Memgraph
In-memory graph database that supplies structured context to AI systems and runs real-time graph analytics on the same engine.
If your agents need structured, traceable context and you also run operational graph analytics, Memgraph puts both on one in-memory engine instead of two systems. The free Community Edition is production-ready, and AI Platform Standard's unlimited vector indexes remove the usual cost anxiety around embeddings. Compare it against Neo4j if Cypher familiarity and migration effort matter most, and against FalkorDB or ArangoDB if you're weighing in-memory mutation models or multi-model breadth. Budget for RAM: memory is the license lever here, and multi-terabyte graphs get expensive.
Verified 4d ago · liveness 83/100 · cite: rightaichoice.com/tools/memgraph
- AI engineers building GraphRAG pipelines that need multi-hop traversal, not just similarity search
- Teams adding long-term semantic, episodic, and procedural memory to AI agents
- Fraud and risk teams scoring connected entities in real time at thousands of tx/sec
- Organizations migrating off Neo4j who want Cypher compatibility on a different engine
- Teams whose data fits a document or key-value store and never needs relationship queries
- Buyers with no graph modeling experience and no time for the Cypher learning curve
- Workloads where vector-only retrieval is genuinely sufficient and graph structure adds nothing
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Skip Memgraph if no one on your team knows Cypher or graph modeling and you only need similarity search over documents, since you'd pay the memory-based license for structure you never traverse.
Licensing is priced on memory capacity (Enterprise counts graph data plus vector indexes together), so growing your graph or adding embeddings raises the bill even though queries, compute, replicas, and algorithms are
Community Edition is $0 and production-ready, which undercuts most operational graph databases for developers getting started. Memgraph Cloud is the low-commitment managed entry for prototyping. Enterprise and AI Platform are custom-priced on memory capacity — Enterprise on graph data plus vector indexes combined, AI Platform on graph data only with unlimited vector indexes — which suits funded AI and fraud teams but gets expensive as graphs grow into the multi-terabyte range where a cheaper
In short
Memgraph — In-memory graph database that supplies structured context to AI systems and runs real-time graph analytics on the same engine. Best for AI engineers building GraphRAG pipelines that need multi-hop traversal, not just similarity search, Teams adding long-term semantic, episodic, and procedural memory to AI agents, Fraud and risk teams scoring connected entities in real time at thousands of tx/sec. Free to use.
What's new in Memgraph
Checked 4 days agoAcross the latest 5 updates: 3 changelog entries and 2 news mentions.
The Single-Writer Bottleneck: Why Write Serialization Limits Real-Time Graph Workloads
Memgraph examines how serialized writes and coarse locks constrain throughput and raise tail latency in real-time graph workloads, framing the case for its in-memory concurrency model.
Why Retrofitting Real-Time Graph Workloads Gets Expensive
The post compares storage, concurrency, and execution models to explain when an in-memory graph engine fits workloads with live updates and GraphRAG.
Lab v3.13.2
Latest release of Memgraph Lab, the visual interface for graph data management, following v3.13.1 on September 11th.
Memgraph v3.13.1
Maintenance release of the Memgraph database engine, following v3.13.0 on September 9th, 2026.
Memgraph v3.13.0
Next minor release of the core database engine in the 3.13 line, listed in the release notes index alongside the matching Lab version.
What people actually say about Memgraph — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
31 mentions across 2 sources (Hacker News, Lemmy) · researched Jul 3, 2026.
Average across the 2 sources that answered — each source counts once, not each post.
- +Sub-millisecond query responses for real-time analytics and AI workloads.
- +Unified architecture for GraphRAG and graph analytics on one in-memory store.
- +Cypher-compatible, making migration from Neo4j straightforward.
- +Native vector search enables semantic AI without separate vector database.
- +High availability replication and ACID transactions with on-disk persistence.
- −Early versions lacked native vector search, pushing users to competitors.
- −Complex Cypher queries can sometimes require manual optimization.
- −Smaller ecosystem and community compared to Neo4j.
- −Memory-based pricing can become expensive for large datasets.
- −Enterprise features like SSO and RBAC are only in paid tier.
