Best Graph Databases for Semantic Kernel Agents in 2026

Semantic Kernel agents lose context between runs unless you give them persistent graph storage. The .NET-native orchestration framework from Microsoft handles planning and plugin coordination, but memory and relationship tracking require dedicated infrastructure. Finding the best database for AI agents means evaluating graph traversal performance, AI-specific features, and integration complexity with the Semantic Kernel SDK.

GraphRAG has moved from experimental to essential in 2026. Microsoft's research shows 70-80% accuracy gains over standard RAG when graph context powers retrieval. We evaluated 18 graph databases based on Semantic Kernel/.NET integration, AI agent memory capabilities, performance benchmarks, and current pricing.

This guide covers the 12 options that matter most for production agent deployments.

Key Takeaways

• HydraDB leads for temporal context with 90.79% accuracy on LongMemEval-S benchmarks and Git-style versioned temporal graphs, critical for agents tracking how facts change over time

• Performance varies dramatically with FalkorDB achieving 6,693 QPS versus Neo4j's 1,010 QPS in independent benchmarks

• Neo4j offers ecosystem maturity with official MCP support and extensive .NET driver community

• Open-source options reduce barriers with Apache AGE, Dgraph, and NebulaGraph offering full-featured graph capabilities without licensing costs

• Model Context Protocol (MCP) support is becoming standard for agent integration, currently confirmed for Neo4j, FalkorDB, and Memgraph

Why Graph Databases Matter for Semantic Kernel Agents

Semantic Kernel provides the orchestration layer for AI agents, but it treats each request as stateless by default. Your agent can call plugins, generate plans, and invoke LLMs, but without external memory infrastructure, it forgets everything between sessions.

Knowledge graphs solve this by storing entities and relationships in traversable structures. When your support agent needs to find "engineers who worked on this system, then find who fixed similar issues," a graph database executes that multi-hop query in milliseconds. Relational databases struggle with these patterns at scale.

The emergence of GraphRAG has accelerated this shift. Traditional RAG retrieves semantically similar chunks, but GraphRAG traverses relationship paths to assemble coherent context. For Semantic Kernel agents handling complex reasoning, this difference determines whether your agent provides accurate, contextual responses or surfaces disconnected fragments.

Every database on this list provides documented integration paths for .NET applications. Some offer native Model Context Protocol (MCP) support for direct LLM communication. Others require custom connector development but deliver specialized capabilities that justify the integration effort.

1) HydraDB - Best for Temporal Context and AI-Native Workflows

Best For: Semantic Kernel agents requiring temporal versioning, relationship-aware retrieval, and cross-session state tracking

Starting Price: Storage-based pricing tiers

HydraDB provides a graph database built on object storage that functions as a context layer for AI agents. The architecture combines temporal versioning, relationship-aware retrieval, and hybrid search in a system designed specifically for AI agent memory workloads.

Key Features

• Git-style versioned temporal graphs tracking how facts change over time

• Hybrid retrieval combining graph traversal, vector search, and BM25 in single queries

• Git-style temporal versioning for point-in-time context and historical state tracking

• Native multi-tenant support with tenant-scoped filtering and dedicated-database isolation options

Why It Made the List

HydraDB achieves 90.79% overall accuracy on LongMemEval-S, including 97.43% accuracy on knowledge update tasks. This temporal accuracy helps agents avoid applying deprecated information, critical for coding assistants tracking outdated architectural decisions or support agents citing superseded policies.

The platform maintains sub-200ms retrieval latency at production scale while running on cost-efficient object storage. For Semantic Kernel agents that need to understand "what was true then" versus "what is true now," temporal knowledge graphs provide the foundation that flat vector stores cannot.

Many retrieval systems emphasize the current state of information rather than preserving how facts and relationships changed over time. For agents that need temporal reasoning, that can make historical and current context harder to distinguish.

HydraDB's versioned temporal graphs solve this by tracking fact validity over time. The platform achieves 97.43% accuracy on knowledge update benchmarks, meaning agents can distinguish between historical and current facts. This capability matters for coding assistants tracking deprecated APIs, support agents referencing policy changes, and research agents analyzing how markets evolved.

HydraDB uses tiered storage across hot memory, warm NVMe SSD, and cold object storage, moving context between tiers based on recency and access patterns. The company says this architecture can reduce costs by up to 10x versus traditional graph database approaches while maintaining sub-200ms retrieval.

