# graph-database Published articles for graph-database. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Philip Rathle on why AI agents keep reaching for a knowledge graph DevFeed: [Philip Rathle on why AI agents keep reaching for a knowledge graph]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-05T23:21:28Z Content type: article Language: en Sources: [WorkOS Blog]() Topics: [Artificial Intelligence](), [Neo4j](), [Graphs](), [graph-database](), [data]() Tags: [agents](), [ai](), [ai-agents](), [data](), [graph](), [graph-database](), [knowledge-graph]() ### AI overview WorkOS CEO Michael Grinich interviews Neo4j CTO Philip Rathle about using knowledge graphs as an AI knowledge layer. Rathle argues that agents need deterministic, explainable access to structured company data for questions where accuracy, sovereignty, and access controls are critical, while noting that graph retrieval can provide context for better model decisions. ### Source excerpt Neo4j CTO Philip Rathle on why over 70% of new business is now the AI knowledge layer, where agents need deterministic answers, and how error rates compound. ## How to Build Unified Agent Memory from Scratch DevFeed: [How to Build Unified Agent Memory from Scratch]() Original publisher: [Read original article]() Author: Paul Iusztin Published: 2026-07-14T05:01:54Z Content type: tutorial Language: en Sources: [Decoding ML]() Topics: [AI Engineering](), [Claude Code](), [Graphs](), [LangChain](), [Large Language Model](), [Database](), [Model Context Protocol]() Tags: [agent](), [ai-engineering](), [claude-code](), [graph](), [graph-database](), [knowledge-graphs](), [langchain](), [llm](), [mcp]() ### AI overview A tutorial on building a unified agent memory layer from scratch using knowledge graphs, including ingestion, querying, and serving. It discusses trade-offs among vector databases, graph databases, temporality, versioning, MCP servers, CLIs, and skills, and explains why understanding the underlying memory layer matters. ### Source excerpt Ingest, query, and serve a unified memory from a single database. ## Graph-Shaped Shared Memory for AI Agents DevFeed: [Graph-Shaped Shared Memory for AI Agents]() Original publisher: [Read original article]() Author: Neo Kim Published: 2026-07-07T11:32:10Z Content type: tutorial Language: en Sources: [System Design Newsletter]() Topics: [AI Agent](), [Graphs](), [graph-database](), [Amazon S3](), [Retrieval Augmented Generation (RAG)](), [Open Source]() Tags: [ai-agent](), [ai-agents](), [amazon-s3](), [graph](), [graph-database](), [open-source](), [rag]() ### AI overview A guide to shared memory for multiple AI agents, using Omnigraph as a case study. It explains why shared folders and vector databases can fail, how graph-shaped memory represents knowledge, and how transactions, versioning, and combined retrieval methods can help agents share context. ### Source excerpt #160: A full guide to graph shaped memory for AI agents ## How Airbnb Built an Internal Identity Graph with JanusGraph and DynamoDB DevFeed: [How Airbnb Built an Internal Identity Graph with JanusGraph and DynamoDB]() Original publisher: [Read original article]() Author: Alexandre Zajac Published: 2026-05-25T15:30:54Z Content type: article Language: en Sources: [Hungry Minds]() Topics: [graph-database](), [Latency](), [Scalability](), [Software Engineering](), [Amazon Web Services](), [Artificial Intelligence]() Tags: [database](), [graph](), [graph-database](), [infrastructure](), [latency](), [low-latency](), [scalability](), [software-engineering]() ### AI overview The article describes Airbnb's internal identity graph infrastructure for trust and safety use cases such as fraud detection, linked-account discovery, and suspicious-activity flagging. It reports that Airbnb replaced a third-party graph database with an internal system using JanusGraph for traversal and DynamoDB for persistence, alongside custom transaction handling, parallel fetches, query rewrites, and tenant isolation. ### Source excerpt PLUS: Vector database deep dive 👨💻, Avoiding AI code slop 🤖, DoorDash clusterless ML feature store ⚡ ## Scaling Airbnb's identity graph with a unified knowledge graph infrastructure DevFeed: [Scaling Airbnb's identity graph with a unified knowledge graph infrastructure]() Original publisher: [Read original article]() Author: Lucen Zhao Published: 2026-05-19T17:01:01Z Content type: article Language: en Sources: [The Airbnb Tech