Agentic RAG - Building an AI Financial Analyst Team
Build a multi-agent system with LangGraph that dynamically plans and retrieves financial data from stock APIs and SEC filings to answer complex questions, moving beyond simple RAG pipelines.
RAG pipelines work great, but they follow a fixed script - retrieve, augment, generate. What happens when a user asks a complex, multi-faceted question like "Analyze Tesla's recent performance and risks"? A simple retriever can't create a plan, it just fetches whatever seems closest. The result is often a shallow answer that misses the big picture.
This is where an Agentic RAG shines. Instead of a fixed process, we'll build a team of AI agents that can reason, create a multi-step research plan, use different tools to gather information, and synthesize a comprehensive analysis.
In this tutorial, you will build FinVault, an agentic RAG system powered by LangGraph. You will learn to construct a "supervisor" agent that delegates tasks to specialized "worker" agents - one for stock prices, another for SEC filings, and a third "synthesizer" to craft a final, data-driven report.
Tutorial Goals
- Understand the difference between simple RAG and agentic RAG
- Implement a multi-agent Supervisor pattern
- Build specialized retrieval tools for financial data (stock prices and SEC filings)
- Orchestrate agent collaboration using LangGraph
- Develop a Streamlit UI application to talk with the agents
Agentic RAG vs. Simple RAG
- 01All 6 modules · 40+ hands-on tutorials
- 02Source code, repositories, and architecture diagrams
- 03Portfolio artifacts: apps, APIs, eval reports, dashboards, capstones
- 04Live sessions + every recording
- 05Discord community access
- 06Certificate of completion with public verification URL
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