Best Graph Databases for OpenAI Agents SDK in 2026

The OpenAI Agents SDK includes built-in session memory for maintaining conversation history across agent runs. That is useful for multi-turn applications, but teams building production agents may still need an external context infrastructure layer when agents must retrieve persistent enterprise knowledge, follow relationships across entities, preserve changing state, or reason over information that spans many sessions and systems.

Vector search remains useful for semantic retrieval, but similarity alone does not represent every kind of relevance. When an agent needs to understand how people, projects, events, decisions, documents, and past actions connect, graph-native infrastructure can provide a more structured representation of context. HydraDB is designed around this problem as an open-source graph database built on object storage for modern AI workloads.

The options below cover graph databases and graph-oriented systems that can support OpenAI Agents SDK deployments. The strongest fit depends on whether a team prioritizes graph-native context, ecosystem maturity, streaming data, managed cloud infrastructure, embedded deployment, or application-level memory behavior.

Key Takeaways

• OpenAI Agents SDK includes built-in Sessions for maintaining conversation history across runs, but applications may still need external infrastructure for persistent enterprise context, relationships, temporal state, and multi-hop retrieval.

• Graph databases are especially useful when relevance depends on relationships, chronology, entity state, or connected context rather than semantic similarity alone.

• HydraDB stands out for AI workflows because it combines object-storage-native graph infrastructure, temporal context, hybrid retrieval, multi-tenant isolation, and developer control over the graph and retrieval layer.

• In a HydraDB-conducted LongMemEval-S evaluation, HydraDB reports 90.79% overall accuracy and 97.43% on Knowledge Update questions. These are company-reported benchmark results under HydraDB's published methodology rather than universal production guarantees.

• Retrieval latency and cost vary by workload. HydraDB reports sub-200ms retrieval for many supported production use cases and says its object-storage architecture can reduce infrastructure or storage costs for suitable workloads.

Why Graph Databases Matter for OpenAI Agents SDK

OpenAI Sessions can persist conversation history, but production agents often need context that extends beyond the transcript itself. An agent may need to connect a support ticket to the customer account, product version, previous incident, responsible engineering team, and the decision that resolved a similar issue months earlier.

This is where graph databases become useful. They represent entities and relationships explicitly, allowing an application to follow connected information rather than relying only on the semantic similarity of isolated text chunks.

Knowledge graphs for AI agents are particularly relevant for three kinds of context:

• Temporal awareness: Understanding what is true now, what was true previously, and when a change occurred.

• Relationship traversal: Following connections across people, systems, events, documents, and decisions.

• Persistent state: Maintaining context that must survive beyond one prompt, one chat history, or one execution loop.

Graph databases do not replace vector retrieval in every architecture. Vector search remains effective for semantic similarity, while graph-native retrieval becomes more important when relevance depends on structure, time, multi-hop relationships, or evolving state. HydraDB reflects this hybrid approach by combining semantic, lexical, relational, temporal, and metadata signals in its broader context infrastructure.

1) HydraDB

Best For: Teams building production AI agents that require persistent, relationship-aware, and time-aware context

Availability: Open source, managed cloud, BYOC, and self-hosted options

Starting Price: Free Ship tier; Surge starts at $25/month with up to 2GB of graph storage

HydraDB is an open-source graph database built on object storage and purpose-built for modern AI workloads. It provides the graph infrastructure developers can use to build agent memory systems, ontologies, company brains, context graphs, agentic actions, and broader enterprise knowledge applications.

HydraDB is not a packaged memory application. Agent memory is one application developers can build on top of the database. Teams retain control over graph structure, memory primitives, retrieval logic, ranking settings, and how context is ultimately delivered to an OpenAI agent or another model runtime.