2) Neo4j

Neo4j holds the position of most widely adopted graph database and serves as a common starting point for developer-led graph projects. With over 240,000 learners on its GraphAcademy platform, finding developers with Neo4j experience is straightforward.

Key Features

• Native Cypher query language with GQL compliance for standardized graph queries

• Official MCP server enabling direct communication between Semantic Kernel agents and the graph

• LLM Knowledge Graph Builder that converts unstructured text to queryable graphs

• AuraDB managed cloud service with 99.95% uptime SLA on Business Critical tier

Why It Made the List

Neo4j combines mature tooling with extensive AI integrations. The platform offers native vector search plus GraphRAG toolkits, addressing both semantic similarity and relationship-aware retrieval. For Semantic Kernel projects, the official .NET driver provides support for Cypher queries, and the MCP integration enables agents to query the graph directly without custom middleware.

The Business Critical tier supports instances up to 1944GB memory on GCP, handling enterprise-scale deployments. Trade-off: pricing can escalate with large graphs.

3) FalkorDB

FalkorDB emerged as the successor to RedisGraph with a focus on AI workload performance. Independent benchmarks show 1.1ms cold start versus 90ms for comparable queries on other platforms.

Key Features

• Sub-millisecond traversal via sparse adjacency matrices and in-memory architecture

• Dedicated GraphRAG SDK with automatic Cypher generation from natural language

• MCP integration for direct Semantic Kernel agent communication

• O(1) relationship insertion supporting 1M+ nodes in under 0.5 seconds

Why It Made the List

FalkorDB delivers high query throughput among specialized graph databases. The 2026 Rust rewrite pushed performance further, and the GraphRAG SDK achieved schema-heavy query accuracy above 90%. For Semantic Kernel agents that need fast context retrieval, FalkorDB's architecture addresses the latency considerations that affect disk-based systems.

The trade-off is ecosystem maturity. Neo4j has more community resources and third-party integrations, though FalkorDB addresses this gap for teams prioritizing raw performance.

4) TigerGraph

TigerGraph targets enterprise-scale graph analytics where other platforms may encounter limitations. A global bank uses TigerGraph to analyze 50 million transactions per day, and Jaguar Land Rover reduced supply chain analysis from 3 weeks to 45 minutes using the platform.

Key Features

• Massively parallel processing (MPP) architecture for real-time analytics at scale

• Native hybrid graph plus vector search for unified GraphRAG queries

• GSQL, openCypher, and GQL pattern matching in a single engine

• Nine pre-built solution kits for fraud, AML, customer 360, and supply chain use cases

Why It Made the List

TigerGraph handles deep, multi-step relationship queries at enterprise scale. The platform processes workloads that would challenge single-server graph databases, making it suitable for Semantic Kernel agents operating against massive knowledge bases.

For .NET integration, TigerGraph supports openCypher (compatible with standard graph drivers) and provides REST APIs for custom connectors. The GraphRAG SDK enables AI workflows, though it requires more setup than some plug-and-play approaches.

5) Memgraph

Memgraph operates as an in-memory graph database optimized for sub-millisecond multi-step relationship queries. The platform maintains Cypher compatibility, functioning as a drop-in alternative for teams migrating from Neo4j.

Key Features

• Microsecond traversal latency via pure in-memory architecture

• Native Kafka, Pulsar, and Redpanda streaming integrations

• MAGE library for graph algorithm extensions

• MCP integration for Semantic Kernel agent connectivity

Why It Made the List

Memgraph targets operational workloads where streaming data feeds continuous graph updates. For Semantic Kernel agents monitoring real-time events, fraud patterns, or network anomalies, Memgraph's streaming-native architecture processes incoming data without batch delays.

The Cypher compatibility means existing Neo4j .NET code works with minimal modifications. MCP support enables direct agent integration, and the MAGE library adds pre-built algorithms for common graph analytics patterns.

6) Amazon Neptune

Amazon Neptune serves as a common choice for AWS-centric teams that need fully managed graph services. The platform uniquely supports both property graphs (via Gremlin/openCypher) and RDF (via SPARQL) in a single database.

