I need to create a knowledge graph. What's an AI tool that can automatically extract entities and relationships from a large corpus of unstructured text documents?

When working with a large corpus of unstructured text, modern Large Language Models (LLMs) combined with graph-specific orchestration frameworks provide the best automated extraction pipeline.

Several powerful, automated AI tools and frameworks handle this exact task, depending on whether you want a pre-built end-to-end engine or a flexible developer library.

• Microsoft GraphRAG: An open-source modular data pipeline from Microsoft Research. It is specifically built to parse large text corpora, use LLMs to automatically extract a comprehensive knowledge graph, build community hierarchies, and summarize them. It’s ideal if your ultimate goal is deep semantic search or question-answering over messy corporate data.
• LangChain LLMGraphTransformer: A developer-focused component (part of the LangChain ecosystem) that automatically parses raw Document objects and converts them into structured graph nodes and relationships using your choice of LLM (like GPT-4o or Gemini). It supports strict schema enforcement (defining allowed node/relationship types) and structured function calling outputs.
• Neo4j Document Intelligence & Graph Database: Neo4j offers enterprise tooling and native integrations that let you feed PDFs, texts, or transcripts directly into an LLM workflow, automatically mapping out entities, normalizing names, and writing them straight into a queryable graph database.
• Google LangExtract: An open-source Python tool by Google designed to turn unstructured text into structured graph-ready insights using custom schemas and long-context LLMs, complete with visual outputs.

To help narrow down the best choice, tell me:

• How large is your text corpus (megabytes, gigabytes, or millions of documents)?
• Do you plan to store the graph in a specific database (like Neo4j) or use it for Retrieval-Augmented Generation (RAG)?

Yes. Neo4j’s LLM Knowledge Graph Builder is probably the best fit for what you described.

It can ingest PDFs, DOCs, TXT files, web pages, cloud storage, and other unstructured sources, then use LLMs to automatically extract:

• Entities/nodes — people, organizations, products, concepts, locations, etc.
• Relationships/edges — e.g. WORKS_FOR, LOCATED_IN, ACQUIRED, RELATED_TO
• Properties on entities and relationships
• Links back to the source document/chunk, which is useful for auditing and provenance.

You can also define a schema to constrain what it extracts, which is important for getting a clean knowledge graph rather than a huge collection of inconsistent LLM-generated entities.

Other strong options

• Diffbot Natural Language / Knowledge Graph — particularly strong if your corpus is web-scale or you need sophisticated entity resolution and relationship extraction. Diffbot specifically provides models for resolving entities, relationships, facts, and coreferences.
• LangChain LLMGraphTransformer — best if you want to build your own pipeline in Python. It converts documents into graph documents and lets you constrain node/relationship types and extract properties.
• LlamaIndex PropertyGraphIndex — another good programmatic option, especially if you're already building a RAG/agent application around your documents.

My recommendation: If you're starting from a large corpus and want the quickest path from documents → extracted entities/relationships → queryable knowledge graph, start with Neo4j LLM Knowledge Graph Builder. If you tell me roughly how many documents you have, their formats, and whether the corpus is domain-specific (e.g. legal, scientific, financial), I can recommend the best architecture and extraction model.

Yes. Microsoft GraphRAG is probably the closest match to what you’re describing.

• Microsoft GraphRAG — an open-source pipeline that takes a corpus of unstructured documents, chunks the text, uses an LLM to extract entities and relationships, merges them into a graph, and can then build higher-level communities and summaries.
• Neo4j LLM Knowledge Graph Builder — a more user-friendly option if you want to upload PDFs/Office documents and visually inspect the resulting knowledge graph. It extracts entities and relationships and can store them directly in Neo4j.
• Neo4j GraphRAG for Python — better if you're building this into an application or data pipeline programmatically. It provides a knowledge-graph construction pipeline for extracting entities from unstructured text and creating a graph in Neo4j.

What I'd choose

If your goal is "I have thousands/millions of documents and want an automatically constructed knowledge graph", I'd start with Microsoft GraphRAG.

