Agent Interoperability Protocol Models (AIPM)

Vision

AIPM is the first open ecosystem for interoperable AI agents, enabling agents from different vendors (OpenAI, Claude, LangGraph, AutoGen, CrewAI, etc.) to communicate securely and consistently using the same protocol.

Architecture Overview

Core Protocol Components

  1. Identity Layer - Agent identification and capabilities
  2. Capability Discovery - Automatic discovery of skills and tools
  3. Secure Handshake - TLS-inspired connection establishment
  4. Task Negotiation - Accept/decline work based on capability
  5. Memory Exchange - Efficient context sharing
  6. Trust Layer - Reputation and reliability tracking
  7. Skill Marketplace - Dynamic capability discovery
  8. Workflow Delegation - Hierarchical task orchestration
  9. Economic Layer - API billing and micropayments
  10. Standard Message Format - JSON-based protocol

Project Structure

aipm/
β”œβ”€β”€ schemas/           # JSON schemas for protocol messages
β”œβ”€β”€ sdk-python/        # Python reference SDK
β”œβ”€β”€ sdk-javascript/    # JavaScript SDK (future)
β”œβ”€β”€ sdk-rust/         # Rust SDK (future)
β”œβ”€β”€ models/           # Fine-tuned AIPM models (future)
β”œβ”€β”€ datasets/         # Training and benchmark datasets (future)
β”œβ”€β”€ examples/         # Example implementations
└── docs/             # Protocol documentation

Quick Start

Installation

cd sdk-python
pip install -e .

Basic Usage

from aipm import AIPMAgent, AgentIdentity, Capabilities

# Create agent identity
identity = AgentIdentity(
    agent_id="my-agent-001",
    organization_id="my-org",
    name="My AI Agent",
    version="1.0.0",
    capabilities=Capabilities(
        skills=["text-generation", "code-review"],
        models=["gpt-4"],
        tools=["code-interpreter"],
    )
)

# Initialize agent
agent = AIPMAgent(identity)

# Initiate handshake with another agent
peer = AgentReference(
    agent_id="peer-agent-001",
    organization_id="peer-org"
)
hello_msg = agent.initiate_handshake(peer)

Run Example

See examples/basic_handshake.py for a complete handshake between OpenAI and LangGraph agents:

cd examples
python basic_handshake.py

Current Status

βœ… Phase 1: COMPLETE

  • JSON schemas defined
  • Python SDK scaffolded
  • Identity & handshake models implemented
  • Basic agent implementation
  • Cryptographic foundation (Ed25519)
  • Example scripts

See PHASE1_COMPLETE.md for full details

🚧 Phase 2: In Planning

  • Task negotiation framework
  • Cryptographic message signing
  • HTTP/WebSocket transport
  • Enhanced error handling
  • Comprehensive test suite

πŸ“‹ Future Phases

Phase 3: Advanced Features

  • Memory exchange protocol
  • Trust scoring system
  • Economic layer implementation

Phase 4: Ecosystem

  • JavaScript SDK
  • Rust SDK
  • Fine-tuned AIPM models
  • Benchmark datasets
  • Public registry/marketplace

Handshake Protocol

Agent A                          Agent B
   |                                |
   |------- HELLO ----------------->|
   |<--- CAPABILITY_EXCHANGE -------|
   |------- AUTHENTICATION -------->|
   |<--- PUBLIC_KEY_EXCHANGE -------|
   |------- TRUST_VERIFICATION ---->|
   |<--- READY ---------------------|
   |                                |
   [Ready for task delegation]

Key Features

Identity Layer

  • Unique agent IDs
  • Organization affiliations
  • Capability declarations
  • Trust scores
  • Public key cryptography

Secure Communication

  • Ed25519 signatures
  • Message authentication
  • Session management
  • Trust verification

Interoperability

  • Vendor-agnostic protocol
  • Standardized message format
  • Capability-based routing
  • Cross-framework communication

Use Cases

  1. Multi-Agent Workflows - Agents from different vendors collaborate on complex tasks
  2. Skill Marketplace - Discover and delegate to specialized agents
  3. Trust Networks - Build reputation across agent interactions
  4. Economic Coordination - Fair billing and micropayments between agents
  5. Memory Sharing - Efficient context exchange without duplication

Technical Stack

  • Protocol: JSON-based message format
  • Cryptography: Ed25519 (EdDSA)
  • Python SDK: Pydantic, cryptography, httpx
  • Schemas: JSON Schema Draft 2020-12

Documentation

Examples

Contributing

We welcome contributions! Areas of focus:

  • Protocol design and specification
  • SDK implementations (Python, JS, Rust, Go)
  • Example applications
  • Documentation and tutorials
  • Test coverage
  • Benchmark datasets

Roadmap

Q3 2026

  • βœ… Phase 1: Core protocol and SDK
  • 🚧 Phase 2: Task negotiation and transport

Q4 2026

  • Phase 3: Advanced features (memory, trust, economic)
  • Additional language SDKs

2027

  • Fine-tuned AIPM models
  • Public agent registry
  • Enterprise features
  • Ecosystem growth

License

Apache 2.0

Contact


Building the future of interoperable AI agents πŸš€

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