Best Graph Databases for Google ADK Agents in 2026

Google ADK gives developers a flexible framework for building agents, but production systems still need durable infrastructure for the context those agents use across sessions. When relationships, evolving facts, decisions, user preferences, and multi-hop dependencies matter, graph databases can provide a more structured foundation than flat retrieval alone.

For Google ADK workloads, the right graph database depends on whether the application prioritizes persistent context, operational graph queries, analytics, global consistency, managed infrastructure, or integration with an existing data platform.

HydraDB stands out for teams building stateful AI applications because it is an open-source, object-store-native graph database purpose-built for modern AI workflows. It provides the graph infrastructure beneath agent memory, context graphs, ontologies, company brains, agentic actions, and other connected knowledge systems.

Key Takeaways

• Graph structure adds relationship-aware context. Graph databases represent entities and explicit connections, which helps agents retrieve information through relationships rather than semantic similarity alone.

• Temporal context matters for stateful agents. Systems that preserve changing facts can help agents distinguish current information from superseded historical context.

• Google now offers graph capabilities across operational and analytical data. BigQuery Graph is designed for graph analytics within BigQuery, while Spanner Graph supports graph-aware operational workloads.

• HydraDB is designed specifically for AI workflows. Its architecture combines graph relationships, time-aware state, semantic and lexical retrieval, metadata signals, and developer-controlled context delivery.

Why Graph Databases Matter for Google ADK Agents

Google ADK can orchestrate agents, tools, sessions, and workflows, but the usefulness of an agent still depends on the context available when it reasons or acts. Traditional semantic retrieval is useful for finding text that resembles a query, yet similarity alone does not explicitly represent how people, systems, decisions, events, and facts connect.

Knowledge graphs model those entities and relationships as a traversable structure. This becomes useful when an agent must answer questions such as which engineer owns a service, which incident affected that service, what remediation was chosen, and whether that remediation is still current.

Graph-native context can support several patterns that matter for stateful Google ADK applications:

• Relationship-aware retrieval: Following explicit connections between entities and events.

• Temporal context: Preserving how information changes over time.

• Cross-session persistence: Retaining relevant state beyond a single conversation.

• Multi-hop reasoning: Retrieving connected evidence across several relationships.

• Context traceability: Maintaining structural links between retrieved facts and their sources.

HydraDB's core technical thesis is that similarity is not the same as relevance. Its relationship-aware retrieval approach combines multiple context signals rather than relying on embeddings alone.

1) HydraDB

HydraDB is an open-source, object-store-native distributed graph database written in Rust and designed for modern AI workloads. It is best understood as graph-native infrastructure beneath AI applications rather than as a packaged memory product.

For Google ADK teams, that distinction matters. Developers can use HydraDB as the database beneath their own memory architecture, context layer, ontology, company brain, or agentic workflow while retaining control over graph structure, retrieval logic, ranking, filtering, and context assembly.

Key Features

• Graph-native context infrastructure for agent memory, ontologies, company brains, context graphs, and agentic actions.

• Temporal versioning for preserving what is true now, what was true previously, and when information changed.

• Hybrid multi-signal retrieval across semantic, BM25, graph, metadata, and temporal signals.

• Entity and relationship extraction during ingestion to structure connected context.

• Object-storage-native architecture designed to support persistent graph workloads without requiring the full graph to remain in memory.

• Developer-controlled retrieval through configurable graph structure, ranking, filtering, and context delivery.

• Official SDKs and APIs for integrating graph-backed context into AI applications.

Why It Made the List

HydraDB is purpose-built for workloads where context evolves over time and where relationships are part of relevance. Its temporal graphs preserve historical and current state so applications can reason about changing information instead of destructively overwriting it.

In HydraDB's company-conducted LongMemEval-S evaluation, the platform reports 90.79% overall accuracy, including 97.43% on Knowledge Update and 90.97% on Temporal Reasoning. These figures are published benchmark results under HydraDB's evaluation configuration and should not be treated as universal production guarantees.

HydraDB also reports sub-200 ms retrieval for supported production workloads, more than one billion documents ingested, approximately one million monthly retrievals, and about 2,000 developers. Actual latency varies with dataset size, graph depth, retrieval mode, infrastructure, and query complexity.

HydraDB is especially relevant for Google ADK applications that need persistent, structured context across sessions rather than a retrieval layer built only around nearest-neighbor similarity.

2) Neo4j AuraDB

Neo4j is a mature property graph database with a broad developer ecosystem and direct integration patterns for Google ADK. Its current tooling includes ADK-compatible memory services, MCP-based graph access, and Google Cloud deployment guidance.

