Building a Finance Research Agent with Claude Agent SDK
11 Apr 2026
I recently built a finance research agent that combines three data sources into autonomous research briefs: real-time market data, semantic search over SEC 10-K filings, and web research.
The system uses Anthropic’s Claude Agent SDK — the same runtime that powers Claude Code, packaged as a Python library. What makes it interesting isn’t any single component, but how they compose:
The agent decides the workflow. You say “research Micron’s HBM thesis” and it autonomously fetches the current stock quote, searches 10-K filings for relevant disclosures about memory production and costs, delegates web research to a subagent for earnings results and analyst targets, and synthesizes everything into a structured bull/bear analysis.
RAG over real financial documents. The retrieval pipeline indexes SEC 10-K filings with section-aware chunking — preserving the boundary between Risk Factors (Item 1A) and MD&A (Item 7) so retrieval returns contextually coherent results, not fragments that span unrelated sections. 4,575 chunks from 28 companies, embedded locally with no API dependency.
Custom tools via MCP. Seven tools exposed as an in-process MCP server — no subprocess overhead, standard interface. The agent calls them autonomously based on the query, and each tool handles its own error cases so the agent can adapt rather than crash.
The whole thing is ~700 lines of Python with three dependencies. The architecture pattern — agent orchestration + domain-specific tools + RAG + subagent delegation — is generalizable to any domain where you need to combine structured data, document retrieval, and live research.