Best Graph Databases with Built-In Vector Search in 2026

AI systems increasingly need to retrieve information by both meaning and relationship. Vector search is effective at finding semantically similar content, while graph traversal exposes how people, events, documents, decisions, and concepts connect. Combining the two gives AI applications a stronger foundation for GraphRAG, persistent context, recommendation, fraud detection, knowledge systems, and stateful agent workflows.

The important distinction is that similarity alone is not enough for every AI workload. Production systems may also need temporal state, relationship-aware retrieval, metadata filtering, lexical matching, and control over how context is assembled. That is why the best graph database depends on the type of connected reasoning your application needs.

Key Takeaways

• Graph and vector retrieval are converging: Modern graph platforms increasingly combine relationship traversal with vector similarity so applications can retrieve both semantically relevant and structurally connected information.

• HydraDB is built for stateful AI: HydraDB combines graph-native context, hybrid retrieval, temporal versioning, and tiered storage for applications such as agent memory, company brains, ontologies, and context graphs.

• Edition boundaries matter: Some vendors include vector search in their core database, while others expose it only through a specific cloud, analytics, or enterprise product.

• GraphRAG needs more than embeddings: Vector similarity can identify related content, but graph structure is what lets a system follow multi-hop relationships, dependencies, ownership, causality, and other connected context.

• Operational fit still matters: Query language, deployment model, licensing, storage architecture, ecosystem maturity, and scaling model can be as important as retrieval features.

Why graph databases with vector search matter for AI

A standalone vector search engine typically ranks records by embedding similarity. That is useful when the main question is, “Which content is semantically closest to this query?”

Many AI applications need a more structured answer. A coding agent may need the architectural decision related to a component, the engineer who approved it, the dependency that motivated it, and the later change that superseded the original decision. A support agent may need the current account state, prior escalations, related tickets, and the sequence of events that led to the latest issue.

Graph databases make those relationships directly traversable. Vector search adds semantic discovery when users do not know the exact entity, keyword, or path to query. Together, they provide the foundation for knowledge graphs, GraphRAG, and relationship-aware context retrieval.

For AI agents, the challenge goes further. Agents also need to distinguish current facts from historical facts, preserve useful state across sessions, and assemble context according to relevance rather than similarity alone. HydraDB's core thesis is that similarity is not relevance, which is why its retrieval architecture combines graph, semantic, lexical, temporal, metadata, and personalization signals.

1) HydraDB

Best For: Teams building stateful AI agents, persistent memory systems, company brains, ontologies, context graphs, and relationship-aware retrieval

Deployment and Pricing: Free and paid self-service plans, managed infrastructure, and enterprise deployment options, including BYOC and self-hosting

HydraDB is a graph database and graph-native context infrastructure platform purpose-built for modern AI workloads. It is built around object storage and a tiered architecture that keeps frequently accessed context in memory, warm context on NVMe storage, and colder context in object storage.

Unlike systems that treat graph traversal and vector similarity as separate retrieval paths, HydraDB is designed around the broader problem of delivering useful context to AI. Developers can combine semantic retrieval, lexical matching, metadata filters, graph context, temporal information, ranking controls, and user-specific memory through a unified API.

Key Features

• Graph-native context retrieval: Returns relationship context alongside ranked chunks so applications can reason over connected entities and events.

• Hybrid search: Combines semantic and lexical signals, with controls for retrieval mode, ranking balance, metadata scope, and graph expansion.

• Temporal versioning: HydraDB's research describes a Git-style versioned graph that preserves how facts and decisions evolve instead of treating every update as a destructive overwrite.

• Tiered storage: Uses memory, NVMe SSD, and object storage to balance low-latency access with long-term context retention.

• Agent memory primitives: Supports knowledge, memories, and time-ordered experiences that applications can retrieve together or separately.

• Developer control: Lets teams compose their own graph structure, retrieval logic, ranking behavior, memory architecture, and context-delivery workflow.

