5 mins
Best GraphRAG Platforms for Mastra in 2026
Soham Ratnaparkhi
Updated on :
Your Mastra agent works in testing. Then it ships, and retrieval accuracy quietly drops 30% because your graph database and vector store are eventually consistent. The question is no longer whether to use GraphRAG, but which platform integrates cleanly with TypeScript agents.
Mastra introduced GraphRAG snapshot support in v1.60.0 in August 2026 with serialize() and GraphRAG.deserialize() capabilities, and the framework reached v1.67 in September 2026. With 28.1k GitHub stars, Mastra developers need graph databases for AI agents that match the framework's production requirements.
We analyzed 10 platforms across direct Mastra integration, TypeScript ecosystem fit, hybrid retrieval capabilities, and production readiness. The graph database market has grown to $4.21 billion with a 27.19% CAGR through 2031, reflecting enterprise demand for relationship-aware AI systems.
Key Takeaways
HydraDB supports AI-focused graph workloads - Its object-storage architecture, hybrid retrieval, temporal versioning, and sub-200ms retrieval make it relevant for Mastra agents that need persistent, relationship-aware context.
Official Mastra integration exists - SurrealDB announced a direct integration on September 3, 2026, making it a platform with documented, official Mastra support.
Temporal context matters - HydraDB achieves 97.43% accuracy on knowledge-update questions by distinguishing past from present facts.
TypeScript ecosystem fit varies - Some platforms are Python-first, creating friction for Mastra's TypeScript architecture.
Database vs. framework distinction - Half the options are orchestration layers, such as Microsoft GraphRAG and Graphiti, rather than persistent storage systems.
Understanding Retrieval Augmented Generation with Graph Databases
Standard RAG retrieves semantically similar chunks from vector databases. GraphRAG extends this by layering relationship traversal, entity extraction, and community detection on top of vector search. The result: agents that understand how entities connect rather than just retrieving isolated text fragments.
The property graph segment holds 61.4% market share, reflecting enterprise preference for graph-native architectures. Property graphs model entities as nodes with attributes and relationships as typed edges, enabling queries like "find all engineers who worked on systems similar to this one, then find who resolved comparable issues."
Why Graph Databases Excel for AI Agent Memory
Agent memory databases must handle three challenges that vector databases struggle with:
Temporal versioning - Tracking how facts change over time ("Alice was CTO at X, now at Y")
Relationship-aware retrieval - Finding connected entities through multi-hop traversals
Cross-session state - Maintaining context across conversations without resetting
GraphRAG implementations using graph databases improve AI agent accuracy by 80% or more compared to vector-only approaches. This improvement stems from the ability to retrieve contextually relevant information based on relationships, not just semantic similarity.
The Limitations of Vector Databases for Advanced AI Agents
Vector databases excel at semantic similarity search but treat every query in isolation. They flatten time, surface outdated data alongside current facts, and require complex metadata filtering to approximate relationship queries.
Single-query composition of graph, vector, and full-text operations remains challenging in vector-first architectures. Most implementations require:
Separate database calls for graph traversal and vector search
Manual stitching of results at the application layer
Custom code to handle temporal filtering
For Mastra agents managing long-running conversations, customer support histories, or multi-month development projects, these limitations compound into retrieval accuracy degradation.
1) HydraDB - Best for AI-Native Graph Workflows
Best for: Production Mastra agents requiring temporal reasoning, hybrid retrieval, and graph infrastructure designed for modern AI workflows.
Mastra Integration: TypeScript SDK available
Price: Free tier available; Ship at $25/month + usage; Scale at $799/month + usage; Enterprise custom pricing.
HydraDB is a fast graph database built for modern AI workflows, running on object storage with a C-based architecture designed for lightweight, cost-efficient operation. The platform serves 2,000+ developers and has processed over 1 billion documents.
Key Features
90.79% overall accuracy on LongMemEval-S
Hybrid semantic, BM25, graph, and temporal retrieval
Sub-200ms context retrieval for production workloads
Git-style versioned temporal graphs for evolving context
Object-storage architecture designed for cost-efficient graph workloads
SOC 2 and ISO 27001 certifications
Why It Made the List
HydraDB's temporal versioning tracks how facts change over time. It achieves 97.43% accuracy on knowledge-update questions, helping agents distinguish current information from historical context. This is useful for coding assistants referencing evolving architectural decisions, support agents working across customer histories, and other Mastra applications that need persistent context across sessions.
Its graph-native architecture also combines relationship traversal with semantic, BM25, and temporal retrieval, while object storage provides an infrastructure model designed for lightweight, scalable AI workloads.
When to Choose: Mastra agents need a graph database that combines temporal context, relationship-aware retrieval, hybrid search, and production-oriented AI infrastructure.
2) SurrealDB
Mastra Integration: Official integration announced September 3, 2026
SurrealDB has a documented official Mastra integration. The platform's multi-model architecture handles graph, document, and vector operations in single atomic statements, eliminating the sync jobs required when combining separate databases.
