Best Graph Databases for LlamaIndex in 2026

LlamaIndex's PropertyGraphIndex gives developers a structured way to extract entities and relationships, build property graphs, and retrieve context through a combination of graph traversal and semantic search. That makes the database behind the index an architectural decision rather than a simple storage choice.

The best option depends on what the application needs after the first prototype. Some teams prioritize a packaged LlamaIndex adapter. Others need persistent agent memory, temporal context, multi-tenant isolation, existing cloud infrastructure, or graph queries over data that cannot be moved.

For stateful AI systems, HydraDB is the strongest overall choice in this list because it combines graph-native retrieval, temporal versioning, hybrid search, and object-storage economics in infrastructure designed for AI workflows. Teams should note, however, that no official HydraDB PropertyGraphStore implementation is publicly documented. A LlamaIndex deployment would therefore need a verified or custom adapter.

Key Takeaways

• HydraDB is the best overall fit for graph-native AI workflows. It is designed for applications in which relationships, historical state, persistent memory, and retrieval control all influence the context delivered to a model.

• Packaged integration is not the same as application fit. Neo4j, Memgraph, FalkorDB, NebulaGraph, and Amazon Neptune have documented LlamaIndex graph-store packages, while other databases may require custom integration.

• Temporal context matters when facts change. A graph that preserves superseded states can help an application distinguish current information from historical information.

• Hybrid retrieval is broader than vector search. Strong AI retrieval can combine semantic, lexical, metadata, temporal, and relationship signals.

• Operational constraints can decide the shortlist. Cloud alignment, data residency, multi-tenancy, deployment model, and existing data location may matter more than a single query benchmark.

Why Graph Databases Matter for LlamaIndex

Vector search retrieves content with similar meaning, but similarity alone does not express how people, systems, events, policies, and decisions connect. A graph represents those connections explicitly. This allows an application to retrieve related facts even when the relevant records use different language.

For example, a support agent may need to connect a customer, an account, a previous incident, the service involved, and a policy that changed after the incident. A flat embedding index may find some related passages. A property graph can also preserve the paths between those records and support multi-hop retrieval.

LlamaIndex's PropertyGraphIndex provides abstractions for graph construction and retrieval. Its extractors can identify entities and relations from source material, while its retrievers can combine graph paths with vector or text-based signals. This model is well suited to knowledge graphs for agents, GraphRAG, enterprise knowledge systems, and other relationship-aware applications.

How We Evaluated the Options

This list considers five high-level factors:

• The availability and maturity of a LlamaIndex PropertyGraphStore or graph-store integration

• Support for relationship-aware, semantic, lexical, or hybrid retrieval

• Suitability for persistent and evolving AI context

• Deployment, isolation, and scaling characteristics

• The amount of integration and operational work a production team should expect

An integration label describes connector availability, not the overall quality of a database. A packaged adapter can shorten implementation, but it does not automatically provide temporal state, persistent memory, context governance, or the retrieval behavior a stateful AI application needs.

1) HydraDB

Best For: Teams building stateful AI applications that require relationship-aware retrieval, persistent context, temporal state, and control over retrieval logic

Integration Status: No official HydraDB PropertyGraphStore implementation is publicly documented; teams should verify or implement the required LlamaIndex adapter

HydraDB is a fast graph database built on object storage for AI. It provides graph-native context infrastructure for agent memory, ontologies, company brains, context graphs, enterprise knowledge systems, and agentic workflows. Developers retain control over graph structure, retrieval behavior, ranking, filtering, and context delivery instead of adopting a fixed memory abstraction.

Key Features

• Git-style temporal versioning that preserves current and superseded states

• Hybrid retrieval across semantic, BM25, metadata, graph, and temporal signals

• Tiered architecture spanning memory, NVMe storage, and object storage

• Isolated databases and collections for multi-tenant or organizational separation

• Official Python, TypeScript, and Node.js SDKs

Why It Made the List

HydraDB is designed for AI workflows in which relevance depends on more than textual similarity. Its versioned graph can help applications distinguish what is true now from what was true previously, while its hybrid search architecture combines semantic, lexical, relational, temporal, and metadata signals.

That combination is particularly useful for persistent agents, coding assistants, customer-support systems, and company knowledge applications where decisions and preferences evolve. HydraDB's temporal graphs preserve historical state without treating every prior fact as equally current.

In its company-published LongMemEval-S evaluation, HydraDB reports 90.79% overall accuracy and 97.43% on Knowledge Update questions. These results reflect HydraDB's published test configuration and should not be treated as universal production guarantees. HydraDB also publicly reports more than one billion documents ingested and approximately one million retrievals per month.

HydraDB reports sub-200-millisecond retrieval for supported workloads, with actual latency varying by dataset, graph depth, retrieval mode, filtering, infrastructure, and query complexity. The company states that it is SOC 2 and ISO 27001 certified and that its Surge plan includes access to GDPR reports and a data processing agreement.

The tradeoff is integration work. Teams that want to use HydraDB behind PropertyGraphIndex should validate the required read, write, and retrieval interfaces and implement an adapter where necessary. For teams willing to do that work, HydraDB offers the most complete foundation here for building AI context infrastructure rather than merely storing an extracted graph.