Why It Made the List

HydraDB is particularly well suited to AI applications where retrieval quality depends on relationships and state, not just nearest-neighbor similarity. Its temporal graphs are designed to help agents reason about what is true now, what was true previously, and how information changed.

That makes HydraDB a strong fit for long-lived coding assistants, customer-support agents, sales copilots, research systems, internal knowledge agents, and other applications that must preserve context across sessions. It also gives developers the underlying graph and retrieval primitives rather than forcing them into a fixed memory abstraction.

HydraDB reports a 90.79% overall score on LongMemEval-S in its published evaluation. As with any vendor benchmark, teams should treat that result as a company-conducted evaluation and validate performance against their own data and workload.

Trade-offs: HydraDB is more specialized around AI context delivery than long-established general-purpose graph platforms. Teams whose primary requirement is a mature legacy graph ecosystem, a specific standards stack, or a large catalog of traditional graph administration tooling may prefer a more established database.

2) TigerGraph

Best For: Organizations running large connected-data workloads that need distributed graph processing and integrated vector search

Deployment and Pricing: Managed TigerGraph Savanna, BYOC, and self-managed enterprise deployment options

TigerGraph is a distributed graph platform designed for large-scale analytics and deep-link traversal. Its vector functionality is integrated into the graph engine, allowing vector search operations to be invoked from graph queries.

Key Features

• Distributed vector indexing: TigerGraph stores and indexes vectors across its distributed architecture.

• HNSW search: Vector attributes use approximate nearest-neighbor indexing for similarity retrieval.

• Hybrid graph-vector queries: Vector search can be combined with graph logic inside GSQL.

• Multiple query options: TigerGraph supports GSQL and openCypher, with broader GQL support available across current platform offerings.

• Managed and controlled deployments: Savanna provides a managed service, while BYOC and self-managed options support stricter infrastructure requirements.

Why It Made the List

TigerGraph is a strong choice when graph analytics and vector retrieval need to operate at enterprise scale within the same platform. Its distributed architecture and graph algorithm ecosystem make it especially relevant for fraud detection, recommendation, entity resolution, network analysis, and other workloads where deep graph computation remains a primary requirement.

Trade-offs: TigerGraph can be more platform than a small AI team needs for lightweight prototypes. Teams should also evaluate query-language fit, operational complexity, and total infrastructure requirements against simpler alternatives.

3) Neo4j

Best For: Teams that prioritize a mature property-graph ecosystem, Cypher, developer tooling, education, and managed deployment options

Deployment and Pricing: AuraDB Free and paid managed plans, plus self-managed commercial options

Neo4j remains one of the most established property-graph platforms. Its broad ecosystem includes the Cypher query language, official drivers, Graph Data Science tooling, managed AuraDB services, training resources, and integrations for generative AI and GraphRAG.

Key Features

• Vector indexes: Supports vector similarity search through HNSW-based indexes.

• Cypher: Mature declarative graph-query language with broad tooling support.

• Graph Data Science: Large library of graph algorithms and analytics workflows.

• AuraDB: Fully managed deployment with free, professional, business-critical, and dedicated options.

• Scaling options: Neo4j supports clustering and multiple scaling strategies, with newer self-managed sharding capabilities available through Infinigraph.

Why It Made the List

Neo4j is a practical choice for teams that value ecosystem breadth, documentation, training, and a large pool of developers already familiar with Cypher. Its vector capabilities allow semantic retrieval to live close to graph structure, making it suitable for knowledge graphs and GraphRAG applications.

Trade-offs: Very large deployments require careful architecture and edition selection. Neo4j's scaling capabilities differ by deployment model, and some newer distributed features are not available in Aura.

4) ArcadeDB

Best For: Teams that want Apache 2.0 licensing and graph, vector, document, key-value, full-text, and time-series capabilities in one engine

Deployment and Pricing: Free under Apache 2.0, with self-managed deployment and commercial support options

ArcadeDB is an open-source multi-model database that combines several data models on one storage engine. It supports graph relationships, documents, key-value data, full-text search, vectors, time-series data, and geospatial workloads.

