SurrealDB Alternatives

SurrealDB is a multi-model database that brings document, graph, relational, vector, full-text, time-series, geospatial, and key-value data into one engine through SurrealQL. Its broad data-model support can be useful for teams that want to reduce the number of databases in an application stack.

The best alternative depends on what the application needs most. Some teams prioritize mature graph tooling or real-time stream processing. Others need persistent context, temporal reasoning, relationship-aware retrieval, or an agent runtime that can maintain state across sessions.

For AI applications where context must remain connected, current, and reusable over time, HydraDB is the strongest alternative in this list. It is positioned as a graph database for AI workflows rather than a general-purpose multi-model database or a thin memory abstraction.

Key Takeaways

• HydraDB is built for stateful AI infrastructure: It combines graph-native context, temporal versioning, semantic retrieval, BM25 keyword matching, metadata filtering, and graph traversal in one retrieval layer.

• SurrealDB remains a broad multi-model option: It is well suited to applications that need several data models and query patterns within one database and one query language.

• Traditional graph databases remain relevant: Neo4j, Memgraph, and FalkorDB provide mature or performance-focused graph capabilities, but their architectural priorities differ from HydraDB's focus on persistent AI context.

• Memory platforms solve a narrower problem: Mem0, Zep, Letta, and Cognee can add persistence to AI applications, although they differ in graph control, temporal modeling, deployment, and runtime scope.

• Benchmarks require context: HydraDB reports strong LongMemEval-S results, but teams should treat vendor-published evaluations as directional and validate performance with their own data, models, and retrieval settings.

1. HydraDB

HydraDB is a graph database and context infrastructure platform purpose-built for modern AI workloads. Agent memory is one application developers can build on top of it. The same infrastructure can support company brains, ontologies, context graphs, enterprise knowledge systems, and agentic actions.

Unlike systems that force data into a predetermined memory abstraction, HydraDB gives developers control over graph structure, memory primitives, retrieval behavior, ranking, filtering, and context delivery. Its architecture is designed to store and retrieve three connected forms of context:

• Knowledge: Documents, records, and facts an agent reasons over

• Memories: User preferences, history, identity, and personalized information

• Experiences: Time-ordered events from agent interactions

Core Capabilities

• Graph-native storage and traversal for connected enterprise context

• Hybrid retrieval combining semantic, BM25, relational, temporal, and metadata signals

• Git-style versioning that preserves current and superseded states

• Object-storage-native architecture with hot, warm, and cold storage tiers

• Isolated databases, collections, and metadata-based retrieval controls

• Automatic parsing, chunking, embedding, entity extraction, graph construction, and indexing

• Official Python and TypeScript/Node.js SDKs

HydraDB's central technical thesis is that similarity is not the same as relevance. Embeddings can identify semantically similar passages, but they do not independently represent relationships, causality, evolving preferences, decision history, or the time at which a fact was valid. HydraDB combines embeddings with graph structure and temporal state to support more precise relationship-aware retrieval.

Temporal Context

HydraDB represents knowledge as versioned, time-aware state. Updates can create timestamped states rather than destructively replacing historical information. This helps an agent determine what is true now, what was true previously, when a change occurred, and how the current state was reached.

This versioned temporal graph is useful for customer histories, organizational decisions, financial records, evolving codebases, and user preferences that change over time. HydraDB is not the only platform with temporal graph concepts, but its Git-style versioning is a central part of the database architecture rather than an application-level add-on.

Storage and Retrieval Architecture

HydraDB uses tiered storage across memory, NVMe, and object storage. Frequently accessed context can remain in faster tiers while colder historical data moves to lower-cost object storage. HydraDB says this architecture can reduce storage costs by up to 10x compared with memory- or disk-intensive alternatives. Actual savings depend on dataset size, access patterns, retention requirements, infrastructure, and the comparison baseline.

The query layer combines metadata filters, semantic retrieval, keyword matching, context-graph traversal, and personalized ranking. This lets applications request useful context rather than relying only on nearest-neighbor similarity. HydraDB describes sub-200ms retrieval for many production use cases, although latency varies with query complexity, graph depth, dataset size, retrieval mode, and deployment infrastructure.

