Why Typed Dict for LangGraph State #143
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One reason is that types in python's stdlib are less likely to see breaking changes. Example: Pydantic moved from v1 to v2 last summer but a large portion of our user base still uses v1, and there are some rough edges in supporting both |
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Just curious, does anyone know the reasoning for using a Typed Dict for the LangGraph State as opposed to something like a Pydantic BaseModel?
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