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Research Article Peer ReviewedAt least two reviewers Open Access

AICrypto‑Assistant: A Multi‑Agent LLM Platform for Democratizing Crypto‑Asset Analysis

1Northeastern University
✉ Corresponding author: Mimoza Dimodugno · m.dimodugno@northeastern.edu
Proceedings International Conference on Open Finance Volume 24 Pages 17–22 Year 2025
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Abstract

Market democratization through conversational AI represents a critical frontier in financial technology, yet cryptocurrency analysis remains dominated by institutional players with sophisticated toolchains. We introduce AICrypto-Assistant, the first open-source platform to integrate multi-agent large language model orchestration with executable technical analysis in a real-time conversational interface. Our system employs supervisor that intelligently routes natural language queries to specialized agents: a Market Analyst executing Python-based indicators (moving averages, volume-price relationships, money flow metrics), a News Researcher synthesizing market developments through filtered sentiment analysis, and a Knowledge Management component delivering contextual information via retrieval-augmented generation. Unlike existing black-box solutions, every analytical step remains transparent and auditable, addressing critical barriers in accessibility, information overload, and methodological opacity that exclude retail participants. Controlled evaluation with 20 participants over four weeks demonstrated remarkable effectiveness: 11% collective portfolio growth, 95% user satisfaction, and 60% achieving positive returns despite market volatility. The modular architecture enables seamless integration of additional indicators without core system modifications, supporting long-term extensibility while advancing Open Finance principles of transparent, inclusive financial intelligence.

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Cite this article

Dimodugno, M., & Mammadov, M. (2025). AICrypto‑Assistant: A Multi‑Agent LLM Platform for Democratizing Crypto‑Asset Analysis. In International Conference on Open Finance (pp. 17–22). SET Technology. https://doi.org/10.36287/setsci.24.2.017

Article History

Published
8 Sep 2025

License

© 2025 SET Technology. This article is distributed under the terms of the CC BY 4.0.

Research Contributions (2019+)Peer reviewed by at least two reviewers
Open AccessCC BY 4.0
Open Discovery Metadata · DOI · OAI-PMH
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