langgraph-html-to-react.md

LangGraph Advice for HTML-to-React Project

Why LangGraph > Prompts

• Modularity: Break your task into nodes (e.g., parse HTML, identify components, generate React code). Update one node for changes, not the whole prompt.
• State Tracking: Persist data (like parsed HTML) across nodes, avoiding repetitive context in prompts.
• Control Flow: Use conditional edges or loops to handle varied HTML structures, unlike linear prompts.
• Debugging: Inspect each node’s output with tools like LangSmith, easier than opaque prompt failures.
• Tool Integration: Add libraries (e.g., BeautifulSoup) or APIs (e.g., code validation) for robust workflows.

Getting Started

Setup (1 Hour)

• Install: pip install langchain langgraph langchain-google-genai.
• Get Gemini API key.
• Skim LangGraph docs: focus on StateGraph, nodes, edges.

Build a Graph (2 Hours)

• State: {"html_input": str, "parsed_structure": dict, "react_code": str}.
• Nodes:
  • Parse HTML (BeautifulSoup or LLM: “Extract elements from: [input]”).
  • Generate React (“Convert this to a React component: [parsed data]”).
  • Assemble output.
• Edges: Parse -> Generate -> End.
• Test: graph.invoke({"html_input": "<div>...</div>"}).

Enhance (1-2 Hours)

• Add conditionals (e.g., branch for forms).
• Use create_react_agent for dynamic routing.
• Add retries for errors.

Tips

• Keep nodes focused; split big prompts.
• Use state to manage context, avoid token limits.
• Check LangGraph GitHub examples for code gen.

What is create_react_agent?

create_react_agent is a LangGraph utility that creates an agent powered by an LLM (like Gemini) to dynamically decide which nodes to execute based on input or context. For your project, it could analyze HTML and choose whether to route to a form-handling node or a static component node, making your workflow adaptive. See the LangChain documentation for details.