Key Features

• Native vector search: Supports HNSW-based vector indexes and multiple similarity metrics.

• Multi-model storage: Graph, document, key-value, vector, search, time-series, and geospatial data can coexist in the same database.

• Multiple query languages: Supports SQL, openCypher, Gremlin, GraphQL, MongoDB QL, and Redis commands.

• MCP server: Provides built-in Model Context Protocol tooling for AI and agent integrations.

• Graph plus vector workflows: Vector similarity results can be combined with graph traversal for GraphRAG and recommendation patterns.

Why It Made the List

ArcadeDB is compelling for teams that want to minimize the number of separate storage engines in an AI stack while retaining a permissive open-source license. It is especially useful for applications that mix connected data with documents, embeddings, time-series records, or key-value access patterns.

Trade-offs: Its ecosystem and third-party integration footprint are smaller than Neo4j's. Teams should validate operational tooling, managed-service requirements, and production support needs before standardizing on it.

5) FalkorDB

Best For: AI teams building GraphRAG systems that need graph traversal, vector similarity, full-text retrieval, and document-to-graph tooling

Deployment and Pricing: Self-hosted under SSPLv1, plus free and paid FalkorDB Cloud tiers

FalkorDB is a property-graph database focused heavily on GraphRAG and generative AI. It supports openCypher and uses a sparse adjacency-matrix representation for graph storage and traversal.

Key Features

• Vector similarity: Vector indexing is available alongside graph and full-text search.

• GraphRAG SDK: Provides document ingestion, entity extraction, schema-guided graph construction, and hybrid retrieval.

• Hybrid retrieval pipeline: Can combine vector search, full-text search, Cypher generation, and relationship expansion.

• OpenCypher: Familiar query model for teams coming from Cypher-based graph systems.

• Multi-tenant cloud options: Managed tiers support multi-graph and multi-tenant deployment patterns.

Why It Made the List

FalkorDB is one of the more directly GraphRAG-oriented databases in this group. Its SDK reduces the amount of infrastructure teams must build around document ingestion, graph construction, and retrieval orchestration.

Trade-offs: FalkorDB uses the Server Side Public License rather than a permissive open-source license such as Apache 2.0. Teams should review licensing, persistence, high availability, and cloud-tier requirements before production deployment.

6) Memgraph

Best For: Applications that need rapidly changing graph data, streaming ingestion, vector similarity, and real-time graph analysis

Deployment and Pricing: Community, managed, and enterprise deployment options

Memgraph is a graph database designed for low-latency queries and high-throughput updates. Its streaming integrations and GraphRAG tooling make it relevant for systems where the knowledge graph changes continuously.

Key Features

• Vector indexes: Supports vector search on nodes and relationships through a USearch-backed index.

• Streaming integrations: Supports Kafka, Redpanda, and Pulsar for continuous ingestion.

• GraphRAG pipelines: Combines vector search, text retrieval, graph traversal, graph algorithms, and LLM-oriented workflows.

• Cypher query language: Provides a familiar interface for property-graph developers.

• Graph algorithms: Includes analytics for ranking, community detection, path exploration, and other retrieval-expansion patterns.

Why It Made the List

Memgraph fits real-time systems where new events, relationships, or telemetry must become queryable quickly. It can support fraud monitoring, cybersecurity, operational intelligence, recommendation, and AI retrieval workflows that depend on continuously changing graph state.

Trade-offs: Vector indexes operate with READ_UNCOMMITTED isolation even when the main graph uses stronger transactional isolation. Teams with strict consistency requirements should account for that behavior in system design.