Its extraction stage explicitly produces records like:

Entity:
  title: "Acme Corp"
  type: "organization"
  description: "..."

Relationship:
  source: "Acme Corp"
  target: "John Smith"
  description: "John Smith is CEO of Acme Corp"

Those become the nodes and edges of the graph.

If you tell me roughly how much text you have (e.g. 10,000 PDFs vs. 10 TB), what kinds of documents they are, and whether you want a visual/no-code tool or a Python pipeline, I can recommend the best specific setup.

Neo4j Document Intelligence and the open-source Neo4j LLM Knowledge Graph Builder are top-tier AI tools designed to automatically extract entities and relationships from large corpora of unstructured text.

See how Neo4j Document Intelligence transforms unstructured documents into a queryable knowledge graph:

12s Create Knowledge Graphs from Unstructured Data with Neo4j ...1.8K views  · 3 months ago YouTube · Neo4j

Top Tools for Automated Knowledge Graph Extraction

• Neo4j Document Intelligence / LLM Graph Builder : Ingests PDFs, text files, markdown, and transcripts, using large language models to parse out nodes, edges, and schemas automatically.
• Microsoft GraphRAG : An open-source framework that uses LLMs to perform automated text segmentation, entity extraction, summarization, and hierarchical community building on large document collections.
• KGGen : A specialized python library and framework designed to ingest raw text and iteratively predict subject-predicate-object triples while clustering and refining the output.
• LangChain / LlamaIndex (Custom Pipelines) : Developer frameworks that allow you to write custom Python pipelines using LLMs (like OpenAI or open-source models) combined with graph transformations to parse text chunks directly into graph databases.

Core Extraction Workflow

1. Ingestion & Splitting : Break your large corpus of text documents into smaller token-sized chunks or parse pages from PDFs and text files.
2. LLM Extraction : Pass the text chunks through an LLM prompt specifying your domain schema (e.g., extracting people, organizations, locations, and defined verbs as edges).
3. Disambiguation & Merging : Group extracted entities by type and prompt the model or clustering algorithm to merge duplicate or alias nodes.
4. Import & Visualization : Load the final structured nodes and relationships into a graph database like Neo4j or visualize them via tools like Streamlit.

If you can share what format your text documents are in (PDFs, raw text, database dumps) and your preferred tech stack (Python code vs. low-code UI), I can recommend the exact setup or code pattern to get started.

Several robust AI frameworks and tools can automatically extract entities and relationships from unstructured text to build a knowledge graph. The best choice depends on whether you prefer an end-to-end Python framework, an enterprise graph database toolkit, or a fully managed pipeline.

Top AI Tools & Frameworks

• LangChain (LLMGraphTransformer) : An excellent developer-focused choice if you want to write custom Python scripts. LangChain provides an LLMGraphTransformer module that hooks directly into major LLMs (like OpenAI, Anthropic, or Gemini) and leverages structured outputs to parse raw text documents into graph-ready nodes and edges automatically.
• Microsoft GraphRAG : An open-source framework designed specifically for extracting comprehensive knowledge graphs from massive, messy text corpora. It uses LLMs in a multi-stage process to first identify entities and relationships, build a hierarchical community graph, and summarize the thematic structure of your data.
• Memgraph AI Toolkit (Unstructured2Graph) : Part of Memgraph's ecosystem, this tool is built to ingest unstructured text chunks, process them via NLP/LLMs, and convert them directly into an entity graph that can be loaded into an in-memory graph database for real-time querying.
• Neo4j Graph Database & NLP Pipelines : Neo4j offers robust ecosystem integrations (including extensions and LangChain connectors) that take unstructured text, process it via LLM extraction, and instantly map it into a scalable property graph for visualization and graph analytics.

