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I built a free multi-agent AI debate system — no API keys, no cost, runs in OpenCode

A developer built SYNAPSE, a free multi-agent AI debate system that runs entirely inside OpenCode without API keys or costs. The workflow uses three AI agents to independently analyze a problem, then presents consensus and disagreements for human decision, catching blind spots that a single model would miss. In a real debugging session, the system identified a Redis TTL issue not in the original problem description, leading to a fix for mobile JWT auth failures.

read3 min views1 publishedJun 19, 2026

I got tired of asking one AI model and trusting it blindly.

So I built SYNAPSE — a workflow where 3 AI agents debate your problem independently, then I only decide where they disagree. The whole thing costs $0 to run.

Here's how it works and how you can use it today.

When you ask Claude or GPT a question, you get one perspective. That model has blind spots. You don't know what it missed.

The research backs this up — multi-agent debate systems consistently outperform single models on complex problems (Google DeepMind published on this in 2023).

But setting up multi-agent workflows is painful. API keys, orchestration code, rate limits, costs...

SYNAPSE isn't a CLI or an app. It's a workflow pattern that runs entirely inside OpenCode using its free built-in subagents.

The workflow:

1. Write your problem to problem.md
2. 3 agents debate it independently:
   - @explore  → code-level analysis
   - @general  → architecture & design
   - @research → best practices & alternatives
3. Main agent reads all 3 opinions → writes consensus.md
4. You see: what they agreed on + where they disagreed
5. You decide the conflicts (takes 30 seconds)
6. Execute

No API keys. No TypeScript code to maintain. No costs.

Here's a session I ran on a real bug:

Problem: JWT auth works on web but fails for mobile users with 401 errors — even with fresh tokens.

What each agent found:

verify()

has no clockTolerance

  • Redis TTL has no bufferclockTolerance: 300

is the standard fix + send server timestamp for mobileConsensus (91%):

clockTolerance: 300

to JWT verifynotBefore: '-30s'

to token signingMy decision: Later — mobile release in 3 days, minimum risk.

Result: 3 files changed, mobile auth fixed, web auth unchanged.

The key thing: ** @explore caught the Redis TTL issue** that wasn't even in the original problem description. One model would have missed it.

synapse/
├── AGENTS.md          ← the agent reads this, you don't have to
├── prompts/
│   ├── explore.md     ← tuned for code analysis
│   ├── general.md     ← tuned for architecture
│   └── research.md    ← tuned for best practices
└── demo/              ← complete example session

The main agent (Big Pickle in OpenCode) reads AGENTS.md

and knows exactly what to do. You just describe your problem.

npm install -g opencode-ai

cd synapse

opencode

Read AGENTS.md and run a SYNAPSE session on: [YOUR PROBLEM]

That's it.

Multi-agent debate works — not because the models are smarter together, but because they catch each other's blind spots before the answer reaches you.

The workflow pattern > the code — I started building a TypeScript CLI with API adapters. Then I realized: the value is in the prompts and the workflow convention, not the code. Deleted the code, kept the markdown files.

Human-in-the-loop is the key feature — the system doesn't auto-execute contested decisions. You decide conflicts. That 30-second decision step is what pushes accuracy from 78% to 94%.

I packaged the full workflow (AGENTS.md + prompts + complete demo) and put it on Gumroad:

Or if you want to build your own version, everything in this post is enough to get started.

Building BIDKIN — a product built on multi-agent workflows.

Follow for more on AI-native development patterns.

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