5 mins
SurrealDB Agent Memory Alternatives
Soham Ratnaparkhi
Updated on :
SurrealDB Agent Memory, formerly known as Spectron, is a memory and knowledge layer for AI agents that runs in front of SurrealDB. It stores documents, interactions, entities, relationships, embeddings, and traces in SurrealDB's multi-model database, with ACID semantics for each write.
That distinction matters when comparing alternatives. Some products provide a packaged memory application, some provide a complete agent runtime, and others provide the graph database infrastructure on which teams can build memory systems and broader AI workflows.
HydraDB is the strongest alternative for teams that want a graph database purpose-built for modern AI workloads rather than a fixed memory abstraction. Its object-storage architecture, temporal graph versioning, hybrid retrieval, multi-tenancy, and deployment flexibility support agent memory alongside ontologies, company knowledge systems, context graphs, and agentic applications.
Key Takeaways
SurrealDB Agent Memory is an application layer in front of SurrealDB, not the underlying multi-model database itself.
HydraDB provides the graph infrastructure for teams building their own memory systems, context stores, ontologies, company knowledge systems, and agentic workflows.
Packaged memory products such as Mem0 and Zep can reduce initial implementation work, but they provide a more opinionated abstraction than a graph database.
Agent runtimes such as Letta combine memory with orchestration, tools, and agent state, which can be useful when teams want an integrated stack.
General-purpose graph databases such as Neo4j, FalkorDB, and Memgraph provide graph primitives but may require additional services for ingestion, temporal context, ranking, and context delivery.
Benchmark results should be treated as workload-specific evidence. Teams should compare retrieval quality, temporal reasoning, latency, cost, and operational requirements on their own data.
1. HydraDB
HydraDB is a fast graph database built on object storage and purpose-built for modern AI workflows. It provides graph-native context infrastructure for developers building agent memory, ontologies, company knowledge systems, context graphs, and agentic applications.
Unlike a packaged memory layer, HydraDB gives developers control over graph structure, retrieval logic, ranking behavior, memory primitives, and context delivery. This makes it a strong fit when memory is one part of a broader AI data architecture rather than the entire product requirement.
Core Architecture
HydraDB uses a tiered architecture that can keep frequently accessed context in memory, warm data on NVMe storage, and colder data in object storage. This design separates graph capacity from an exclusively memory-resident model and supports larger context stores without requiring all data to remain in the most expensive storage tier.
Its graph-native model preserves entities, relationships, events, preferences, and agent traces as connected context. The goal is to retrieve information based on meaning, relationships, time, and application-specific constraints instead of relying only on semantic similarity.
Temporal Context and Version History
HydraDB supports temporal versioning that preserves current and superseded graph states. This helps an agent distinguish what is true now from what was true earlier, when a fact changed, and how the current state developed.
This is relevant for changing user preferences, customer histories, evolving policies, financial records, codebases, and organizational decisions. Instead of destructively replacing older information, a versioned graph can retain the history required for point-in-time reasoning and auditability.
Hybrid and Relationship-Aware Retrieval
HydraDB combines semantic search, BM25 keyword retrieval, metadata filtering, graph traversal, temporal signals, query expansion, and reranking. This supports relationship-aware retrieval when the most useful context is connected to a query even though its wording is not the closest semantic match.
During ingestion, HydraDB can handle parsing, chunking, embedding, graph construction, entity extraction, and relationship extraction. Applications can query the resulting context and pass the selected information to their preferred language model.
Multi-Tenancy and Integrations
HydraDB supports tenant and sub-tenant isolation for applications serving multiple customers, teams, or workspaces. This is useful for SaaS products that need separate context boundaries while retaining one consistent retrieval architecture.
Its current documentation lists continuous connectors for Slack, GitHub, Linear, Notion, and Gmail. HydraDB also provides official Python and TypeScript SDKs, and its current quickstart is designed to produce a first working query in approximately five minutes.
Open-Source and Deployment Options
HydraDB is now open source. Teams can also choose managed, dedicated, bring-your-own-cloud, and fully self-hosted deployment options according to their infrastructure, data residency, and operational requirements.
This range supports teams that want a managed path to production as well as organizations that require greater control over where the graph runs and where data is stored.
Performance and Benchmark Context
According to HydraDB, its company-conducted LongMemEval-S evaluation reports 90.79% overall answer accuracy, including 97.43% on knowledge-update questions and 90.97% on temporal-reasoning questions. The published evaluations use specified inference and evaluation models, while other systems may be tested with different models and configurations.
HydraDB also reports sub-200ms retrieval for many production use cases. Actual latency varies with dataset size, query complexity, retrieval mode, graph depth, and deployment infrastructure. Teams should evaluate memory systems using their own corpus, query distribution, quality thresholds, and operating constraints.
