LlamaIndex is an open-source framework and platform for building AI-powered applications that can retrieve, understand and work with information from different data sources. It helps developers connect large language models (LLMs) to private and external data, making it possible to build applications that answer questions, search documents, automate workflows and perform tasks using company-specific information.

The platform addresses a common limitation of large language models: although they can generate natural-language responses, they may not have access to an organisation’s latest documents, internal records or specialised information. LlamaIndex provides tools for connecting these sources to language models so applications can retrieve relevant information before generating responses. This approach is commonly associated with retrieval-augmented generation (RAG).

LlamaIndex supports the development of data-aware applications through a collection of components for data ingestion, indexing, retrieval, query processing and response generation. Developers can load information from sources such as documents, databases, websites and cloud storage, transform it into a format suitable for retrieval, and make it available to an LLM. The framework also provides retrieval methods that help applications find relevant information based on a user’s question.

Beyond traditional RAG, LlamaIndex supports AI agent development. Its agent capabilities allow applications to use tools, interact with data sources and complete multi-step tasks. Developers can create agents that retrieve information, call external services, reason through a workflow and return results in natural language. These capabilities are useful for building research assistants, customer support applications, knowledge assistants and business process automation systems.

The listing below distinguishes the free, open-source LlamaIndex Framework from LlamaParse, its commercial document-processing platform, because they have different licensing and pricing arrangements.

LlamaIndex began as an open-source RAG and agent framework in 2023. Its current product ecosystem also includes managed document parsing, structured extraction, indexing and agent workflows.

LlamaIndex – AI Agent Development and Document Processing Platform

About

LlamaIndex is an open-source framework and commercial AI platform for building applications that can retrieve, understand, process and act on information from different data sources. It helps developers connect large language models (LLMs) to private and external data, making it possible to build intelligent assistants, document-processing systems, knowledge management applications and automated business workflows.

The platform addresses a common limitation of large language models: although they can generate natural-language responses, they may not have access to an organisation’s latest documents, internal records or specialised information. LlamaIndex provides tools for connecting these sources to language models so applications can retrieve relevant information before generating responses. This approach is commonly known as retrieval-augmented generation (RAG).

LlamaIndex began as a framework for building data-aware LLM applications. Its original framework provides components for data ingestion, indexing, retrieval, query processing and response generation. Developers can load information from documents, databases, websites, cloud storage and other sources, transform it into a suitable format, and make it available to an LLM. Retrieval components then help applications locate relevant information when users submit questions.

Beyond traditional RAG, LlamaIndex supports the development of AI agents and multi-step workflows. Its agent capabilities allow applications to use external tools, interact with data sources, maintain state and complete tasks involving multiple operations. Developers can build agents that conduct research, retrieve information, extract data, call external services and return structured results. Its Workflows system provides event-driven orchestration, allowing developers to combine multiple steps, loops and parallel operations into more controlled processes.

An important part of the ecosystem is LlamaParse, a managed document-processing platform that converts complex files into information suitable for AI applications. It supports document parsing, agentic optical character recognition (OCR), structured data extraction, classification, splitting and indexing. Its document-processing capabilities are designed to handle challenging files containing tables, charts, images, handwriting, forms and complex layouts.

LlamaParse can produce outputs in formats such as Markdown, plain text, JSON, spreadsheets and HTML tables. Developers can use these results to build applications that search documents, extract business information, analyse records and automate document-heavy operations. The platform advertises support for more than 130 file formats and over 80 languages for document parsing.

LlamaIndex also provides LiteParse, an open-source document parser designed for local processing. It can extract text and layout information, provide bounding boxes and perform OCR without requiring a cloud API connection. This gives developers another option when they need local document processing or want to minimise reliance on hosted services.

The platform supports multiple model providers, embedding systems and vector databases. This flexibility allows developers to select different technologies depending on their application requirements, infrastructure and budget. Its integration ecosystem includes providers such as OpenAI, Anthropic, Google, Hugging Face, Cohere and Mistral, as well as storage technologies such as Pinecone, Chroma, Weaviate, Qdrant and MongoDB Atlas.

LlamaIndex is suitable for software developers, AI engineers, startups and enterprise teams building applications that need access to large collections of documents or specialised business information. Typical applications include internal knowledge assistants, research tools, customer support agents, automated document extraction, financial document analysis, contract processing and enterprise search.

