I made a state machine framework for guided conversations with LLMs
It’s written in Python and I call it GoalChain. It lets you build a conversation flow graph that the user traverses. When there’s enough input it spits out a dictionary with the defined fields. Otherwise it will jump state to state as led by the user. It was fun to write, and it’s surprisingly effective if you keep in mind you’re prompt-engineering every string and field name. README.md has a mini-tutorial. Would be cool to get some ideas for how to build it further and what improvements I could make.
In plain words
GoalChain is a Python framework for building guided conversations with large language models using state machine graphs. It structures dialogue flows where users navigate between states, and the system collects information until it has enough data to output a structured dictionary with defined fields. The framework is designed for developers who need to guide LLM interactions toward specific information-gathering goals, requiring careful attention to prompt engineering for field names and conversational strings.
written from the facts on this page · September 2026
Does the same job
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