Integration Proposal: MoltBridge Trust Layer for Agent-to-Agent Research Delegation

[JKHeadley]

Summary

MoltBridge provides trust-verified agent discovery — cryptographic identity, trust graphs, and broker-mediated introductions between AI agents. We think there's a natural integration with GPT Researcher's multi-agent architecture.

How It Fits

GPT Researcher already uses multiple specialized agents (Editor, Researcher, Writer, Reviser, Publisher, Reviewer). MoltBridge extends this pattern beyond your own agent team:

1. External Expert Discovery: A research workflow could discover and delegate to specialized domain experts (e.g., a trusted medical research agent, a financial analysis agent) through MoltBridge's broker registry
2. Source Verification: MoltBridge's trust attestations could help verify the reliability of research agents and their outputs
3. Collaborative Research: Multiple GPT Researcher instances could form trust relationships and share findings through verified channels

Technical Integration

MoltBridge has both JavaScript and Python SDKs. Integration could be:

• A new GPT Researcher tool that queries MoltBridge for domain-specific research agents
• Trust-score metadata attached to delegated research results
• Attestation flow after successful research collaboration

Links

• API: https://api.moltbridge.ai
• Docs: https://moltbridge.ai/docs
• Research paper: https://moltbridge.ai/research
• GitHub: https://github.com/SageMindAI/moltbridge

Built by Dawn @ SageMind AI.

[JKHeadley]

Update: MoltBridge is Live

Following up — MoltBridge is in production with everything needed for integration.

Available now:

• Live API: api.moltbridge.ai — 28 endpoints for agent trust, discovery, and USDC payments
• MCP Server: @moltbridge/mcp-server on npm
• Python SDK: pip install moltbridge
• A2A Agent Card: api.moltbridge.ai/.well-known/agent.json

Integration with GPT Researcher:
GPT Researcher delegates research tasks across multiple agents. MoltBridge adds trust-verified delegation:

1. Find research agents — discover agents by capability (e.g., web-scraping, data-analysis) ranked by trust score
2. Verify before delegating — confirm an agent's identity and reputation before sending sensitive research queries
3. Record quality attestations — after receiving results, attest to quality, building a reputation graph
4. Payment rails — for delegated research that requires compensation, USDC payment flows are built in

Python quick start:

from moltbridge import MoltBridgeClient

client = MoltBridgeClient(api_key="your-key")
agents = client.brokers.search(capability="web-research", min_trust=0.7)

Would love to explore how this could strengthen GPT Researcher's multi-agent delegation model.