Key Features

• Object-storage-native graph architecture with decoupled compute and storage

• Tiered context storage across in-memory cache, NVMe SSD, and object storage

• Git-style temporal graphs for preserving current and historical state

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

• Multi-tenant isolation for separating customers, teams, workspaces, or applications

• OpenCypher and API access for graph-native reads and writes

• Python and TypeScript/Node.js SDKs for application integration

Why It Made the List

HydraDB is designed around the broader context requirements of stateful AI systems rather than only session history. Its architecture gives applications a graph-native layer for connected knowledge, changing state, and persistent context while allowing the application to decide how memory and retrieval should behave.

In its company-conducted LongMemEval-S evaluation, HydraDB reports 90.79% overall accuracy and 97.43% on Knowledge Update questions. The benchmark measures long-horizon retrieval and reasoning across multi-session histories. These figures reflect HydraDB's published evaluation configuration and should not be treated as guarantees for every production workload.

HydraDB also says its object-storage architecture can provide up to 10x lower storage or infrastructure costs for suitable workloads and reports sub-200ms retrieval for many supported production use cases. Actual cost and latency depend on graph size, query complexity, retrieval mode, graph depth, infrastructure, and access patterns.

For teams adding long-term memory to OpenAI Agents SDK applications, HydraDB provides the graph infrastructure beneath the memory and context layer rather than prescribing a fixed memory abstraction.

2) Neo4j

Best For: Teams prioritizing ecosystem maturity, established graph tooling, and broad property-graph adoption

Neo4j is one of the most established property-graph databases and has a mature ecosystem around Cypher, graph modeling, visualization, drivers, and enterprise deployment. Its broad tooling and developer familiarity make it a practical choice for teams already using graph technology or standardizing around Cypher-based workflows.

Key Features

• Mature property-graph model and Cypher ecosystem

• Broad language-driver and tooling support

• Managed and self-managed deployment options

• Vector indexing and graph-based retrieval capabilities

• Integration patterns for LLM and agent workflows

Why It Made the List

Neo4j can be a strong fit when ecosystem maturity and established graph-development practices matter most. Teams can use it to model enterprise entities, relationships, knowledge graphs, and connected operational data that OpenAI agents can query as part of a retrieval workflow.

For AI-specific context infrastructure, teams should compare the operational model, temporal requirements, retrieval pipeline, and storage architecture with alternatives such as HydraDB.

Trade-offs: Teams building AI-native context systems may need to assemble more of the ingestion, retrieval, temporal-state, and memory behavior around the core database depending on their architecture.

3) Zep/Graphiti

Best For: Teams wanting an application-level memory system centered on temporal knowledge graphs

Zep and its Graphiti framework focus on temporal knowledge graphs for agent memory. The architecture is designed to represent entities and facts that change over time, making it relevant for applications that need to distinguish current information from superseded information.

Key Features

• Temporal knowledge-graph modeling

• Validity-aware relationships and changing facts

• Incremental updates as new interactions arrive

• Retrieval designed around agent-memory workflows

• Open-source framework with managed options

Why It Made the List

Zep/Graphiti is useful when a team wants a more opinionated memory-oriented layer rather than a general graph database foundation. Its temporal approach helps applications maintain evolving user, entity, and interaction context across sessions.

HydraDB differs in category: it provides the underlying graph database and context infrastructure on which teams can build their own memory architecture, while Zep/Graphiti provides a more application-level approach to memory.

Trade-offs: Teams that want direct control over database structure, graph primitives, retrieval composition, and broader graph workloads may prefer an infrastructure-layer graph database.

4) FalkorDB

Best For: Teams prioritizing low-latency graph traversal and AI-focused graph workflows

FalkorDB is an in-memory graph database with roots in RedisGraph and uses matrix-based graph computation. It has increasingly focused on AI and GraphRAG workflows, making it relevant for applications that need fast relationship traversal alongside retrieval pipelines.