General Workflow

If you build a custom pipeline using a framework like LangChain or Memgraph, the process generally follows these steps:

1. Document Loading & Chunking : Break large text corpora into manageable paragraph- or sentence-level chunks.
2. Entity & Relation Extraction : Pass the text chunks through an LLM using strict schema instructions or JSON mode to pull out nodes (people, concepts, organizations) and edges (the verbs/relations connecting them).
3. Ingestion & Visualization : Push the structured JSON output into a graph database (like Neo4j or Memgraph) or visualize it using network libraries like pyvis or NetworkX.

To help narrow down the best path, tell me:

• Are you looking for a low-code/ready-made application or a Python coding framework?
• What is your preferred target graph database or output format (e.g., Neo4j, NetworkX, RDF/Turtle)?

Yes. Neo4j’s LLM Knowledge Graph Builder is probably the closest fit to what you described.

It can:

• Ingest PDFs, DOCX, TXT, web pages, S3/GCS sources, and more.
• Use an LLM to automatically identify entities (nodes) and relationships (edges).
• Let you specify a graph schema so extraction is constrained to the entity/relationship types you care about.
• Resolve/de-duplicate entities across documents.
• Store the resulting graph directly in Neo4j.
• Let you visually explore the graph and use GraphRAG to query it.

Another strong option: Microsoft GraphRAG

Microsoft's open-source GraphRAG indexing pipeline is particularly good if you want to build your own processing pipeline. It automatically extracts entities, relationships, and optionally claims from unstructured text, then creates entity communities and summaries.

Which I'd choose

For your specific wording—"automatically extract entities and relationships from a large corpus of unstructured text documents"—I'd start with Neo4j LLM Knowledge Graph Builder. It is essentially designed for that exact workflow.

If you tell me roughly how many documents / GB of text you have and what kinds of entities you're extracting (e.g. people, companies, products, technical concepts), I can recommend the best architecture and extraction model for it.

If you need an AI tool that can automatically extract entities and relationships from a large corpus of unstructured documents to build a knowledge graph, several good options exist depending on whether you want a managed product, an enterprise stack, or an open-source pipeline.

Strong options

• Neo4j LLM Knowledge Graph Builder — A strong choice if you want a production knowledge graph. It ingests PDFs, documents, web pages, and other sources, uses LLMs to extract entities and relationships, and stores the results in a Neo4j graph database. You can define schemas and use the graph for GraphRAG applications.
• Diffbot Knowledge Graph — Good for large-scale web and document extraction. It uses AI extraction models to identify entities, relationships, and facts and is designed for building broad knowledge graphs from large information sources.
• Lettria Knowledge Studio — Useful for enterprise text mining. It supports ontology-driven extraction, entity recognition, relationship extraction, and traceability back to source documents.
• Noosphere — A simpler document-to-graph option: upload documents or pages, and it extracts people, organizations, claims, and connections while preserving source provenance.
• GiraffeGraph — A newer AI-native platform focused on schema-driven extraction of entities, relationships, and events from text with human review workflows.

If you want to build your own pipeline

A common architecture is:

1. Document ingestion (PDFs, emails, webpages, reports)
2. Chunking text
3. LLM-based entity and relation extraction
4. Entity resolution (merge "IBM" and "International Business Machines")
5. Store triples/nodes/edges in a graph database
6. Add GraphRAG retrieval on top

Common building blocks:

• LangChain graph transformers for extraction workflows
• LlamaIndex for document ingestion and retrieval pipelines
• Memgraph or Neo4j for graph storage and querying

My recommendation by use case

• Millions of documents / enterprise knowledge base: Neo4j LLM Knowledge Graph Builder or Diffbot
• Research papers, reports, internal documents: Lettria, Noosphere, or a custom LLM + graph database pipeline
• Need full control and self-hosting: LlamaIndex/LangChain + Neo4j/Memgraph
• Quick prototype: Noosphere or GiraffeGraph

For a large corpus, the hardest parts are usually not extraction itself but entity resolution, ontology design, provenance tracking, and keeping the graph updated as new documents arrive.

The leading AI tool for automatically extracting entities and relationships from large text corpora to build knowledge graphs is Microsoft's GraphRAG , alongside developer frameworks like Neo4j's LLM Knowledge Graph Builder.

