# AGENTiGraph: A Multi-Agent Knowledge Graph Framework for Interactive, Domain-Specific LLM Chatbots

arXiv: https://arxiv.org/abs/2508.02999 · HTML: https://mlsanity.com/paper/2508.02999/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 21 of its 36 references that are in the map.
- First posted on arXiv 5 Aug 2025.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [Knowledge Conflicts for LLMs: A Survey](https://mlsanity.com/paper/2403.08319/) (2024)
- [ToolQA: A Dataset for LLM Question Answering with External Tools](https://mlsanity.com/paper/2306.13304/) (2023)
- [A Review of Relational Machine Learning for Knowledge Graphs](https://mlsanity.com/paper/1503.00759/) (2015)
- [End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures](https://mlsanity.com/paper/1601.00770/) (2016)
- [Factuality Challenges in the Era of Large Language Models](https://mlsanity.com/paper/2310.05189/) (2023)
- [KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques](https://mlsanity.com/paper/2403.05881/) (2024)
- [Modern Baselines for SPARQL Semantic Parsing](https://mlsanity.com/paper/2204.12793/) (2022)
- [DetermLR: Augmenting LLM-based Logical Reasoning from Indeterminacy to Determinacy](https://mlsanity.com/paper/2310.18659/) (2023)
- [LLM-based SPARQL Query Generation from Natural Language over Federated Knowledge Graphs](https://mlsanity.com/paper/2410.06062/) (2024)
- [Enhancing Diagnostic Accuracy through Multi-Agent Conversations: Using Large Language Models to Mitigate Cognitive Bias](https://mlsanity.com/paper/2401.14589/) (2024)
- [Natural Language Interfaces to Data](https://mlsanity.com/paper/2212.13074/) (2022)
- [Deriving Contextualised Semantic Features from BERT (and Other Transformer Model) Embeddings](https://mlsanity.com/paper/2012.15353/) (2020)
- [Large Language Models on Wikipedia-Style Survey Generation: an Evaluation in NLP Concepts](https://mlsanity.com/paper/2308.10410/) (2023)
- [Graphusion: Leveraging Large Language Models for Scientific Knowledge Graph Fusion and Construction in NLP Education](https://mlsanity.com/paper/2407.10794/) (2024)
- [NNKGC: Improving Knowledge Graph Completion with Node Neighborhoods](https://mlsanity.com/paper/2302.06132/) (2023)
- [Application of Pre-training Models in Named Entity Recognition](https://mlsanity.com/paper/2002.08902/) (2020)

## Built on by (most cited first, 10 of 10)

- [InfiAgent: An Infinite-Horizon Framework for General-Purpose Autonomous Agents](https://mlsanity.com/paper/2601.03204/) (2026)
- [InfiAgent: Self-Evolving Pyramid Agent Framework for Infinite Scenarios](https://mlsanity.com/paper/2509.22502/) (2025)
- [Deterministic Legal Agents: A Canonical Primitive API for Auditable Reasoning over Temporal Knowledge Graphs](https://mlsanity.com/paper/2510.06002/) (2025)
- [Cost and Accuracy of Long-Term Memory in Distributed Multi-Agent Systems Based on Large Language Models](https://mlsanity.com/paper/2601.07978/) (2026)
- [MAGE: Multi-Agent Self-Evolution with Co-Evolutionary Knowledge Graphs](https://mlsanity.com/paper/2605.10064/) (2026)
- [LogosKG: Hardware-Optimized Scalable and Interpretable Knowledge Graph Retrieval](https://mlsanity.com/paper/2604.18913/) (2026)
- [LLMs+Graphs: Toward Graph-Native, Synergistic AI Systems](https://mlsanity.com/paper/2606.11560/) (2026)
- [Trust-Aware Multi-Agent Traceability: Confidence-Calibrated Knowledge Graphs for Consistent Software Artifact Management](https://mlsanity.com/paper/2606.17203/) (2026)
- [Natural Language Access to Domain-Specific Metadata: A Reusable Framework for LLM Query Generation](https://mlsanity.com/paper/2607.18029/) (2026)
- [Curriculum Brain: Constructing Curriculum Knowledge Graphs as a Substrate for Cognitive Diagnosis](https://mlsanity.com/paper/2610.05860/) (2026)

## Abstract (arXiv)

AGENTiGraph is a user-friendly, agent-driven system that enables intuitive interaction and management of domain-specific data through the manipulation of knowledge graphs in natural language. It gives non-technical users a complete, visual solution to incrementally build and refine their knowledge bases, allowing multi-round dialogues and dynamic updates without specialized query languages. The flexible design of AGENTiGraph, including intent classification, task planning, and automatic knowledge integration, ensures seamless reasoning between diverse tasks. Evaluated on a 3,500-query benchmark within an educational scenario, the system outperforms strong zero-shot baselines (achieving 95.12% classification accuracy, 90.45% execution success), indicating potential scalability to compliance-critical or multi-step queries in legal and medical domains, e.g., incorporating new statutes or research on the fly. Our open-source demo offers a powerful new paradigm for multi-turn enterprise knowledge management that bridges LLMs and structured graphs.