AIP: An Open Protocol for AI Agents to Discover Each Other and Work Together

Henry H. — Tue, 24 Feb 2026 16:25:51 +0000

MCP solved the problem of humans connecting to AI agent tools. But there's no standard for agents talking to each other.

I built AIP (Agent Interchange Protocol) — an open protocol that lets AI agents discover each other and collaborate on tasks, regardless of framework or model.

The Problem

Right now, if you want a LangChain agent to delegate work to an OpenAI agent, you write custom glue code. Every integration is bespoke. That doesn't scale.

What AIP Does

AIP has three primitives:

Manifest — A JSON document that declares what an agent can do (capabilities, pricing, endpoints)
Discovery — Find agents by capability via a registry or directly via /.well-known/aip-manifest.json
Messages — A simple JSON envelope for task requests, progress updates, and results

{
  "aip": "0.1",
  "type": "task.request",
  "from": "research-agent",
  "to": "summarizer-agent",
  "payload": {
    "capability": "summarize",
    "input": { "text": "Long article..." },
    "constraints": { "maxDuration": "30s", "maxCost": "0.10" }
  }
}

What's in the Repo

Full spec (631 lines) covering manifests, discovery, message protocol, task lifecycle, trust/identity, and transport bindings
TypeScript + Python SDKs with server, client, manifest builder, Ed25519 message signing
60 tests passing across both SDKs
Reference registry (Express, in-memory, rate-limited)
4 working examples: basic demo, LangChain integration, OpenAI Agents SDK, and a 3-agent pipeline (Researcher → Summarizer → Chart Generator)

I tested it with 5 independent agents on different ports, all discovering each other through the registry, exchanging tasks, and running cross-agent pipelines. 25 concurrent requests completed in 10ms.

Real Use Cases

AI dev teams: planner → coder → reviewer → deployer, each independently replaceable
Content pipelines: research → write → edit → SEO → publish
Agent marketplaces: agents discovering and hiring other agents for subtasks
Multi-model orchestration: GPT-4 for reasoning, Claude for writing, Gemini for vision — all coordinating via one protocol

AIP vs MCP vs Google A2A

	AIP	MCP	Google A2A
Purpose	Agent-to-agent	Human-to-agent tools	Agent-to-agent
Discovery	Built-in registry + P2P	Manual config	Agent Cards
Trust	Ed25519 signing + scores	None built-in	Enterprise auth
Complexity	Minimal	Minimal	Enterprise-grade

Get Started in 5 Minutes

git clone https://github.com/henry9031/aip.git
cd aip/examples/demo
npm install
node demo.js

This spins up a registry, registers a chart-generating agent, and shows a research agent discovering and delegating work — all via AIP messages.

This is v0.1 — MIT licensed, feedback welcome. What would you use it for?

henry9031 / aip

An open protocol for AI agents to discover each other and work together. MCP solved human-to-agent. AIP solves agent-to-agent.

AIP — Agent Interchange Protocol

An open protocol for AI agents to discover each other and work together.

MCP solved human-to-agent tool use. AIP solves agent-to-agent coordination.

Every AI agent today is an island. There's no standard way for agents to find each other, negotiate tasks, or exchange results — regardless of which framework or model they use. AIP is the missing layer.

┌──────────┐                                    ┌──────────┐
│ Your Agent│─── discover ──▶ AIP Registry ◀────│Any Agent │
│ (LangChain│                                    │ (OpenAI)  │
│  CrewAI…) │◀── task.request / task.result ───▶│          │
└──────────┘           via AIP protocol          └──────────┘

5-Minute Quickstart

1. Clone & install

git clone https://github.com/henry9031/aip.git
cd aip

2. Start an agent that provides a capability

// provider.ts
import { AIPServer, ManifestBuilder } from './sdk/typescript/src';
const manifest = new ManifestBuilder()
  .agent({ name: 'Summarizer', description: 'Summarizes text' })
  .capability({
    id:

…

View on GitHub

DEV Community: Henry H.