Memgraph in GraphRAG use cases

Is this guide for you?

This guide is for you if you’re exploring or building GraphRAG (Graph-Augmented Retrieval-Augmented Generation) systems. You’ll benefit from this content if:

• You want to query your graph using natural language through a proxy LLM interface.
• You need to seamlessly extract knowledge graphs from multiple source systems, especially when a graph structure naturally fits the data.
• You’re building a system where business stakeholders need fast insights without relying on engineers to expose data through APIs.
• Your engineers are deep in the trenches at midnight, and you’d rather they write simple questions instead of Cypher queries.
• You have embeddings and need to perform vector search across your graph, along with structured Cypher query language expressiveness.

If any of these resonate with your project, this guide will walk you through the best practices and configurations to bring GraphRAG to life using Memgraph.

Why choose Memgraph for GraphRAG use cases?

Here’s what makes Memgraph a perfect fit for GraphRAG:

• In-memory architecture: Delivers consistent, predictable response times, avoiding the inconsistency of disk-based caching strategies like LRU.
• Built-in Vector search (powered by usearch): Supports high-performance retrieval across all your GraphRAG workloads.
• Unified analytical engine: Seamlessly integrates with legacy systems and supports a wide range of data ingestion sources to populate your knowledge graph with ease.
• Instant schema metadata access: Makes schema metadata accessible in constant time, allowing your LLM to construct Cypher queries efficiently and without overhead.
• Enterprise-grade access control: Secure your data with role-based permissions, including read-only access, so your GraphRAG queries never risk modifying your data.

What is covered?

The suggestions for GraphRAG use cases complement several key sections in the general suggestions guide. These sections offer important context and additional best practices tailored for performance, stability, and scalability in GraphRAG systems:

• Hardware sizing If you’re using embeddings for vector search, be aware that they can significantly impact memory consumption.

• Choosing the right Memgraph flag set Configure runtime flags to enable constant-time schema retrieval for the LLM. This reduces overhead during text2Cypher construction.

• Choosing the right Memgraph storage mode Guidance on selecting the optimal storage mode for GraphRAG use cases, depending on whether your focus is analytical speed or transactional safety.

• Enterprise features you might require Understand which enterprise features — such as security, access controls, and dynamic graph algorithms are essential for production-ready GraphRAG deployments.

• Queries that best suit your workload Learn how to use deep path traversals, vector search, and dynamic MAGE algorithms to efficiently retrieve contextual data and handle high-velocity graphs.

• Memgraph ecosystem Explore the tools and integrations within the Memgraph ecosystem that can accelerate the development and operation of your GraphRAG pipeline.

Hardware sizing

If your GraphRAG use case involves vector embeddings, it’s important to account for their impact on memory usage. Memgraph stores each indexed vector once, in the vector index (USearch). The property store keeps only a reference to it, for both node and edge vector indexes. The vector is still readable as a regular property through Cypher.

To estimate the memory needed for the vectors themselves, use the following formula:

number of indexed nodes or edges × embedding dimension × bytes per component

The bytes per component depend on the scalar kind of the index: 4 bytes for the default f32, 2 bytes for f16. For example, one million 768-dimensional f32 vectors take about 3.1 GB.

On top of that, plan for:

• Index structure: the graph USearch uses for approximate nearest-neighbor search.
• Reserved capacity: memory follows the index capacity and grows by the resize_coefficient. Deleting nodes or removing vector properties does not free it, but new vectors reuse it. It is released only when the index is dropped.
• Overlapping indexes: a node or edge that matches several vector indexes is stored in each of them.
• Dropping an index: Memgraph moves the vectors back into the property store at the precision set by --storage-floating-point-resolution-bits (64-bit by default), so the property store grows. See Drop vector index.

To see how much memory your vector indexes actually use, check vector_index_memory_tracked in SHOW STORAGE INFO. See Monitor vector index memory, which also explains how vector index memory counts against the ai_platform license limit.