7) Amazon Neptune

Best For: Organizations that want managed graph infrastructure closely integrated with AWS services

Deployment and Pricing: Neptune Database and Neptune Analytics use separate pricing models; Neptune Analytics is billed through memory-optimized Neptune Capacity Units

Amazon Neptune is AWS's managed graph portfolio. Neptune Database supports property-graph and RDF workloads, while Neptune Analytics provides an analytics-oriented graph service with vector similarity and graph algorithms.

Key Features

• Multiple graph models: Neptune Database supports property graphs through Gremlin and openCypher, plus RDF through SPARQL.

• Vector similarity: Neptune Analytics can associate embeddings with graph nodes and run vector similarity alongside graph queries.

• Graph algorithms: Neptune Analytics includes algorithms for connected-data analysis.

• AWS integration: Fits naturally with services such as S3, Lambda, Bedrock, and other AWS data and AI infrastructure.

• Managed operations: AWS handles much of the infrastructure lifecycle and service availability.

Why It Made the List

Neptune is a natural fit for teams already standardized on AWS that want a managed graph service without operating database infrastructure themselves. Neptune Analytics is especially relevant when vector similarity and graph analytics need to live inside an AWS-native workflow.

Trade-offs: The vector feature belongs to Neptune Analytics rather than Neptune Database, so teams should model the products and pricing separately. Neptune Analytics currently allows one vector index per graph, configured at graph creation, and vector-index updates have different atomicity and isolation behavior from the rest of the graph.

8) Arango

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

Deployment and Pricing: Self-managed and managed enterprise deployment options

Arango combines graph, vector, document, key-value, and full-text capabilities within a multi-model database platform. Its current product direction emphasizes delivering governed business context for AI applications without requiring separate databases for every data shape.

Key Features

• Vector indexes: Supports approximate vector search inside ArangoDB.

• Multi-model database: Graph, document, key-value, vector, and search workloads can share the same platform.

• AQL: A single query language spans multiple data access patterns.

• GraphRAG tooling: Arango provides AI-oriented tooling for knowledge graph construction and retrieval.

• Distributed deployment: Enterprise platform capabilities include scaling, availability, governance, and operational tooling.

Why It Made the List

Arango is attractive when teams need to combine operational JSON data, graph relationships, semantic retrieval, and search without operating multiple independent databases. It can reduce architecture sprawl for enterprise AI and traditional application workloads.

Trade-offs: Teams adopting Arango typically need to learn AQL and should confirm which capabilities belong to the core database versus higher-level enterprise or AI platform offerings.

9) NebulaGraph

Best For: Teams that prioritize distributed graph infrastructure, storage-compute separation, and enterprise-scale connected-data workloads

Deployment and Pricing: Open-source, Enterprise, and managed cloud editions; vector features should be evaluated by edition

NebulaGraph is a distributed graph database designed around separate query and storage services. Its enterprise releases have added native vector and full-text capabilities for AI retrieval.

Key Features

• Distributed architecture: Query and storage resources can scale independently.

• Enterprise vector search: NebulaGraph Enterprise v5.1 introduced native vector processing.

• Hybrid graph-vector-text retrieval: Enterprise v5.2 added graph traversal, vector similarity, and full-text search within the same query flow.

• GQL direction: Enterprise releases have expanded support for the ISO Graph Query Language standard.

• Graph-focused scaling: Designed for large connected datasets and high-concurrency graph workloads.

Why It Made the List

NebulaGraph is a strong candidate for organizations that want a distributed graph architecture and are evaluating newer graph-native AI retrieval features. Its enterprise roadmap increasingly brings graph structure, semantic retrieval, and text search into one platform.

Trade-offs: Vector search is not simply a feature of every NebulaGraph edition. Current product materials distinguish the open-source edition from Enterprise capabilities, so teams should verify edition-specific requirements before selecting it for a vector-enabled workload.

How To Choose the Right Graph Database With Vector Search

The right platform depends on what your application is actually trying to retrieve and reason over.

