Framework for building multi-agent equity research agents
Details
- External ID
- 47156540
- Source
- HN
- Company
- —
- Product
- Framework for building multi-agent equity research agents
- Website domain
- github.com
- Launched
- Feb. 25, 2026
- Cohort
- —
- Upvotes
- 6
- Upvotes percentile
- 0.28099730458221023
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:25 p.m.
- Updated at
- Sept. 7, 2026, 9:25 p.m.
Description
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
Enrichment
- Theme
- algorithmic trading bots and platforms
- Vertical
- Fintech
- Function
- Agent / copilot
- Audience
- B2B
- AI stance
- AI-native
- Project type
- Commercial product
- Normalized one-liner
- multi-agent system for equity research
- Manually corrected
- False
Could you build this?
Partial While multi-agent orchestration frameworks can be vibe-coded, robustly parsing complex SEC XBRL taxonomies and extracting labeled 10-K/10-Q sections requires specialized domain-specific financial engineering.
What it would actually take: The architecture requires an SEC EDGAR ingestion pipeline using parsers like python-xbrl and Arelle, coupled with an orchestration layer (e.g., LangGraph or custom DAG) and LLM tool calling. The difficult piece is handling inconsistent XBRL tagging across filings, non-standard disclosures, and financial statement normalization. Building a dependable version requires deep familiarity with US GAAP/SEC reporting standards and financial data engineering.
Discussion
No comments on this launch.
Competitors
Other products that read as similar to this one — 290 launches clear the similarity bar, closest 8 shown.
Attention rank: #247 of 291 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 117 days after the earliest competitor.
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Same idea, different domain
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