Key Features

• Property graph modeling with Cypher.

• Google ADK integration for persistent graph-backed agent memory.

• MCP-based access for agent tools and graph queries.

• Managed deployment through AuraDB as well as self-managed options.

• Broad ecosystem support across graph applications, analytics, and enterprise development.

Why It Made the List

Neo4j is a practical option for organizations that already use Cypher, operate existing Neo4j graphs, or want a well-established graph ecosystem around Google ADK. It is particularly suitable when agent workloads need to connect to an existing enterprise knowledge graph rather than introduce a new context architecture.

3) BigQuery Graph

BigQuery Graph brings native graph capabilities into BigQuery so teams can analyze entities and relationships without moving their analytical data into a separate graph platform. It supports graph queries alongside the broader SQL and analytics environment used by BigQuery teams.

Key Features

• Native graph modeling over data in BigQuery.

• Graph Query Language support for connected-data analysis.

• Integration with existing BigQuery governance and analytical workflows.

• Ability to combine graph analysis with large-scale warehouse queries.

• No separate graph ETL layer for data already stored in BigQuery.

Why It Made the List

BigQuery Graph is a strong fit when Google ADK agents need analytical reasoning over large historical datasets already governed in BigQuery. It is oriented toward deep graph analysis and large-scale exploration rather than serving as a purpose-built persistent context database for every agent interaction.

4) Spanner Graph

Spanner Graph adds native graph capabilities to Google's globally distributed Spanner database. It lets teams work across relational and graph representations in one operational data platform while retaining Spanner's transactional model.

Key Features

• Native graph queries alongside relational data.

• Support for Graph Query Language.

• Integration with Spanner's operational database capabilities.

• Graph, relational, full-text, and vector-oriented access patterns within the same platform.

• Suitable for graph-aware applications that also require transactional data management.

Why It Made the List

Spanner Graph is relevant when a Google ADK application needs graph reasoning directly over operational data already managed in Spanner. It can reduce the need to maintain a separate graph copy for workloads where relational and connected views of the same data must remain closely aligned.

5) FalkorDB

FalkorDB is a graph database built around sparse matrix and GraphBLAS-style computation. It is commonly used for workloads where fast graph traversal and in-memory execution are important, including graph-backed retrieval and agent-memory architectures built with surrounding tooling.

Key Features

• GraphBLAS-based graph computation.

• Cypher-compatible graph querying.

• In-memory architecture optimized for low-latency graph access.

• Integration patterns with Graphiti for persistent, temporal knowledge graphs.

• Self-hosted and managed deployment options.

Why It Made the List

FalkorDB is a relevant option for interactive agent applications where the active graph can remain in memory and fast traversal is a primary design requirement. Teams should evaluate memory capacity, persistence, and operating cost alongside query speed when sizing larger workloads.

6) Cognee

Cognee is an agent-memory and knowledge platform that combines structured graph context with semantic retrieval. It offers a Google ADK integration that exposes memory operations as agent tools, allowing ADK applications to store and retrieve persistent knowledge across sessions.

Key Features

• Google ADK integration through asynchronous agent tools.

• Persistent cross-session memory.

• Knowledge-graph construction with semantic retrieval.

• Multi-hop graph context for agent queries.

• Session and user isolation patterns for agent applications.

Why It Made the List

Cognee is useful for teams that want a higher-level memory workflow rather than direct control over graph database infrastructure. That makes it structurally different from HydraDB: Cognee provides a more opinionated memory layer, while HydraDB provides the underlying graph database and retrieval primitives developers can use to build their own memory architecture.

7) NebulaGraph

NebulaGraph is a distributed graph database designed for large connected datasets and horizontally scaled deployments. It supports graph-oriented query patterns and is suited to teams that want to operate a dedicated distributed graph system.

Key Features

• Distributed graph architecture.

• Graph Query Language support in its current platform direction.

• Horizontal scaling for large graph workloads.

• Graph analytics and traversal capabilities.

• Self-managed deployment options for infrastructure teams.

Why It Made the List

NebulaGraph is relevant when the core requirement is operating a large distributed graph with dedicated graph infrastructure. For Google ADK applications, the main consideration is how much custom work the team wants to own around ingestion, retrieval orchestration, temporal context, and agent-facing APIs.

8) Amazon Neptune

Amazon Neptune is a managed graph database for teams already operating heavily within AWS. It supports property graph workloads through Gremlin and openCypher as well as RDF graphs through SPARQL.

Key Features

• Managed graph database infrastructure on AWS.

• Property graph support through Gremlin and openCypher.

• RDF graph support through SPARQL.

• Integration with the broader AWS platform.