The framework is primarily intended for developers who want control over how their AI applications retrieve information and execute workflows. Its managed services provide an additional option for teams that prefer hosted document processing rather than maintaining every component themselves.

Overall, LlamaIndex combines open-source development tools with commercial document-processing services. Its main strength is helping developers transform information stored across different sources into useful context for AI agents and automated applications.

Pricing

LlamaIndex has two distinct pricing arrangements: the open-source framework is free, while LlamaParse and its managed document-processing services use credit-based pricing.

LlamaIndex Framework

• Free to download and use.

• Open-source MIT licence.

• No LlamaIndex subscription is required for the framework itself.

• Developers may still pay separately for external LLM APIs, hosting, vector databases or other infrastructure.

LlamaParse – Pay-as-you-go

• $0 monthly subscription.

• 10,000 free credits per month.

• Additional credits cost $1.50 per 1,000 credits.

• Basic email support.

• Five concurrent parsing jobs.

LlamaParse – Pro

• $500 per month.

• 400,000 monthly credits.

• Additional credits cost $1.25 per 1,000 credits.

• Twenty concurrent parsing and extraction jobs.

• Priority support.

• Higher storage and project limits.

Enterprise

• Custom pricing.

• Volume discounts on credits.

• Up to 100 concurrent parsing, extraction and indexing jobs.

• Enterprise security and access controls.

• Private cloud or hybrid deployment options.

• Dedicated support and account management.

The Starter plan previously advertised on the pricing page is no longer offered to new customers, according to the current billing documentation.

Review

LlamaIndex is a strong development platform for teams building AI applications that need to work with private, specialised or document-heavy information. Its combination of retrieval, data connectors, agent development and workflow orchestration makes it useful for applications that require more than basic chatbot responses.

One of its strongest advantages is its RAG functionality. Developers can connect different data sources, index their contents and retrieve relevant information when an application receives a question. This helps build assistants that respond using an organisation’s own documents instead of relying entirely on the general knowledge of an LLM. It is particularly valuable for internal knowledge systems, research applications and enterprise search.

Its agent and workflow capabilities provide another important advantage. Developers can create applications that perform multiple operations, interact with tools and follow controlled processes. The event-driven Workflows system supports asynchronous execution, state management, loops and parallel steps. These features make it suitable for more complex automation projects, including research assistants, information extraction pipelines and document-processing agents.

LlamaParse strengthens the ecosystem by providing managed document processing. Many business documents contain information that ordinary text extraction tools struggle to interpret, especially when they include tables, charts, diagrams, scanned pages or handwritten content. LlamaParse’s agentic OCR and structured extraction capabilities are designed to make this information more accessible to AI applications. Its ability to return structured outputs is useful for systems that need to extract specific fields from invoices, reports, forms and other records.

Another advantage is its extensive integration ecosystem. Developers can choose from different language models, embedding providers, vector databases and data connectors. This reduces dependence on one model provider and makes it easier to adapt applications when technical requirements change. The framework also supports Python and TypeScript, making it accessible to developers working with either ecosystem.

The open-source framework is particularly attractive to developers who want to experiment, customise components and manage their own infrastructure. Its MIT licence also provides flexibility for commercial development. LiteParse adds another local-processing option for projects that need document extraction without relying on a hosted parsing service.

However, LlamaIndex has a learning curve. Developers need to understand concepts such as embeddings, indexing, retrieval, context management, LLMs and workflow orchestration to use its more advanced capabilities effectively. Building reliable production applications also requires testing retrieval quality, managing errors, controlling API costs and evaluating generated responses.

The commercial services introduce additional cost considerations. LlamaParse uses credits, and the number consumed depends on the processing mode and the complexity of the document. More advanced parsing and extraction operations can cost more than basic text extraction. Organisations processing large document collections therefore need to monitor usage and select suitable processing settings.

The framework itself also does not eliminate the need for external services. Developers may need separate model-provider accounts, vector databases, hosting infrastructure and monitoring tools. These dependencies can increase the complexity of deployment and ongoing maintenance.

LlamaIndex is best suited to developers, AI engineers, startups and organisations building data-aware AI applications. It may be less suitable for beginners seeking a ready-made chatbot without programming or for users who simply want an out-of-the-box document assistant.

Its strongest value comes from combining an extensible open-source framework with managed document-processing capabilities. For projects involving RAG, AI agents, document extraction and multi-step automation, LlamaIndex is a capable option with a broad development ecosystem.