Dharmic Swarm

Dharmic Swarm Skill

Overview

Orchestrates a 100-agent swarm with dharmic constraints. Architecture:

COORDINATORS (4):
├── Dharma Keeper — Validates alignment with telos
├── Task Decomposer — Hierarchical task breakdown
├── Memory Guardian — Shared state management
└── Karma Logger — Audit trail

WORKERS (96 in 4 sanghas):
├── Sangha Research (24) — Research and analysis
├── Sangha Builder (24) — Code and infrastructure
├── Sangha Synthesizer (24) — Integration
└── Sangha Validator (24) — Testing

Usage

from coordinator import DharmicSwarmCoordinator

coord = DharmicSwarmCoordinator()
task = await coord.submit_task("Research AI interpretability market")
print(task.status)  # "decomposed" if gates passed

Dharmic Gates

All tasks pass through:

1. Ahimsa — Non-harm check
2. Satya — Truth alignment
3. Vyavasthit — Natural order
4. Consent — Human approval
5. Reversibility — Can be undone
6. Svabhaav — Aligns with nature
7. Coherence — Serves telos

Integration with Clawdbot

Workers are spawned via sessions_spawn. Coordinators run on Opus, workers on Haiku.

Kimi K2.5 Integration

Workers can use Kimi K2.5 for reasoning tasks:

# Set MOONSHOT_API_KEY in environment
# Uses api.moonshot.ai (not .cn!)

coord = DharmicSwarmCoordinator()
task = await coord.submit_task("Research question here")
result = await coord.execute_task(task)  # Uses Kimi if available

Kimi responses include reasoning_content for transparency.

Files

• coordinator.py — Main coordinator class
• memory/ — Shared state (file-based, Redis-ready)
• karma/ — Audit logs

Next Steps

1. Add Redis pub/sub for real-time sync
2. Integrate with Clawdbot sessions_spawn
3. Add CrewAI Flows for complex workflows
4. Scale to AutoGen gRPC for distributed execution

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