AI Tools
LlamaIndex
Open-source framework for retrieval and agentic data applications.
What it is
LlamaIndex is an MIT-licensed application framework for connecting language models to external data. Source documents are brought in through data connectors and turned into the framework's document and node structures, indexed so that retrievers can pull relevant context, and served to the model through query engines or agents. Models, embedding providers and vector stores are added as separate integration packages rather than bundled, so the stack underneath can be swapped without rewriting the application, and supported index state can be persisted to disk and reloaded later instead of being rebuilt on every run. The company also sells hosted document parsing and extraction services, but the framework itself is the open-source part and carries no licence cost.
LlamaIndex pricing
LlamaIndex does not publish a plan ladder we could verify. Free tier available.
Prices read from the vendor's own pricing page on 2026-08-24. Vendors change plans without notice — check the source before you budget.
Key features
Data connectors
Loads source documents into framework document and node structures.
Indexes and retrievers
Structures external data and retrieves context for model calls.
Provider integrations
Adds models, embeddings and vector stores as separate packages.
Local persistence
Persists supported index state to disk and reloads it later.
Strengths and trade-offs
What works well
- Retrieval components: Provides ingestion, indexing, storage and query abstractions.
- Modular integrations: Core packages connect to separately installed model, embedding and vector-store providers.
- Local persistence: Index state can be persisted and loaded without requiring the hosted platform.
- Permissive license: The main Python framework is distributed under MIT terms.
Where it falls short
- Framework boundary: It does not replace the model, embedding service or production database you select.
- Security responsibility: Input validation, authentication, rate limits and prompt-injection controls belong to the host application.
- Package selection: The starter package installs defaults; customized deployments must choose integration packages explicitly.
- Hosted product separation: LlamaParse and LlamaCloud capabilities and terms are separate from the open-source framework.
Who it is for
Developers building retrieval or agent workflows over external data who want modular Python components rather than a complete hosted application.
Pick it or skip it
Pick LlamaIndex if
- Retrieval-augmented applications that coordinate ingestion, indexing and querying
- Teams that want to choose their own model, embedding and storage providers
- Python applications that benefit from reusable retrieval and agent abstractions
Skip it if
- You need a database or hosted model rather than an application framework
- A direct provider SDK and a small amount of application code already cover the workflow
- Your team cannot implement web-layer security and untrusted-input controls around the library
Our verdict
Use LlamaIndex when its data and retrieval abstractions remove real integration work; evaluate the open-source framework separately from paid document services and underlying providers.
Frequently asked questions
- Does LlamaIndex have a free plan?
- Yes. LlamaIndex offers a free tier with limits; paid plans unlock higher usage or team features.
- Who is LlamaIndex best for?
- Developers building retrieval or agent workflows over external data who want modular Python components rather than a complete hosted application.
- What are the main drawbacks of LlamaIndex?
- The trade-offs we noted: Framework boundary: It does not replace the model, embedding service or production database you select.; Security responsibility: Input validation, authentication, rate limits and prompt-injection controls belong to the host application.; Package selection: The starter package installs defaults; customized deployments must choose integration packages explicitly..
- When should you not use LlamaIndex?
- Skip LlamaIndex if: You need a database or hosted model rather than an application framework; A direct provider SDK and a small amount of application code already cover the workflow; Your team cannot implement web-layer security and untrusted-input controls around the library.
What we checked
- Public APIOffers a documented API you can build against.Yes
- Mobile appHas a native app for iOS or Android, not just a mobile website.No
- Open source / self-hostableSource is open and the tool can be run on your own infrastructure.Yes
- SSO (SAML)Supports SAML single sign-on on at least one plan.Yes
“Not checked” means exactly that — we have not verified it, and we do not guess.
Evidence and freshness
Status: Source listed, figures not recordedReviewed: 2026-08-24
- LlamaIndex — Official repository ↗
Checked 2026-08-24 · Supports: MIT license, framework scope, core and integration packages, data connectors, retrieval components, local persistence
- LlamaIndex — Package metadata ↗
Checked 2026-08-24 · Supports: Python requirement, package dependencies, MIT license, framework classification
- LlamaIndex — Security policy ↗
Checked 2026-08-24 · Supports: trusted execution assumption, host application validation, authentication responsibility, prompt-injection boundary, hosted-product separation
- LlamaIndex — Pricing (automated check) ↗
Checked 2026-09-03 · Supports: An automated check confirmed this vendor pricing page was reachable on the date shown. No plan name, price or limit was extracted from it.
Limit: Official repository documentation was reviewed; ToolCompare did not independently test retrieval quality, integration stability, framework overhead or hosted LlamaParse services. The pricing source was added by an automated check: the page was fetched on this date and contained pricing information. The other sources on this record were read by an editor.
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