LangChain vs LlamaIndex: Which one should you use and when
When I first started building LLM-powered apps, two libraries consistently emerged: LangChain and LlamaIndex. At first, they seemed to be doing the same thing. But the deeper I explored, the more I realized that they solve very different problems and understanding that is the key to building better AI apps. In this blog, I’ll break down what each library does, provide real-world use cases, and help you determine which one to use when, along with some code examples to make it all click.
- LlamaIndex is excellent when you want to connect LLMs to your own data (like PDFs, Notion, databases, etc.).
- LangChain is ideal for building chains, doing multi-step reasoning, and orchestrating agent-style workflows.
- You can, and must use both together.
What is LangChain?
LangChain is a framework for the logic and orchestration of large language models that lets you:
- Build multi-step chains (e.g., extract → calculate → summarize)
- Use tools like search engines , APIs, and calculators
- Manage memory, prompt templates, and agents
It’s the go-to if your LLM needs to “do things” not just answer a one-off question.
For a deeper dive into how LangChain can help structure clean, object-based responses from LLMs, check out this guide on using LangChain for structured outputs.
When should you use LangChain
Use LangChain when:
- You need chained logic — e.g., extract → transform → analyze
- You’re building agent-style apps (e.g., AI that uses tools or browses the web)
- You want more control over prompt engineering, memory, or dynamic flows
Example: Multi-step reasoning with LangChain
from langchain.chains import LLMChain, SequentialChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplatelocation_prompt = PromptTemplate.from_template("Give a short travel summary of {location}")
llm_chain1 = LLMChain(llm=OpenAI(), prompt=location_prompt, output_key="summary")activity_prompt = PromptTemplate.from_template("Based on this: {summary}, suggest one fun activity.")
llm_chain2 = LLMChain(llm=OpenAI(), prompt=activity_prompt, output_key="activity")overall_chain = SequentialChain(
chains=[llm_chain1, llm_chain2],
input_variables=["location"],
output_variables=["summary", "activity"]
)result = overall_chain({"location": "Goa"})
print(result["activity"])
Takeaway: LangChain is perfect when your app needs to think in steps or use tools.
What is LlamaIndex?
LlamaIndex, previously known as GPT Index, assists large language models in…….Read More
