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Neo4j LLM Knowledge Graph Builder Review

The Neo4j LLM Knowledge Graph Builder is an open-source tool designed to extract entities and relationships from unstructured data to construct knowledge graphs for large language models.

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Neo4j LLM Knowledge Graph Builder — product screenshot

Why it matters

1Launched in June 2024, it became the fourth most popular source of user interaction on AuraDB Free.

2Supports a wide range of LLMs including OpenAI (GPT-4o, GPT-4o mini), Google (Gemini 1.5, 2.0 Pro, Flash), Llama 3, and Claude Sonnet (3.5).

3Compliant with ISO 27001, SOC 2 Type 2, and HIPAA Type 1 Attestation standards.

4The February 2025 update introduced Community Summaries and Parallel Retrievers.

overview

What is Neo4j LLM Knowledge Graph Builder?

Neo4j LLM Knowledge Graph Builder is an AI tool developed by Neo4j that enables developers, data scientists, and users building GenAI applications to extract entities and relationships from unstructured data and construct knowledge graphs. It leverages the Neo4j graph database to create and manage a knowledge graph specifically for large language models (LLMs), supporting AI applications with connected data. The tool automates the extraction of entities and relationships from diverse unstructured data sources, including PDF documents, web pages, YouTube transcripts, general documents, and images. It utilizes various advanced LLMs, such as OpenAI (GPT-4o, GPT-4o mini), Google (Gemini 1.5, 2.0 Pro, Flash), Llama 3, Diffbot, Claude Sonnet (3.5), Qwen 2.5, Amazon Nova models, Groq, Ollama, DeepSeek, and Microsoft Phi-4, to identify and extract information. This extracted data is then converted into a graph format and stored in a Neo4j database, forming both a lexical graph of documents and chunks (with embeddings) and an entity graph.

features

Key Features of Neo4j LLM Knowledge Graph Builder

The Neo4j LLM Knowledge Graph Builder provides a suite of functionalities designed to streamline the creation and utilization of knowledge graphs for AI applications. Its core capabilities focus on data extraction, graph construction, and integration with LLMs.

Extracts entities and relationships from unstructured data sources (PDFs, web pages, YouTube transcripts, documents, images).
Leverages the Neo4j graph database for knowledge graph construction and storage.
Supports integration with various LLMs, including OpenAI GPT-4o, Google Gemini 1.5, Llama 3, and Claude 3.5 Sonnet.
Creates and manages a knowledge graph specifically optimized for LLM memory and context.
Provides functionality for defining and integrating graph structures to support AI applications.
Enables the visualization and interaction with constructed knowledge graphs.
Offers Community Summaries and Parallel Retrievers (introduced in February 2025 update).
Forms both a lexical graph of documents and chunks (with embeddings) and an entity graph.

use cases

Who Should Use Neo4j LLM Knowledge Graph Builder?

The Neo4j LLM Knowledge Graph Builder is primarily designed for technical users and organizations focused on leveraging structured data for advanced AI applications. Its capabilities are particularly beneficial for those working with large volumes of unstructured text.

Developers building Generative AI applications: To provide structured, connected data for more accurate and contextually rich AI responses.
Data Scientists: For transforming complex unstructured datasets into queryable knowledge graphs for enhanced analysis and knowledge discovery.
Users building Retrieval-Augmented Generation (RAG) applications: To improve the relevance and explainability of AI agent responses through multi-hop reasoning on a knowledge graph.
Organizations seeking to automate knowledge graph construction: To reduce manual effort in building and maintaining knowledge graphs from diverse text sources.
Researchers and analysts: For uncovering deeper insights and connections within large text corpora by structuring information into a graph format.

how to use

How to Use Neo4j LLM Knowledge Graph Builder

The Neo4j LLM Knowledge Graph Builder is an online application that facilitates the conversion of unstructured text into knowledge graphs. Users typically interact with the tool through its web interface to upload data and configure LLM integrations.

