The Sovereignty of Massive Orchestration
Hermes Swarm (Prime V7.2) is a specialized platform engineered for Sovereign Prediction Intelligence. By leveraging a localized orchestration engine and massive agent reasoning offload via the Gemini CLI MCP Bridge, we've eliminated cloud dependency and API bottlenecks.
The platform simulates complex scenarios by drafting up to 160+ unique agent personas into a weighted Bayesian consensus loop, each with distinct domain knowledge, psychological profiles, and data priorities.
Prediction Domains
📈 Financial Markets
Multi-agent analysis of market sentiment, whale movements, on-chain data, and macroeconomic indicators to forecast price action and volatility windows.
🌍 Geopolitical Events
Swarm simulations model how nation-state actors, institutions, and market participants respond to policy shifts, sanctions, and conflict escalation.
🏛️ Policy Impact Analysis
Predict how groups of people will respond to government policy changes — from regulatory shifts to fiscal decisions — using adversarial persona modeling.
Swarm Architecture
From objective definition to final predictive model — every step is reputation-weighted and mathematically grounded.
01. OBJECTIVE DEFINITION
Parameterization of mission goals and boundary conditions via Tactical Pane.
02. AGENT DRAFTING
Selection of 160+ specialized personas based on domain relevance and AIS score.
03. REASONING OFFLOAD
Parallelized local inference via Gemini CLI MCP Bridge with zero-latency local execution.
04. BAYESIAN SYNTHESIS
Final consensus weighting where AAA-Tier agents carry 10x predictive weight.
Bayesian Consensus Logic
Instead of simple majority voting, the swarm uses the Integrity Protocol to weight each agent's prediction. A "Sovereign Master" (AAA Tier) contributes 10x more to the final probability synthesis than an unverified agent.
Swarm Dynamics
- Dynamic Pruning: Agents showing high technical entropy are automatically ejected from the simulation.
- Adversarial Personas: Swarms include contrarian nodes to stress-test consensus and prevent groupthink.
- Grounding Oracle: Predictions cross-checked against historical accuracy to build long-term Swarm Reputation.
- Gemini CLI MCP Bridge: Localized LLM offloading via Model Context Protocol — zero cloud API latency.
def calculate_consensus(predictions, ais_scores):
weights = [score ** 2 for score in ais_scores]
weighted_avg = sum(p * w for p, w in zip(predictions, weights)) / sum(weights)
return weighted_avg
# Current Swarm Confidence: 0.892
The Sovereign Cockpit
🎯 Tactical Pane
Edge-anchored mission parameterization, goal definition, and Fusion Intelligence toggles for alternative data streams.
🌐 Spatial Intelligence
Full-bleed viewport with immersive 3D Blue Marble globe or Neural Map visualizations for real-time swarm analytics.
📊 Strategic Synthesis
High-density Neural Stream Uplink and Alpha Feed tracking sentiment criticalities and whale movements in real-time.
Hermes UI
A visual Command Center for zero-code automation and recursive agent orchestration. Hermes UI provides the human interface layer for configuring swarm parameters, monitoring agent health, and reviewing prediction outputs.
- Drag-and-Drop Automation Engine with Neural Node Library
- Graph-Based Global Settings with real-time property configuration
- OpenAI-like Chat Interface with markdown code blocks and telemetry stream
Contribute to Hermes Swarm.
We're building the future of prediction intelligence in the open. Star the repo, open an issue, or submit a PR.