Data framework for AI

How to use
LlamaIndex.

Loading and indexing private data, building source-grounded retrieval applications, composing workflows and agents, and evaluating retrieval and response quality.

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What it isopen-source data framework for retrieval-augmented generation and agent workflows Workflows2 UpdatedJuly 2026

LlamaIndex is a open-source data framework for retrieval-augmented generation and agent workflows. Loading and indexing private data, building source-grounded retrieval applications, composing workflows and agents, and evaluating retrieval and response quality. This guide covers the whole path in one place: official access, a first session that produces something reviewable, the checks that make output trustworthy, and the permissions worth limiting before you connect real work.

Troiana principle

AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.

01

Access & setup

Find, install, and sign in to LlamaIndex

Get into the official LlamaIndex experience with the right account and a setup you understand.

  1. 01

    Start at https://www.llamaindex.ai/ and confirm the domain before entering account or payment information.

  2. 02

    Availability: LlamaIndex is installed as Python or TypeScript packages and used in an application development environment.

  3. 03

    A supported programming runtime, model and embedding access, a controlled source corpus, and storage or vector infrastructure appropriate to the workload.

  4. 04

    Sign in with the account you intend to keep using, then review plan, data, notification, and permission settings.

  5. 05

    Run one low-risk test task before connecting sensitive files, repositories, or workspace data.

  • Use official download pages.
  • Review permissions during setup.
  • Keep installers and applications updated.
02

First session

Your first useful LlamaIndex session

Learn the interaction loop using a small task with a clear outcome.

  1. 01

    Define a small approved document corpus, access rules, expected questions, and a benchmark with expected source IDs.

  2. 02

    Load documents with stable metadata, choose chunking and embeddings, and build a persistent index.

  3. 03

    Query the index and inspect retrieved nodes and citations before judging the generated answer.

  4. 04

    Measure retrieval relevance and response faithfulness, then version the corpus and settings before iteration.

  • State the outcome before the background.
  • Provide the real source material.
  • Review the result before expanding the task.
03

Quality control

Check the quality of LlamaIndex output

Establish that an autonomous run did the right thing, not merely that it finished.

  1. 01

    Define what the run should achieve and what it must never touch before granting it a single tool.

  2. 02

    Read the full execution trace: which tools were called, with what arguments, and in what order.

  3. 03

    Verify the side effects directly in the target system rather than trusting the agent's own report of success.

  4. 04

    Confirm failures surfaced as failures — a silent retry loop or a swallowed error is more dangerous than a crash.

  5. 05

    Re-run the same task and compare: an agent that behaves differently across identical runs is not yet production-ready.

  • Verify side effects in the system of record, not in the agent's summary.
  • Require human approval for any irreversible or outward-facing action.
  • Log every tool call so a run can be reconstructed afterwards.
04

Privacy & permissions

Use LlamaIndex safely

Bound what an autonomous system can reach before you let it run unattended.

  1. 01

    Enumerate every tool, credential, and system the agent can reach, and remove the ones it does not need.

  2. 02

    Require explicit human approval for irreversible actions: sending, publishing, paying, deleting, or deploying.

  3. 03

    Run against non-production data until behaviour is predictable across repeated runs.

  4. 04

    Set hard limits on spend, iterations, and runtime so a failure loop cannot run unbounded.

  5. 05

    Treat anything the agent reads from the web or a document as data, never as instructions it may follow.

  • RAG can retrieve irrelevant, stale, private, or malicious content and still produce fluent answers. Enforce access during retrieval, sanitize sources, preserve citations, separate retrieval and response evaluation, and protect traces and model credentials.
  • Follow your organisation's approved-use policy.
  • Never treat fluent output as authorization to act.
05

Core workflows

Step-by-step ways to use LlamaIndex for the work it does best.

Each workflow is a separate guide with its own steps and review checkpoints.

01 WorkflowBuild a source-grounded RAG pipeline with LlamaIndex

Load controlled documents, retrieve relevant evidence, and produce answers whose citations can be inspected.

 02 WorkflowEvaluate and monitor a LlamaIndex application

Measure retrieval and response quality with a versioned dataset and trace failures to individual pipeline stages.

06

Official references

Check the current product documentation.

Features, plan limits, availability, and data controls change. These official pages are the starting points used for this guide.

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