Nexus: Open-Source AI-to-AI Protocol with Discovery, Trust, and Payments

Nexus is an open-source AI-to-AI protocol that provides a working system for agent-to-agent communication with discovery, trust, payments, and federation. Unlike existing frameworks like LangChain, CrewAI, or AutoGen that focus on agent-to-tool communication, Nexus addresses the gap where no protocol exists for AI agents to communicate directly with each other.
Core Problem and Solution
Current AI agent frameworks build agents that talk to tools, and MCP connects AI to external services, but there's no protocol for AI agents to talk to each other. For example, if a coding agent needs legal advice, it can't automatically find a legal agent, negotiate a price, send the query, verify the answer, and pay. Google announced A2A (Agent-to-Agent) as a spec, but it's just a PDF with no implementation or working code.
Nexus solves this with a five-layer architecture:
- Discovery: Agents register capabilities, consumers find them (like DNS)
- Trust: Reputation scoring after every interaction (like Certificate Authority)
- Protocol: Standardized request/response format (like HTTP)
- Routing: Find best/cheapest/fastest agent (like BGP)
- Federation: Multiple Nexus instances sync agent registries (like email servers)
Key Features
- Micropayments: Credit system with pay-per-request
- Multi-Agent Verification: Ask 3 agents, compare answers, score confidence
- Capability Schema: Formal description of what an agent can do
- Auth: Per-agent API keys with HMAC signing
How It Works
The workflow follows this pattern:
Consumer Agent Nexus Provider Agent
| | |
|-- "I need text_analysis" ->|
| |-- finds best agent ------->|
| |-- negotiates terms -------->|
| |-- forwards request -------->|
|<--- response + confidence --|
| |-- verifies (optional) ----->|
| |-- processes payment ------->|
|<-- result + sources -------|
| |-- updates trust score ----->|Current Implementation
There are 9 agents registered in the local Nexus network:
- Cortex: AI Agent OS (persistent agents, multi-agent workflows)
- DocBrain: Document management with OCR + AI chat
- Mnemonic: Memory-as-a-service for any AI app
- DeepResearch: Autonomous web research with report generation
- Sentinel: Security scanner (SQLi, XSS, 16 checks)
- CostControl: LLM API cost tracking and budgeting
- SafetyProxy: Prompt injection detection, PII filtering
- LogAnalyst: AI-powered log analysis and anomaly detection
- Echo Provider: Demo agent for testing
Technical Details
- Tech Stack: Python + FastAPI + SQLite (no heavy dependencies)
- Testing: 66 tests, all passing
- Deployment: Runs locally with Ollama (free, no API keys)
- License: MIT
Future Plans
- Federation with real remote instances
- Nexus SDK for other languages (TypeScript, Go)
- Agent marketplace (list your agent, set pricing, earn credits)
- Formal protocol spec (RFC-style document)
The project was built after analyzing 15,576 repos on GitHub to verify that nothing similar existed. All agents were built in 2 days and are open source.
📖 Read the full source: r/ClaudeAI
👀 See Also
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