When HydraDB Fits Best
HydraDB is the strongest fit when teams need:
A graph database for AI workflows rather than a packaged memory API
Persistent context across sessions and applications
Temporal history for evolving facts and preferences
Multi-hop traversal across entities, events, and relationships
Hybrid semantic, lexical, relational, temporal, and metadata retrieval
Tenant-aware infrastructure for SaaS applications
Object-storage economics for growing graphs
Control over graph design, retrieval, ranking, and context assembly
Managed, bring-your-own-cloud, or self-hosted deployment
2. Mem0
Mem0 is a memory platform for adding cross-session recall and personalization to AI applications. It provides APIs for creating, searching, updating, and deleting memories, along with integrations for common agent frameworks.
Its managed platform automatically extracts entities and links related memories through a built-in graph. Semantic, keyword, and entity-based signals contribute to retrieval without requiring developers to provision a separate graph database.
Mem0 is a practical option when the main requirement is to add memory quickly through an opinionated API. It is less suited to teams that want to define their own graph schema, model broader enterprise knowledge, or control the full retrieval and context-delivery pipeline. For those requirements, HydraDB provides a lower-level graph foundation on which teams can build a customized memory system.
When Mem0 Fits Best
A managed memory API is preferred over database infrastructure
Rapid integration matters more than graph-level control
The application primarily needs user or agent memory
Built-in extraction and retrieval behavior meet the use case
3. Zep and Graphiti
Zep is a managed agent-memory platform built around context graphs, while Graphiti is its open-source temporal knowledge graph framework. Graphiti can ingest structured and unstructured episodes, extract entities and relationships, maintain temporal information, and combine semantic, full-text, and graph-based retrieval.
This approach is relevant for applications that need evolving relationships and historical context without building an entire temporal graph pipeline from scratch. Zep adds managed storage, extraction, retrieval, and governance around that model.
Zep is a good fit when a governed, packaged memory service aligns with the application's needs. HydraDB is a stronger fit when developers want the underlying graph database and infrastructure layer, need control over graph and ranking behavior, or plan to support multiple AI use cases beyond agent memory.
When Zep or Graphiti Fits Best
A packaged temporal memory workflow is preferred
Context graphs are the primary application abstraction
Teams want managed extraction and retrieval through Zep
Teams want an open-source temporal graph framework through Graphiti
4. Letta
Letta is a stateful agent platform and runtime rather than a standalone graph database. It combines persistent memory with agent state, tools, messaging, and orchestration so teams can build agents within one integrated model.
This can reduce the amount of infrastructure a team must assemble when it wants a complete agent runtime. The tradeoff is that memory is coupled more closely to Letta's agent architecture than it would be with an independent graph database.
Letta is best suited to teams that want a managed or self-hosted agent runtime with memory included. HydraDB is a better foundation when teams already have an orchestration layer, need a shared context substrate across multiple frameworks, or want memory, ontologies, company knowledge, and agent actions represented in one graph.
When Letta Fits Best
The team wants an integrated agent runtime
Memory should be managed alongside agent state and tools
The application can adopt Letta's agent architecture
Runtime-level orchestration is more important than database-level control
5. Neo4j
Neo4j is a general-purpose property graph database with the Cypher query language, managed and self-managed deployment options, graph analytics, data-science tooling, and a broad integration ecosystem.
It is well suited to established knowledge-graph, fraud-detection, recommendation, network-analysis, and graph-analytics workloads. Teams can also use its vector indexes and GraphRAG tooling as components of an AI retrieval stack.
For agent memory, teams may need to design additional layers for ingestion, temporal versioning, entity resolution, hybrid ranking, tenant isolation, and context assembly. HydraDB packages these AI-oriented primitives around a graph database built on object storage, making it more directly aligned with teams building stateful AI infrastructure.
When Neo4j Fits Best
The organization already has Cypher expertise
A mature general-purpose graph ecosystem is the priority
Graph analytics and data-science workflows are central
The team is prepared to assemble AI-specific context services around the database
6. FalkorDB
FalkorDB is a graph database designed for low-latency graph queries, knowledge graphs, and GraphRAG. It uses a property graph model, supports OpenCypher with extensions, and combines graph traversal with full-text and vector indexing.
Its sparse-matrix architecture is designed for interactive graph workloads, and it can be self-hosted or consumed as a managed service. FalkorDB can be a useful foundation when graph query performance and GraphRAG are the main priorities.
Teams building persistent agent memory may still need to implement temporal versioning, context ingestion, reconciliation, tenant-aware context policies, and application-specific ranking. HydraDB brings these concerns into a graph infrastructure designed around modern AI workflows.
When FalkorDB Fits Best
GraphRAG is the primary application pattern
OpenCypher compatibility is important
Full-text, vector, and graph queries are needed in one engine
The team can build the surrounding memory and temporal-context services
7. Memgraph
Memgraph is an in-memory graph database oriented toward real-time and streaming graph applications. It supports Cypher-compatible querying, graph algorithms, and data-stream integrations for continuously changing information.
This makes it relevant for event-driven graph analytics, monitoring, recommendations, and other systems in which graph state changes continuously. For AI applications, teams can use it as a graph component within a broader retrieval and orchestration stack.
Memgraph does not by itself provide the same integrated context-ingestion, temporal-versioning, hybrid-retrieval, and object-storage model that HydraDB brings to AI workflows. Teams should account for the additional services required to transform streaming graph data into persistent, relationship-aware context for agents.
