on the architecture and requirements of the application rather than one framework being universally suitable. Developers building document-heavy or retrieval-focused systems may find LlamaIndex especially relevant, while developers building complex agentic workflows may benefit from LangChain’s broader orchestration capabilities. They can also be used together. For example, LlamaIndex can handle specialized document retrieval while LangChain coordinates that retrieval with other tools and application steps.
Frequently Asked Questions (FAQs)
1. What is the difference between LlamaIndex and LangChain?
LlamaIndex primarily focuses on connecting large language models with external data through ingestion, indexing, retrieval, and query capabilities. LangChain provides a broader framework for building LLM applications using models, prompts, tools, agents, retrieval systems, and workflows.
2. Which is better, LlamaIndex or LangChain?
Neither framework is universally better. The appropriate choice depends on the application. LlamaIndex is particularly relevant when data ingestion, indexing, and retrieval are central requirements, while LangChain is useful for applications involving broader workflow orchestration, tools, and agents.
3. What are the key differences between LlamaIndex and LangChain?
The main difference is their emphasis. LlamaIndex is strongly oriented toward data and retrieval for LLM applications, while LangChain provides a broader set of abstractions for composing models, prompts, tools, agents, and workflows. Both frameworks also provide retrieval and RAG capabilities.
4. When should you use LlamaIndex instead of LangChain?
LlamaIndex can be a suitable choice when the primary requirement is building an application around external data, such as documents, knowledge bases, or other information sources. It is particularly relevant for retrieval-heavy applications such as document question answering and RAG systems.
5. Can LlamaIndex and LangChain be used together?
Yes. They can be combined when an application needs LlamaIndex’s data ingestion and retrieval capabilities alongside LangChain’s broader workflow, model, tool, or agent orchestration. The exact integration depends on the architecture and requirements of the application.
