04-19, 15:30–17:00 (US/Eastern), Room 120
Agents have become one of the most talked-about topics in the AI community, but much of the discussion focuses on its potential impact rather than practical implementation. This hands-on workshop will guide data scientists and engineers through building a complete workflow using LangGraph, and will show how to define custom tools, implement vector retrieval, leverage semantic caching, incorporate allow/block list routing, and structure model output for downstream consumption. In order to participate, attendees will need to have python (>=3.11), docker, an OpenAI api key, and the starter code for the project cloned.
Starter code: https://github.com/redis-developer/agents-redis-lang-graph-workshop
Note: participants can test their environment setup ahead of time by following the Readme and running python test_setup.py before the workshop.
Despite the growing excitement around AI agents, many practitioners lack clear guidance on how to implement them effectively. This workshop aims to bridge that gap by providing a structured, hands-on approach to building AI agent workflows with LangGraph. Participants will create an agent capable of playing the Oregon Trail and making in-game decisions, illustrating in a fun way not only how to implement agents but also when, why, and for what sorts of problems.
Session outline:
1. Understanding Agent Workflows (10 min)
- Overview of agentic workflows and their importance
- When and why to build agent workflows
2. Building a Basic LangGraph Agent (20 min)
- Setting up the LangGraph framework
- Defining discrete operations with custom tools
3. Enhancing Agent Capabilities (20 min)
- Structuring output for API interactions
- Implementing vector retrieval for RAG to improve contextual responses
4. Optimizing for Performance and Control (25 min)
- Creating a semantic cache to reduce LLM latency and cost
- Implementing allow/block list routing for controlled execution
5. Review and Discuss (15 min)
- Review what was just accomplished and why
- Discuss any design challenges or open debugging questions
- Open Q&A for questions related to best practice
This workshop has been tested with participants at a variety of levels and typically takes ~60 minutes to complete if environment setup has been confirmed as noted above.
Previous knowledge expected
Robert is an Applied AI Engineer at Redis, where he specializes in vector search and AI applications, supporting the development of the redisvl package and collaborating with a wide range of customers, from startups to enterprise organizations. His expertise spans diverse use cases, including financial chat applications, e-commerce recommendation systems, and more. Prior to Redis, Robert honed his skills as a Data Scientist and Full-Stack Engineer in the logistics industry, leading innovative projects that bridged software development and the complexities of physical goods movement.
When he's not diving into AI and data challenges, you can find Robert enjoying the great outdoors—most likely savoring some camp stove ramen along the Appalachian Trail in his native Virginia.