Blog - Medium]() Topics: [Graphs](), [graph-database](), [Architecture & Design](), [Scalability](), [Caching](), [Low Latency](), [real-time](), [data]() Tags: [architecture](), [caching](), [data](), [engineering](), [graph-database](), [infrastructure](), [knowledge-graph](), [latency](), [low-latency](), [safety](), [scalability](), [technology]() ### AI overview Airbnb describes scaling its identity graph into a unified, internally managed knowledge graph infrastructure. The article covers the graph database and KV caching storage layer, graph service, near-real-time ingestion, low-latency access, and architectural evolution driven by increasing graph density and query complexity. ### Source excerpt How Airbnb shifts from PaaS to an internal knowledge graph infrastructure at scale. By: Lucen Zhao, Shukun Yang, Ashish Jain Knowledge graphs offer a natural and powerful way to represent relationships between entities. Many real-world systems are fundamentally about connections. Airbnb's identity graph captures relationships between users in a graph database. The identity graph serves aggregated insights that enable user identity resolution and relationship understanding. These capabilities support a wide range of Trust and Safety use cases, from detecting suspicious activities to identifying linked accounts. Over time, the identity graph has grown into one of the largest and most complex graph data products at Airbnb, both in terms of scale and the complexity of queries it supports. In 2024, Airbnb began investing in a new, internally managed, paved-path graph data platform to build a unified knowledge graph infrastructure. Airbnb's identity graph became one of the first systems to adopt this platform. In this post, we'll walk through the foundations and challenges of the identity graph, introduce the architecture behind the graph infrastructure, and highlight several key optimizations that emerged during the onboarding process. Airbnb's identity graph Airbnb's identity graph is a critical foundation layer, playing an important role in Trust and Safety applications. It contains two major components: Graph data storage: a storage layer composed of a graph database and a key-value (KV) caching layer. It models users and relationships as vertices and edges. Most data is ingested in near real-time through asynchronous events and served through low-latency, real-time service calls. Graph service: this service provides a unified interface for accessing graph data. It retrieves data from underlying sources, including the graph database, applies aggregation logic or models as needed, and serves the results to downstream customer services. Evolution of the identity graph a ## Zero-Copy Graph Analytics: Getting Started With LakeHouse Graph DevFeed: [Zero-Copy Graph Analytics: Getting Started With LakeHouse Graph]() Original publisher: [Read original article]() Author: Maruthi Lokanathan Published: 2026-02-03T15:39:34Z Content type: tutorial Language: en Sources: [ClickHouse Blog]() Topics: [graph-database](), [clickhouse](), [Graphs](), [Databases](), [schema-evolution](), [Latency](), [SQL](), [data]() Tags: [clickhouse](), [database](), [graph-database](), [latency](), [real-time](), [schema-evolution](), [sql](), [systems]() ### AI overview This tutorial explains why copying analytical data into a separate graph database creates ETL, synchronization, schema-evolution, storage, and latency problems. It introduces zero-copy graph analytics, using PuppyGraph with ClickHouse to query relationships directly over existing tables through Cypher or Gremlin without moving the data. ### Source excerpt Why copying data for graph analytics doesn't scale, and what you can do instead ## Infrahub with Damien Garros DevFeed: [Infrahub with Damien Garros]() Original publisher: [Read original article]() Published: 2026-01-16T06:47:00Z Content type: opinion Language: en Sources: [ipSpace.net blog]() Topics: [Automation](), [Database](), [Network]() Tags: [automation](), [database](), [graph-database](), [network-automation](), [podcast](), [software-gone-wild](), [tool]() ### AI overview A podcast discussion with Damien Garros examines Infrahub, a network automation tool, including its purpose, intended users, and use of a graph database. ### Source excerpt Why do we need Infrahub, another network automation tool? What does it bring to the table, who should be using it, and why is it using a graph database internally? I