The Gremlin Graph Traversal Machine and Language
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Abstract (arXiv)
Gremlin is a graph traversal machine and language designed, developed, and distributed by the Apache TinkerPop project. Gremlin, as a graph traversal machine, is composed of three interacting components: a graph G, a traversal Psi, and a set of traversers T. The traversers move about the graph according to the instructions specified in the traversal, where the result of the computation is the ultimate locations of all halted traversers. A Gremlin machine can be executed over any supporting graph computing system such as an OLTP graph database and/or an OLAP graph processor. Gremlin, as a graph traversal language, is a functional language implemented in the user's native programming language and is used to define the Psi of a Gremlin machine. This article provides a mathematical description of Gremlin and details its automaton and functional properties. These properties enable Gremlin to naturally support imperative and declarative querying, host language agnosticism, user-defined domain specific languages, an extensible compiler/optimizer, single- and multi-machine execution models, hybrid depth- and breadth-first evaluation, as well as the existence of a Universal Gremlin Machine and its respective entailments.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Aug 2015.
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Citing papers per year: 2022: 2; 2023: 2; 2024: 1; 2025: 3; 2026: 2.
10 papers.
- 2024A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applicationscited by 17
- 2022GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representationcited by 13
- 2023Construction of Knowledge Graphs: State and Challengescited by 12
- 2025Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learningcited by 12
- 2023Knowledge Graphs Queryingcited by 5
- 2022Expressive Reasoning Graph Store: A Unified Framework for Managing RDF and Property Graph Databasescited by 1
- 2025ZOGRASCOPE: A New Benchmark for Semantic Parsing over Property Graphscited by 1
- 2026Achieving Precise Text-To-Cypher Via Grounded Knowledge Graph Data Generationcited by 1
- 2025Managing FAIR Knowledge Graphs as Polyglot Data End Points: A Benchmark based on the rdf2pg Framework and Plant Biology Datacited by 0
- 2026Graph Memory for LLM Agents: At What Cost? A Comparative Evaluation of Query, Ingest, and Update Performance Across Graph Database Enginescited by 0
- 2026Graph Memory for LLM Agents: At What Cost? A Comparative Evaluation of Query, Ingest, and Update Performance Across Graph Database Engines20 Sep 2026
- 2026Achieving Precise Text-To-Cypher Via Grounded Knowledge Graph Data Generation12 Jun 2026
- 2025Managing FAIR Knowledge Graphs as Polyglot Data End Points: A Benchmark based on the rdf2pg Framework and Plant Biology Data23 May 2025
- 2025ZOGRASCOPE: A New Benchmark for Semantic Parsing over Property Graphs7 Mar 2025
- 2025Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learning2 Jan 2025
- 2024A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applications23 Apr 2024
- 2023Knowledge Graphs Querying23 May 2023
- 2023Construction of Knowledge Graphs: State and Challenges22 Feb 2023
- 2022Expressive Reasoning Graph Store: A Unified Framework for Managing RDF and Property Graph Databases13 Sep 2022
- 2022GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation24 May 2022