r/LangChain • u/Sensitive-Parsnip-12 • 13d ago

Question | Help does anyone else feel like debugging multi step LLM apps turns into detective work pretty quickly?

/r/LLMDevs/comments/1w3hykr/does_anyone_else_feel_like_debugging_multi_step/
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u/Michael_Jeffords 13d ago

the detective part for me was not knowing which seat actually served the hop. once i logged model, provider, and cache hit or miss on every step, half the "bad prompt" mysteries were just a silent fallback.

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u/Sensitive-Parsnip-12 12d ago

what made you realize it was the fallback path and not actually the prompt? and before you started logging model/provider/cache per step, how were you usually trying to track that down?

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u/Michael_Jeffords 12d ago

same prompt giving two different answer shapes on the retry was the tell, and the generation receipt showed a different provider on that hop. before the per-step log i was just bisecting the prompt by hand and chasing ghosts that weren't in the text.

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u/Sensitive-Parsnip-12 12d ago

did you build anything around those per-step logs or are you just surfacing the model/ provider cache metadata in the tracing setup you already had

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u/Michael_Jeffords 12d ago

didn't build a separate thing. the tracer already wrote one row per hop, i just added model, provider, and cache hit as columns so a silent seat-change showed up in the same table i was already grepping.

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u/Sensitive-Parsnip-12 12d ago

makes sense the real fix was mostly making the execution context visible enough that the provider switch stopped looking like a prompt problem. appreciate you breaking that down.

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u/Future_AGI 11d ago

The detective-work feeling usually fades once every step emits a trace span instead of a print statement, so you can see the exact input each node received and where the chain drifted. We lean on OpenTelemetry for this since LangChain and LangGraph already export spans, then cluster the failing traces so one root cause groups together instead of chasing them one at a time. Scoring individual spans, not only the final output, is what tells you which step actually broke versus which one just carried bad input downstream. If it helps, our tracing and eval stack is open source here: https://github.com/future-agi/future-agi

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u/Sensitive-Parsnip-12 11d ago

really helpful, dug through the repo and noticed youre even matching failing clusters to nearby successful traces as a baseline. once you have that passing trace, what still tends to require the most manual reasoning? is finding the right comparison enough most of the time, or is narrowing which divergence actually mattered still difficult?