LlamaIndex
LlamaIndex is an open-source framework that connects language models to your own data. It ingests data sources, structures them in indices and provides query and retrieval interfaces for RAG and agents.
- Project
- LlamaIndex
- Publisher
- LlamaIndex
- Repository
- github.com/run-llama/llama_index
- Documentation
- developers.llamaindex.ai/python/framework/
- Languages
- Python
- Category
- AI agent frameworks
What is LlamaIndex?
LlamaIndex describes itself as a data framework for applications built on language models. Its core concern is how a model can access private or company-internal data. The framework is available in Python, released under the MIT licence and offered as a core package with more than 300 integration packages for models, embeddings and vector databases.
According to its README, the company behind LlamaIndex now focuses on LlamaParse, a commercial platform for document parsing and extraction. The open-source framework remains available and can be used with or without LlamaParse.
Core capabilities
- Data connectors
- Ingesting existing sources and formats such as APIs, PDFs, documents or SQL databases.
- Indices
- Structuring data, for example as a vector index or graph, so that a model can use it in a targeted way.
- Retrieval and querying
- Interfaces that find context relevant to a query and return a knowledge-augmented answer.
- Extensibility
- Getting started takes a few lines of code; connectors, indices, retrievers, query engines and reranking can each be customised.
When LlamaIndex fits
LlamaIndex fits when the hard part of your application is the data: many documents, different formats and the question of which passage is relevant to an answer. For orchestrating complex agent workflows, it can be combined with other frameworks.
Using LlamaIndex effectively in an AI-first business model
An agent acts only as well as the knowledge it can access. Before indexing documents, decide which sources are authoritative, who maintains them and which data an agent may use for which purpose.
Retrieval quality can be measured: does the system find the right passages, and is the answer grounded in them? Such test cases belong in ongoing evaluation so that changes to data or models do not degrade results unnoticed.
From tool to outcome
In AI-first coaching you clarify which outcomes AI agents should be accountable for in your go-to-market and within which framework. REVAGO then designs, builds and runs your system. You pay for results – not for activity.
Frequently asked questions
What is LlamaIndex?
LlamaIndex is an open-source Python framework (MIT licence) that connects language models to your own data sources. It provides data connectors, indices and retrieval interfaces for RAG and agentic applications.
What is the difference between LlamaIndex and LlamaParse?
LlamaIndex is the open-source framework. LlamaParse is a commercial platform by the same company for document parsing, extraction, indexing and document agents. They can be used together or separately.
Can I use LlamaIndex with local models?
Yes. Integration packages connect various model providers, including local models via Ollama and embeddings from Hugging Face.
Further reading in the wiki
A neutral overview, not a recommendation of individual providers. REVAGO is not affiliated with the listed projects. Descriptions are based on the projects’ public information; features and licences may change.