Pith. sign in

REVIEW 3 cited by

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

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2508.02999 v1 pith:IRE4C3L6 submitted 2025-08-05 cs.AI cs.CL

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

Xinjie Zhao , Moritz Blum , Fan Gao , Yingjian Chen , Boming Yang , Luis Marquez-Carpintero , M\'onica Pina-Navarro , Yanran Fu
show 5 more authors
So Morikawa Yusuke Iwasawa Yutaka Matsuo Chanjun Park Irene Li
This is my paper
classification cs.AI cs.CL
keywords knowledgeagentigraphclassificationdomain-specificgraphsmanagementquerysystem
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

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.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Graph-Grounded Optimization: Rao-Family Metaheuristics, Classical OR, and SLM-Driven Formulation over Knowledge Graphs

    cs.DB 2026-05 unverdicted novelty 6.0

    Graph-grounded optimization pulls problem elements from knowledge graphs via Cypher and shows that a portfolio of Rao-family metaheuristics outperforms single variants while OR-tools fails on non-linear objectives tha...

  2. Graph-Grounded Optimization: Rao-Family Metaheuristics, Classical OR, and SLM-Driven Formulation over Knowledge Graphs

    cs.DB 2026-05 unverdicted novelty 6.0

    Graph-grounded optimization sources problem elements from knowledge graphs and shows Rao-family metaheuristics plus OR-tools perform differently across seven real-world KG-backed problems while surfacing data issues.

  3. Trust-Aware Multi-Agent Traceability: Confidence-Calibrated Knowledge Graphs for Consistent Software Artifact Management

    cs.SE 2026-06 unverdicted novelty 4.0

    A confidence-calibrated knowledge graph framework for multi-agent traceability coordination with a two-stage link prediction pipeline, seeding mechanism, and consistency protocol, evaluated on an automotive case study.