# LLM-Assisted Modeling of Semantic Web-Enabled Multi-Agents Systems with AJAN

arXiv: https://arxiv.org/abs/2510.06911 · HTML: https://mlsanity.com/paper/2510.06911/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 8 Oct 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 6 of 6)

- [Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases](https://mlsanity.com/paper/1903.02188/) (2019)
- [Robot Behavior-Tree-Based Task Generation with Large Language Models](https://mlsanity.com/paper/2302.12927/) (2023)
- [EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs](https://mlsanity.com/paper/1801.03825/) (2018)
- [Bio-SODA: Enabling Natural Language Question Answering over Knowledge Graphs without Training Data](https://mlsanity.com/paper/2104.13744/) (2021)
- [Learning and Executing Re-usable Behaviour Trees from Natural Language Instruction](https://mlsanity.com/paper/2106.01650/) (2021)
- [Integrating AI Planning with Natural Language Processing: A Combination of Explicit and Tacit Knowledge](https://mlsanity.com/paper/2202.07138/) (2022)

## Abstract (arXiv)

There are many established semantic Web standards for implementing multi-agent driven applications. The AJAN framework allows to engineer multi-agent systems based on these standards. In particular, agent knowledge is represented in RDF/RDFS and OWL, while agent behavior models are defined with Behavior Trees and SPARQL to access and manipulate this knowledge. However, the appropriate definition of RDF/RDFS and SPARQL-based agent behaviors still remains a major hurdle not only for agent modelers in practice. For example, dealing with URIs is very error-prone regarding typos and dealing with complex SPARQL queries in large-scale environments requires a high learning curve. In this paper, we present an integrated development environment to overcome such hurdles of modeling AJAN agents and at the same time to extend the user community for AJAN by the possibility to leverage Large Language Models for agent engineering.