discussed these questions with Damien Garros, the driving force behind Infrahub, the founder of OpsMill (the company developing it), and a speaker in the ipSpace.net Network Automation course. Listen to the podcast ## Postgres as a Graph Database: (Ab)using pgRouting DevFeed: [Postgres as a Graph Database: (Ab)using pgRouting]() Original publisher: [Read original article]() Author: Paul Copplestone Published: 2025-02-25T07:00:00Z Content type: tutorial Language: en Sources: [Supabase Blog]() Topics: [Databases](), [graph-database](), [Graphs](), [Geographic Information System](), [Algorithms](), [Neo4j]() Tags: [algorithm](), [geospatial](), [graph-database](), [graphs](), [how-to](), [learn](), [postgres](), [routing]() ### AI overview This article explains how pgRouting, a Postgres extension commonly used with PostGIS, can provide lightweight graph functionality. It covers shortest-path and network analysis, GIS applications, task scheduling with dependency graphs, critical-path analysis, and the use of Dijkstra's algorithm. ### Source excerpt Learn how to use pgRouting as a lightweight graph database solution in Postgres. ## Math databases DevFeed: [Math databases]() Original publisher: [Read original article]() Published: 2024-05-17T22:55:50Z Content type: article Language: en Sources: [Jeremy Kun]() Topics: [Databases](), [math](), [Graphs](), [graph-database](), [Pull Request]() Tags: [databases](), [graph-database](), [graphs](), [math](), [pull-request](), [research](), [shortform]() ### AI overview The article discusses code4math, an effort to advance mathematical research by building software infrastructure such as packages and databases of mathematical objects. It highlights Mathbases and the House of Graphs database, which supports graph searches by drawing and provides relevant statistics for records. ### Source excerpt Steven Clontz informed me of an effort he's involved in called code4math. It's described as a professional organization for the advancement of mathematical research through building non-research software infrastructure. By that he means, for example, writing software packages like Macaulay2 or databases of mathematical objects that other researchers can use to do their research. Clontz recently gave a talk on the topic, with ample discussion of the evaluation material they can provide to justify the academic value of this sort of work. ## What is a Graph Database? | Why GraphQL is Not a Graph Database DevFeed: [What is a Graph Database? | Why GraphQL is Not a Graph Database]() Original publisher: [Read original article]() Author: Khalil Stemmler Published: 2021-11-25T21:40:19Z Content type: article Language: en Sources: [Apollo Blog]() Topics: [graph-database](), [GraphQL](), [Databases](), [Database](), [Transactions]() Tags: [database](), [fraud-detection](), [graph](), [graph-database](), [graphql](), [management](), [network](), [relational-databases](), [transactions]() ### AI overview This educational article explains what graph databases are, how they model data with nodes, edges, and properties, when they are useful for relationship analysis, and how they differ from GraphQL and relational databases. ### Source excerpt GraphQL is a query language and a server-side runtime (typically served over HTTP). However, you may have also heard of graph databases. What does GraphQL have to do with graph databases? Not so much. In this post, we'll clear up any confusion you may have about GraphQL and graph databases by learning about what graph databases are, the main ideas behind them, when they're most valuable, and how they differ from GraphQL. What is a graph database? ## Elle: inferring isolation anomalies from experimental observations DevFeed: [Elle: inferring isolation anomalies from experimental observations]() Original publisher: [Read original article]() Author: adriancolyer Published: 2020-11-23T13:56:00Z Content type: article Language: en Sources: [Adrian Colyer]() Topics: [Databases](), [Transactions](), [Concurrency](), [test](), [SQL](), [graph-database](), [NoSQL]() Tags: [concurrency](), [database](), [evaluation](), [graph-database](), [sql](), [systems](), [test](), [transactions](), [uncategorized]() ### AI overview This article discusses the Elle checker from the Jepsen project, which infers database isolation anomalies from experimental execution histories. Elle can check histories involving hundreds of thousands of transactions in tens of