Multi-Agent Document Analysis with LangGraph

Build a production-ready CV analysis pipeline with LangGraph. Design stateful workflows where parser, strengths, weaknesses, suggester, and scorer agents run in parallel with shared state.

Start learning at learnwithparam.com. Regional pricing available with discounts of up to 60%.

What You'll Learn

• Build complex multi-agent workflows with LangGraph orchestration
• Design stateful agent workflows with parallel execution
• Implement section-aware document chunking strategies
• Create context-preserving embeddings for document analysis

Tech Stack

• LangGraph - Stateful workflow orchestration
• LangChain - Agent framework and tools
• PyMuPDF4LLM - Layout-aware PDF parsing
• FastAPI - High-performance async Python web framework
• Docker - Containerized development

Getting Started

make dev    # One command to set up and run

Open http://localhost:8000/docs for the interactive API docs.

Challenges

1. The Monolith - Single prompt approach (why it fails)
2. The Specialist - First dedicated agents
3. The Workflow - Intro to LangGraph (nodes, edges, state)
4. The Smart Workflow - Agent collaboration via shared state
5. The Parser Agent - Structured data extraction with Pydantic
6. The Context-Aware Graph - Job description matching
7. The Parallel Graph - Parallel agent execution for speed

Learn more

• Start the course: learnwithparam.com/courses/langgraph-multi-agent-workflows
• AI Bootcamp for Software Engineers: learnwithparam.com/ai-bootcamp
• All courses: learnwithparam.com/courses