LlamaIndex vs Neo4j
Relationship
LlamaExtract and Neo4j LLM Knowledge Graph Builder both list document extraction; both also serve buyers who need to extract facts from text; LlamaIndex's scale not recorded.
Assembled from the recorded fields for this pair, not hand-checked. The comparison below is read from each company’s own profile.
4 shared capabilities — Across all their products, both list agent orchestration, document extraction, knowledge retrieval and 1 more.
Ludbee capability tags · every product each company sellsShared product type — Both ship agent platform.
Ludbee product recordsAligned comparison
Capability overlap
Shared · 4
Not verified for Neo4j · 2
Recorded for LlamaIndex. Neo4j’s product records say nothing either way — a missing record is not a missing capability.
Not verified for LlamaIndex · 1
Recorded for Neo4j. LlamaIndex’s product records say nothing either way — a missing record is not a missing capability.
Products, side by side
Algorithmic pairing — assembled from recorded fields, not hand-checked
LlamaIndex
Agent platform
LlamaAgents lets developers build, run, and deploy document-agent workflows.
Neo4j
Agent platform
Low-code builder for agents grounded in a Neo4j knowledge graph, run as managed endpoints on Aura.
No counterpart
LlamaIndex sells these in a stack layer with no product recorded for Neo4j yet — nothing on the other side to compare them against.
API service
Document parsing for LLM pipelines: layout-aware, agentic extraction that turns PDFs, contracts and forms into structured text a retrieval system can index, with an LLM or VLM reading the page when basic parsing is not enough.
Developer tool
LlamaIndex's open-source document parser, published on GitHub for teams that want parsing in their own process rather than through LlamaCloud.
The open-source framework the company is named for: SDKs for building context-aware agents over a company's own data, with Workflows for orchestration.
Workflows is a framework for building multi-step agentic AI applications in Python and TypeScript.
Data service
Extracts structured data from complex documents using custom schemas, with field-level confidence scores and citations back to source.
The Index part of LlamaParse: intelligent chunking and embedding that turns parsed documents into a searchable knowledge base for agents.
Neo4j sells these in a stack layer with no product recorded for LlamaIndex yet — nothing on the other side to compare them against.
Application
Graph-powered intelligence analysis platform for government and enterprise, connecting people, organizations, locations and events into a shared intelligence picture; positioned as an open-standard alternative to Palantir Gotham.
Neo4j LLM Knowledge Graph Builder uses machine-learning models to transform documents into knowledge graphs of entities and relationships.
Platform
Serverless graph analytics on any data platform with no ETL: run graph algorithms on data from AuraDB or other sources, billed per GB-hour used.
Fully managed Neo4j graph database in the cloud, with a vector index for GenAI and GraphRAG retrieval; Free, Professional, Business Critical and Virtual Dedicated Cloud tiers.
Graph analytics and machine-learning library for Neo4j: 65+ graph algorithms, node embeddings and graph-native ML pipelines for clustering, similarity and classification.
Graph queries and 65+ graph algorithms run directly on an existing data warehouse or lake such as Snowflake, Databricks or Fabric with no ETL, with GenAI text2cypher and automated schema design to build knowledge graphs.