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Framework for building LLM applications with RAG. Data framework for AI.
Founded by: Jerry Liu
Metrics computed from HN discussion, GitHub activity, and funding data.
According to AI-Buzz, LlamaIndex ranks #18 in Foundation Models for HN discussion share (out of 33 tracked with 0.2% of HN discussion), with 56% positive developer sentiment (9 HN comments analyzed), with 24 commits/week (steady), with 376,616 npm downloads in 30 days, with 8,992,103 PyPI downloads in 30 days.
Source: https://ai-buzz.com/companies/llamaindex?utm_source=citation&utm_medium=referral&utm_campaign=cite_this_data
Metrics derived from public APIs (HN Algolia, GitHub, npm/PyPI). Sentiment classified by AI. See methodology for details →
Description
Framework for building LLM applications with RAG. Data framework for AI.
Estimated Company Size
11 - 50 employees
Website
llamaindex.aiFounded
2022
Description
LlamaIndex is a data framework for LLM applications. Enables RAG and data-augmented AI. Popular with developers building AI apps.
Community engagement metrics that indicate developer traction and interest.
Last updated: 1 day ago
Mentions in HN discussions. Source: Hacker News Algolia API.
Sentiment analysis of Hacker News comments only. Does not include Reddit, Discord, or other platforms.
Total stars indicate project popularity and developer adoption.
Forks indicate active developer engagement and contribution interest.
Weekly commit activity indicates ongoing development momentum and team productivity.
Package download volume indicates real-world adoption and integration into production projects.
An open-source data framework for building LLM applications with Retrieval Augmented Generation (RAG).
A managed service for ingesting, parsing, and indexing data to power RAG applications.
An API for robustly parsing complex documents into structured formats for RAG, often integrated with LlamaCloud.
Pre-built, end-to-end LlamaIndex applications that developers can download and use for various use cases.
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Package downloads and ecosystem metrics — 30-day window
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