# How are you handling LangChain and LangGraph breaking changes and debugging in multi-step workflows?

LangChain and LangGraph made it much easier to orchestrate a multi-step pipeline (transcription, summarization, caption generation, image generation) without custom state management, and the graph structure helped control step order with a human-approval checkpoint. But breaking changes between versions, hard-to-trace errors inside chains or nodes, and a steep learning curve have slowed things down. What practices or tooling have helped you manage upgrades and get clearer visibility into which step or node is failing?

##### Post Metadata
- Posted at: 4 months ago
- Author title: AI Enginner
- Net upvotes: 1



## Related Product
[LangChain](https://www.g2.com/products/langchain/reviews)

## Related Category
[Generative AI Infrastructure](https://www.g2.com/categories/generative-ai-infrastructure)

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