- • Memory-based pricing can be expensive for large datasets
- • Enterprise edition cost is not publicly transparent
- • Some advanced features (SSO, RBAC) only in paid tier
Viability Score
How well maintained and how widely used is Memgraph? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- In-memory ACID transactions with on-disk persistence
- Sub-millisecond multi-hop graph traversals
- 1,000+ transactions per second reads and writes
- Graph sizes from 100 GB to 4 TB
- Cypher query language with documented Neo4j migration path
- Built-in vector search for semantic AI applications
- GraphRAG pipelines that traverse a knowledge graph for multi-hop context
- AI memory: semantic, episodic, and procedural memory in one graph
- Agentic AI reasoning graph with inspectable, scored decision traces
- MAGE algorithm library (PageRank, community detection, shortest path, and more)
- Stream connectors for Kafka, Pulsar, and Redpanda
- Zero-ETL querying of existing data as a graph
- Memgraph Lab visual interface, including GraphChat and Graph Style Script
- Python client, NetworkX, LangChain, and LlamaIndex integrations
- High-availability replication and automatic failover
About Memgraph
Memgraph is an in-memory graph database that runs two jobs on one engine: feeding structured, connected context to AI systems, and powering real-time graph analytics. Because the connected data lives in memory, Memgraph claims sub-millisecond multi-hop traversals at 1,000+ transactions per second, with ACID transactions and on-disk persistence, across graphs from 100 GB to 4 TB. Cypher is the query language, and the docs include a dedicated Migrate from Neo4j path, so teams with Neo4j experience can reuse familiar interfaces and protocols. The AI side is where the roadmap points. GraphRAG traverses a knowledge graph rather than retrieving text chunks by similarity, following multi-hop relationships across entities that similarity matching can't reach. AI Memory stores semantic, episodic, and procedural memory in one queryable graph. Agentic AI turns the agent's reasoning into an explicit action space, where the traversed path becomes an inspectable trace and alternative branches can be scored and compared. Vector search is built in and, on the AI Platform Standard tier, vector indexes are unlimited and don't count toward the license. For data teams, the same engine covers fraud and risk detection, 360° network exploration, data lineage, knowledge graphs, identity and access management, and supply chain modeling. NASA, Cedars-Sinai, and Capitec Bank appear as production references; Capitec reports scoring 3.5 million-plus clients daily. MAGE supplies the graph algorithm library (PageRank, community detection, shortest path and more), Memgraph Lab is the visual interface, and stream connectors cover Kafka, Pulsar, and Redpanda. Python, LangChain, LlamaIndex, and NetworkX integrations keep the developer path familiar. Release notes list Memgraph v3.13.1 (September 14th, 2026) and Lab v3.13.2 (September 18th, 2026).
Behind the Verdict
Memgraph's pitch is architectural rather than linguistic. It speaks Cypher, the same query language as Neo4j, so the interesting claim isn't the interface — it's the engine underneath. Connected data sits in memory, which is what makes sub-millisecond multi-hop traversals and 1,000+ transactions per second plausible, with ACID transactions and on-disk persistence for durability. The company itself frames the comparison that way: on its own comparison page, Memgraph vs Neo4j is "same query language, fundamentally different engine." The AI features are the reason this page exists in 2026. GraphRAG here means traversing a knowledge graph to follow multi-hop relationships rather than retrieving text chunks by embedding similarity — the vendor's own framing is that similarity matching "can't reach" connected evidence. AI Memory unifies semantic, episodic, and procedural memory in one queryable graph, which addresses the fact that LLMs are stateless. The agentic story is the most differentiated: a reasoning graph is described as an action space where graph algorithms find the highest-scoring path from current state to goal, and the traversed path becomes an inspectable trace with scored alternatives. That is a genuinely different auditability story from reading token probabilities. Strengths in practice: one engine covers both the AI context layer and classic graph analytics — fraud detection, network analysis, data lineage, knowledge graphs, IAM, and supply chain modeling all sit on the same substrate. MAGE ships the algorithm library (PageRank, community detection, shortest path, and a long list of others), Memgraph Lab handles visual exploration, and stream connectors cover Kafka, Pulsar, and Redpanda. The integration story for developers is unusually concrete: Python, NetworkX, LangChain, and LlamaIndex are all documented, and there's a Migrate from Neo4j guide covering CSV, single-Cypher-query, and RDBMS paths. Weaknesses worth weighing. Memory is the licensing lever — pricing scales with memory capacity, so a multi-terabyte graph is a multi-terabyte bill, even though there are no per-query charges, compute fees, or charges for replicas or algorithms. You need Cypher and graph data modeling skill; if your team has neither and no time to learn, this is the wrong tool. And if vector-only retrieval genuinely answers your questions, the graph layer adds modeling work for no return. Pick it when relationships are the point, not the decoration.
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Real-world workflow fit
Concrete scenarios for the personas Memgraph actually fits — and what changes day-one when you adopt it.
