QuantMind is an open-source, agent-native knowledge extraction and retrieval framework built specifically for quantitative finance, from the LLMQuant community. Released under the MIT license and hosted on GitHub with about 3,100 stars, it is actively developed in 2026. Its goal is to turn the flood of financial research (papers, reports, filings and notes) into structured, retrievable knowledge that quant researchers and their AI agents can actually use.
The framework provides pipelines that ingest financial documents, extract structured knowledge with large language models, and organise it for retrieval-augmented workflows. Because it is designed agent-natively, the knowledge layer exposes interfaces that LLM agents can query as part of research and strategy-development loops, rather than being a passive document store. QuantMind grew out of the LLMQuant community’s production work on financial data, and it includes evaluation harnesses so teams can measure extraction quality instead of trusting it blindly.
QuantMind reflects a specific design philosophy the LLMQuant community calls knowledge engineering: rather than dumping raw documents into a vector store, financial texts are parsed into structured units (claims, factors, methodologies and results) that preserve the semantics quant workflows depend on. The framework’s agent-native interfaces then let research agents retrieve this knowledge mid-task, for example while drafting a strategy hypothesis or checking prior evidence on a factor. An accompanying data platform from the same community runs the approach in production, which keeps the open-source framework grounded in real research usage.
QuantMind is free and open source under the MIT license. You supply your own LLM API access for the extraction pipelines.
Quantitative researchers, systematic trading teams and financial-data engineers who want to build LLM-powered knowledge systems over financial research corpora.