Citation :

Selvakumar Ayyanar, 2024. "Building Multi-AI Agent Systems for Complex Financial Analysis - A Comparative Study of all Open source frameworks", ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 2, Issue 4: 120-124.

Abstract :

The increasing demand for intelligent systems capable of handling complex workflows has driven innovations in multi-agent AI platforms. Phi Data, an open-source framework, and LangChain, a dynamic toolchain for LLM-powered applications, offer comprehensive toolkits for building, deploying, and monitoring multi-agent systems. This paper explores the potential of both platforms, focusing on the design and implementation of a financial analysis multi-agent system. By integrating real-time data retrieval, web search capabilities, and robust large language models (LLMs), the systems effectively analyze stock trends, summarize analyst recommendations, and provide actionable insights. Detailed use cases on NVIDIA’s and Tesla’s stock performances over five years illustrate the efficacy and versatility of these platforms, alongside comparisons with other popular frameworks like Microsoft Autogen and CrewAI.

References :

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Keywords :

Multi-AI Agent Systems, Complex Financial Analysis, Open Source Frameworks, Artificial Intelligence, Financial Modeling, Agent-based Systems, Machine Learning, Financial Data Analysis, Comparative Study, Distributed AI Systems.