Installs Community Edition, models an entity-and-relationship knowledge graph from SQL, CSV, PDF and XLSX sources, then queries it from Python with the LangChain or LlamaIndex integration so retrieval follows multi-hop relationships instead of embedding similarity.
Outcome: Retrieval that surfaces connected evidence similarity search misses, on an engine that's free to run while you evaluate.
Streams transaction events through the Kafka connector, runs MAGE algorithms over entity relationships, and scores connected accounts in real time via Cypher rather than scoring each entity in isolation.
Outcome: Ring-level fraud signals caught as they form, mirroring the Capitec pipeline that scores 3.5 million-plus clients daily.
Follows the Migrate from Neo4j guides — CSV, a single Cypher query, or the RDBMS path — keeps using Cypher and familiar protocols, and reorganizes around an in-memory engine.
Outcome: A working graph on a different engine with minimal retraining, which is how NASA describes its move.
Use Cases
- Build a GraphRAG system that enriches LLM prompts with structured knowledge graph context instead of text chunks retrieved by similarity.
- Detect fraud rings in real time by mining relationships between entities with sub-millisecond queries.
- Give an AI agent long-term memory across semantic, episodic, and procedural types in a single queryable graph.
- Perform 360-degree network exploration and risk analysis across complex entity relationships.
- Unify data silos into a queryable knowledge graph for cross-team data lineage and compliance.
- Model multi-tier supplier networks, simulate disruption, and reroute in real time.
- Build IAM systems that track complex permissions and check access rules in milliseconds at scale.
Limitations
- Memgraph is an in-memory graph database, so memory sizing is the central operational and commercial constraint: price scales with memory capacity (100 GB to 4 TB graph sizes are quoted).
- You need working knowledge of Cypher and graph data modeling to get value quickly, though client libraries exist for Python, Java, JavaScript, Go, Rust, C#, PHP, and Node.js and there's a documented Migrate from Neo4j path.
- Community Edition is free forever and open source; Enterprise and AI Platform editions are licensed on memory — Enterprise on graph data plus vector indexes combined, AI Platform on graph data only.
- There are no per-query charges, compute fees, or charges for replicas or algorithms.
as of 2026-10-04
Verification history
We have re-verified Memgraph 9 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 9 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Memgraph tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Community Edition
$0
Ideal for
Individual developers and small teams who want to build and evaluate a Memgraph graph on their own hardware with no license conversation
What this tier adds
Starting tier: free forever and open source, including the full in-memory engine, ACID transactions with persistence, HA replication, Cypher, stream connectors, vector search, and MAGE.
Memgraph Cloud
Free trial, then paid
Ideal for
Developers and small teams prototyping, learning, or running early-stage projects who don't want to manage infrastructure
What this tier adds
Adds fully managed AWS hosting across 6 regions with automatic updates and backups, in 1 GB to 32 GB RAM instances, purchased through AWS Marketplace.
AI Platform Standard
Custom
Ideal for
AI and embedding-heavy teams building GraphRAG, agent memory, or agentic systems who expect vector indexes to grow
What this tier adds
Same Enterprise feature set as Enterprise Edition but priced on graph data only — vector indexes are unlimited and don't count toward your license.
Enterprise Edition
Custom
Ideal for
Companies running production graph workloads — fraud detection, network analysis, supply chain, infrastructure monitoring — that need security and compliance controls
What this tier adds
Adds RBAC and label-based access control, SSO (Entra ID, Okta, OIDC, SAML), LDAP/PAM, multi-tenancy, automatic failover, no-downtime updates, disaster recovery, query audit logging, Prometheus monitoring, and Slack support from Memgraph engineers.
OEM / SaaS
Custom
Ideal for
Product companies embedding a graph engine inside their own application or hosted service and shipping it to their customers
What this tier adds
Adds redistribution rights: embed the engine on-prem, as private or public SaaS, single- or multi-tenant, with you handling end-user terms and Memgraph engineering backing you.
Where the pricing makes sense
The company stage and team size where Memgraph's pricing actually pencils out — and where peers do it cheaper.
Community Edition is $0 and production-ready, which undercuts most operational graph databases for developers getting started. Memgraph Cloud is the low-commitment managed entry for prototyping. Enterprise and AI Platform are custom-priced on memory capacity — Enterprise on graph data plus vector indexes combined, AI Platform on graph data only with unlimited vector indexes — which suits funded AI and fraud teams but gets expensive as graphs grow into the multi-terabyte range where a cheaper
Setup time & first value
How long it actually takes to get something useful out of Memgraph — broken out by persona, not the marketing-page minute.
