🗂️ LlamaIndex 🦙

](https://pypi.org/project/llama-index/) [ ](https://github.com/jerryjliu/llama_index/graphs/contributors) [ ](https://x.com/llama_index) [ [](https://www.phorm.ai/query?projectId=c5863b56-6703-4a5d-87b6-7e6031bf16b6)

LlamaIndex OSS (by LlamaIndex) is an open-source framework to build agentic applications. [Parse](https://cloud.llamaindex.ai?utm_medium=li_github&utm_source=github&utm_campaign=2026--) is our enterprise platform for agentic OCR, parsing, extraction, indexing and more. You can use LlamaParse with this framework or on its own; see LlamaParse below for signup and product links.

### 📚 Documentation:

>

- LlamaParse - LlamaIndex OSS - LlamaAgents

Building with LlamaIndex typically involves working with LlamaIndex core and a chosen set of integrations (or plugins). There are two ways to start building with LlamaIndex in Python:

1. 1.Starter: `llama-index`. A starter Python package that includes core LlamaIndex as well as a selection of integrations.

1. 1.Customized: `llama-index-core`. Install core LlamaIndex and add your chosen LlamaIndex integration packages on LlamaHub

that are required for your application. There are over 300 LlamaIndex integration packages that work seamlessly with core, allowing you to build with your preferred LLM, embedding, and vector store providers.

The LlamaIndex Python library is namespaced such that import statements which include core imply that the core package is being used. In contrast, those statements without core imply that an integration package is being used.

# typical pattern
from llama_index.core.xxx import ClassABC  # core submodule xxx
from llama_index.xxx.yyy import (
    SubclassABC,
)  # integration yyy for submodule xxx

# concrete example
from llama_index.core.llms import LLM
from llama_index.llms.openai import OpenAI

LlamaParse (document agent platform)

LlamaParse is its own platform—focused on document agents and agentic OCR. It includes Parse (parsing), LlamaAgents (deployed document agents), Extract (structured extraction), and Index (ingest and RAG). You can use it with the LlamaIndex framework or standalone.