In Agentflow, you write functions by inheriting from the BaseFunction class. You need to provide the definition in JSON that GPT-3.5/4 uses to understand how to call a function, and also the function logic itself. This just means creating a get_definition() function that returns a JSON Schema object, and an execute() function that performs your logic and returns a string. Once you have those, you can then just use the function in your workflow by adding "function_call": "your_function". The application does the rest. Here's the create_image function, for example, which uses the Dall-e API: https://github.com/simonmesmith/agentflow/blob/main/agentflo...

What I mean by "you don't need to write any code with LangChain" is that you don't need to write any Python at all to use Agentflow, unless you want to create a new function. Creating workflows just involves creating JSON files. It's not like LangChain, for which you'd have to chain together multiple prompts in Python.

Does that help clarify?

PS: You'll notice heavy documentation in the link above. I want to experiment with automatically generating documentation using Sphinx, so I documented everything with Sphinx formatting. It might be overkill.