graphrag and llm-graph-builder
These are **complements**: GraphRAG provides the RAG framework and query patterns while LLM Graph Builder supplies the upstream graph construction pipeline from unstructured data, making them useful in sequence within the same workflow.
About graphrag
microsoft/graphrag
A modular graph-based Retrieval-Augmented Generation (RAG) system
Extracts knowledge graphs from unstructured text using LLMs, then uses those graph structures to improve retrieval and reasoning for private data. Implements a data indexing pipeline that transforms narrative documents into entity-relationship graphs, enabling more contextual and discovery-oriented query responses compared to standard vector retrieval. Supports prompt tuning workflows and integrates with major LLM providers through a configuration-driven architecture.
About llm-graph-builder
neo4j-labs/llm-graph-builder
Neo4j graph construction from unstructured data using LLMs
Supports multiple input sources (PDFs, videos, web pages, S3/GCS buckets) and 10+ LLM providers through LangChain, with configurable embedding models and vector search capabilities. Built on a FastAPI backend and React frontend, featuring conversational graph querying, token usage tracking per user, and real-time visualization in Neo4j Bloom. Deployable locally via Docker Compose, separately for development, or to Google Cloud Run with support for both cloud and local LLMs like Ollama.
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