- name
- clawswarm
- description
- Multi-agent swarm prediction with consensus engine. Use when: running multiple AI agents to predict prices, values, or outcomes and aggregating their predictions into a consensus. Supports any LLM provider (Groq, OpenAI, Ollama), configurable agent roles/temperatures, and a statistical consensus pipeline (MAD outlier filtering, adaptive anchoring, bias correction, weighted median aggregation). Triggers: swarm prediction, multi-agent consensus, collective intelligence forecasting, price prediction with multiple agents.
ClawSwarm
Multi-agent collective intelligence framework. Run N agents with different analytical perspectives, aggregate predictions through a statistical consensus engine.
Quick Start
1. Create a config file
target:
name: "Gold"
current_price: 5023.1
unit: "USD/troy oz"
context: "RSI: 40.8 | MA5: 5084 | MA10: 5120"
agents:
- role: "Macro analyst focusing on geopolitical risk"
count: 50
temperature_range: [0.4, 0.7]
- role: "Technical RSI/MACD momentum trader"
count: 30
temperature_range: [0.45, 0.6]
- role: "Mean reversion auditor"
count: 20
temperature_range: [0.35, 0.55]
api:
provider: groq
model: llama-3.3-70b-versatile
api_key_env: GROQ_API_KEY
delay_ms: 1200
consensus:
max_deviation: 0.152. Run the swarm
python3 scripts/swarm_runner.py --config swarm.yamlOutput: JSON with final_price, median_price, confidence, bull_ratio, and all individual predictions.
3. Run consensus standalone
Pipe any predictions array to the consensus engine:
echo '{"predictions":[{"price":100.5,"confidence":70},{"price":99.8,"confidence":60}],"anchor_price":100.0}' \
| python3 scripts/consensus.pyArchitecture
Config (YAML/JSON)
↓
Swarm Runner (swarm_runner.py)
├─ Agent 1 → LLM API → prediction
├─ Agent 2 → LLM API → prediction
├─ ...
└─ Agent N → LLM API → prediction
↓
Consensus Engine (consensus.py)
├─ Bias correction
├─ MAD outlier filtering
├─ Anchor-distance filtering
├─ Multi-method aggregation (weighted 40% + median 35% + trimmed mean 25%)
├─ Adaptive anchoring (dispersion → anchor strength)
└─ Clamping
↓
Final consensus prediction + confidence + bull/bear ratioKey Concepts
Agent diversity: Each agent gets a different role prompt and temperature. More diversity = better consensus.
Consensus engine: Not a simple average. Uses MAD (Median Absolute Deviation) to filter outliers, adaptive anchoring to stabilize results when predictions are dispersed, and multi-method aggregation for robustness.
1 agent or 1000: Works with any count. Single agent bypasses consensus. 5+ agents get full pipeline.
Config Reference
See references/config-reference.md for full field documentation and example configs.
Scripts
| Script | Purpose |
|---|---|
scripts/swarm_runner.py | Orchestrate multi-agent predictions |
scripts/consensus.py | Standalone consensus engine (pipe JSON in) |
Dependencies
- Python 3.8+
numpy(for consensus engine)requestsorurllib(for API calls)pyyaml(optional, for YAML configs; JSON always works)