The 2026 change most comparisons miss
Most “LangChain vs LlamaIndex” articles were written in 2024 and 2025 and compare two equal, general-purpose frameworks. That picture changed this year.
From LlamaIndex’s own README:
“While we still have the OSS framework available as an open toolkit that you’re welcome to use, our primary focus has shifted towards LlamaParse, along with liteparse and our benchmarking efforts.”
Two more facts follow from it:
- LlamaIndex.TS is deprecated. The TypeScript repository was archived in March 2026, so LlamaIndex is effectively Python-only.
- LlamaCloud is now LlamaParse, a platform for parsing, extraction, indexing and document agents.
The framework is still maintained and still good at what it was built for: retrieval over your documents. But if you’re choosing a long-term foundation for agents, the momentum is with LangChain, which is its company’s core product. LangChain’s agents also run on LangGraph.
What the code looks like
LlamaIndex. Index a folder of documents and query it, from the LlamaIndex README:
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
documents = SimpleDirectoryReader("YOUR_DATA_DIRECTORY").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
query_engine.query("YOUR_QUESTION")LangChain. A tool-calling agent, from the LangChain overview:
from langchain.agents import create_agent
def get_weather(city: str) -> str:
"""Get weather for a given city."""
return f"It's always sunny in {city}!"
agent = create_agent(
model="openai:gpt-5.5",
tools=[get_weather],
system_prompt="You are a helpful assistant",
)The two snippets show where each library starts. LlamaIndex starts from your data: load, index, query. LangChain starts from the agent: model, tools, prompt. Each can do the other’s job, but less directly.
Side by side
| LangChain | LlamaIndex | |
|---|---|---|
| Starts from | Agents and tool calling | Data: ingestion, indexing, retrieval |
| Agent API | create_agent, running on LangGraph |
Agents and event-driven Workflows |
| Retrieval | Document loaders, text splitters, vector store integrations | Indices and query engines, many data loaders and vector stores |
| Languages | Python, TypeScript | Python (TypeScript port deprecated) |
| Versioning | 1.x | Pre-1.0 (0.14.x) |
| Company focus | Core product (with LangGraph, Deep Agents, LangSmith) | The framework is maintained, but the company’s focus is LlamaParse |
| Paid platform | LangSmith: Developer free, Plus $39/seat/month | LlamaParse: free plan with 10K credits/month, paid plans from $50/month |
| MCP | Via langchain-mcp-adapters |
Use MCP tools, and expose LlamaIndex tools and workflows via MCP |
| License | MIT | MIT |
Platform prices are from langchain.com/pricing and LlamaIndex’s pricing page, checked October 22, 2026.
Who should pick which
Choose LangChain if:
- you’re building agents that call tools, with retrieval as one of those tools
- you need TypeScript, or the same framework in Python and TypeScript
- you want a framework that’s its company’s main focus, with LangGraph underneath when you need more control
Choose LlamaIndex if:
- your app is mostly question-answering over your own documents, in Python
- you want the shortest path from a folder of files to a working query engine
- you already use, or plan to use, LlamaParse for document parsing
Use both if: you want LlamaIndex’s or LlamaParse’s document handling with a LangChain or LangGraph agent on top. Wrap the LlamaIndex query engine as a tool and give it to create_agent.
Hands-on test
We haven’t benchmarked these frameworks against each other. This comparison is based on each project’s README, official documentation and pricing pages, linked on the LangChain and LlamaIndex pages.