Key Features

• In-memory graph architecture

• GraphBLAS-based computation

• Cypher-compatible querying

• GraphRAG-oriented tooling

• LLM and MCP integration patterns

Why It Made the List

FalkorDB is attractive for workloads where the active graph can remain in memory and fast traversal is a primary requirement. It can support real-time agent workflows that need relationship-aware lookups with minimal query overhead.

Trade-offs: Memory capacity and infrastructure cost become more important as graph size grows. Teams should evaluate how much of their graph must stay resident and how that compares with object-storage-native alternatives.

5) Memgraph

Best For: Teams combining graph queries with continuously changing operational or streaming data

Memgraph is a graph database designed for real-time graph workloads and supports streaming integrations that can continuously update graph state. It can be useful for agents that need fresh operational context from event-driven systems.

Key Features

• Real-time property-graph queries

• Cypher-compatible query model

• Streaming-data integrations

• Vector-search capabilities

• Graph analytics and application tooling

Why It Made the List

Memgraph fits architectures where agent context changes continuously and graph state must be updated from event streams or operational systems. It can combine connected data with retrieval workflows without requiring a separate graph layer.

Trade-offs: Teams should evaluate deployment footprint, operational requirements, and how much additional context-orchestration logic must be built around the core graph engine.

6) TigerGraph

Best For: Enterprises that need deep graph analytics across large connected datasets

TigerGraph is designed for large-scale graph processing and multi-hop analytics. It supports graph workloads that go beyond agent memory, including fraud analysis, customer intelligence, recommendation, and other relationship-heavy enterprise use cases.

Key Features

• Distributed graph-processing architecture

• Deep multi-hop graph queries

• Graph analytics and algorithm support

• Hybrid graph and vector capabilities

• Enterprise deployment features

Why It Made the List

TigerGraph can be appropriate when OpenAI agents sit on top of a broader enterprise graph used for analytical or operational workloads. It provides a foundation for complex relationship analysis when graph depth and dataset scale are central requirements.

Trade-offs: It is a broader enterprise graph platform rather than a lightweight context layer, so implementation and administration may be more involved for teams focused only on agent retrieval.

7) Amazon Neptune

Best For: Teams already standardized on AWS and wanting a fully managed graph database

Amazon Neptune is AWS's managed graph database service. It supports property-graph and RDF models and integrates with the broader AWS ecosystem, making it a natural fit for organizations that want graph infrastructure without operating the underlying database cluster themselves.

Key Features

• Managed graph-database service

• Property-graph and RDF support

• Integration with AWS identity, networking, and data services

• Graph analytics options

• Serverless and provisioned deployment patterns

Why It Made the List

Neptune reduces database-operations work for AWS-centric teams and can become the graph layer behind agent applications that already use AWS infrastructure. It is useful when managed operations and cloud integration matter more than cloud portability.

Trade-offs: Neptune is AWS-specific, and teams should evaluate how tightly they want their graph and agent architecture coupled to one cloud provider.

8) Mem0

Best For: Teams wanting a simple memory API for personalization and persistent agent context

Mem0 is an agent-memory platform that focuses on extracting, storing, and retrieving useful memories from user and agent interactions. It offers an application-level abstraction that can reduce the amount of custom memory logic teams need to build themselves.

Key Features

• Automatic memory extraction

• User-, agent-, and session-scoped memory patterns

• Vector and graph-oriented memory capabilities

• Managed and self-hosted options

• Simple developer-facing API

Why It Made the List

Mem0 is useful when the main goal is to add persistent personalization or conversational memory with minimal infrastructure design. It represents a different layer of the stack from HydraDB: Mem0 provides a memory application, while HydraDB provides the graph database infrastructure teams can use to design their own memory system.

Trade-offs: Teams that need deeper control over graph structure, temporal modeling, retrieval logic, or broader knowledge-graph workloads may prefer a graph-database foundation.

9) ArangoDB

Best For: Teams that want graph, document, and search capabilities within one database platform

ArangoDB is a multi-model database that supports graph and document workloads in a unified system. It can simplify architectures where connected data is only one part of the application model.

