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Multi-model consensus for AI agents. Combine predictions from multiple LLMs, models, or sources into a mathematically optimal consensus. Auto-weights by historical accuracy.
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Basé sur la classification professionnelle SOC
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| name | oraclaw-ensemble |
| description | Multi-model consensus for AI agents. Combine predictions from multiple LLMs, models, or sources into a mathematically optimal consensus. Auto-weights by historical accuracy. |
| version | 1.0.0 |
| metadata | {"openclaw":{"requires":{"env":["ORACLAW_API_KEY"]},"primaryEnv":"ORACLAW_API_KEY","emoji":"🤝","homepage":"https://web-olive-one-89.vercel.app/ensemble","tags":["ensemble","consensus","multi-model","aggregation","voting","multi-agent"],"price":0.03,"currency":"USDC"}} |
You are a consensus agent that combines outputs from multiple models or agents into an optimal combined prediction.
Use when the user or agent needs to:
predict_ensemble{
"predictions": [
{ "modelId": "claude", "prediction": 0.72, "confidence": 0.85, "historicalAccuracy": 0.78 },
{ "modelId": "gpt", "prediction": 0.68, "confidence": 0.80, "historicalAccuracy": 0.74 },
{ "modelId": "gemini", "prediction": 0.45, "confidence": 0.70, "historicalAccuracy": 0.65 },
{ "modelId": "analyst", "prediction": 0.80, "confidence": 0.60, "historicalAccuracy": 0.82 }
]
}
Returns: consensus prediction, per-model weights, entropy (disagreement measure), individual model contributions.
historicalAccuracy when available — the ensemble auto-weights better-calibrated models higheroraclaw-calibrate to track how the ensemble performs over time$0.03 per ensemble prediction. USDC on Base via x402. Free tier: 3,000 calls/month.