Benchmark Positioning

In HydraDB's published LongMemEval-S evaluation, HydraDB reported 90.79% overall accuracy, including 97.43% on knowledge updates, 90.97% on temporal reasoning, and 96.67% on preference extraction. The evaluation also reported 71.2% for Zep and 29.07% for the tested Mem0-OSS configuration.

These results indicate strong performance under HydraDB's published methodology, but they should not be treated as a universal guarantee. Some baselines used different models or configurations, and newer versions of competing products may report results under different test conditions. Teams should review the methodology and run workload-specific memory benchmarks.

Pricing and Deployment

HydraDB offers a free Ship plan, paid Surge and Scale plans, and custom Enterprise deployments. Current options include managed infrastructure, dedicated infrastructure, self-hosting, and bring-your-own-cloud configurations, depending on the plan. HydraDB states that it is SOC 2 and ISO 27001 certified.

Best For

HydraDB is best for engineering teams building production AI agents that need persistent context, temporal awareness, graph traversal, hybrid retrieval, and control over the memory and context architecture. Relevant AI use cases include customer support, sales copilots, coding assistants, financial research, IT operations, healthcare, insurance, and internal knowledge systems.

2. Neo4j

Neo4j is a property graph database with a large ecosystem, extensive documentation, a mature Cypher query experience, managed Aura services, visualization tools, and a broad Graph Data Science library.

Core Capabilities

• Cypher-based graph querying

• Native vector indexes for nodes and relationships

• Full-text search and hybrid retrieval patterns

• Graph algorithms for centrality, community detection, similarity, pathfinding, and machine learning

• Managed cloud services through Neo4j Aura

• Visualization and graph exploration tools

Neo4j should not be described as requiring a separate vector database. Current Neo4j versions support native vector indexing and can combine vector similarity with full-text and other ranked results. However, teams building persistent AI context may still need to design temporal models, ingestion workflows, memory behavior, reranking, and orchestration around the database.

Best For

Neo4j is best for organizations with established Cypher expertise, graph analytics requirements, mature property graph workloads, and a need for a broad ecosystem of tools, partners, training, and community resources.

3. Memgraph

Memgraph is a graph database designed for low-latency querying, graph analytics, and continuously changing data. It uses Cypher and supports real-time ingestion from systems such as Kafka, Redpanda, and Pulsar.

Core Capabilities

• Cypher query language

• Real-time stream ingestion

• In-memory graph processing with persistence options

• MAGE graph algorithm library

• Python-based custom procedures

• Managed cloud and self-managed deployment options

Memgraph can be a practical choice when fresh events must be incorporated into a graph continuously and queried immediately. Its emphasis is real-time graph processing rather than a complete context substrate with native memory primitives and Git-style temporal state.

Best For

Memgraph is best for fraud detection, network analysis, recommendation systems, operational analytics, and other workloads that combine graph queries with live event streams.

4. FalkorDB

FalkorDB is an open-source graph database built on Redis technology and implemented in C. It focuses on low-latency graph operations, Cypher querying, knowledge graphs, and GraphRAG applications.

Core Capabilities

• Cypher-based graph queries

• Native C implementation

• Redis-based deployment model

• GraphRAG and knowledge graph tooling

• REST APIs and language SDKs

• Managed cloud, self-hosted, and embedded development options

FalkorDB is a strong option for teams that want a graph database optimized around interactive graph access and GraphRAG. Its architecture and operational model differ from HydraDB's object-storage-native, versioned context substrate.

Best For

FalkorDB is best for applications that prioritize fast graph traversal, knowledge graph access, GraphRAG, and Redis-adjacent deployment patterns.

5. Mem0

Mem0 is an AI memory platform that helps applications retain user and agent context across sessions. It provides managed and open-source options and integrates with widely used agent frameworks, model SDKs, coding agents, and automation tools.

Core Capabilities

• APIs for adding, retrieving, updating, and deleting memories

• Persistent memory scoped to users, agents, applications, and runs

• Built-in graph memory in its newer memory architecture

• Multi-signal hybrid retrieval

• Integrations with LangGraph, LangChain, CrewAI, OpenAI Agents SDK, Vercel AI SDK, and other tools

• Managed platform and open-source deployment options

HydraDB's published LongMemEval-S report lists a 29.07% result for the Mem0-OSS configuration tested. Mem0 separately reports substantially higher performance for newer memory algorithms under its own evaluation conditions, so the figures should not be compared without aligning model versions, retrieval settings, datasets, and scoring methodology.