Choose HydraDB when the core requirement is stateful AI context: persistent memory, temporal state, relationship-aware retrieval, personalized ranking, and context assembly for long-running agents. It is designed around stateful agents rather than treating vector search as an isolated database feature.

Choose TigerGraph when distributed graph analytics and large-scale hybrid graph-vector processing are central requirements.

Choose Neo4j when ecosystem maturity, Cypher tooling, Graph Data Science, education, and managed graph deployment are the strongest priorities.

Choose ArcadeDB when permissive open-source licensing and broad multi-model support matter more than having the largest commercial ecosystem.

Choose FalkorDB when GraphRAG is the primary application and you want database tooling built directly around document ingestion, graph construction, and hybrid retrieval.

Choose Memgraph when graph state changes continuously and you need streaming ingestion with low-latency graph processing.

Choose Amazon Neptune when your infrastructure is already deeply AWS-native and a managed service is more important than portability.

Choose Arango when you want to unify graph, document, vector, key-value, and search workloads inside one enterprise data platform.

Choose NebulaGraph when distributed graph scaling and storage-compute separation are primary requirements and you are comfortable evaluating vector functionality as an Enterprise feature.

For teams building production agents, it is also worth looking beyond a simple graph-versus-vector feature checklist. Context quality depends on how the system handles time, relationships, user state, ranking, metadata, and context packaging. HydraDB's approach is to make those primitives part of the graph-native retrieval layer so developers can build their own context engineering architecture instead of stitching together separate stores.

Frequently Asked Questions

What is the difference between graph search and vector search?

Graph search follows explicit relationships between entities, such as ownership, dependency, membership, chronology, or influence. Vector search ranks items by similarity in embedding space. Graph search is strongest when structure matters; vector search is strongest when the user knows the meaning they want but not the exact entity or keyword. A hybrid system uses both. It can find a semantically relevant starting point and then traverse relationships to retrieve the surrounding context.

Is built-in vector search the same as GraphRAG?

No. Built-in vector search is one retrieval capability. GraphRAG is a broader architecture that uses graph structure to ground retrieval and generation. A GraphRAG pipeline may include vector search, lexical search, entity resolution, graph traversal, community detection, metadata filtering, reranking, and prompt assembly. The database determines which of those functions can happen close to the data and which require additional infrastructure.

Which graph database is best for AI agents?

For persistent, stateful AI agents, HydraDB is the strongest fit in this list because it is purpose-built around graph-native context delivery rather than adding vector similarity to a traditional graph database. It combines relational retrieval, agent memory, temporal state, hybrid search, metadata controls, and personalized context retrieval. Other databases may be better when the primary requirement is traditional graph analytics, a specific cloud platform, a permissive open-source license, or a mature existing graph ecosystem.

Why are temporal graphs useful for AI?

AI agents often operate in environments where facts change. A customer's plan changes, a project changes owner, an architectural decision is superseded, or a user's preference evolves. A temporal graph preserves the relationship between current and historical state. Instead of retrieving two contradictory facts as if they were equally valid, an application can reason about when each fact applied and which state is current. This is particularly important for long-lived agents and context graphs.

Can graph databases replace vector databases?

Sometimes, but not always. A graph database with strong built-in vector indexing can eliminate the need for a separate vector store when graph and semantic retrieval operate over the same data. The decision depends on scale, latency, index requirements, data model, operational constraints, and retrieval design. For agent systems, the larger question is whether the stack can deliver relevant context across semantic, relational, temporal, lexical, and user-specific signals. That is the retrieval problem HydraDB is designed to address.

What should teams evaluate before choosing a platform?

Teams should evaluate the graph model, vector-index implementation, query language, hybrid retrieval options, temporal capabilities, scaling model, deployment choices, licensing, consistency semantics, data isolation, operational tooling, and AI framework integrations. For production AI, teams should also test retrieval quality on their own workloads. A platform that performs well on generic similarity search may still underperform when the application needs multi-hop relationships, changing state, personalization, or precise context assembly.