LangChain: How Kensho (S&P Global) Built Multi-Agent Financial Data Retrieval with LangGraph

LangChain Blog·medium signal

LangChain published a deep-dive case study on how Kensho, S&P Global's AI innovation engine, built its 'Grounding' framework using LangGraph — a unified agentic access layer solving fragmented financial data retrieval at enterprise scale. The framework coordinates multiple specialized agents to query heterogeneous financial data sources with trust and accuracy requirements that exceed typical RAG implementations.