What happens when 500K AI agents get real memory?
A side-by-side benchmark comparing default context-window memory vs Clude cognitive memory retrieval in large-scale agent simulations inspired by MiroFish.
Visit clude-miro.vercel.app and click "Run Simulation" to see the results in real time.
| Metric | What It Tells You |
|---|---|
| Hallucination Rate | How often agents "remember" things that never happened |
| Fact Retention | What % of original facts survive after N rounds of interaction |
| Prediction Accuracy | How well agents predict outcomes based on their memories |
| Cost Per Round | Dollar cost of memory operations across all agents |
- 1,000 agents are seeded with 10 ground-truth facts each
- Two parallel simulations run for 50 rounds:
- Default: Context window stuffing ($0.25/query, 2-5% memory degradation per round)
- Clude: Vector memory retrieval ($0.001/query, 0.1-0.2% degradation per round)
- Each round: agents interact, share info, make predictions, and update memories
- Results stream in real-time via SSE
After 50 rounds with 1,000 agents:
| Metric | Default | Clude | Improvement |
|---|---|---|---|
| Hallucination Rate | ~23% | ~1% | 23x less |
| Fact Retention | ~41% | ~94% | +53% |
| Cost Per Round | $250 | $1 | 250x cheaper |
npm install
npm run devOpen localhost:3000
- Next.js 14, React 18, Tailwind CSS
- Framer Motion for animations
- SSE for real-time streaming
- Clude API for memory retrieval
At 500K agents, memory failures compound exponentially. One agent that "misremembers" a fact shares it with others, who share it further. In a swarm simulation, hallucination isn't just wrong — it's contagious.
Clude's 1% hallucination rate vs 23% default means your simulation stays accurate at scale.
MIT