Building a Financial Agent with LangGraph and LangSmith
Financial LLMs Overview Financial LLM applications fail for predictable reasons: ungrounded numbers, opaque reasoning, and workflows that are hard to debug or evaluate. In this article we'll try to overcome some of the issues and build a tool-first, graph-based agent for financial research - similar in spirit to FinChat or Koyfin - but that actually useful ;) The goal is not to predict prices or trade, but to answer structured financial questions such as: “Compare NVDA and AMD margins using their latest quarterly reports.” This problem is representative of real production constraints: structured data, deterministic calculations, clear provenance, and observable failures. Scope and assumptions We assume that quarterly financial statements for different companies are already available locally in on the disk in some structured form . The process that produces these files will be treated as an upstream data engineering concern and is explicitly out of scope. This will be somewh...