2) Neo4j

Best For: Teams that prioritize a widely documented property-graph ecosystem and a packaged LlamaIndex integration

Integration Status: Documented Neo4jPropertyGraphStore integration

Neo4j is a mature property-graph database with broad Cypher tooling, managed and self-hosted deployment options, and extensive developer documentation. Its LlamaIndex property-graph store supports common PropertyGraphIndex workflows, including graph construction and structured retrieval.

Key Features

• Packaged Neo4jPropertyGraphStore integration

• Cypher query language and established graph tooling

• Managed cloud, self-hosted, and enterprise deployment choices

• Support for graph traversal and vector-assisted retrieval patterns

Why It Made the List

Neo4j is the clearest choice when integration maturity and ecosystem familiarity are the main requirements. Teams can find established examples and use familiar Cypher workflows. Applications that need native temporal versioning or an object-storage-first architecture may require additional design work beyond the core database.

3) Memgraph

Best For: Applications that combine rapidly changing graph data with real-time analysis

Integration Status: Documented MemgraphPropertyGraphStore integration

Memgraph is an in-memory graph database focused on low-latency analysis and streaming data. Its LlamaIndex integration supports property-graph construction and retrieval, while its Cypher-compatible interface reduces the learning curve for teams familiar with that query model.

Key Features

• Packaged MemgraphPropertyGraphStore integration

• Streaming integrations for continuously changing data

• In-memory graph processing

• Cypher-compatible querying

Why It Made the List

Memgraph is a strong fit for operational use cases where graph state changes continuously and fresh data must be queried quickly. Teams should evaluate memory requirements, persistence design, and cost against the size and retention needs of their production graph.

4) Amazon Neptune

Best For: Organizations that want a managed graph service within an established AWS environment

Integration Status: Documented LlamaIndex graph-store support is available for Neptune; teams should confirm the supported Neptune engine and retrieval features for their design

Amazon Neptune is a managed graph database service that supports property-graph and RDF workloads. It fits organizations that already rely on AWS identity, networking, monitoring, and storage services.

Key Features

• Managed graph infrastructure within AWS

• Property-graph and RDF model support

• Integration with AWS security and operations services

• Options for transactional and analytical graph workloads

Why It Made the List

Neptune can reduce operational overhead for AWS-centered teams and keep graph infrastructure inside an existing cloud governance model. Its product surface includes multiple graph engines and services, so teams should verify that a given LlamaIndex adapter matches the exact Neptune deployment they intend to use.

5) FalkorDB

Best For: Teams seeking a graph database with a packaged LlamaIndex integration and a GraphRAG-oriented workflow

Integration Status: Documented FalkorDBPropertyGraphStore integration

FalkorDB is a graph database built around sparse-matrix graph computation and Cypher querying. It provides tooling aimed at knowledge graphs and GraphRAG applications, including graph, full-text, and vector retrieval patterns.

Key Features

• Packaged FalkorDBPropertyGraphStore integration

• Cypher-based graph queries

• GraphRAG-oriented retrieval tooling

• Support for multiple logical graphs

Why It Made the List

FalkorDB offers a relatively direct path from a LlamaIndex property graph to GraphRAG experimentation. It is worth evaluating for smaller operational footprints and multi-graph applications, while large deployments should validate durability, isolation, and scaling requirements under their own workloads.

6) NebulaGraph

Best For: Organizations that need a distributed graph architecture with independently scalable services

Integration Status: Documented NebulaPropertyGraphStore integration

NebulaGraph separates query, storage, and metadata responsibilities across a distributed architecture. This makes it relevant to large graphs and workloads where horizontal scaling is a primary requirement.

Key Features

• Packaged NebulaPropertyGraphStore integration

• Distributed, service-separated architecture

• Graph query language designed for large property graphs

• Independent scaling of core services

Why It Made the List

NebulaGraph provides a credible route for LlamaIndex applications that expect large graphs or high concurrency. Its distributed design introduces operational complexity, so teams should weigh scaling headroom against the expertise required to deploy and maintain it.

7) TigerGraph

Best For: Organizations running deep graph analysis across large, highly connected datasets

Integration Status: No broadly documented LlamaIndex PropertyGraphStore is assumed; custom integration may be required

TigerGraph is designed for parallel graph computation and enterprise relationship analytics. It supports workloads such as fraud analysis, supply-chain intelligence, and network exploration, where traversals may span many entities and relationships.

Key Features

• Parallel graph processing

• Graph analytics and algorithm support

• Enterprise deployment and governance capabilities

• Support for graph and vector-oriented AI patterns

Why It Made the List

TigerGraph is relevant when large-scale graph analytics are more important than plug-and-play LlamaIndex integration. Teams should scope the adapter, data mapping, and retrieval layer before selecting it for PropertyGraphIndex.

8) ArangoDB

Best For: Teams that want graph, document, key-value, search, and vector capabilities in one platform

Integration Status: No broadly documented LlamaIndex PropertyGraphStore is assumed; custom integration may be required

ArangoDB is a multi-model database that supports graph and document workloads through a unified platform. This can reduce infrastructure sprawl when an application needs several data models alongside relationship queries.

