llama_index

🗂️ LlamaIndex Framework 🦙

[!NOTE] The current focus of LlamaIndex is to build the best AI-powered engine for document parsing and extraction. [LlamaParse](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. LiteParse represents our efforts to build the best free, fast, cheap text parser in the market. ParseBench and ExtractBench represent our commitment towards open benchmarking for parsing and extraction. The company itself has undergone an evolution since when this OSS framework first launched 3 years ago in 2023. Since the early days, the framework has consisted of a broad set of orchestration tools enabling developers to build various RAG and agent applications. 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. We have a strong belief that agents are the new consumers of documents, and they fundamentally need the right tools to unlock context from the world's hardest documents accurately/cheaply at scale. Whether you're an AI startup processing documents or an enterprise looking to automate document workflows, come talk to us.

LlamaIndex Framework (by LlamaIndex, the company behind LlamaParse) is an open-source framework to build agentic applications. You can use LlamaParse with this framework or on its own; see LlamaParse below for signup and product links.

### 📚 Documentation: - LlamaParse - LlamaIndex Framework - 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. Starter: `llama-index`. A starter Python package that includes core LlamaIndex as well as a selection of integrations.

1. Customized: `llama-index-core`. Install core LlamaIndex and add your chosen LlamaIndex integration packages from the integrations page

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.

• [Sign up for LlamaParse](https://cloud.llamaindex.ai?utm_medium=li_github&utm_source=github&utm_campaign=2026--) — Create an account and get your API key.
• Parse — Agentic OCR and document parsing (130+ formats). Docs
• Extract — Structured data extraction from documents. Docs
• Index — Ingest, index, and RAG pipelines. Docs
• Split — Split large documents into subcategories. Docs
• Agents — Build end-to-end document agents with Workflows and Agent Builder. Docs

Important Links