Memgraph

Memgraph is a high-performance, in-memory graph database that powers real-time AI context and graph analytics at scale.

[Memgraph @memgraphdb]

Your pipeline created 12,000 relationships. That proves it was busy, not that they’re right.

At a million documents in your #KnowledgeGraph, how do you find the one bad edge?

Sara Tilly explores this with @tonilastre here 👉 https://t.co/e9ArpjSfFk

#Memgraph

[Memgraph @memgraphdb]

Your application has 12 workers. But with a single-writer model per graph, only one write may execute while the other 11 wait.

More workers cannot make those writes run concurrently.

Why this matters for real-time graph workloads 👉 https://t.co/euygpms6G3

#Memgraph

[Memgraph @memgraphdb]

Beyond #GraphDatabase features, remember to ask:
▪️Where does the active topology live?
▪️How are concurrent writes handled?
▪️Which graph state does #analytics use?

The answers set the latency floor, #throughput ceiling & freshness boundary 👉 https://t.co/l9KthMSwaM

#Memgraph

[Memgraph @memgraphdb]

Fraud rarely announces itself in a single document.
The signal may sit several hops away:

claimant → address → prior claim → payment account

See where #VectorRAG falls short and what graph traversal adds 👉 https://t.co/Pzwzk6wygN

#GraphRAG #FraudDetection #Memgraph

[Memgraph @memgraphdb]

Your #GraphRAG system found the right context. But can it explain why a decision was made six months later?

See how GraphLogic stores evidence, assumptions, risks and actions in #Memgraph.

Full walkthrough 👉 https://t.co/mwDr04cM9G
Key highlights 👉 https://t.co/RcNmuscWPH

[Memgraph @memgraphdb]

#KYC documents are messy. Handwriting, scanned PDFs, half-filled forms & facts spread across filings.

In this Community Call, @QuarkLabs showed how to turn them into a queryable #KnowledgeGraph in #Memgraph.

Watch 👉 https://t.co/N6DPi8ghEK
Recap 👉 https://t.co/pvvH5ngUuT

[Memgraph @memgraphdb]

A transaction can look normal and still sit inside a suspicious network.

#FraudRings hide across accounts, devices, addresses and intermediaries. That’s why row-by-row #FraudDetection misses the bigger picture.

Read more 👉 https://t.co/ZFK8Tem3wh

#GraphAnalytics #Memgraph

[Memgraph @memgraphdb]

How do you stop #AI agents from inventing facts?
Give them structure.

Schema-first #GraphRAG grounds #LLMs in real entities, valid relationships & defined properties.

Full walkthrough 👉 https://t.co/IB5pNQ1qG6
Key highlights 👉 https://t.co/TnDn9WEqXq

#Memgraph #GraphDatabase

[Memgraph @memgraphdb]

@CedarsSinai connected 20+ biomedical sources into a knowledge graph with 234,000+ entities and 1.67M relationships.

The point was not more data. It was making existing evidence easier to connect, trace, and investigate.

Learn more 👉 https://t.co/GvER43muTT

#Memgraph

[Memgraph @memgraphdb]

Fraud rings do not operate in silos. #Fraud detection systems often do.

A login anomaly, suspicious payment and #AML signal may look minor alone, but reveal a network when connected.

See why multi-hop analysis matters 👉 https://t.co/TYgBnKaVhb

#Memgraph

[Memgraph @memgraphdb]

$485.6 billion was lost globally to fraud scams and bank fraud.

This is not because of lack of data but because financial teams struggle to connect what they already know before the risk disappears into another system.

Read why 🔗 https://t.co/mjw5Bs7tnQ

#Memgraph

[Memgraph @memgraphdb]

Another dashboard will not solve fragmented research data.

Drug discovery teams need to query relationships across sources without manually stitching the context together first.

Read how #KnowledgeGraphs help 👉 https://t.co/t6O3iQ12Ep

#Memgraph #DrugDiscovery #BiotechResearch

[Memgraph @memgraphdb]

#PostgreSQL 19 vs #Memgraph. Same dataset. Same #traversal task.

Very different story as the number of hops increased.

#Benchmarking by Josip Mrđen.

See the results 👉 https://t.co/hFx6eGvfCc

[Memgraph @memgraphdb]

The hard part of multi-tenant graph workloads is keeping boundaries without losing access.

Memgraph 3.11 is built around that production reality by making multi-tenant graph workloads more practical to run.

Full breakdown 👉 https://t.co/LqYWRLn22y

[Memgraph @memgraphdb]

Memgraph is featured in a new @Arm Learning Path install guide.

Learn how to install Memgraph on Arm with Docker or native Linux packages, run Cypher queries with mgconsole, and optionally add MAGE for graph algorithms.

https://t.co/QikbAZVFcK

[Memgraph @memgraphdb]

Is data copying dead? In the latest GraphGeeks Podcast episode, @amyhodler talks with our Co-founder and CTO, @mbudiselicbuda , about Memgraph Zero and MemGQL.

Watch the full podcast here: https://t.co/zQabpsNK65

[Memgraph @memgraphdb]

Futurum Group covered Memgraph Zero and MemGQL, our federated GQL query engine for querying heterogeneous enterprise data sources as a unified graph, without the burden of physical data movement.
https://t.co/N6i1Inla6t

[Marko Budiselić @mbudiselicbuda]

Introducing @memgraphdb Zero and our first associated product: MemGQL -> https://t.co/TdF3mKjpFy

Query (you or your agent) all of your data source as a graph. Live. No ETL. No pipelines. No stale data.

One GQL query. Every backend. Zero copies.

Join our Community Call to learn more -> https://t.co/jhJGtNzOdt

#graphs #GQL #agents

[Memgraph @memgraphdb]

Want graph intelligence without moving all your data first? #Memgraph Zero introduces MemGQL, a federated #GQL engine for querying live data across graph, relational, OLAP, and lakehouse systems.
Lean more 👉 https://t.co/W1hg49y3OS

[Memgraph @memgraphdb]

With local graph search you can find the right node, expand the graph, filter the noise, return the context that matters.

Why Atomic GraphRAG helps:

✓ less code to review
✓ smaller context
✓ better accuracy

Full walkthrough 👉 https://t.co/zd2XYe34Q8

#GraphRAG #Memgraph