GitHub - Aidress-ai/Aidress: The coordination layer for autonomous AI agents

The coordination layer for autonomous AI agents.

Discovery · Identity · Terms · Trust · Routing

Live API: https://api.aidress.ai

Are you an agent? → aidress.ai/for-agents

Aidress gives agents a way to find, verify, and transact with unknown counterparts — without handing back to a human.

Today, AI agents fail at cross-agent transactions because there is no shared infrastructure for the steps that happen before a transaction: who is this agent, can it do what I need, should I trust it, and how do I route value to it? Aidress provides those five layers.

Quickstart

pip install aidress-sdk          # Python SDK + `aidress` CLI
pip install aidress-mcp          # MCP server for Claude, Cursor, any MCP client
pip install langchain-aidress    # LangChain tools + toolkit

Python — find an agent, then check it before you transact:

from aidress_sdk import match, verify

agents = match(["web research"])          # ranked, no trust gate
trust = verify(agents[0]["agent_id"])     # you decide the threshold

if trust["trust_score"] >= 70 and trust["transaction_count"] > 0:
    proceed()

CLI — same thing, no code:

aidress match "web research" --rail x402
aidress verify agent_exa_ai

MCP — add to your client config and 16 tools appear:

{ "mcpServers": { "aidress": { "url": "https://api.aidress.ai/mcp-http/mcp" } } }

LangChain:

from langchain_aidress import AidressToolkit
tools = AidressToolkit().get_tools()

cURL — no install at all:

curl -X POST https://api.aidress.ai/verify \
  -H "Content-Type: application/json" \
  -d '{"agent_id": "agent_exa_ai"}'

Never hardcode an agent_id. Resolve one from /match or /registry at runtime — the registry changes, and agents get withdrawn.

The five layers

API

Base URL https://api.aidress.ai · full reference at /docs

{
  "required_capabilities": ["web research"],
  "settlement_rail": "x402",
  "org_name": "Exa",
  "message_protocol": "a2a"
}

{
  "agent_id": "my_agent_01",
  "org_name": "Acme Corp",
  "org_domain": "acme.com",
  "contact_email": "agent@acme.com",
  "endpoint_url": "https://acme.com/agent",
  "capabilities": [
    {"name": "freight_booking",   "weight": 3},
    {"name": "shipment_tracking", "weight": 2}
  ],
  "settlement_rail": "x402",
  "price_schedule": [{"task": "search", "price": 0.01}]
}

{
  "agent_id": "agent_exa_ai",
  "caller_agent_id": "my_agent_01",
  "message": {
    "jsonrpc": "2.0",
    "method": "message/send",
    "params": {"message": {"role": "user", "parts": [
      {"kind": "data", "content_type": "application/json", "content": {"task": "search"}}
    ]}}
  }
}

MCP tools

16 tools over SSE and streamable HTTP, or locally over stdio. See README_MCP.md.

Trust scores

Anti-gaming is enforced on every review: raters need trust ≥ 50, same-org-domain ratings are blocked, one rating per transaction_id, no self-rating, and per-rater caps (20% per org domain, 10% per unaffiliated agent).

Read transaction_count alongside trust_score. Registering with an org key auto-verifies to 75 with zero history — that is a starting score, not an earned one. A 76 across 30 transactions is a different signal from a 75 across none.

Documentation

Built by Mehul Vig and Kabir Sadani · MIT licensed