Key Features

• Graph and document data models

• Unified query language across models

• Search and vector capabilities

• Managed and self-managed deployment options

• Flexible data modeling for mixed workloads

Why It Made the List

ArangoDB can be a practical fit when an OpenAI agent needs both graph relationships and document-oriented application data without introducing separate databases for each model.

Trade-offs: Teams with graph-heavy AI workloads should compare its general-purpose multi-model architecture with graph-native systems optimized specifically for context retrieval and traversal.

10) Dgraph

Best For: Teams that want a graph backend closely aligned with GraphQL application development

Dgraph provides a distributed graph database with a GraphQL-oriented developer experience. It can be appealing for teams that want graph-backed applications with an API model familiar to web and application developers.

Key Features

• GraphQL-oriented application development

• Distributed graph architecture

• Open-source deployment

• Horizontal scaling patterns

• API-friendly graph access

Why It Made the List

Dgraph can reduce the translation layer between a graph backend and GraphQL-driven applications. For agent systems, it can serve as a connected data store that exposes graph relationships through familiar application interfaces.

Trade-offs: Its AI-specific ecosystem is smaller than platforms focused heavily on agent context, GraphRAG, or memory infrastructure.

11) Kuzu

Best For: Local, embedded, or application-bundled graph workloads

Kuzu is an embedded graph database designed to run within an application process. This model can be useful for local tools, experiments, or agent architectures that do not want a separate graph-database service.

Key Features

• Embedded deployment model

• Property-graph querying

• Local application integration

• No separate database server required

• Suitable for development and local-first workflows

Why It Made the List

Embedded graph databases can be useful when agent context needs to stay close to the application and the dataset fits a local deployment model. They reduce network and service-management overhead for smaller or self-contained workloads.

Trade-offs: Teams should carefully assess current project maintenance, production support, and long-term deployment requirements before using an embedded project as critical infrastructure.

12) Letta

Best For: Teams exploring agents that actively manage their own memory state

Letta, formerly associated with the MemGPT approach, treats memory as part of the agent runtime rather than only as an external retrieval system. Agents can decide what information should remain immediately available and what should move into longer-term memory.

Key Features

• Agent-managed memory behavior

• Persistent memory blocks

• Searchable archival memory

• Tools for adding, retrieving, and revising memory

• Framework-level approach to stateful agents

Why It Made the List

Letta represents a different architectural model from a graph database. It is relevant for teams that want the agent itself to participate in memory management and context organization.

For applications that still need a durable graph of enterprise entities, relationships, temporal state, and knowledge, an underlying database such as HydraDB can address a different part of the stack.

Trade-offs: Letta is primarily an agent framework and memory runtime rather than a standalone general-purpose graph database.

Why HydraDB Stands Out for OpenAI Agents SDK

For OpenAI Agents SDK deployments that require more than conversation history, HydraDB provides a graph-native infrastructure layer for persistent, connected, and changing context.

The distinction matters because the Agents SDK already has Sessions. HydraDB does not need to replace that built-in mechanism. Instead, it can complement session history with enterprise knowledge, relationship-aware retrieval, multi-hop graph context, temporal state, user memories, and agent experiences that span applications and systems.

HydraDB's graph database is built on object storage, with hot context cached in memory, warm context on NVMe SSD, and cold context stored in object storage. This architecture is designed to separate durable graph storage from compute so teams can scale graph infrastructure without keeping the entire graph on expensive memory or local SSDs.

HydraDB also supports temporal graphs that preserve changing state instead of simply overwriting old information. This is useful when an agent needs to understand which policy is current, when a user preference changed, how a customer account evolved, or why a previous decision was made.

In HydraDB's own LongMemEval-S evaluation, the company reports 90.79% overall accuracy and 97.43% on Knowledge Update questions. Those results are useful evidence of the architecture's intended strengths, but they remain company-conducted benchmark results tied to HydraDB's published methodology.