seconds and was evaluated on SQL, document, and graph databases, where it found unexpected anomalies. The article notes that Elle supports many anomaly types and reproducible bug reports but does not guarantee completeness. ### Source excerpt Elle: inferring isolation anomalies from experimental observations, Kingsbury & Alvaro, VLDB'20 Is there anything more terrifying, and at the same time more useful, to a database vendor than Kyle Kingsbury's Jepsen? As the abstract to today's paper choice wryly puts it, "experience shows that many databases do not provide the isolation guarantees they claim." Jepsen captures ... Continue reading Elle: inferring isolation anomalies from experimental observations ## Neo4j storage internals DevFeed: [Neo4j storage internals]() Original publisher: [Read original article]() Published: 2020-08-09T00:00:00Z Content type: tutorial Language: en Sources: [Gaurav Sarma's Blog]() Topics: [Neo4j](), [graph-database](), [Graphs](), [Databases]() Tags: [graph-database](), [indexes](), [internals](), [storage]() ### AI overview This article examines Neo4j storage internals and compares graph database relationship traversal with the join-based approach used by MySQL. It explains Neo4j's index-free adjacency and fixed-record-size pointer scheme, while discussing storage and cluster-size comparisons with MongoDB and Cassandra. ### Source excerpt I was exploring Neo4j and came upon this [video](https://www. youtube... ## Using Amundsen to Support User Privacy via Metadata Collection at Square DevFeed: [Using Amundsen to Support User Privacy via Metadata Collection at Square]() Original publisher: [Read original article]() Author: Alyssa Ransbury Published: 2020-07-14T19:00:00Z Content type: article Language: en Sources: [Square Corner Blog RSS Feed]() Topics: [data](), [data-processing](), [graph-database](), [pii](), [BigQuery](), [MySQL](), [App]() Tags: [bigquery](), [collection](), [data](), [database](), [engineering](), [graph-database](), [lyft](), [mysql](), [netflix](), [pii](), [privacy]() ### AI overview Square describes using its own version of Amundsen to automate metadata collection and improve understanding of data stored, collected, and processed across many services while supporting user privacy. The system ingests schema information from Snowflake, BigQuery, and MySQL into a graph database, enabling search and richer column-level metadata such as PII semantic types and data storage security. ### Source excerpt More scalable, automated data insights while preserving users' privacy ## Dgraph Database Semantics DevFeed: [Dgraph Database Semantics]() Original publisher: [Read original article]() Published: 2020-06-26T00:00:00Z Content type: tutorial Language: en Sources: [William Kennedy]() Topics: [Database](), [graph-database](), [Scalability](), [Architecture & Design](), [Latency](), [Availability](), [Replication](), [Transactions](), [Resilience]() Tags: [acid](), [architecture](), [ardan-labs](), [availability](), [blog](), [database](), [distributed](), [engineering](), [go](), [go-programming](), [golang](), [graph-database](), [high-availability](), [latency](), [low-latency](), [programming](), [replication](), [resilience](), [scalability](), [servers](), [sharding](), [software](), [transactions]() ### AI overview This article explains the semantics behind Dgraph's description as a distributed graph database. It provides a high-level mental model for claims involving horizontal scalability, cluster-wide ACID transactions, low-latency arbitrary-depth joins, synchronous replication, high availability, and crash resilience, while leaving lower-level mechanics to the referenced paper. ### Source excerpt Introduction In this paper written by Manish Jain (the founder of Dgraph) he describes Dgraph as: a distributed graph database which provides horizontal scalability, distributed cluster-wide ACID transactions, low-latency arbitrary-depth joins, synchronous replication, high availability, and crash resilience. There are many claims being stated here which frankly I didn't understand the meaning of the first time I read this sentence. I decided I wanted to better understand these claims with the hope that it would help me understand Dgraph's architecture and engineering decisions better. ## Dgraph, GraphQL, Schemas, and CRUD DevFeed: [Dgraph, GraphQL, Schemas, and CRUD]() Original publisher: [Read original article]() Published: 2020-05-15T00:00:00Z