dialogwright/dialogwright

dialogwright.com

Nobody calls an IVR to chat. People call to pay a bill, report an outage or move an appointment, and they judge your IVR or chatbot by one thing: whether it understood them. Large language models finally made real understanding possible, and generation came bundled with it. DialogWright unbundles them: understanding is flexible, and what the agent says and does is predictable. Your agent does not need to talk about everything. It needs to understand its own domain thoroughly and act on it safely.

DialogWright is an open-source TypeScript framework for voice and chat agents. A fast decision model reads what the caller said and answers typed questions (judgments, never generated text), so callers talk naturally. Every line the agent says is written and approved, deterministic code decides what happens next, and every action goes through a policy gate that enforces identity levels, scope, confirmation and attempt limits, with each decision recorded in a hash-chained audit log. Replies are fast, because a decision is a short typed question and not a composed answer. An app is a folder: YAML for what is data (intents, forms, prompts, policy, identity, locales, a knowledge base of approved answers) and TypeScript for what runs (tools, hooks, rules of its own), so the same engine runs a clinic’s appointment line, a utility’s outage line or yours.

Try it, with no keys

You need git, Node 22.19 or later and pnpm 9 (corepack enable gives the version this repository pins). Nothing here needs an account or a key: a stub stands in for the decision model.

git clone https://github.com/DialogWright/dialogwright.git
cd dialogwright
pnpm install
pnpm --filter @dialogwright/example-clinic cli --client heuristic --today 2026-09-18

That is the text console of the example clinic, Example Family Practice (a fictional appointment line). Type what a caller would say (“I need to move my appointment”, “cancel”, “can I talk to someone”) and it prints each question the model was asked, what came back, the gate’s decision and the line the caller hears. --client heuristic answers from keywords, so it understands less than the real model; --today pins the day that “Tuesday” is read against. Then replay the clinic’s scripted calls and labelled lines against their baseline:

pnpm --filter @dialogwright/example-clinic regress