Building KillMyIdea: A Fast Multi-Agent Debate App with Next.js + LangChain

KillMyIdea is a developer-focused experiment: can we make AI feedback feel like a real argument instead of a long report?

Short answer: yes, by combining:

• strict output schemas,
• parallel orchestration,
• role-specific prompts,
• and UI pacing that feels live.

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Product Goal

Given a user idea, generate a sharp debate between:

• Optimist (upside)
• Skeptic (assumption attack)
• Risk Analyst (legal/financial/operational failure modes)
• Moderator (verdict + scores + actions)

Target latency: under ~8–10 seconds.

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High-Level Architecture

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Why a Custom Orchestrator (Instead of Full LangGraph)?

LangGraph is powerful for long-lived workflows, but this app needs:

• fast and deterministic flow,
• shallow state,
• predictable output shape.

So a lightweight custom orchestrator in lib/debate-orchestrator.ts is enough and simpler to maintain.

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Core Orchestration Code

Parallelized agent execution

const [optimistInit, skepticInit, riskInit] = await Promise.all([
  initialRound("Optimist", optimistPrompt),
  initialRound("Skeptic", skepticPrompt),
  initialRound("Risk Analyst", riskPrompt)
]);

Strict structured moderator output

const moderatorSchema = z.object({
  summary: z.string().min(20).max(900),
  verdict: z.enum(["Likely to Fail", "Needs Pivot", "Promising with Conditions"]),
  optimistScore: z.number().int().min(1).max(10),
  skepticScore: z.number().int().min(1).max(10),
  riskLevel: z.enum(["Low", "Medium", "High"]),
  actions: z.array(z.string().min(5).max(140)).min(1).max(2)
});

Robust parsing strategy

• Agent lines can fallback from plain text into { text }.
• Structured outputs (context/moderator) run strict parsing + repair retry.

This protects UX from model format drift.

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Frontend UX Decisions

The UI avoids wall-of-text. It’s paced like a live debate:

• sequential reveal of cards,
• explicit stage tracker during generation,
• final verdict card with 3 metrics,
• share-card export (text + image clipboard).

Relevant file: app/page.tsx.

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API Contract

POST /api/debate accepts:

• rich payload (title, description, context), or
• compact payload (idea, context).

Returns both concise fields and full structured result.

This dual-format input makes the endpoint easier to integrate with other clients.

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Performance Notes

Performance wins come from:

• Promise.all in all multi-agent stages,
• short prompt constraints,
• low-token outputs (3–4 sentences max).

Primary latency bottleneck is provider round-trip time, not local compute.

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Developer Experience

• npm run lint and npm run build validate deployment health.
• App is Vercel-ready with env-driven model/provider switching.
• OpenAI-compatible setup supports non-OpenAI providers via OPENAI_BASE_URL.

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What I’d Build Next

1. SSE streaming per stage (context, initial, critique, refine, moderator).
2. Persistent DB + analytics for controversial ideas.
3. Qdrant memory for historical retrieval and better context grounding.
4. Auth + rate limits for public launch.
5. Prompt versioning with offline evaluation harness.

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KillMyIdea works because it treats AI output as structured debate events, not freeform chat.

That shift makes the product faster, clearer, and more shareable.

Github Repo: https://github.com/harishkotra/kill-my-idea