File size: 6,472 Bytes
2effa7f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 | #!/usr/bin/env python3
"""
Basic AIPM Handshake Example
Demonstrates two AI agents (OpenAI-based and LangGraph-based) performing
a complete handshake using the AIPM protocol.
"""
import json
from aipm import (
AIPMAgent,
AgentIdentity,
Capabilities,
TrustScore,
MessageType,
)
def print_message(label: str, message):
"""Pretty print message"""
print(f"\n{'='*60}")
print(f" {label}")
print(f"{'='*60}")
print(f"Type: {message.type.value}")
print(f"From: {message.sender.agent_id}")
print(f"To: {message.receiver.agent_id}")
print(f"Payload: {json.dumps(message.payload, indent=2)}")
def main():
"""Run basic handshake example"""
print("\n" + "="*60)
print(" AIPM BASIC HANDSHAKE EXAMPLE")
print("="*60)
# Create OpenAI-based agent
print("\n[1] Creating OpenAI-based Agent...")
openai_identity = AgentIdentity(
agent_id="agent-openai-001",
organization_id="openai",
name="OpenAI Assistant",
version="1.0.0",
capabilities=Capabilities(
skills=["text-generation", "code-review", "summarization"],
models=["gpt-4", "gpt-3.5-turbo"],
tools=["code-interpreter", "web-browser"],
max_context=128000,
memory_support=True,
languages=["en", "es", "fr", "de"],
),
trust_score=TrustScore(
reliability=0.99,
accuracy=0.95,
avg_latency_ms=250.0,
success_rate=0.98,
total_interactions=10000,
),
)
openai_agent = AIPMAgent(openai_identity)
print(f"✓ Created {openai_agent}")
# Create LangGraph-based agent
print("\n[2] Creating LangGraph-based Agent...")
langgraph_identity = AgentIdentity(
agent_id="agent-langgraph-001",
organization_id="langchain",
name="LangGraph Orchestrator",
version="1.0.0",
capabilities=Capabilities(
skills=["workflow-orchestration", "multi-agent-coordination", "data-processing"],
models=["claude-3-opus", "claude-3-sonnet"],
tools=["database", "api-caller", "document-processor"],
max_context=200000,
memory_support=True,
languages=["en", "zh", "ja"],
),
trust_score=TrustScore(
reliability=0.97,
accuracy=0.93,
avg_latency_ms=300.0,
success_rate=0.96,
total_interactions=5000,
),
)
langgraph_agent = AIPMAgent(langgraph_identity)
print(f"✓ Created {langgraph_agent}")
# Start handshake
print("\n" + "="*60)
print(" HANDSHAKE PROTOCOL")
print("="*60)
# Step 1: OpenAI agent initiates with HELLO
print("\n[Step 1] OpenAI Agent → LangGraph Agent: HELLO")
hello_msg = openai_agent.initiate_handshake(langgraph_identity.to_reference())
print_message("HELLO Message", hello_msg)
# Step 2: LangGraph agent responds with CAPABILITY_EXCHANGE
print("\n[Step 2] LangGraph Agent → OpenAI Agent: CAPABILITY_EXCHANGE")
capability_msg = langgraph_agent.process_message(hello_msg)
print_message("CAPABILITY_EXCHANGE Message", capability_msg)
# Step 3: OpenAI agent continues with AUTHENTICATION
print("\n[Step 3] OpenAI Agent → LangGraph Agent: AUTHENTICATION")
auth_msg = openai_agent.process_message(capability_msg)
print_message("AUTHENTICATION Message", auth_msg)
# Step 4: LangGraph agent exchanges PUBLIC_KEY
print("\n[Step 4] LangGraph Agent → OpenAI Agent: PUBLIC_KEY_EXCHANGE")
key_msg = langgraph_agent.process_message(auth_msg)
print_message("PUBLIC_KEY_EXCHANGE Message", key_msg)
# Step 5: OpenAI agent verifies TRUST
print("\n[Step 5] OpenAI Agent → LangGraph Agent: TRUST_VERIFICATION")
trust_msg = openai_agent.process_message(key_msg)
print_message("TRUST_VERIFICATION Message", trust_msg)
# Step 6: LangGraph agent sends READY
print("\n[Step 6] LangGraph Agent → OpenAI Agent: READY")
ready_msg = langgraph_agent.process_message(trust_msg)
print_message("READY Message", ready_msg)
# Step 7: OpenAI agent confirms READY
print("\n[Step 7] OpenAI Agent confirms READY")
openai_agent.process_message(ready_msg)
# Verify handshake completion
print("\n" + "="*60)
print(" HANDSHAKE COMPLETE")
print("="*60)
openai_ready = openai_agent.is_ready(langgraph_identity.to_reference())
langgraph_ready = langgraph_agent.is_ready(openai_identity.to_reference())
print(f"\nOpenAI Agent Ready: {openai_ready} ✓")
print(f"LangGraph Agent Ready: {langgraph_ready} ✓")
# Exchange identity information
print("\n" + "="*60)
print(" PEER IDENTITY EXCHANGE")
print("="*60)
openai_peer = openai_agent.get_peer_identity(langgraph_identity.to_reference())
langgraph_peer = langgraph_agent.get_peer_identity(openai_identity.to_reference())
print(f"\nOpenAI knows LangGraph as:")
print(f" - Name: {openai_peer.name}")
print(f" - Skills: {', '.join(openai_peer.capabilities.skills)}")
print(f" - Models: {', '.join(openai_peer.capabilities.models)}")
print(f" - Trust Score: {openai_peer.trust_score.reliability:.2f}")
print(f"\nLangGraph knows OpenAI as:")
print(f" - Name: {langgraph_peer.name}")
print(f" - Skills: {', '.join(langgraph_peer.capabilities.skills)}")
print(f" - Models: {', '.join(langgraph_peer.capabilities.models)}")
print(f" - Trust Score: {langgraph_peer.trust_score.reliability:.2f}")
# Create a task request
print("\n" + "="*60)
print(" TASK REQUEST EXAMPLE")
print("="*60)
task_msg = openai_agent.create_task_request(
langgraph_identity.to_reference(),
task_description="Process customer feedback data and generate insights",
priority="high",
dataset_size=1000,
deadline="2026-07-10T00:00:00Z",
)
print_message("Task Request from OpenAI to LangGraph", task_msg)
print("\n" + "="*60)
print(" ✓ EXAMPLE COMPLETE")
print("="*60)
print("\nTwo agents from different vendors successfully:")
print(" 1. Completed secure handshake")
print(" 2. Exchanged capabilities")
print(" 3. Verified trust")
print(" 4. Ready for task delegation")
print("\nThis is the foundation for cross-vendor agent interoperability! 🚀")
print()
if __name__ == "__main__":
main()
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