Build AI Research Agents with LangGraph: React, Planning, and Memory

Build a financial research agent using five LangGraph patterns and real SEC filings. Learn to compare company performance, track financial trends, identify emerging risks, validate findings, and deliver evidence-based answers while controlling execution costs and errors.

###

Workshop description

• Build financial research agents using five LangGraph patterns and real SEC filings.
• Develop agents that plan, evaluate evidence, maintain memory, and control costs.

###

Who this workshop is for

• Data scientists, ML engineers, AI developers, Python developers, and software engineers.
• Technical leads and AI practitioners building reliable, production-focused agents.

###

What you will learn

• Implement fixed workflows, ReAct, reflection, planning, and four memory types.
• Evaluate agent reliability, execution traces, LLM calls, and costs.

###

By the end, our agent can handle questions such as

• How have company revenue and R&D spending changed over five years?
• Which financial risks emerged, and how do companies compare?

###

Prerequisites

• Python 3.11+, type hints, Pydantic, and basic LLM API knowledge.
• An LLM provider or Ollama, plus Docker or Neo4j. No LangGraph experience required.

###

Key takeaways

• Build reliable agents with validated plans, controlled retries, and traceable decisions.
• Develop reusable research workflows that deliver evidence-based answers efficiently.

###

Meet your Instructor

• **Dr. Alessandro Negro**, Senior Director, GraphAware Products at Neo4j.
• PhD in Computer Science and author of Graph-Powered Machine Learning.