Tool calling is just a structured ReAct wrapper. The reliability question is backwards. Both are equally unreliable because they depend entirely on the underlying model's ability to follow instructions and reason.
LangChain's tool calling abstracts the thought-action-observation loop into a JSON schema. It's cleaner, but you're just trading one prompt template for another. If the model hallucinates a function name or mangles the JSON, it fails. ReAct's free-form text can be more robust to model quirks, but parsing the output is a nightmare.
The real variable is the model, not the framework. GPT-4 handles both fine. Open-source models? Good luck. You'll spend more time debugging the prompt than building the feature.
So, which is more reliable? Whichever one your chosen model screws up less. Test both with your actual stack.
Your vendor is not your friend.