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LlamaIndex

by LlamaIndex, Inc. · Framework · also known as llama_index, GPT Index, LlamaIndex Framework

Open-source Python framework for retrieval-augmented agents over your data, with connectors, indexes, query engines and event-driven workflows, alongside the commercial LlamaParse document platform

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Link verified Nov 2, 2026

Overview

LlamaIndex is an MIT-licensed Python toolkit for building LLM agents and retrieval pipelines over private data. Its building blocks are data connectors (loaders for files, APIs and databases), indexes backed by many vector stores, query and chat engines, agents with tools, and Workflows, an event-driven system for multi-step processes with branching, retries and human review that can be deployed as services. A retrieval pipeline is treated as one tool among others that an agent may call. Documentation moved in 2026 from docs.llamaindex.ai to developers.llamaindex.ai, and a TypeScript edition exists alongside the Python core.

The company behind the framework sells LlamaParse, a hosted document-processing platform (formerly named LlamaCloud) that parses, extracts, classifies and splits documents and offers a managed index; its output feeds directly into framework indexes. Plans for the hosted platform range from a free tier through Starter, Pro and Enterprise, while the framework itself stays free. LlamaIndex is a good fit when documents are the center of the problem; teams needing generic agent orchestration may find LangGraph or Pydantic AI more direct, and the breadth of integrations means dependency management deserves attention.

Capabilities

  • Data connectors for files, APIs and databases
  • Indexes over many vector stores with query and chat engines
  • Agents with tools and event-driven Workflows
  • Evaluation and observability hooks
  • Integration with LlamaParse for document parsing and extraction
  • Python core with a TypeScript edition

Typical workflows

  1. Index a folder of PDFs and answer questions with citations
  2. Build a workflow that parses invoices, extracts fields and routes exceptions to a human
  3. Combine a retrieval tool with web search in one agent

Strengths

  • Document-centric design with mature retrieval abstractions
  • MIT license and broad integration catalog
  • Commercial parsing service available when needed

Limitations

  • Many optional dependencies to manage
  • Hosted LlamaParse features are proprietary and metered
  • Product renames (LlamaCloud to LlamaParse) date older guides

Prerequisites

  • Python 3.10+
  • An LLM and embedding provider or local equivalents

Who it is for

  • Developers building RAG applications
  • Teams processing document-heavy workflows
  • Researchers indexing corpora for question answering

Alternatives & comparison

  • LangChain

    Open-source Python and JavaScript framework whose create_agent harness builds tool-using agents on the LangGraph runtime with swappable model providers and middleware

    Open sourceOpen source
  • LangGraph

    Low-level orchestration framework and runtime for stateful, long-running agents expressed as graphs, with durable execution, checkpointing, human-in-the-loop and streaming

    Open sourceOpen source
  • Pydantic AI

    Type-safe Python agent framework from the Pydantic team with validated structured outputs, dependency injection, swappable model providers, MCP, durable execution and evals

    Open sourceOpen source
  • Ragas

    Open-source Python library for evaluating LLM and RAG applications with LLM-driven and custom metrics, experiments, dataset management and result tracking

    Open sourceOpen source
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Sources & verification

Primary sources consulted for this record. Claims are tied to the fields each source supports; the automated link checker re-verifies every URL on a schedule.

Confidence
High
Reviewed
Nov 1, 2026
Links
Verified · Nov 2, 2026

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