• Suitable for knowledge graphs, dependency graphs, identity relationships, and other connected-data applications.

Why It Made the List

Neptune is a practical option when graph data is already part of an AWS-centered architecture and Google ADK is being used as the agent framework on top. Cross-cloud networking, identity, latency, and operational boundaries should be considered when the agent runtime and graph database live in different cloud environments.

Why HydraDB Excels for Production AI Agents

When Google ADK applications move from short-lived prototypes to stateful production workflows, the database must do more than store text for semantic search. The system may need to preserve relationships, changing facts, historical states, user preferences, agent outcomes, and structured enterprise knowledge over long periods.

HydraDB is designed around that broader requirement. It is the graph database beneath the context architecture, giving developers the infrastructure to build memory systems without forcing them into one memory abstraction.

Its Google ADK memory guidance explains the distinction between framework-level session handling and durable context infrastructure. Google ADK can manage an agent's runtime and orchestration, while HydraDB can persist and retrieve the connected context the application wants to carry forward.

What Sets HydraDB Apart

• Graph infrastructure, not a packaged memory layer: Developers own the graph structure, memory primitives, retrieval logic, and ranking behavior.

• Temporal versioning: HydraDB can preserve previous and current states instead of treating every update as a destructive replacement.

• Hybrid retrieval: Semantic, lexical, relational, temporal, and metadata signals can be combined in one retrieval pipeline.

• Object-storage architecture: HydraDB is designed around durable object storage with tiered access patterns for graph context.

• Bring-your-own-graph patterns: Structured application data can be represented alongside ingested unstructured context.

• Multi-tenant isolation: Databases and collections can scope context across customers, users, teams, and workspaces.

• Open-source foundation: Teams can build on HydraDB's open-source graph database while choosing managed or self-hosted deployment patterns as appropriate.

HydraDB's versioned context is particularly important for agents operating on information that changes. A coding agent may need to know which architecture decision is current. A support agent may need to distinguish an old policy from its replacement. A company-brain application may need to trace how a decision evolved rather than return only the latest sentence mentioning it.

The result is a graph-native context layer designed for stateful AI systems, not only for one memory use case. Teams can also use HydraDB for AI context graphs, ontologies, company knowledge, and other relationship-heavy workflows.

Frequently Asked Questions

What is the primary difference between a graph database and a vector database for AI agents?

Vector databases are optimized for similarity search over embeddings. Graph databases represent explicit entities and relationships, which makes them useful when an agent must traverse dependencies, ownership, chronology, or other connected structure. Many production AI systems benefit from both. HydraDB's vector and memory guidance explains why semantic similarity and structured context solve different parts of the retrieval problem.

How does temporal context improve AI agent accuracy?

Temporal context helps an agent distinguish the current state from the historical state. Without it, a system can retrieve an older preference, policy, decision, or configuration that is semantically relevant but no longer valid. HydraDB uses temporal graph context to preserve changes over time. In its company-conducted LongMemEval-S evaluation, HydraDB reports 97.43% accuracy on Knowledge Update and 90.97% on Temporal Reasoning. These are benchmark results under the published evaluation setup rather than guarantees for every application.

Can graph databases integrate with existing enterprise applications?

Yes. Integration approaches vary by product and may include application connectors, APIs, ingestion pipelines, event streams, or custom adapters. HydraDB documents continuous connectors for Slack, GitHub, Linear, Notion, and Gmail. Its app-source ingestion model can also preserve structured records from tickets, CRM systems, email, and other business applications without implying that every source is available as a managed native connector.

What use cases benefit most from graph databases for AI agents?

Graph databases are especially useful when agents need to follow multi-hop relationships, track evolving state, connect information across systems, preserve decision history, reason over dependencies, or maintain persistent context across sessions.

Common examples include coding assistants, support agents, internal knowledge systems, research agents, CRM copilots, IT operations agents, and company-brain applications.

Is HydraDB suitable for small teams and startups?

Yes. HydraDB currently offers a free Ship tier with unlimited API calls and tenants, while its paid plans add larger storage allocations and additional infrastructure or support features. Teams should check the current HydraDB pricing before making deployment decisions because plan details can change.

How does HydraDB address data security and compliance for sensitive enterprise data?

HydraDB states that it is SOC 2 and ISO 27001 certified. Its current Surge plan lists SOC 2 and GDPR reports plus a DPA, while larger deployment options add dedicated infrastructure and self-hosting choices. Security and regulatory requirements still depend on the deployment. Teams should verify access controls, data residency, encryption, audit logging, isolation, and industry-specific obligations for their own environment. HydraDB's enterprise security guidance provides additional context for evaluating these requirements.