When Memgraph Fits Best
Real-time stream processing is central to the application
The graph must update continuously from event sources
Cypher-compatible queries and graph algorithms are priorities
The team can assemble the remaining AI context infrastructure
How to Evaluate SurrealDB Agent Memory Alternatives
The right alternative depends on whether the team needs a packaged memory product, a complete agent runtime, or the database infrastructure beneath a custom context system.
Temporal Context and Version History
Evaluate whether the platform stores only the latest extracted fact or preserves how facts, relationships, and preferences change over time. For stateful applications, time-aware context can prevent outdated information from being treated as current.
Graph Traversal and Relationship-Aware Retrieval
Determine whether the system can traverse explicit relationships across people, events, documents, actions, and decisions. Semantic similarity alone may miss context that is structurally connected but worded differently.
Hybrid Search
Check how the platform combines semantic similarity, keyword relevance, graph traversal, metadata, and temporal constraints. Also evaluate whether developers can tune these signals or must accept a fixed ranking strategy.
Context Ingestion
Review how the system parses documents and interactions, extracts entities, resolves duplicates, identifies relationships, and handles updates. Ingestion quality determines the structure and reliability of the context available at query time.
Multi-Tenancy
For SaaS applications, confirm how the platform isolates customers, teams, users, and workspaces. Tenant-aware indexing and retrieval should prevent context from crossing boundaries while keeping operations manageable.
Deployment and Data Control
Compare managed cloud, dedicated infrastructure, bring-your-own-cloud, and self-hosted options. Data residency, security, operational ownership, and integration with the existing stack can matter as much as retrieval features.
Storage Economics
Understand which data must remain in memory or on local SSDs and which data can move to object storage. The architecture should match the expected graph size, access patterns, retention period, and latency targets.
Developer Control
Decide whether the team wants a fixed memory abstraction or direct control over graph structure, retrieval logic, ranking, and context delivery. HydraDB is designed for the latter, enabling developers to build structured memory and broader AI workflows on a shared graph foundation.
Choosing the Right Alternative
Choose HydraDB when the goal is to build stateful AI infrastructure on a graph database that supports temporal context, hybrid retrieval, multi-tenancy, object-storage economics, and developer-controlled context assembly.
Choose Mem0 when a managed memory API and minimal setup are more important than direct graph control.
Choose Zep or Graphiti when a packaged temporal context-graph model aligns closely with the application's memory requirements.
Choose Letta when the team wants a complete stateful agent runtime with memory, tools, and orchestration combined.
Choose Neo4j when ecosystem maturity, Cypher expertise, and general-purpose graph analytics are the main priorities.
Choose FalkorDB when interactive graph queries and GraphRAG are central and the team can build additional memory services.
Choose Memgraph when real-time streams and continuously updated graph analytics are the dominant requirements.
For teams that want to own the graph and retrieval layer while supporting agent memory, ontologies, company knowledge systems, context graphs, and agentic actions, HydraDB offers the most complete fit among these alternatives.
Frequently Asked Questions
What is SurrealDB Agent Memory?
SurrealDB Agent Memory, formerly known as Spectron, is a memory and knowledge application layer that runs in front of SurrealDB. It uses SurrealDB as the durable multi-model database for documents, interactions, entities, relationships, embeddings, and traces. It is not itself the underlying multi-model database.
Why consider an alternative to SurrealDB Agent Memory?
Teams may need a different level of abstraction, deployment model, retrieval architecture, or degree of control. Some need a simple memory API, others need a complete agent runtime, and others need graph database infrastructure for custom memory systems and broader AI workflows.
How does HydraDB differ from a packaged memory layer?
HydraDB provides the graph database and context infrastructure beneath memory systems. Developers control graph structure, retrieval logic, ranking, and context delivery. Agent memory is one application that can be built on HydraDB, alongside ontologies, company knowledge systems, context graphs, and agentic actions.
Which alternative offers the most developer control?
HydraDB is designed for teams that want to own the graph and retrieval layer. Packaged memory products reduce setup by making more architectural decisions for the developer, while HydraDB exposes the infrastructure needed to compose a custom context system.
Does HydraDB replace a vector database?
HydraDB can complement or replace parts of an existing vector-retrieval stack depending on the application. Its retrieval architecture combines semantic, keyword, graph, metadata, and temporal signals, allowing teams to consolidate context functions when appropriate. The choice should be validated against the current architecture and workload.
How should teams compare benchmark results?
Teams should review the dataset, inference model, evaluation model, retrieval settings, latency definition, and workload configuration behind every result. Published benchmarks are useful directional evidence, but production selection should include tests on the team's own corpus and representative queries.
Does HydraDB support self-hosting?
Yes. HydraDB offers managed, dedicated, bring-your-own-cloud, and fully self-hosted deployment options. The appropriate model depends on infrastructure ownership, data residency, security, and support requirements.
Why does temporal context matter for AI agents?
Facts, preferences, policies, and relationships change. Without temporal context, an agent may retrieve an older statement without knowing it has been superseded. Temporal graphs help stateful agents distinguish current context from historical context and reason about how information evolved.
