LangGraph Agent Engineering
Goal: Build complex, multi-step AI workflows that are reliable, debuggable, and capable of long-running operations.
- Core Concepts (The Graph)
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State: A explicitly defined schema (TypedDict/Pydantic) that tracks the agent's memory snapshot.
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Nodes: Functions that perform work (call LLM, run tool, modify state).
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Edges: Logic that routes flow between nodes (Conditional edges based on LLM output).
- Architecture Patterns
🧠 Knowledge Modules (Fractal Skills)
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A. The ReAct Agent (Standard)
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B. Plan-and-Execute (Advanced)
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C. Human-in-the-Loop