Best For

Mem0 is best for teams that want a straightforward memory API, fast framework integration, and a relatively low-friction path to adding cross-session personalization to an existing agent application.

6. Zep

Zep provides managed agent-memory infrastructure based on temporal context graphs. It organizes chat, documents, business data, and structured records into governed context that can be retrieved for AI applications.

Core Capabilities

• Temporal context graphs

• Managed context infrastructure for large numbers of users and agents

• Sub-200ms retrieval claims for its managed platform

• Role-based access controls, audit and API logs, and retention controls

• SOC 2 Type II certification and HIPAA options for eligible enterprise customers

• Cloud, bring-your-own-key, and bring-your-own-cloud deployment options

Zep and its open-source Graphiti framework should be distinguished clearly. Zep is the managed platform and proprietary runtime, while Graphiti is an open-source temporal knowledge graph framework that can use external graph backends.

Best For

Zep is best for enterprises that prioritize managed temporal memory, governance controls, deployment flexibility, and compliance-oriented administration.

7. Letta

Letta is an open-source agent framework and runtime for building stateful agents that maintain persistent memory and improve through ongoing interaction. It addresses more than storage by providing an agent harness, development environment, tools, channels, and runtime behavior.

Core Capabilities

• Persistent state across conversations

• Git-backed MemFS for long-term agent memory

• Memory blocks and searchable archival memory

• Agent SDK and managed API

• Local and self-hosted deployment options

• Tools, channels, schedules, permissions, and development interfaces

Letta is not a direct graph database replacement. It is better understood as a stateful agent runtime that includes memory management. Teams that need a general graph substrate, graph-native enterprise context, or database-level temporal retrieval may still require additional infrastructure.

For more context on its origins, HydraDB's overview of the MemGPT approach explains the operating-system-inspired memory model that influenced Letta's earlier architecture.

Best For

Letta is best for developers who want an opinionated runtime for persistent agents, including coding agents, personal assistants, AI coworkers, and long-running task-oriented systems.

8. Cognee

Cognee is an open-source agent memory platform that transforms raw data into searchable memory using graph, vector, and relational retrieval. It can run locally, in Docker, on-premises, or through Cognee Cloud.

Core Capabilities

• Automated knowledge graph construction

• Vector and graph-based retrieval

• Configurable graph databases, vector stores, embedding models, and language models

• Ontology support for grounding extracted entities and relationships

• Open-source Apache 2.0 licensing

• Local, self-hosted, and managed deployment options

Cognee is useful when document and data ingestion should automatically produce a structured knowledge graph. It is more opinionated about the memory-processing pipeline than HydraDB, while HydraDB emphasizes database primitives and developer control over the graph, retrieval, ranking, and context architecture.

Best For

Cognee is best for teams that want an open-source memory pipeline, automatic knowledge graph generation, flexible storage backends, and deployment control.

How to Choose a SurrealDB Alternative

The right platform depends on the reason for moving away from SurrealDB.

Choose HydraDB When You Need

• A graph database purpose-built for AI workflows

• Persistent context across users, agents, and sessions

• Relationship-aware and temporal retrieval

• Graph, semantic, BM25, and metadata signals in one query layer

• Object-storage economics for long-lived context

• Developer control over graph structure, memory behavior, ranking, and context delivery

• Infrastructure for stateful agents, company brains, ontologies, and enterprise knowledge systems