Key Features

• Dual-model support for property graph and RDF workloads

• Managed GraphRAG via Amazon Bedrock Knowledge Bases

• Multi-AZ architecture with 99.95% uptime and automatic failover

• Neptune Analytics for analyzing tens of billions of relationships in seconds

Why It Made the List

Neptune eliminates infrastructure management for teams already invested in AWS. The Bedrock GraphRAG integration provides a path to graph-enhanced retrieval for Semantic Kernel agents. IAM-native access control, encryption at rest and in transit, and VPC isolation address enterprise security requirements.

The trade-off is AWS lock-in. Neptune's pricing model (I/O-heavy workloads require careful cost modeling) and AWS-only deployment affect flexibility compared to portable alternatives.

7) ArangoDB

ArangoDB operates as a multi-model database where a single AQL query can span graph, document, and key-value operations. This architectural approach reduces database sprawl for Semantic Kernel applications requiring mixed workloads.

Key Features

• Unified AQL query language across all data models

• Built-in search and vector capabilities

• Distributed clusters with sharding and replication

• Flexible schema evolution for rapidly changing applications

Why It Made the List

ArangoDB suits Semantic Kernel agents that need graph traversal alongside document storage and key-value lookups. Rather than integrating separate databases for each data model, teams can consolidate on ArangoDB and reduce operational complexity.

The trade-off is performance. Multi-model flexibility involves considerations on demanding graph analytics workloads. Purpose-built graph databases may outperform ArangoDB on deep traversals, though ArangoDB offers architectural simplicity.

8) Zep (with Graphiti)

Zep and its open-source Graphiti core focus on temporal knowledge graphs for agent memory. The platform uses validity windows (valid_at, invalid_at timestamps) to track historical invalidation.

Key Features

• Temporal knowledge graph memory with explicit validity windows

• Historical invalidation tracking for "what was true when" queries

• SOC 2 Type 2, HIPAA compliance, and ABAC for enterprise deployments

• Native integrations with LangChain, LangGraph, LlamaIndex, and CrewAI

Why It Made the List

Zep specializes in temporal correctness for long-running agents. For Semantic Kernel deployments in regulated industries (healthcare, finance, insurance), the platform's audit trail capabilities and compliance certifications address requirements. The open-source Graphiti core (Apache 2.0 license) allows teams to start without commercial commitment.

The trade-off: Zep requires an external graph database (Neo4j, FalkorDB, or Kuzu) as its backend, adding integration complexity compared to self-contained options.

9) Mem0

Mem0 delivers a managed long-term memory and personalization service with minimal integration overhead. The dual-store architecture combines vector search with knowledge graph capabilities.

Key Features

• Drop-in managed service with lightweight local footprint

• Hierarchical distillation and semantic traces for memory organization

• Support for Qdrant, Chroma, Milvus, pgvector, and Redis as vector backends

• Graph features (Pro tier) adding entity tracking and relationships

Why It Made the List

Mem0 provides a path to adding personalized memory to Semantic Kernel agents. The extraction pipeline converts conversation messages into atomic memory facts, scoped to users, sessions, or agents. For teams that want memory capabilities without managing graph infrastructure, Mem0's managed approach reduces operational burden.

The trade-off is control. Self-hosted options provide more customization, but Mem0 optimizes for speed of deployment over architectural flexibility.

10) NebulaGraph

NebulaGraph operates as a distributed, scalable, native graph database designed for large-scale data handling with millisecond latencies. The Apache 2.0 license means no feature gating between community and enterprise versions.

Key Features

• Distributed architecture handling tens of billions of edges

• Millisecond latencies at enterprise scale

• Apache 2.0 licensing with no commercial restrictions

• Strong LDBC-SNB benchmark performance

Why It Made the List

NebulaGraph provides enterprise-scale graph capabilities without licensing costs. For Semantic Kernel agents operating against massive knowledge bases, NebulaGraph's distributed architecture scales horizontally. The fully open-source model allows complete control over deployment and customization.

The trade-off is ecosystem maturity. Some platforms have more .NET drivers and community resources, requiring custom connector development for Semantic Kernel integration.

11) Dgraph

Dgraph provides a GraphQL-native graph database with all enterprise features now in the open-source build as of version 25. The distributed architecture scales horizontally with no feature gating.

