Memgraph

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

[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

[Memgraph @memgraphdb]

If your #GraphRAG pipeline runs embeddings for a COUNT query…
You’re doing it wrong.

#Text2Cypher is what you need for direct, analytical questions.

See how → https://t.co/ubXs6yfQGH

#AIEngineering #Memgraph

[Memgraph @memgraphdb]

Prompt engineering: Which instructions and examples should I give the model? Context engineering: Which external information shall the model's response be based on?

Have a look 👉 https://t.co/Wkx6LUGWmA

#AIEngineering #LLMs #GraphTech #Nemgraph

[Memgraph @memgraphdb]

A leading retail bank, Capitec Bank, used #Memgraph to surface the network patterns behind APP scams, then ran the pipeline daily at scale.

Results? Runs 3.5M+ scores daily in ~2 hours.

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

Key highlights 👉 https://t.co/LE4Yu5TeXx

[Memgraph @memgraphdb]

#Infrastructure decisions age. Memgraph moved from co-located hardware to #Hetzner for:

✓ lower costs
✓ faster CI
✓ more reproducible benchmarks
✓ less ops overhead

Full story 👉 https://t.co/YtQ4xVCnfq

#Memgraph #DevOps #GraphDatabase

[Ante Javor @AnteJavor]

We built the Graph of public skills -> https://t.co/EFJHg9ioJ0

From a non-technical standpoint, Agent Skills are the procedural memory you keep in your head on how to solve a particular problem, which can be very valuable, whether you are aware of that or not. You are constantly adapting and changing that procedural memory since the task is usually not fully deterministic, hence it cannot be a script. Parallel to that, skills have caused some controversy for being a security vulnerability and hallucinated LLM brain fog, but more on that in the future. 

Staying on the positive side of things and ignoring the negatives for now, agent skills could hold all the operational knowledge, allowing agents to operate semi-autonomously or autonomously to solve the particular operational problem. 

An example of that would be compiling a Memgraph Rust query module, which is not an easy task since you need the environment, the Memgraph query module API dependency, and knowledge of how to actually do it. Most advanced LLMs, like Codex or Opus, succeed at this after many tries and failures. This is why we build skills for compiling and deploying C++, Rust, and Python query modules that let LLMs practically single-shot the whole process. 

Back to the topic of the graph of skills, what is the actuall problem here? So if you have hundreds or thousands of skills in your organisation, the question is: how are you going to maintain them, how will they learn and evolve, and how will agents access them? If the tool's API changes, so should the skills, which causes a cascade of events across the files. Then the question becomes: how are those connected and correlated? 

This is what graphs as a structure are built for, and this is what we in Memgraph are trying to solve from different angles. The graph of skills will serve as our test bench for running the evolution, traceability, and access to the skills, while improving @memgraphdb as the graph database that serves as a real-time context engine for AI.

[Memgraph @memgraphdb]

571M+ Amazon reviews isn’t a prompt. It’s a graph problem.

Build the #KnowledgeGraph, then run Atomic #GraphRAG in #Memgraph.

Key session highlights 👉 https://t.co/4A1ToNiJMS

[Memgraph @memgraphdb]

Your AI did not suddenly get worse. It aged.

#Context rot is what happens when old facts never get removed and new facts keep stacking. The result is slow drift, subtle wrong answers, and longer prompts that still do not stabilize behavior.

👉 https://t.co/ddcgLRe5qA

#GraphRAG

[Memgraph @memgraphdb]

Here is what Memgraph’s production telemetry stack looks like:
✓ AWS for ingestion and batching
✓ S3 as the source of truth
✓ ClickHouse for fast analytics over semi-structured events
✓ Grafana for dashboards and alerts
Full write-up 👉 https://t.co/FmEk3Auu0t
#Grafana #AWS

[Memgraph @memgraphdb]

GraphRAG often fails due to pipeline sprawl, not retrieval.

Atomic GraphRAG: run the pipeline as a single Cypher query in the DB, with 10x less orchestration code.

Explainer by @mbudiselicbuda 👉 https://t.co/KC9jMzw7CH

#GraphData #Memgraph

[Memgraph @memgraphdb]

Need vector search that scales without doubling storage?
In this blog, David Iveković breaks down how Memgraph avoids duplicate vector copies and improves memory efficiency.
Full write-up 👉 https://t.co/JEwS5Ys3PY

#Memgraph #VectorSearch #VectorIndex #GraphData #GraphRAG #RAG

[Memgraph @memgraphdb]

#AI doesn’t fail because models are weak.
AI fails because meaning is missing.

And the companies that turn their implicit knowledge into structured context will see their AI systems become a competitive advantage that compounds.
@dtomicevic explains how 👉https://t.co/pF0jWqZeYT

[Memgraph @memgraphdb]

LLM apps don’t fail because prompts are bad. They fail because context is missing.

This beginner’s guide explains what #ContextEngineering actually is and how developers use it to ship reliable RAG and agent workflows.

Have a look 👉 https://t.co/oWS8MoqU4m

#GraphRAG #Memgraph

[Memgraph @memgraphdb]

#Memgraph 3.8 is out 🎉

✨ Parallel Runtime
✨ Concurrent Edge WRITES on Supernodes
✨ Atomic GraphRAG
✨ Single Store Vector Index

Release highlights 👉 https://t.co/2SRW6At4p3

#GraphRAG #Cypher #VectorSearch

[Memgraph @memgraphdb]

Tools without context is how MCP turns into a security problem.

@dtomicevic breaks down in this article why that is so 👉 https://t.co/nUxnhJhuHx

#MCP #EnterpriseAI #GraphTech #Memgraph