FinNLP

FinNLP is a comprehensive ecosystem developed by the AI4Finance Foundation designed to bridge the gap between complex financial data and Natural Language Processing. As of 2026, it has become the standard open-source framework for building Financial Large Language Models (FinLLMs). The technical architecture focuses on three core pillars: automated data curation from heterogeneous sources (Finnhub, Seeking Alpha, Yahoo Finance), domain-specific instruction tuning, and the deployment of Financial Retrieval-Augmented Generation (FinRAG) systems. Unlike general-purpose NLP libraries, FinNLP provides pre-configured pipelines for processing earnings call transcripts, 10-K/10-Q regulatory filings, and real-time market news. By 2026, it has expanded to include multi-modal support, allowing researchers to correlate textual sentiment with tabular market data. The framework supports the training of specialized models such as FinMA and provides benchmarking tools to evaluate LLM performance against financial datasets like FiQA and FPB. It operates within the Python ecosystem, integrating seamlessly with PyTorch, Hugging Face Transformers, and LangChain, making it an essential tool for quantitative analysts, hedge funds, and fintech developers seeking to extract alpha from unstructured text.