Key Features

• GraphQL-first development model

• Distributed architecture with horizontal scaling

• Apache 2.0 licensing (v25+) removing commercial restrictions

• No feature gating between community and enterprise

Why It Made the List

Dgraph suits Semantic Kernel applications using GraphQL APIs. The GraphQL-native approach simplifies integration with modern .NET applications using GraphQL clients like StrawberryShake. Full Apache 2.0 licensing removes barriers for production deployment.

The trade-off is stability. Two acquisitions in two years (Hypermode in 2023, Istari Digital in October 2025) mean the commercial roadmap is still evolving.

12) Apache AGE

Apache AGE adds openCypher graph query capabilities to existing PostgreSQL installations without requiring a separate database. This approach offers a low barrier to entry for Postgres shops.

Key Features

• PostgreSQL extension requiring no separate database deployment

• OpenCypher support for standardized graph queries

• Leverages existing PostgreSQL tooling, monitoring, and backup systems

• Suitable for teams with established Postgres expertise

Why It Made the List

Apache AGE minimizes adoption barriers for .NET applications already using PostgreSQL. Standard Npgsql drivers work with Cypher queries, enabling graph capabilities without new infrastructure. For Semantic Kernel projects where graph requirements are moderate, AGE provides a pragmatic starting point.

The trade-off is performance. Graph-specific query performance may lag behind dedicated graph engines on deep traversals. Teams with demanding graph workloads should consider purpose-built options.

Why Choose HydraDB for Production Semantic Kernel Agents

Choosing the right graph database for Semantic Kernel agents depends on how much your application relies on persistent memory, relationship-aware retrieval, and changing context over time. For teams building production agents that need to remember across sessions and distinguish current facts from historical ones, HydraDB stands out as a strong fit.

HydraDB combines graph-native context with temporal versioning and hybrid retrieval, helping agents retrieve connected, time-aware information instead of isolated semantic matches. Key advantages include:

• 90.79% overall accuracy on LongMemEval-S

• 97.43% accuracy on knowledge updates

• Sub-200ms retrieval latency for real-time agent workflows

• Git-style temporal graphs for tracking how facts change

• Hybrid retrieval across graph traversal, vector search, and BM25

• Storage-based architecture designed to scale cost-effectively

If your Semantic Kernel agents need persistent, structured context that evolves with every interaction, schedule a HydraDB demo to see how temporal graph memory can support your production workloads.

Frequently Asked Questions

What is Model Context Protocol (MCP) and why does it matter for Semantic Kernel?

MCP provides a standardized interface for LLMs to communicate with external data sources. For Semantic Kernel agents, MCP-enabled graph databases allow direct queries without custom middleware. Neo4j, FalkorDB, and Memgraph currently offer MCP support, enabling agents to traverse relationships and retrieve context through a unified protocol.

Can I use multiple graph databases in a single Semantic Kernel application?

Yes. Semantic Kernel's plugin architecture supports multiple data sources. Teams commonly use a high-performance option like FalkorDB for real-time queries alongside a managed service like Neptune for analytics. The key is maintaining clear boundaries between operational and analytical workloads.

How does GraphRAG differ from traditional RAG for Semantic Kernel agents?

Traditional RAG retrieves semantically similar text chunks based on embedding distance. GraphRAG traverses relationship paths to assemble contextually coherent responses. Microsoft research shows 70-80% accuracy improvements on complex reasoning tasks when graph context powers retrieval. For Semantic Kernel agents handling multi-step reasoning, GraphRAG provides more reliable context assembly.

What .NET drivers are available for these graph databases?

Neo4j provides official .NET drivers with full Cypher support. Memgraph and FalkorDB maintain Cypher compatibility, allowing Neo4j driver usage with minor modifications. Amazon Neptune supports Gremlin via Apache TinkerPop .NET clients. For databases without official .NET support, REST APIs enable custom integration through HttpClient.

How do I choose between in-memory and persistent graph databases?

In-memory options (FalkorDB, Memgraph) deliver sub-millisecond latency but require sufficient RAM for your entire graph. Persistent options (Neo4j, HydraDB, Neptune) handle larger datasets but introduce storage I/O latency. For Semantic Kernel agents, consider your latency requirements and graph size. Real-time decision agents benefit from in-memory speed, while knowledge bases exceeding available RAM require persistent storage with caching layers.