Best practices

• Use a smaller scalar kind: f16 halves the memory of f32 vectors, usually with little loss in accuracy.
• Choose the right embedding dimension: While models like OpenAI use 1536 or 3072 dimensions, lower-dimensional vectors like 512 or 768 often result in only a 5–6% drop in accuracy and drastically reduce memory consumption.
• Offload if needed: If in-memory embedding storage is too demanding, you can offload embeddings to a third-party vector database and still integrate it into your GraphRAG pipeline alongside Memgraph.
• Watch for string context size: If you’re also storing document context in Memgraph, keep in mind that these long strings are currently stored in memory as well. A short-term roadmap item will enable offloading static text content to disk, since these strings rarely change. This will further increase your memory efficiency and scalability.

Choosing the right Memgraph flag set

For GraphRAG use cases, it’s essential to run Memgraph with the following additional flag:

--schema-info-enabled=true

This enables constant-time schema retrieval, which drastically reduces the time needed to supply the LLM with schema information required for generating Cypher queries. Without this flag, schema discovery can introduce unnecessary latency, negatively affecting query responsiveness and the overall user experience.

Choosing the right Memgraph storage mode

Schema retrieval is fully supported in both IN_MEMORY_TRANSACTIONAL and IN_MEMORY_ANALYTICAL storage modes. This means you can choose the mode that best fits your workload without compromising the ability to serve schema metadata to the LLM.

• Use IN_MEMORY_TRANSACTIONAL if your use case requires ACID guarantees, replication, or high availability.
• Use IN_MEMORY_ANALYTICAL if you’re focused on read-only, high-ingestion, or analytics-heavy workloads where maximum write throughput is a priority.

Both modes are compatible with GraphRAG workflows — choose based on your performance and reliability needs.

Enterprise features you might require

To ensure your GraphRAG setup is secure, scalable, and suitable for multi-user environments, Memgraph offers several enterprise features that are especially relevant:

• Role-based access control
  GraphRAG systems often generate queries automatically via LLMs, so it’s critical to enforce strict read-only permissions to prevent accidental data modification. Memgraph supports role-based access control, allowing you to limit access to read-only roles for GraphRAG users and services.

• Label-based access control
  For more granular permissions, Memgraph supports label-based access control. This enables defining access rules at the label and edge type level, so users can only view and query the parts of the graph that are relevant to them—ensuring data isolation and privacy across teams or tenants.

• SSO support for GraphChat
  If you’re using GraphChat inside Memgraph Lab to power LLM-based querying, single sign-on (SSO) makes it easy to integrate with your existing identity provider and streamline user access across your organization.

• Dynamic graph algorithms
  In scenarios where the graph is changing in real-time and you need continuous summarization or insights, Memgraph provides enterprise-only dynamic graph algorithms, such as online community detection. These allow your GraphRAG pipeline to stay up-to-date with evolving data without expensive full re-computation.

Queries that best suit your workload

When integrating Memgraph into open-source GraphRAG frameworks like LlamaIndex, LangGraph or MCP, it’s essential to incorporate this query in your retrieval pipeline:

SHOW SCHEMA INFO;

This command retrieves the graph schema in constant time, enabling the LLM to construct valid Cypher queries with minimal overhead. It’s a key part of the retrieval pipeline that helps bridge natural language questions and structured graph queries.

Additionally, SHOW SCHEMA INFO; is integrated with fine-grained access control,

allowing you to restrict specific users or agents from viewing the full graph schema. This is particularly useful when working with LLMs, as it helps reduce schema noise in large enterprise graphs with many use cases. Limiting the visible schema ensures that the LLM focuses on relevant parts of the graph, improving its ability to generate meaningful answers or queries.

While there’s no single query pattern that fits all GraphRAG use cases. Since LLMs generate a wide variety of questions, multi-hop queries (i.e., traversals across multiple relationships) stand to benefit significantly from Memgraph’s in-memory architecture, offering fast and consistent response times even under complex traversal logic.

Memgraph ecosystem

To accelerate your GraphRAG journey, we recommend exploring the AI ecosystem page. There, you’ll find detailed information about:

• GraphChat, Memgraph’s natural language interface powered by LLMs
• Open-source GraphRAG integrations with frameworks like LlamaIndex and LangGraph
• Practical examples and usage patterns to help you get started quickly

Whether you’re building a custom retrieval pipeline or experimenting with LLM-driven interfaces, this ecosystem is designed to give you a head start and reduce development time.