Content type: tutorial Language: en Sources: [William Kennedy]() Topics: [graph-database](), [GraphQL](), [CRUD](), [Structured-data](), [integrity](), [Scalability](), [MongoDB](), [browser](), [ide](), [Transactions]() Tags: [ardan-labs](), [blog](), [browser](), [database](), [databases](), [go](), [go-programming](), [golang](), [graph-database](), [graphql](), [how-to](), [ide](), [mongodb](), [programming](), [scalability](), [schema](), [transactions]() ### AI overview A tutorial on defining GraphQL schemas in Dgraph and using the generated CRUD API through GraphQL Playground. It also compares graph, relational, and document databases, discussing integrity, flexibility, distribution, and scalability. ### Source excerpt Introduction In most of the reviews for this post, I was asked why choose a graph database over something else? This is a hard question to answer since my experience right now is limited on the graph database side. My guess is you're wondering the same thing, so this is my best answer to date. At this point in my career, I would only choose a relational database if I was writing financial software. Relational databases are very rigid so you need to really make sure you understand your data upfront. Changing the database is a big effort, but the database when designed correctly will give you very high levels of integrity and you can get good performance. These databases are hard to distribute and scale so you tend to end up with single instances that are very large and require replication for backup. The cloud providers have relational databases today that are supposed to scale, but I have no experience with them. ## I wish I knew my consumers - Maven Reverse Dependency DevFeed: [I wish I knew my consumers - Maven Reverse Dependency]() Original publisher: [Read original article]() Author: roar Published: 2013-01-31T12:49:05Z Content type: article Language: en Sources: [Finn.no]() Topics: [Reverse Dependencies](), [Maven](), [Neo4j](), [graph-database](), [Library](), [bug]() Tags: [bug](), [dependency](), [graph-database](), [libraries](), [maven](), [reverse-dependencies]() ### AI overview The article describes a Maven plugin that records project dependencies in a Neo4j graph database and examines reverse dependencies to identify applications using a Maven project, including the versions they use. The plugin was created to help developers notify users when shared libraries are fixed or changed. ### Source excerpt At FINN.no being a developer fixing bugs in a library is a breeze. Getting every user of your library to use the fix, however, is a different story. How to know who to notify? I mean, I know my library's dependencies, but who "out there" has dependency to the component where I just fixed a bug? I wish. Enter maven-dependency-graph. The idea was born on the plane back home from a Copenhagen hosted conference. Graph database. Download neo4j and start dabbling at a maven plugin. Flying time Copenhagen - Oslo was too short, all of a sudden. From there, the idea slept for a couple of years. Until the need arose somewhere among the developers. With 100+ different applications running with common core services and libraries, everybody suddenly needed to know who depended on their code which had recently been bugfixed. So the old idea was dusted off and once more saw the light of day. We needed to upgrade the server installation and the API to neo4j - which took some time to grasp; but after some playing around, it became obvious and easy. The idea was to have every project report its dependencies to a graph database, building the tree of dependencies on each commit. This constitutes one half of the plugin. Over time, all projects will have reported their dependencies, and from there on part two of the plugin comes into use. It will examine the reverse dependencies to the current maven project, and report all incoming dependencies to it in the maven log. Hey, presto! We now know who out there uses us! And even which version they are using, thanks to two different keys into the built-in lucene index engine. The plugin is published on github @ Finn Technology's account. Feel free! @gardleopard and @roarjoh Usage examples Dependencies to current maven project: mvn no.finntech:dependency-mapper-maven-plugin:read [INFO] Scanning for projects... [INFO] [INFO] ------------------------------------------------------------------------ [INFO] Building greenpages thrift-client 3.4.5-SN