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.

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 buffer clockTolerance: 300 is the standard fix + send server timestamp for mobile Consensus 91% : clockTolerance: 300 to JWT verify notBefore: '-30s' to token signing My 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. 1. Install OpenCode free npm install -g opencode-ai 2. Clone/download SYNAPSE cd synapse 3. Open OpenCode opencode 4. Tell the agent: 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.