Show HN: AI-powered multi-agent equity research in Python (github.com)

A new multi-agent financial research framework called Hermes has been announced, built on LlamaIndex. Hermes equips AI engineers with a suite of tools for financial data retrieval and analysis, including document ingestion pipelines and output generation capabilities for Excel, Word, and PDF reports. Users can utilize pre-built agents for tasks such as SEC filing analysis, macroeconomic data acquisition, and financial news sentiment analysis. The framework allows for customization by enabling users to swap agents or integrate their own data sources seamlessly. This development is significant for the AI/ML community as it leverages advanced tools and composable agents to streamline equity research, making it accessible for a broader audience of financial analysts and developers. Key technical features of Hermes include modules for interacting with various financial data sources such as SEC EDGAR and Yahoo Finance, and the ability to create Excel models and reports programmatically. Its integration with asynchronous functions and a token bucket rate limiter enhances performance for retrieving vast amounts of financial data, establishing Hermes as a powerful asset for modern financial research automation.