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The 4 product components of LlamaIndex: Parsing, Extraction, Knowledge Management & Agent Framework (2025 Guide)

Tech Lead, CTO AXI Technologies

Romain DE LA SOUCHÈRE

Published on 23 mai 2025 · 10 min of reading

Table of content

Overview of the LlamaIndex suite

LlamaParse – GenAI-native Document Parsing

LlamaExtract – Structured Data Extraction

Knowledge Management (KM) – Indexing & Search

Agent Framework – Multi-Agent Orchestration

How to choose (or combine) the components?

Strengths and limitations of a modular approach

Conclusion & resources

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Since its inception as GPT-Index, LlamaIndex has evolved to meet the increasing complexity of generative AI projects in enterprises. Segmenting the offering into four components — Parsing, Extraction, Knowledge Management, and Agent Framework — allows for targeted responses to various business needs: document ingestion, structured data extraction, knowledge management, and agent orchestration. This modularity promotes gradual adoption and fine integration into existing stacks.

The LlamaIndex suite is aimed at data engineers, AI developers, IT architects, and product managers. The benefits include: accelerating the deployment of RAG (Retrieval-Augmented Generation) solutions, automating data structuring, ensuring reliable knowledge governance, and industrializing the orchestration of LLM agents.

TL;DR:

4 components = 1 stack: Parsing → Extraction → KM → Agents.
LlamaParse v2025 adds skew detection and multi-language support.
LlamaExtract (beta) outputs your data in JSON/SQL with a simple YAML schema.
Knowledge Management centralizes your vector + hybrid indexes, prepared for RAG.
Agent Framework + AgentWorkflow = scalable multi-tool orchestration.

Overview of the LlamaIndex suite

From GPT-Index to 4 products (2023 → 2025)

LlamaIndex started as an open-source library for document indexing with LLMs. By 2025, the offering has been structured around 4 major products, each addressing a key step in the AI pipeline:

LlamaParse: GenAI-native document parsing.
LlamaExtract: Schema-driven extraction of structured data.
Knowledge Management (KM): Indexing, searching, and governance of knowledge.
Agent Framework: Orchestration of multi-step agents with AgentWorkflow.

Business model: open-source + managed LlamaCloud

LlamaIndex offers a dual approach: a very active open-source base (>4M developers), and managed services via LlamaCloud, providing scalability, monitoring, and enterprise support.

How do the components fit together?

Each component can be used alone or combined in a complete pipeline. Example: LlamaParse ingests a PDF, LlamaExtract structures the data, KM indexes everything, and Agent Framework orchestrates an agent that responds to business queries.

👉 For a complete overview of LlamaIndex and its key features, you can refer to our 2025 guide on LlamaIndex.

LlamaParse – GenAI-native Document Parsing

Key features: OCR, tables, skew detection 2025

LlamaParse excels in analyzing complex documents thanks to:

Advanced OCR (multilingual optical character recognition)
Intelligent table extraction
Skew detection (new for 2025): automatic correction of tilted scanned documents
New parsing models (May 2025), optimized for accuracy across various formats

Supported formats (PDF, DOCX, PPTX, images)

LlamaParse natively handles:

PDF (text and scanned)
DOCX, PPTX
Images (JPEG, PNG, TIFF)
Multi-page and mixed documents

Architecture: parsing → chunking → embedding

The LlamaParse pipeline:

Parsing: raw content extraction
Chunking: intelligent segmentation (pages, sections, tables)
Embedding: vectorization for semantic search

Use cases & best practices

Legal contracts: reliable extraction of clauses
Invoices: OCR + table parsing
Financial reports: managing complex layouts

Best practices: validate the quality of parsing on a sample, adjust chunking parameters according to the document type.

Announced roadmap

Support for CAD formats and emails (Q3 2025)
Improvement of multilingual parsing
User feedback API to refine models

👉 To get started with LlamaIndex in Python, check out our essential tutorial

LlamaExtract – Structured Data Extraction

Schema-driven engine: YAML → JSON/SQL

LlamaExtract allows you to define extraction schemas in YAML, automatically generating structured outputs (JSON, SQL-ready). Ideal for transforming unstructured documents into usable databases.