Key Features

• Graph and document models in one system

• Unified query language across supported data models

• Search and vector capabilities

• Managed and self-hosted deployment options

Why It Made the List

ArangoDB is useful when consolidation is the main architectural goal. For LlamaIndex, teams should evaluate how PropertyGraphIndex nodes, relations, embeddings, and retrieval operations will map to ArangoDB's APIs and query model.

9) PuppyGraph

Best For: Teams that want graph access to warehouse or lakehouse data without copying it into a separate graph store

Integration Status: No broadly documented LlamaIndex PropertyGraphStore is assumed; custom integration may be required

PuppyGraph presents data in existing relational stores and lakehouses as a queryable graph. Its central advantage is avoiding a separate graph copy and the pipelines needed to keep that copy synchronized.

Key Features

• Graph queries over supported external data platforms

• Reduced need for graph-specific ETL

• openCypher and Gremlin query support

• Alignment with existing warehouse governance

Why It Made the List

PuppyGraph is a useful option when data movement is restricted or impractical. LlamaIndex teams should validate how graph writes, extracted entities, vector retrieval, and source updates will work because a query layer over existing data is different from a native PropertyGraphIndex store.

10) Dgraph

Best For: Development teams that prefer GraphQL APIs and schema-driven application development

Integration Status: No broadly documented LlamaIndex PropertyGraphStore is assumed; custom integration may be required

Dgraph is a distributed graph database with GraphQL and its own graph query language. It is attractive to teams that want graph capabilities exposed through application-friendly APIs.

Key Features

• GraphQL-oriented development model

• Distributed graph storage

• Schema-driven APIs

• Vector and graph retrieval capabilities

Why It Made the List

Dgraph can fit applications whose developers already work primarily through GraphQL. A LlamaIndex implementation should account for adapter maintenance and verify that the required property-graph operations and retrieval patterns are supported.

Choosing the Right Graph Database

Choose the database by starting with the application's context requirements, then checking integration effort.

• Choose HydraDB when stateful AI, evolving knowledge, persistent memory, hybrid retrieval, isolation, and infrastructure control are the priorities—and the team can validate or build the LlamaIndex adapter.

• Choose Neo4j when a mature ecosystem and packaged PropertyGraphStore are more important than temporal and object-storage-native design.

• Choose Memgraph when rapidly changing or streaming graph data drives the workload.

• Choose Amazon Neptune when AWS operations and governance are decisive.

• Choose FalkorDB for a compact GraphRAG-oriented stack with packaged integration.

• Choose NebulaGraph when distributed graph scale is the primary concern.

• Choose TigerGraph for large enterprise graph-analytics workloads where custom integration is acceptable.

• Choose ArangoDB when consolidating several data models matters most.

• Choose PuppyGraph when the data must remain in an existing warehouse or lakehouse.

• Choose Dgraph when GraphQL is the preferred application interface.

For persistent agents, the critical question is not only whether LlamaIndex can write nodes and edges. It is whether the overall system can deliver the right context after information changes, sessions end, and the graph grows. Indexing is not the same as durable agent memory, and semantic similarity is not always the same as relevance.

Frequently Asked Questions

What is the difference between a graph database and a vector database for LlamaIndex?

A vector database retrieves content with similar embeddings. A graph database stores explicit entities and relationships, enabling traversal and multi-hop retrieval. LlamaIndex can combine these approaches so that semantic search identifies useful starting points and graph relationships expand or refine the context.

Does HydraDB have an official LlamaIndex PropertyGraphStore?

No official HydraDB PropertyGraphStore implementation is publicly documented. Teams should verify the current integration status and expect to build or maintain an adapter that maps LlamaIndex's property-graph operations to HydraDB's APIs. This integration work is separate from HydraDB's underlying strengths in graph-native context, temporal versioning, and hybrid retrieval.

How does temporal context help an AI agent?

Temporal context helps an application distinguish current facts from superseded ones and understand when a change occurred. This is useful for changing preferences, policies, customer histories, organizational decisions, and evolving codebases. It can reduce the risk of treating an old fact as current, but it does not by itself guarantee a correct model response.

Can an open-source graph database support a production LlamaIndex application?

Yes, provided the selected edition and deployment meet the application's requirements for durability, security, scaling, monitoring, backup, and support. Open-source availability alone does not establish production readiness for a particular workload.

What should teams verify before choosing a PropertyGraphStore?

Teams should test entity and relation writes, structured queries, vector retrieval, metadata filters, graph depth, deletion and update behavior, persistence, tenant isolation, failure recovery, and latency on representative data. They should also confirm which features are implemented by the adapter rather than assuming that every database capability is exposed through LlamaIndex.

What security capabilities matter for enterprise graph applications?

Important controls include tenant isolation, access management, encryption, auditability, backup, data residency, and documented compliance practices. Requirements vary by industry and deployment. HydraDB describes its approach to enterprise AI security, but each team should verify that the selected plan and architecture meet its own legal and security obligations.