1Access the Neo4j LLM Knowledge Graph Builder application via its web interface.
2Select an LLM provider and configure API keys (e.g., OpenAI, Google Gemini, Llama 3).
3Upload unstructured data from sources such as PDF documents, web pages, YouTube transcripts, or general text files.
4Define the desired graph schema or allow the tool to infer entities and relationships.
5Initiate the knowledge graph construction process, which extracts entities and relationships using the configured LLM.
6Review and interact with the generated knowledge graph within the Neo4j database, utilizing its visualization and querying capabilities.

pricing

Neo4j LLM Knowledge Graph Builder Pricing & Plans

The Neo4j LLM Knowledge Graph Builder operates under a paid model. Specific pricing tiers and detailed cost structures are typically associated with Neo4j's broader cloud offerings, such as AuraDB, which provides the underlying graph database infrastructure. While the tool itself is open-source, its effective use often involves a paid Neo4j database instance and consumption of external LLM APIs, which incur their own costs. Neo4j AuraDB offers various tiers, including a free tier for initial exploration, with paid plans scaling based on database size, performance, and data transfer.

Neo4j AuraDB Free: Includes 500MB storage and 10,000 read/write operations per hour.
Neo4j AuraDB Professional: Tiered pricing based on database size and performance requirements, starting from specific GB/hour rates.
Neo4j AuraDB Enterprise: Custom pricing for large-scale deployments with advanced features and dedicated support.

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Pros

+Directly transforms diverse unstructured text (PDFs, web pages, YouTube transcripts) into structured knowledge graphs.
+Integrates with a wide array of advanced LLMs, including OpenAI GPT-4o, Google Gemini 1.5, Llama 3, and Claude 3.5 Sonnet.
+Leverages the native capabilities of the Neo4j graph database for efficient storage and querying of interconnected data.
+Enhances Retrieval-Augmented Generation (RAG) applications by providing rich context and enabling multi-hop reasoning.
+Compliant with industry standards including ISO 27001, SOC 2 Type 2, and HIPAA Type 1 Attestation.
+Offers features like Community Summaries and Parallel Retrievers for improved knowledge organization and retrieval efficiency.

Cons

−Some user feedback indicates mixed experiences regarding practical implementation and performance, with concerns about complexity and cost compared to vector-only search in specific scenarios.
−While the core tool is open-source, effective deployment often requires a paid Neo4j database instance (e.g., AuraDB) and incurs costs for external LLM API usage.
−Requires familiarity with graph database concepts and potentially Cypher query language for advanced interaction and optimization.
−The 'magical text-to-graph experience' may still require significant configuration and fine-tuning for optimal results with specific datasets.
−The tool's utility can be limited if the underlying unstructured data quality is poor or lacks clear entity-relationship structures.

Similar Tools

Neo4j LLM Knowledge Graph Builder vs Competitors

The Neo4j LLM Knowledge Graph Builder is positioned within the ecosystem of graph databases and AI tools designed for knowledge extraction and RAG applications. It differentiates itself by offering a direct, application-level approach to text-to-graph conversion, leveraging Neo4j's native graph capabilities.

LangChainOn Stork

A comprehensive framework for developing applications powered by LLMs, offering modules for various tasks including knowledge graph construction and integration with different data sources and LLMs.

The Neo4j LLM Knowledge Graph Builder offers a more integrated, application-like experience for direct text-to-graph conversion. LangChain, as a framework, provides the building blocks and flexibility to construct a similar system, but requires more development effort and explicit integration with a graph database.

LlamaIndexOn Stork

A data framework for LLM applications, specializing in connecting custom data sources (like documents) to LLMs, including tools and integrations for knowledge graph creation and retrieval augmented generation (RAG).

While the Neo4j LLM Knowledge Graph Builder provides a ready-to-use application for knowledge graph creation, LlamaIndex is a framework that requires more hands-on development to set up the data ingestion, LLM integration, and graph database connection.

Graphiti↗

An open-source Python framework specifically for building and querying dynamic, temporal knowledge graphs designed to provide context for AI agents.

The Neo4j LLM Knowledge Graph Builder is a general-purpose tool for creating knowledge graphs. Graphiti is more specialized for temporal knowledge graphs tailored for AI agent context, and while open-source, its core Context Graph Engine (part of Zep) can be proprietary, or it requires integration with another graph database.

eknowledge↗

A Python package designed to facilitate the generation of knowledge graphs from textual inputs by leveraging language models to parse text and extract relationships between entities.

The Neo4j LLM Knowledge Graph Builder offers a more complete application experience with UI and direct database integration. eknowledge is a lightweight Python package, providing core text-to-graph extraction functionality programmatically, which means more manual effort for data loading, database storage, and visualization.

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