FinRobot

FinRobot organizes financial AI into four layers: a financial AI agents layer using chain-of-thought prompting, a financial LLM algorithms layer of domain-tuned models, LLMOps and DataOps layers for multi-source integration and model selection, and a foundation-model layer supporting plug-and-play LLMs. It builds on existing multi-agent frameworks including AutoGen and LangChain.

A documented use case is automated equity research that produces structured stock analysis reports covering financial statement analysis, valuation multiples such as P/E and EV/EBITDA, and risk assessment. Paper: arXiv:2405.14767.