Creation, testing, and versioning of schemas

UI/CLI for creating and testing schemas
Native versioning to track the evolution of extraction models
Automatic validation of outputs

Current limitations of the beta and costs

Limited to 1000 extractions/day in public beta (May 2025)
Cost per extraction on LlamaCloud, with volume packages
Support for nested schemas is being improved

Business examples: finance, supply chain, compliance

Finance: automatic extraction of KPIs in balance sheets
Supply chain: structuring delivery notes
Compliance: extraction of legal mentions in contracts

Knowledge Management (KM) – Indexing & Search

ETL pipeline: connectors, transformations, indexes

KM offers a complete ETL pipeline:

Connectors: SQL, NoSQL, APIs, S3, SharePoint, etc.
Transformations: cleaning, enrichment, metadata mapping
Indexing: creating indexes optimized for search

Index types: Vector, Keyword, Hybrid, Composable

Vector: semantic search (embeddings)
Keyword: exact search (full-text)
Hybrid: combination of both
Composable: multi-source and multi-format indexes

Incremental refresh, monitoring & governance

Incremental ingestion (new for 2025)
Monitoring: indexing dashboards, error alerting
Governance: access management, query auditing

Integration with RAG and AutoRAG

KM natively feeds RAG pipelines, with AutoRAG support for automatic optimization of prompts and context retrieval.

Agent Framework – Multi-Agent Orchestration

AgentWorkflow Architecture: Agent, Tool, Memory, State

The new AgentWorkflow structures agents around:

Agent: business logic
Tool: access to APIs, databases, external functions
Memory: management of conversational context
State: tracking the state of the workflow

-> Discover how to create a conversational agent with LlamaIndex in 5 min.

Agent patterns: Simple QA, Planner-Executor, Workflow

Simple QA: question/answer based on documentation
Planner-Executor: decomposition and execution of complex tasks
Workflow: multi-step orchestrations (e.g., HR onboarding)

Production deployment: Docker, FastAPI, LlamaCloud

Docker for containerization
FastAPI for exposing REST endpoints
LlamaCloud for scalability and monitoring

LLM Compatibility: OpenAI, Anthropic, Mistral, local Llama 3

Native support for major LLMs on the market, including open-source and on-premise deployed models.

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How to choose (or combine) the components?

Decision matrix by need (parse, extract, KM, agent)

Business need	LlamaIndex component to activate
Document ingestion	LlamaParse
Data structuring	LlamaExtract
Search/Indexing	Knowledge Management
Agent automation	Agent Framework

End-to-end scenarios: from raw data to operational agent

Example: A consulting firm wants to automate contract analysis:

LlamaParse ingests and cleans PDFs
LlamaExtract extracts key clauses
KM indexes the data for search
Agent Framework deploys an agent that responds to legal questions

Strengths and limitations of a modular approach

Strengths: flexibility, connectors, community of 4M + devs

Flexibility: gradual adoption, tailored integration
Rich ecosystem: numerous connectors, plugins, integrations
Community: support, feedback, contributions

Points of caution: GPU overhead, monitoring maturity, security

Costs: intensive parsing/extraction may generate GPU overhead
Monitoring: still improvable on some complex workflows
Security: attention to access rights management and data confidentiality

Conclusion & resources

Quick implementation checklist

Identify needs (parse, extract, KM, agent)
Prototype each component on a pilot dataset
Monitor costs and performance
Involve business users to validate outputs
Go into production via LlamaCloud or on-premise

Official documentation, starter templates

-> Llama Parse

-> Llama Extract

-> Knowledge Management

-> Agent framework

-> LlamaIndex Documentation

-> Our guide on creating a secure internal chatbot for your sensitive data.

If you are still unsure or need specific support, our team is here to help

Want to go further?

This topic is part of our Generative AI for Developers course. Browse the full programme, or get it by email.

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Romain DE LA SOUCHÈRE

Tech Lead, CTO AXI Technologies

Expert Data Engineering et Cloud, Romain affiche plus de 11 ans d'expérience, dont plusieurs années comme Lead Developer sur des solutions Smart Building haute performance. Il y a conçu et mis en production des moteurs de traitement capables d'absorber des centaines de milliers de données de capteurs par minute, ainsi que des bases clusterisées gérant plus de 10 millions de données dynamiques. Certifié Microsoft Azure DevOps Engineer Expert, il maîtrise aussi bien le développement back-end (Python, C#) que le DevOps (Docker, Kubernetes, Terraform) et les agents LLM. Formateur en Python, cloud, DevOps et IA générative appliquée, il forme avec une obsession : Amener chaque apprenant à concevoir et déployer des architectures réellement scalables en production.

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