LangGraph Agent Engineering

Goal: Build complex, multi-step AI workflows that are reliable, debuggable, and capable of long-running operations.

1. Core Concepts (The Graph)

• State: A explicitly defined schema (TypedDict/Pydantic) that tracks the agent's memory snapshot.
• Nodes: Functions that perform work (call LLM, run tool, modify state).
• Edges: Logic that routes flow between nodes (Conditional edges based on LLM output).

2. Architecture Patterns

🧠 Knowledge Modules (Fractal Skills)

1. A. The ReAct Agent (Standard)

2. B. Plan-and-Execute (Advanced)

3. C. Human-in-the-Loop