At a Glance

Name: Structuring LLM Outputs with CrewAI and Pydantic for Agents
Price: Free CAD
Rating: 0.0 (0 reviews)
Author: kunal_makwana, joseph_santarcangelo

Transform chaotic LLM outputs into consistent, professional documents by implementing structured data models with Pydantic and multi-agent workflows in CrewAI. This project teaches you to design specialized AI agents that collaborate to research topics and generate formatted reports, ensuring reliable outputs regardless of the subject matter. By creating a hierarchical data architecture that enforces validation and formatting rules, you'll learn to build AI systems that consistently deliver client-ready documents with proper structure, citations, and visual formatting.

In today's rapidly evolving AI landscape, AI systems often produce inconsistent, unpredictable outputs when multiple agents need to communicate with each other. When one agent's output becomes another's input, the lack of structure leads to cascading errors, misinterpretations, and unusable results. This guided project addresses this fundamental challenge by showing you how to enforce structured data exchanges between AI agents.

You'll learn how to ensure consistent, structured outputs from AI agents - a common challenge in AI development. Working with a research report scenario, you'll build a system that transforms unstructured AI responses into professional documents.

You'll design hierarchical data models with Pydantic as blueprints for your AI outputs, then create specialized CrewAI agents with distinct roles: a researcher, an analyst, and a formatter. You'll coordinate these agents in a workflow where each agent's output becomes structured input for the next, using data models to eliminate inconsistencies. By the end, you'll have a reusable framework for generating well-structured reports on any subject, automating what would normally take days of manual work into a process that produces professional results within hours.

What You'll Learn

By completing this lab, you will:

Create structured data models using Pydantic to enforce consistency in LLM outputs
Design specialized AI agents with specific roles using CrewAI
Implement multi-stage workflows that transform research into organized documents
Build hierarchical data structures for proper validation and formatting
Generate professional reports with consistent formatting and citations
Apply data validation techniques to prevent incomplete LLM outputs

Who should enroll

This project is perfect for:

AI developers looking to build more reliable agent-based systems
Data scientists who need to generate consistent reports from AI research
Software engineers interested in structured data handling with AI
Business analysts wanting to automate research and reporting workflows

What you'll need

Before beginning this guided project, you should have:

Basic understanding of Python programming
Access to a modern web browser for the IBM Skills Network Labs environment

Why enroll

The ability to create AI systems that produce structured, consistent outputs is increasingly critical as organizations move AI from experimentation to production. By learning to enforce data models and orchestrate specialized agents, you'll gain valuable skills for building reliable AI applications that deliver professional results in research, content generation, and data analysis.

Offered By: IBMSkillsNetwork

Structuring LLM Outputs with CrewAI and Pydantic for Agents

At a Glance

What You'll Learn

Who should enroll

What you'll need

Why enroll