Developers installing Community Edition reach a first Cypher query in minutes: the docs give one-line install commands for Linux/macOS and Windows, plus Docker and a first-steps-with-Docker guide. Memgraph Cloud is faster still — fully managed with no setup, so you can start querying right after provisioning. Production work is slower: expect meaningful time for graph data modeling, connecting
Switching to or from Memgraph
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Neo4j: use the Migrate from Neo4j guides, which cover CSV files, a single Cypher query, and RDBMS paths, keeping familiar Cypher interfaces and protocols.
- →From PostgreSQL, MySQL, SQL Server, Oracle, or MongoDB: start with the Migrate from RDBMS using CSV files guide, or use SQL2Graph and the Dremio integration for Iceberg tables.
- →From Apache Iceberg: migrate Iceberg tables from the data lake using Dremio.
- →From Hadoop/Spark pipelines: migrate to Memgraph using Apache Spark.
- →From Memgraph Platform: move to Memgraph plus MAGE with the documented export path and migration guide.
- ↗To Neo4j: Cypher queries largely carry over, but you'll need to re-import data and account for the different engine architecture.
- ↗To FalkorDB or ArangoDB: rebuild the graph in the target model — Memgraph's docs cover the differences in Cypher implementations that you'll hit along the way.
- ↗To a vector-only store: export embeddings and move the retrieval layer, accepting the loss of multi-hop graph traversal.
- ↗To a managed graph service: export via the CSV, PARQUET, JSON, or CYPHERL export utilities, then load into the target platform.
Integrations
Resources & Guides
- Documentationmemgraph.com
Docs · Memgraph
Full product docs from memgraph.com
- Quickstartmemgraph.com
Getting Started · Memgraph
Get up and running fast from memgraph.com
- Documentationmemgraph.com
Migrate From Neo4j · Memgraph
Full product docs from memgraph.com
- Documentationmemgraph.com
Advanced Algorithms · Memgraph
Full product docs from memgraph.com
- Documentationmemgraph.com
Help Center · Memgraph
Full product docs from memgraph.com
- Documentationmemgraph.com
Llms · Memgraph
Full product docs from memgraph.com
- Resourcememgraph.com
Release Notes · Memgraph
Helpful link from memgraph.com
- Resourcememgraph.com
Pricing · Memgraph
Helpful link from memgraph.com
Tutorials & Learning
YouTube returned 6 videos for “Memgraph”, and we withheld 6: 6 could not be judged, because “Memgraph” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Memgraph.
Tools that pair well with Memgraph
Common stack mates teams adopt alongside Memgraph, with the specific reason each pairing earns its keep.
Vespa AI
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Doris
Apache Doris: an open-source real-time SQL database that unifies OLAP analytics, full-text search, and vector search in one engine.
Mem0
AI memory layer that gives agents and apps persistent, cross-session context
Featured Head-to-Head Comparisons
Memgraph vs Spider Cloud
Choose Spider Cloud if you need real-time web data for RAG pipelines and AI agents, with minimal coding and a pay-per-page model. Choose Memgraph if you need an in-memory graph database for GraphRAG, AI memory, and real-time analytics, and you're comfortable with Cypher. They solve fundamentally different problems: Spider Cloud gets data from the web; Memgraph stores and queries graph data.
Memgraph vs Screenplayiq
ScreenplayIQ and Memgraph serve completely different needs: one is a niche AI script analyzer for film industry professionals, the other a high-performance graph database for developers building AI systems. Your choice depends on whether you need box office predictions from a screenplay or real-time graph analytics with GraphRAG. For screenwriters, ScreenplayIQ is the clear pick; for AI engineers, Memgraph excels.
Memgraph vs Temporal Ai
Temporal AI and Memgraph serve fundamentally different purposes: Temporal is a durable execution engine for orchestrating complex, fault-tolerant workflows (great for AI agents and microservices), while Memgraph is an in-memory graph database optimized for real-time graph analytics, GraphRAG, and AI memory. Choose Temporal if you need reliable process orchestration with automatic retries and state persistence; choose Memgraph if you need sub-millisecond graph traversals and Cypher-based graph analytics. They are not direct competitors.
Alternatives to Memgraph
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Vespa AI
Vespa AI is an open-source, distributed search and serving engine that runs vector, text, and structured retrieval with machine-learned ranking in the same
Frequently Asked Questions
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