LLMQuant/quant-mind
QuantMind is an agent-native knowledge extraction and retrieval framework for quantitative finance. observed · 2026-08-28
Health v2 · maintenance only
64/100
- Activity 97
- Release rhythm 35
- Longevity 38
Flags: no_releases
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 537
- days_rel: n/a
- days_push: 18
- n_releases_24m: 0
Adoption not part of the score
2630 stars · 450 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
QuantMind is an agent-native Python framework that refines raw financial information such as papers, news, and SEC filings into typed, timestamped, citation-preserving structured knowledge for retrieval and reasoning. It is designed so coding agents can build and verify knowledge pipelines inside the checkout, and it complements the LLMQuant Data platform with its MCP server and financial data APIs.
Use cases
- extract structured knowledge from financial research papers
- build a pipeline to parse SEC filings into typed knowledge
- collect and structure financial news for LLM agents
- give trading agents retrieval-ready financial context
- run deterministic preprocessing of financial documents with provenance
- connect financial data to Claude or Cursor via MCP
- build time-queryable knowledge bases of market information
When to choose
- you need reproducible, citation-preserving knowledge extraction from financial sources
- you want an agent-oriented repo where coding agents build pipelines against contracts and skills
- you are building LLM trading or quant research agents that need structured financial context
- you want an importable Python library plus MCP integration for financial data
When to avoid
- you need a general-purpose RAG framework outside finance
- you want a turnkey backtesting or live-trading engine rather than knowledge extraction
- you need a no-code tool for non-technical users
- you require guaranteed long-term support from a vendor rather than an MIT-licensed open-source project
Facets
framework · maturity active
rag etl nlp agent-framework mcp search-engine parser data-science fintech large-language-models python cli cross-platform quantitative-finance knowledge-extraction financial-data agent-native context-engineering sec-filings research-papers trading mcp-server llm-pipeline ai-agents retrieval-augmented-generation data-engineering natural-language-processing
5 sources
- readme: https://github.com/LLMQuant/quant-mind · fetched 2026-08-28 · a99a80943b8d
- homepage: http://llmquantdata.com/ · fetched 2026-08-29 · 3485a5b85ca0
- site_page: https://docs.llmquantdata.com/ · fetched 2026-08-29 · d1f86a007b0f
- site_page: https://llmquantdata.com/pricing · fetched 2026-08-29 · 7716c6b35041
- site_page: https://llmquantdata.com/changelog · fetched 2026-08-29 · cd43cdfaf393
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| LLMQuant/quant-mind | main | 64 |
For agents
markdown · JSON · MCP: product_card(name="LLMQuant/quant-mind")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem