I've noticed a recurring pattern in discussions about building robust LangGraphs, especially when integrating external tools. People often focus on validating and sanitizing *inputs* to a tool node, which is crucial, but there's a critical second step that sometimes gets overlooked: validating and sanitizing the *output* from a tool before it propagates through the rest of your graph.

Consider a tool that fetches user data or scrapes a webpage. Even if you trust the tool call itself, the data it returns could be malformed, excessively large, contain unexpected PII, or include prompt injection strings aimed at a downstream LLM node. Passing this raw, unchecked output to the next node, especially an LLM, can break your graph's logic or create a security risk.

So, my question to the community is about your patterns and safeguards for this phase. What are you doing between the tool node and the next node?

I'm thinking of practices like:
* Defining a strict Pydantic model for the tool's expected output and validating against it, failing the edge if it doesn't conform.
* Truncating or redacting specific fields (like stripping HTML tags or masking credit card numbers) before the data enters the main graph state.
* Implementing a dedicated "sanitization node" on every edge coming out of a tool call.

How are you handling this? Are you doing validation within a custom tool wrapper, or as a separate step in the graph logic? I'm particularly interested in examples where the *structure* of the output is correct, but the *content* needs cleaning before proceeding.

- mod mike