Local Agent Discovery for A2A (LAD-A2A)

An open protocol for discovering A2A-capable agents on local networks.

LAD-A2A addresses a critical gap in the AI agent ecosystem: when a device joins a network—hotel Wi-Fi, office LAN, cruise ship, hospital campus—how does the user's AI assistant discover and connect to local agents? While A2A defines agent-to-agent communication and MCP defines agent-to-tool integration, LAD-A2A defines agent discovery.

With LAD-A2A, agents can:

• Automatically discover local A2A agents when joining a network
• Verify identity through signed AgentCards and DIDs
• Obtain user consent before establishing connections
• Negotiate capabilities via standard A2A mechanisms

See It In Action

Why LAD-A2A?

AI agents are increasingly capable, but they remain isolated from their physical environment. A guest's AI assistant has no way to discover the hotel's concierge agent. An employee's assistant can't find the building's room booking agent. A patient's assistant doesn't know the hospital offers a wayfinding agent.

LAD-A2A solves this by providing:

• Zero-Configuration Discovery: Agents find each other automatically via mDNS/DNS-SD (_lad-a2a._tcp) and well-known endpoints.
• An honest trust model: Local networks are hostile by default. LAD-A2A carefully separates channel authentication (TLS proves you reached the host in the URL) from identity verification (domain/JWS/DID anchored to the org). TLS to an attacker-chosen host proves nothing about identity — see the Trust Model.
• Explicit user consent: Human approval before first contact, keyed to a verified identity — never auto-approved.
• Ecosystem Alignment: LAD-A2A only handles discovery—once an agent is verified and approved, standard A2A communication takes over.

How It Fits

LAD-A2A is the first handshake. It answers "who's here?" so that A2A can answer "what can you do?" and MCP can answer "how do I do it?"

Discovery Mechanisms

LAD-A2A supports multiple discovery paths with automatic fallback:

Getting Started

Interactive Demo (Recommended)

Experience LAD-A2A with a fully working demo featuring two AI agents:

cd demo
cp .env.example .env
# Add your OpenAI API key to .env
./run_demo.sh

Open http://localhost:8000 to see:

• Real mDNS discovery (_a2a._tcp.local)
• LAD-A2A protocol in action
• A2A JSON-RPC 2.0 communication
• LLM-based query routing

See demo/README.md for full documentation.

Run Locally

cd reference
pip install -e .

# Start a discovery server (development only)
python -m server.lad_server --name "My Agent" --port 8080

# Discover agents (in another terminal). --no-verify-tls opts into plaintext for
# local dev; without it the client refuses non-https URLs as a discovery failure.
python -m client.lad_client --url http://localhost:8080 --no-verify-tls

Note: The example above uses HTTP for local development. In production, TLS is required per the security spec: all endpoints MUST use TLS 1.2+, and a non-https URL is a discovery failure, not merely an unverified agent.

Example: Hotel Concierge

1. Guest device joins "ExampleHotel-Guest" Wi-Fi
2. Client queries mDNS for _lad-a2a._tcp.local AND the well-known endpoint,
   then aggregates + dedups by identity key
3. Discovery response includes an AgentCard URL (https) and a DID identity key
4. Client fetches the card over verified TLS and verifies IDENTITY
   (JWS/DID anchored to the org domain); a failed method is fatal
5. Client prompts the user, showing channel and identity separately:
   "Domain-authenticated: concierge.examplehotel.com — Connect?"
6. User approves → Standard A2A session begins (this is first contact)
7. User asks: "What time is breakfast?" → Agent responds via A2A

Security Model

Local networks are hostile by default — a valid TLS certificate for an attacker-owned look-alike domain is part of the threat model, not a defense against it. LAD-A2A mandates:

See the Trust Model for why the channel/identity split is the crux.

Documentation

📚 Full documentation at lad-a2a.org

Use Cases

Contributing

We welcome contributions to enhance the LAD-A2A protocol.

• Questions & Discussions: GitHub Discussions
• Issues & Feedback: GitHub Issues
• Contribution Guide: CONTRIBUTING.md

License

This project is licensed under the Apache License 2.0.