--- title: "llama_index" type: "tool" slug: "run-llama-llama-index" canonical_url: "https://www.graphcanon.com/tools/run-llama-llama-index" github_url: "https://github.com/run-llama/llama_index" homepage_url: "https://developers.llamaindex.ai" stars: 50709 forks: 7705 primary_language: "Python" license: "MIT" categories: ["ai-agents", "llm-frameworks", "vector-databases"] tags: ["llamaindex", "fine-tuning", "agents", "llm", "application", "multi-agents", "data", "framework"] updated_at: "2026-07-07T17:31:15.24703+00:00" --- # llama_index > LlamaIndex is the leading document agent and OCR platform LlamaIndex is the leading document agent and OCR platform ## Facts - Repository: https://github.com/run-llama/llama_index - Homepage: https://developers.llamaindex.ai - Stars: 50,709 · Forks: 7,705 · Open issues: 489 · Watchers: 281 - Primary language: Python - License: MIT - Last pushed: 2026-07-02T17:54:20+00:00 ## Categories - [AI Agents](/categories/ai-agents.md) - [LLM Frameworks](/categories/llm-frameworks.md) - [Vector Databases](/categories/vector-databases.md) ## Tags llamaindex, fine-tuning, agents, llm, application, multi-agents, data, framework ## Related tools - [ECC](/tools/affaan-m-ecc.md) - The agent harness performance optimization system. 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You can use LlamaParse with this framework or on its own; see [LlamaParse](#llamacloud-document-agent-platform) below for signup and product links. > ### 📚 **Documentation:** > > - [LlamaParse](https://developers.llamaindex.ai/python/cloud/llamaparse/?utm_medium=li_github&utm_source=github&utm_campaign=2026--) > - [LlamaIndex OSS](https://developers.llamaindex.ai/python/framework/?utm_medium=li_github&utm_source=github&utm_campaign=2026--) > - [LlamaAgents](https://developers.llamaindex.ai/python/llamaagents/overview/?utm_medium=li_github&utm_source=github&utm_campaign=2026--) 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`](https://pypi.org/project/llama-index/). A starter Python package that includes core LlamaIndex as well as a selection of integrations. 2. **Customized**: [`llama-index-core`](https://pypi.org/project/llama-index-core/). Install core LlamaIndex and add your chosen LlamaIndex integration packages on [LlamaHub](https://llamahub.ai/) 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. ```python # 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](https://developers.llamaindex.ai/python/cloud/llamaparse/?utm_medium=li_github&utm_source=github&utm_campaign=2026--) - **Extract** — Structured data extraction from documents. [Docs](https://developers.llamaindex.ai/python/cloud/llamaextract/?utm_medium=li_github&utm_source=github&utm_campaign=2026--) - **Index** — Ingest, index, and RAG pipelines. [Docs](https://developers.llamaindex.ai/python/cloud/llamacloud/?utm_medium=li_github&utm_source=github&utm_campaign=2026--) - **Split** — Split large documents into subcategories. [Docs](https://developers.llamaindex.ai/python/cloud/split/getting_started/?utm_medium=li_github&utm_source=github&utm_campaign=2026--) - **Agents** — Build end-to-end document agents with `Workflows` and Agent Builder. [Docs](https://developers.llamaindex.ai/python/llamaagents/overview/?utm_medium=li_github&utm_source=github&utm_campaign=2026--) ### Important Links [Documentation](https://developers.llamaindex.ai/python/framework/?utm_medium=li_github&utm_source=github&utm_campaign=2026--) [X (formerly Twitter)](https://x.com/llama_index) [LinkedIn](https://www.linkedin.com/company/llamaindex/) [Reddit](https://www.reddit.com/r/Llam ``` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/run-llama-llama-index`](/api/graphcanon/tools/run-llama-llama-index) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_