How do I implement a multi-agent workflow using LangGraph for complex SEO audits?

All answers to this question.

For a multi-step process like an SEO audit, LangGraph is definitely the superior choice over the legacy AgentExecutor. While the standard executor is great for simple "thought-action-observation" loops, it struggles with the branching logic you need for a crawler-to-analyzer handoff. In LangGraph, you can define a state machine where each node is a specialized agent. This allows you to implement "human-in-the-loop" checkpoints, so you can manually approve the keyword list before the meta-tag agent starts writing. It’s much more robust for production-grade software development.

   Answered 2025-05-16 by Deborah Lewis

That makes sense, but how do you handle the shared state between the crawler and the analyzer without hitting token limits if the website is huge?

   Answered 2025-05-18 by Charles Walker

• Charles, you should use "State Filtering" or a "Summarizer" node. Instead of passing the entire HTML crawl to the next agent, have the crawler node extract only the relevant SEO entities—like headers, existing metas, and word counts—into a structured Pydantic model. This keeps your context window clean and reduces costs significantly when processing large enterprise-level sites.

     Commented 2025-05-19 by Michael Young

I've found that LangGraph's persistence layer is the real game-changer here. It saves the thread state automatically, so if your crawler fails halfway, you can resume exactly where you left off.

   Answered 2025-05-20 by Karen Scott

• Agreed, Karen! The checkpointing feature in LangGraph is a lifesaver for long-running SEO jobs that might encounter rate limits or network timeouts during the crawling phase.

     Commented 2025-05-21 by Kimberly Evans