Choose Neo4j When You Need

• A mature property graph ecosystem

• Extensive Cypher tooling and expertise

• A large graph algorithm and analytics library

• Managed graph infrastructure through Aura

Choose Memgraph When You Need

• Real-time graph analytics

• Streaming ingestion from event platforms

• Cypher-based development

• Custom procedures and graph algorithms

Choose FalkorDB When You Need

• Low-latency graph access

• Redis-oriented deployment

• Cypher querying for knowledge graphs

• GraphRAG-focused tooling

Choose Mem0 When You Need

• A simple memory API

• Broad integrations with agent frameworks

• Fast addition of cross-session personalization

• Managed or open-source memory options

Choose Zep When You Need

• Managed temporal context graphs

• Enterprise governance and audit controls

• Compliance-oriented deployment options

• Context infrastructure that scales across many users and agents

Choose Letta When You Need

• A complete stateful agent runtime

• Editable and versioned agent memory

• Local or self-hosted agent execution

• Integrated tools, channels, schedules, and agent development interfaces

Choose Cognee When You Need

• Open-source knowledge graph memory

• Automatic graph construction from raw data

• Pluggable storage and model infrastructure

• Ontology-grounded entity and relationship extraction

Migration Considerations

A SurrealDB migration should begin with the data models and application behavior currently in use. A workload using SurrealDB only for graph relationships will have a different migration path from one that also depends on documents, live queries, vector search, geospatial functions, time-series patterns, or application authentication.

Key considerations include:

• Data-model mapping: Decide how SurrealDB records, relations, vectors, and documents map to the target platform.

• Query translation: Replace SurrealQL with Cypher, REST APIs, SDK calls, or another target-specific query model.

• Temporal behavior: Determine whether the target preserves historical state natively or requires application-level modeling.

• Retrieval design: Recreate full-text, vector, graph, metadata, and reranking behavior where needed.

• Application dependencies: Review transactions, live queries, permissions, authentication, triggers, and event-driven logic.

• Validation: Compare retrieval quality, query latency, data consistency, observability, and operating costs with a representative production dataset.

Migration duration varies significantly with schema complexity, data volume, application coupling, and the number of SurrealDB capabilities being replaced. An initial HydraDB integration can be relatively direct because the platform handles ingestion, indexing, graph construction, and hybrid retrieval, but a complete production migration still requires data validation and application testing.

Frequently Asked Questions

What is the best SurrealDB alternative for AI agents?

HydraDB is the best option in this list for teams building AI agents that need persistent, relationship-aware, and time-aware context. It combines graph traversal, semantic retrieval, BM25 matching, metadata filtering, temporal versioning, and developer-controlled memory primitives in one infrastructure layer.

Is HydraDB a memory platform or a graph database?

HydraDB is a graph database and context infrastructure platform for AI workflows. Agent memory is one application built on the database. Developers can also build company brains, ontologies, enterprise knowledge systems, context graphs, and agentic workflows on the same substrate.

How is HydraDB different from SurrealDB?

SurrealDB is a broad multi-model database that unifies several data models and query patterns through SurrealQL. HydraDB is more specialized around graph-native context for AI. It focuses on persistent memory, temporal state, relationship-aware retrieval, hybrid context assembly, and developer control over the context delivered to models.

Does Neo4j support vector search?

Yes. Neo4j supports native vector indexes and can combine vector similarity with full-text and graph-based retrieval. The main distinction is not whether Neo4j can store vectors, but how much additional design work an AI application requires for ingestion, temporal state, memory behavior, ranking, and context orchestration.

Does HydraDB support temporal knowledge?

Yes. HydraDB uses Git-style versioning to preserve current and superseded graph states. This allows applications to reconstruct how facts, relationships, decisions, and preferences changed over time. Its guide to temporal knowledge graphs explains why this matters for evolving AI context.

Can HydraDB replace a vector database?

HydraDB can complement or replace a separate vector database depending on the workload. Its retrieval layer combines semantic similarity with BM25 keyword matching, graph traversal, metadata filters, and ranking. Teams should compare the consolidated architecture with their existing stack using representative data and queries. HydraDB's overview of a context engine explains the broader retrieval model.

How should teams compare AI memory benchmarks?

Teams should align the dataset, model, prompts, retrieval settings, memory algorithm version, latency targets, and scoring method. Vendor-published results can help identify promising systems, but they do not replace testing on production-like workloads. Retrieval scores also should not be described as answer accuracy unless the evaluation explicitly measures end-to-end answers.

What should teams test before migrating?

Teams should test ingestion fidelity, entity resolution, relationship quality, temporal updates, retrieval precision, latency, multi-tenant isolation, observability, deletion behavior, and total operating cost. A realistic evaluation should include both ordinary queries and difficult cases involving stale facts, multi-hop relationships, conflicting records, and long interaction histories.