Framework for building multi-agent equity research agents

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I built Hermes, an open-source Python framework for multi-agent financial research. Most AI “equity research” demos stop at generating text. In practice, real workflows require pulling structured XBRL financials from SEC filings, extracting labeled sections like MD&A and Risk Factors, merging macro and market data, building actual Excel models with formulas, and generating investment memos in Word or PDF. Hermes is designed to handle that full pipeline end to end. It includes 35 financial data tools covering SEC EDGAR (via edgartools), FRED, Yahoo Finance market data, and RSS-based financial news. It also provides composable specialist agents for filings, macro data, market data, modeling, report generation, and multi-agent orchestration. On the output side, it can generate Excel workbooks using openpyxl, create Word documents, export PDFs, and index filings with ChromaDB for semantic search. It includes async rate limiting, file-based caching (filings cached permanently, quotes never cached), and streaming progress events. Hermes is MIT licensed and designed to be extended. You can register custom tools and agents and plug in your own data sources or models. I’d love feedback from both AI engineers and finance professionals, especially around validation, reliability, and real-world research workflows. Repo: https://github.com/schnetzlerjoe/hermes

Visit product View on Hacker NewsFeb 25, 2026

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, pipe, io · Missing: https docs, excited, just released

57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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