Key Features
SurrealQL enables co-equal predicates for graph, vector, full-text, and filters in one query
ACID transactions ensure ground truth edges match transaction records
No intermediate materialization compared to Cypher's procedural steps
Rust-based architecture for performance
Why It Made the List
SurrealDB provides an official Mastra integration, giving TypeScript teams a documented path for connecting the framework with its multi-model database architecture.
When to Choose: A documented official Mastra integration and multi-model database architecture are priorities.
3) Neo4j
Mastra Integration: Indirect via drivers and GraphRAG libraries
Neo4j is an established property-graph database with a mature ecosystem of tools, integrations, and community resources. The platform's GraphRAG Python package enables knowledge graph construction, and native vector search was added in v2026.01.
Key Features
Cypher query language with extensive learning resources
Graph Data Science library for analytics
Graphiti framework runs on Neo4j for temporal knowledge graphs
Broad integration support across AI frameworks
Why It Made the List
Neo4j's ecosystem includes extensive documentation, tooling, community resources, and developer expertise that can support teams operating graph workloads over the long term.
When to Choose: Existing Cypher expertise or broad graph tooling and integrations are important requirements.
4) TypeGraph
Mastra Integration: MCP server compatibility
TypeGraph emerged as a TypeScript-native GraphRAG tool built specifically for developers working in Node.js environments. The platform achieved 67.68% accuracy on medical benchmarks and runs on Postgres for familiar infrastructure.
Key Features
Native TypeScript implementation
MCP server for agent integration
Postgres backend for operational simplicity
Lower inference costs than Microsoft GraphRAG
Why It Made the List
TypeGraph aligns with Mastra's TypeScript-first philosophy. Rather than wrapping Python libraries or managing cross-language dependencies, Mastra developers can work entirely in their preferred ecosystem.
When to Choose: A TypeScript-native GraphRAG implementation is a primary requirement.
5) Microsoft GraphRAG
Mastra Integration: Indirect, Python-based
Microsoft GraphRAG defined the category with its April 2024 paper. The framework uses community detection via the Leiden algorithm to create hierarchical summaries for global queries.
Key Features
Community detection for corpus-wide theme identification
Hierarchical summarization for complex queries
Entity extraction and relationship mapping
Modular indexing and query framework
Why It Made the List
Microsoft GraphRAG achieved 50.93% accuracy on novel benchmarks and handles "global" questions requiring corpus-wide understanding. Its approach involves substantial indexing pipelines and LLM calls during graph construction.
When to Choose: Global or sensemaking queries over large document corpora are central to the application.
6) Graphiti
Mastra Integration: Unknown, Python-based
Graphiti implements bi-temporal graphs tracking both event time and system time, enabling fact invalidation and historical context retrieval. The framework supports fast incremental ingestion without full re-indexing.
Key Features
Bi-temporal model distinguishes "when it happened" from "when we learned it"
Incremental updates for long-running agent sessions
Built specifically for agent memory use cases
Runs on Neo4j infrastructure
Why It Made the List
Graphiti addresses temporal knowledge management by maintaining historical context as facts change instead of simply overwriting previous states.
When to Choose: Agents manage evolving facts over extended timeframes.
7) Memgraph
Mastra Integration: Indirect via drivers
Memgraph's in-memory architecture delivers low-latency graph processing for streaming workloads. The Orbis case study showed accuracy improvement from 20% to 90% on a graph with approximately 100 million nodes.
Key Features
Kafka, Redpanda, and Pulsar streaming integrations
Real-time graph updates without batch delays
MCP integration for agent development
Cypher-compatible queries
Why It Made the List
For Mastra agents that process live events and update knowledge graphs in real time, Memgraph provides an architecture centered on streaming graph workloads.
When to Choose: Agents consume streaming data and require immediate graph updates.
8) FalkorDB
Mastra Integration: Indirect via drivers
FalkorDB evolved from RedisGraph with a GraphBLAS sparse-matrix architecture and native multi-tenant graph isolation. The platform includes a GraphRAG SDK with Cypher generation capabilities.
Key Features
Native tenant isolation for SaaS platforms
Matrix-based execution
GraphRAG SDK with entity extraction
Cypher, vector, full-text, and graph retrieval combined
Why It Made the List
Multi-tenant isolation is relevant when a Mastra application serves multiple customers whose data must remain separated. FalkorDB incorporates tenant-oriented graph capabilities into its architecture.
When to Choose: Per-customer graph isolation is an important requirement for a SaaS application.
9) Amazon Neptune
Mastra Integration: Indirect via Bedrock and AWS SDKs
Amazon Neptune integrates with Bedrock Knowledge Bases for managed GraphRAG capabilities. Neptune Analytics provides graph plus vector search, though the service operates as two separate products, Database and Analytics.