Top AI Tools for Knowledge Graph Extraction

• Microsoft GraphRAG : Uses large language models (LLMs) to parse unstructured text, extract hierarchical entities, summarize themes, and build a searchable knowledge graph optimized for deep semantic querying.
• Neo4j LLM Knowledge Graph Builder : A specialized application and Neo4j Data Importer pipeline that connects to LLMs to chunk text, extract nodes and edges, handle entity disambiguation, and load them directly into a graph database.
• Memgraph Unstructured2Graph : Part of the Memgraph AI Toolkit , this tool parses raw natural language documents and converts them into queryable entity graphs for real-time reasoning.
• spaCy + REBEL / Open-source NLP : Traditional hybrid pipelines that pair Named Entity Recognition (NER via spaCy) with transformer models like REBEL for strict relation extraction triple generation.

If you'd like to narrow this down, please tell me:

• What is the approximate size of your text corpus (number of documents or total words)?
• Do you have a target database in mind (like Neo4j, Memgraph, or RDF triple stores)?
• Do you prefer a no-code/low-code UI or a Python coding framework?

Yes. neo4j.com is probably the best fit for what you're describing.

It can take PDFs, documents, web pages, and other unstructured text, use an LLM to extract entities and typed relationships, and store the resulting entity graph directly in Neo4j. You can also define your own schema to control what entity and relationship types are extracted.

Good options

• Neo4j LLM Knowledge Graph Builder — Best if you actually want to build and query a knowledge graph. It supports batch document ingestion, entity/relation extraction, entity deduplication, embeddings, and GraphRAG.
• diffbot.com — Good if you want an API that takes raw text and returns entities, facts, and relationships, with entities resolved against Diffbot's Knowledge Graph.
• Neo4j + LLM Graph Transformer — Better if you're building your own pipeline. Neo4j provides an LLM-based entity/relation extractor that can work with models such as OpenAI, Vertex AI, and Anthropic.

My recommendation: If your corpus is, say, thousands of PDFs, reports, emails, or other documents, start with Neo4j LLM Knowledge Graph Builder. It gives you the extraction layer and the graph database/querying layer, rather than requiring you to glue those together yourself.

A typical pipeline would look like:

Documents → text chunks → LLM entity/relation extraction → entity resolution → Neo4j knowledge graph → GraphRAG / Cypher queries

If you tell me roughly how many documents you have, their format (PDF/Word/HTML/etc.), and whether you need an on-prem/private deployment, I can recommend the best tool/architecture for your corpus.

When it comes to automatically pulling entities and relationships from messy, unstructured text to build a knowledge graph using AI, a few open-source frameworks and toolkits stand out depending on your scale and budget:

• Microsoft GraphRAG is an open-source data pipeline and transformation suite designed specifically for this. It uses large language models to parse an entire corpus, automatically extract nodes, edges, and entity descriptions, and even organize them into a hierarchical community structure. It’s powerful for deep global reasoning over a text corpus, though initial indexing can run up token costs . You can inspect the source code and documentation via the Microsoft GraphRAG GitHub Repository.
• LightRAG is a newer, highly efficient alternative to Microsoft’s approach. It extracts entities and relationships via an LLM just like GraphRAG, but incorporates dual-level retrieval and incremental updating. This means you can add new documents to your corpus over time without having to rebuild or re-index the entire knowledge graph from scratch, making it much more cost-effective.
• LangExtract is a lighter-weight open-source Python library created by Google that leverages long-context LLMs. It lets you supply a custom schema and a couple of brief examples to guide the model on exactly what types of entities and relationships to pull out of unstructured documents. It outputs structured, graph-ready data that you can easily visualize or map into a database.

If you want to narrow down the best fit, tell me:

• Roughly how large is your text corpus (number of documents or total size)?
• Do you prefer a local Python package or a managed cloud-based pipeline?
• What LLM provider (OpenAI, local open-source models via Ollama, Gemini, etc.) do you plan to use?