How I Built an Adversarial AI Council in React (and Why It Argues With You)

A local-first, single-file SPA where multiple agents debate your decision and hand you a verdict.

The problem: every AI I asked just agreed with me

I almost named this project wrong.

I'd picked a name that sounded powerful. I asked ChatGPT, and it loved it. I asked Claude, and it nodded along. Nobody warned me about the trademark conflict, the wrong search intent, or the SEO fight I'd pick with the BBC.

That was the moment I realized the problem wasn't the name. It was the feedback loop. Most AI assistants are tuned to please, so they hide your blind spots instead of showing them. When you need to make a consequential decision, "sounds great" is the most expensive answer you can get.

So I built the opposite: a council of AI agents that disagree on purpose.

What NoFlattery does

NoFlattery puts 2–4 agents in a room, gives them different reasoning biases, and makes them debate your decision. The output isn't another chat transcript. It's a Decision Record: a clear verdict, the reasoning behind it, the main risk, what would change the call, and a next step.

Use it for product decisions, pricing, tech stack, hiring, or any call where one perspective isn't enough.

Key product choices:

Local-first: your chats and API keys stay in your browser.
BYOK: bring your own OpenAI, Anthropic, OpenRouter, or Ollama key.
One-time price: no subscription, no account, no data harvesting.

The stack

The whole app is a single-file SPA built with:

React 19 + TypeScript
Zustand for state
Dexie over IndexedDB for local-first storage
Vite + vite-plugin-singlefile for a single index.html deploy
An OpenAI-compatible provider runtime so users can plug in their own keys

Why single-file? Because the deploy becomes dead simple. One HTML file. No server for the data. No build orchestration. I can ship the app to Cloudflare Pages and forget about it.

The turn engine: deterministic, not magical

The heart of NoFlattery is a turn-based multi-agent engine. One user message triggers one round. Each agent speaks in order, with a defined bias. There are no hidden selector models deciding who talks next. No silent fallbacks that break identity contracts.

The flow looks like this:

User asks a question.
Each agent generates a response based on its role and the conversation so far.
Agents can @mention each other to force a direct response.
At the end of the round, the user can reply, ask for a summary, or start a new decision.

Discussion modes are prompt injections, not separate state machines. Adversarial mode makes agents challenge harder. Audit mode makes them look for flaws. Vote mode forces a clear verdict. This keeps the runtime small and predictable.

Local-first by default

Every conversation, every API key, every preference lives in IndexedDB via Dexie. Nothing leaves the browser unless you choose to call a provider with your own key.

This isn't a compromise. It's a feature. For a tool about honest decisions, the privacy model should match the message: your data is yours.

The only server touch is license validation for Pro. Even that is just a key plus a device hash. No chat history. No prompts. No telemetry.

Lessons learned

1. Users don't want another chatbot.

They want a decision they can defend. The transcript matters less than the verdict. That's why the Decision Record is the first-class output, not the chat.

2. Single-file deploy removes a lot of headache.

No Docker. No DB migrations. No "works on my machine." I run npm run build and get one index.html. The infrastructure complexity drops to near zero.

3. Local-first is a trust shortcut.

Especially for early users who don't know you. "Your data stays in your browser" is easier to believe than "we promise not to look."

4. Determinism matters more than cleverness.

I tried smarter routing. Hidden selectors. Model-based orchestration. Every time, the system became harder to debug and less trustworthy. One round, one order, one identity per agent. That's the constraint that makes the product feel reliable.