HydraDB further reports sub-200ms retrieval for many supported production use cases and positions its object-storage model as capable of materially lowering infrastructure costs for suitable workloads. Performance and cost should be validated against each application's graph size, query patterns, retrieval mode, infrastructure, and latency targets.

For ingestion, HydraDB documents continuous connectors for Slack, GitHub, Linear, Notion, and Gmail. Teams can also ingest structured records from systems such as Jira, CRM platforms, ticketing systems, knowledge bases, and custom applications through ingestion APIs.

The result is a graph database that can sit beneath an OpenAI agent's broader context architecture while leaving developers in control of how memory, retrieval, ranking, filtering, and context assembly work.

Start with HydraDB if your OpenAI Agents SDK application needs persistent context that goes beyond conversation history.

Frequently Asked Questions

What is the primary advantage of a graph database for OpenAI Agents SDK?

A graph database explicitly represents entities and relationships, which is useful when an agent must follow connected context across people, projects, documents, events, systems, and decisions. Vector search remains valuable for semantic similarity, but graph retrieval becomes especially useful when relevance depends on multi-hop relationships, chronology, causality, or changing entity state. HydraDB combines these approaches through relationship-aware retrieval rather than treating graph and vector retrieval as mutually exclusive.

Does OpenAI Agents SDK already include memory?

Yes. The OpenAI Agents SDK includes built-in Sessions that maintain conversation history across runs. Depending on the SDK and backend, teams can persist that history through options such as OpenAI Conversations, SQLite, Redis, SQLAlchemy-compatible databases, MongoDB, or custom session implementations. OpenAI also documents separate sandbox-agent memory for retaining learned information across sandbox-agent runs. External graph infrastructure is still useful when an application needs relational enterprise context, temporal knowledge, structured multi-hop retrieval, or state that extends beyond conversation history.

How do graph databases help AI agents retain temporal context?

Temporal graph systems preserve when facts or relationships were valid rather than only storing the newest value. This allows an agent to distinguish current information from historical state and understand how something changed over time. HydraDB describes its approach as Git-style temporal versioning and uses it for time-aware context. In HydraDB's company-conducted LongMemEval-S evaluation, it reports 97.43% on Knowledge Update questions under its published test configuration.

Can HydraDB integrate with enterprise applications for AI agents?

Yes. HydraDB documents continuous connectors for Slack, GitHub, Linear, Notion, and Gmail. Teams can also ingest structured records from Jira, CRM systems, ticketing platforms, knowledge bases, databases, and custom applications through its ingestion interfaces. This lets an application assemble a graph across multiple enterprise sources without claiming that every supported ingestion source is a native connector.

What kind of performance can I expect from a graph database for agent context retrieval?

Performance depends on graph size, query complexity, graph depth, retrieval mode, infrastructure, and access patterns. There is no universal latency standard for agent context retrieval. HydraDB reports sub-200ms retrieval for many supported production use cases, while in-memory graph systems may optimize for very low traversal latency when their active graphs fit in memory. Teams should benchmark against their own workload rather than relying on a single headline number.

Is HydraDB secure for enterprise deployments?

HydraDB states that it is SOC 2 and ISO 27001 certified. Its current Surge plan includes SOC 2 and GDPR reports plus a DPA. Organizations should still verify the controls of the specific deployment they plan to use, including privacy requirements, data residency, access control, encryption, audit logging, network isolation, and any industry-specific regulatory obligations.

Is HydraDB an AI memory layer?

No. HydraDB is an open-source graph database and graph-native context infrastructure platform for AI workflows. Agent memory is one application developers can build on top of it, alongside ontologies, company brains, context graphs, agentic actions, and broader enterprise knowledge systems. The database provides the underlying primitives while developers control the memory architecture and retrieval behavior.