LlamaIndex: The Data Framework for AI Agents

LlamaIndex specializes in connecting LLMs with your data. 160+ connectors, advanced RAG pipelines, and agent-ready query engines make it the go-to framework for data-intensive AI applications.

38K+
GitHub Stars
160+
Data Connectors
None
Model Lock-in
Moderate
Learning Curve

What is LlamaIndex?

LlamaIndex (formerly GPT Index) is a data framework that makes it easy to connect LLMs with external data sources. While not a pure agent framework, its query engines, ReAct agents, and RAG capabilities make it essential for building data-aware AI agents.

With 160+ data connectors (databases, APIs, documents, cloud storage), LlamaIndex can ingest data from virtually any source, create optimized index structures, and enable agents to reason over your data with high accuracy.

Core Concepts

🔗

Data Connectors

160+ integrations to load data from databases, APIs, PDFs, cloud storage, and more.

📊

Index Structures

Optimized vector, keyword, and tree indices for fast and accurate retrieval.

🔍

Query Engines

Natural language interfaces over your data with agent-like reasoning capabilities.

🤖

ReAct Agents

Full agent capabilities using tools, query engines, and multi-step reasoning.

Popular Use Cases

Document Q&A

Ask questions about PDFs, internal docs, and knowledge bases in natural language.

Multi-Source Analysis

Combine SQL databases, documents, and APIs for cross-source reasoning.

Enterprise Search

Build intelligent search across company wikis, Slack, email, and code repos.

Financial Analysis

Analyze earnings reports, SEC filings, and market data with structured queries.

Quick Example

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.agent import ReActAgent
from llama_index.core.tools import QueryEngineTool

# Load and index your documents
documents = SimpleDirectoryReader("./data").load_data()
index = VectorStoreIndex.from_documents(documents)

# Create a query tool
query_tool = QueryEngineTool.from_defaults(
    query_engine=index.as_query_engine(),
    description="Search company documents"
)

# Build an agent with the tool
agent = ReActAgent.from_tools([query_tool], verbose=True)
response = agent.chat("What were our Q1 revenue trends?")
📊

LlamaIndex vs Other Frameworks

See how LlamaIndex compares to LangGraph, CrewAI, and 9 other frameworks in our definitive 2026 comparison.

Compare Frameworks

🔗

LangGraph

Graph-based workflows

👥

CrewAI

Multi-agent collaboration

⚡

Agno

Production-ready agents

Explore More

Best AI Data Analysis Tools

Compare 7 data analysis agents

Best AI Agent Frameworks

Ranked framework comparison

How to Build an AI Agent

10-step guide from zero to production

Related Articles