Key Features
Native integration with Bedrock, S3, and IAM
AWS GraphRAG Toolkit available
Fully managed with AWS security and networking
Gremlin and SPARQL query support
Why It Made the List
For Mastra teams already using AWS Bedrock for inference, Neptune keeps graph infrastructure within the broader AWS ecosystem.
When to Choose: The application is already built around AWS infrastructure and services.
10) TigerGraph
Mastra Integration: Indirect via drivers
TigerGraph supports massively parallel graph processing with GSQL, openCypher, and GQL support. The platform includes built-in graph algorithms and solution kits for common enterprise patterns.
Key Features
Distributed architecture for large connected datasets
Graph analytics and algorithm library
Real-time analytics for large analytical workloads
Enterprise security and compliance capabilities
Why It Made the List
TigerGraph is designed for large-scale graph analytics and distributed processing, making it relevant when Mastra applications need to operate over very large connected datasets.
When to Choose: Large-scale graph analytics and distributed processing are primary requirements.
Why HydraDB Fits Production Mastra AI Workflows
Mastra gives developers a TypeScript framework for building AI applications, but production agents still need infrastructure that can retrieve connected, time-aware context as their knowledge evolves.
HydraDB addresses that layer as a graph database built for modern AI workflows. Rather than positioning memory as the database itself, HydraDB provides the underlying graph infrastructure on which persistent agent memory, context retrieval, knowledge graphs, ontologies, and other AI applications can be built.
Temporal Context for Long-Running Agents
HydraDB's versioned temporal graphs preserve how information changes instead of treating every stored fact as equally current. That matters when Mastra agents work with architectural decisions, customer histories, policies, account relationships, or other information that evolves over time.
Multiple Retrieval Signals
Production retrieval often needs more than semantic similarity. HydraDB combines:
Semantic retrieval for conceptually related information
BM25 for lexical matching
Graph traversal for relationship-aware context
Temporal retrieval for time-sensitive facts
The result is graph infrastructure designed to give AI applications access to both the relevant information and the relationships and history surrounding it.
Production-Oriented Infrastructure
HydraDB runs on object storage and is built in C, with an architecture designed for lightweight, cost-efficient graph workloads. It also supports sub-200ms retrieval and enterprise deployment requirements, including self-hosting and BYOC options.
For Mastra teams moving beyond prototype retrieval, these capabilities provide a graph foundation for agents that need persistent, structured, and evolving context.
Choose HydraDB for Graph-Native Mastra Workloads
Choosing a GraphRAG platform for Mastra depends on more than framework compatibility. Production agents may need to maintain context across sessions, understand relationships between entities, retrieve current rather than superseded information, and support increasingly large knowledge stores.
HydraDB brings these requirements together in a graph database designed for modern AI workflows. Its architecture supports agent memory as one application while also providing infrastructure for context retrieval, ontologies, company knowledge systems, and broader graph-based AI workloads.
For Mastra applications, key capabilities include:
Temporal versioning to preserve evolving facts and historical context
Hybrid retrieval across semantic, BM25, graph, and temporal signals
Relationship-aware queries for connected context rather than isolated chunks
Sub-200ms retrieval for real-time agent workflows
Object-storage architecture designed for scalable, cost-efficient graph infrastructure
Deployment flexibility for teams that require managed, BYOC, or self-hosted environments
SurrealDB provides a documented official Mastra integration, while other options address different graph and GraphRAG requirements. HydraDB is positioned for teams that need the underlying graph database to support persistent, temporal, relationship-aware AI workflows in production.
Book a demo to see how HydraDB can support graph-native context for production Mastra agents.
Frequently Asked Questions
What is the difference between a GraphRAG platform and a vector database for AI agents?
Vector databases retrieve semantically similar text chunks based on embedding distances. GraphRAG platforms add relationship traversal, entity extraction, and community detection, enabling queries that understand how concepts connect. This produces contextually relevant results rather than just similar-sounding text.
How does HydraDB help AI agents handle outdated information?
HydraDB uses Git-style versioned temporal graphs to track how facts change over time. The system achieves 97.43% accuracy on knowledge-update benchmarks, helping retrieval distinguish between historical and current facts.
What are the main use cases where GraphRAG platforms outperform traditional RAG?
GraphRAG is relevant for coding assistants tracking architectural decisions across multi-month projects, support agents requiring customer history and cross-ticket relationships, sales copilots maintaining multi-year account context, and research agents performing temporal analysis over market intelligence.
Is it possible to deploy HydraDB in a self-hosted environment?
Yes. HydraDB supports managed cloud deployments and enterprise deployment options that include dedicated cloud or BYOC. Enterprise teams can use BYOC when data needs to remain within customer-controlled infrastructure.
How does Mastra integrate with GraphRAG platforms?
Mastra v1.60.0 and later include the @mastra/rag package with GraphRAG snapshot support through capabilities such as serialize() and GraphRAG.deserialize(). SurrealDB has an official integration announced in September 2026. Other platforms can integrate through TypeScript drivers, REST APIs, or other supported interfaces depending on their architecture.
