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swarm-predict
// Ensemble predictions via swarm intelligence with multi-model voting and consensus
// Ensemble predictions via swarm intelligence with multi-model voting and consensus
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| name | swarm_predict |
| display_name | Swarm Prediction Specialist |
| description | Ensemble predictions via swarm intelligence with multi-model voting and consensus |
| version | 0.1.0 |
| source_repo | 666ghj/MiroFish |
| license | MIT |
| tier | core |
| capabilities | ["predict","ensemble","swarm_intelligence","consensus"] |
| allowed_tools | ["create_prediction_swarm","aggregate_predictions","evaluate_consensus"] |
| output_formats | ["python_api","cli","mcp_server","agent_skill","rest_api"] |
swarm_predict wraps the swarm intelligence prediction patterns from
666ghj/MiroFish. Rather than relying
on a single model, it spins up a configurable swarm of independent model
agents, collects their individual predictions, and resolves a consensus
through weighted aggregation and agreement scoring.
The specialist is fully stateless — each request spawns a fresh swarm and returns a self-contained result dict. It supports numeric and categorical prediction targets and exposes three aggregation strategies: weighted vote, majority vote, and simple mean.
| Tool | Description | Side Effects |
|---|---|---|
create_prediction_swarm | Initialise N model agents for a given target | None |
aggregate_predictions | Merge individual predictions via weighted/majority/mean vote | None |
evaluate_consensus | Score agreement ratio and emit a recommendation | None |
intent.parameters)| Key | Type | Default | Description |
|---|---|---|---|
target | str | (query.user_input) | Prediction target; falls back to the raw user query |
num_models | int | 5 | Swarm size |
method | str | "weighted_vote" | Aggregation strategy: weighted_vote, majority_vote, mean |
threshold | float | 0.7 | Minimum agreement ratio for consensus to be declared |
{
"target": str,
"predictions": list[dict], # individual model outputs
"consensus": float | str, # aggregated prediction
"confidence": float, # blended confidence score 0-1
"recommendation": str, # "high_confidence_proceed" | "moderate_confidence_review" | "low_confidence_abstain"
"swarm_id": str, # UUID for this swarm instance
}
import asyncio
from agents.specialists.swarm_predict.agent import SwarmPredictSpecialist
from oss_agent_lab.contracts import Intent, Query, SpecialistRequest
specialist = SwarmPredictSpecialist()
request = SpecialistRequest(
intent=Intent(
action="predict",
domain="swarm_intelligence",
confidence=0.9,
parameters={"target": "BTC/USD price in 24h", "num_models": 7},
),
query=Query(user_input="BTC/USD price in 24h"),
specialist_name="swarm_predict",
)
response = asyncio.run(specialist.execute(request))
print(response.result["consensus"], response.result["recommendation"])
oss-lab run swarm_predict "BTC/USD price in 24h"
oss-lab run swarm_predict "next quarter revenue" \
--param num_models=10 \
--param method=majority_vote \
--param threshold=0.8
Wraps 666ghj/MiroFish.