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
- Identity Layer - Agent identification and capabilities
- Capability Discovery - Automatic discovery of skills and tools
- Secure Handshake - TLS-inspired connection establishment
- Task Negotiation - Accept/decline work based on capability
- Memory Exchange - Efficient context sharing
- Trust Layer - Reputation and reliability tracking
- Skill Marketplace - Dynamic capability discovery
- Workflow Delegation - Hierarchical task orchestration
- Economic Layer - API billing and micropayments
- Standard Message Format - JSON-based protocol
Project Structure
aipm/
βββ schemas/ # JSON schemas for protocol messages
βββ sdk-python/ # Python reference SDK
βββ sdk-javascript/ # JavaScript SDK (future)
βββ sdk-rust/ # Rust SDK (future)
βββ models/ # Fine-tuned AIPM models (future)
βββ datasets/ # Training and benchmark datasets (future)
βββ examples/ # Example implementations
βββ 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
β 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
π§ Phase 2: In Planning
- Task negotiation framework
- Cryptographic message signing
- HTTP/WebSocket transport
- Enhanced error handling
- Comprehensive test suite
π 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
- Multi-Agent Workflows - Agents from different vendors collaborate on complex tasks
- Skill Marketplace - Discover and delegate to specialized agents
- Trust Networks - Build reputation across agent interactions
- Economic Coordination - Fair billing and micropayments between agents
- 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
- Phase 1 Complete - Detailed Phase 1 documentation
- SDK README - Python SDK documentation
- Schemas - JSON schema specifications
Examples
- basic_handshake.py - Complete handshake demo
- verify_phase1.py - Verification script
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
- β Phase 1: Core protocol and SDK
- π§ 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
- GitHub: https://github.com/aipm/aipm
- Documentation: https://docs.aipm.org
- Community: https://discord.gg/aipm
Building the future of interoperable AI agents π
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