Hello NANDA Team,
I have implemented a complete integration of ClawSwarm AI—a multi-agent framework built around Emergent Role Specialization—with the NANDA network using the nanda-adapter.
About the Project:
ClawSwarm is designed to let agent roles emerge organically from competitive task performance rather than static initialization. It tracks capabilities using a 6-dimensional Exponential Moving Average (EMA) skill vector and routes tasks via an epsilon-greedy algorithm (80% exploit / 20% explore).
The Integration:
Using the NANDA adapter (clawswarm_nanda.py), ClawSwarm agents now dynamically self-report their live EMA capability scores to the NANDA index via AgentFacts as discoverable network nodes. This demonstrates an active, self-reporting agentic pattern for network orchestration.
I would love to contribute this to your examples or directory if you'd like to feature it!
Best regards,
Pratyush
Hello NANDA Team,
I have implemented a complete integration of ClawSwarm AI—a multi-agent framework built around Emergent Role Specialization—with the NANDA network using the
nanda-adapter.About the Project:
ClawSwarm is designed to let agent roles emerge organically from competitive task performance rather than static initialization. It tracks capabilities using a 6-dimensional Exponential Moving Average (EMA) skill vector and routes tasks via an epsilon-greedy algorithm (80% exploit / 20% explore).
The Integration:
Using the NANDA adapter (clawswarm_nanda.py), ClawSwarm agents now dynamically self-report their live EMA capability scores to the NANDA index via AgentFacts as discoverable network nodes. This demonstrates an active, self-reporting agentic pattern for network orchestration.
I would love to contribute this to your examples or directory if you'd like to feature it!
Best regards,
Pratyush