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docs: add PydanticAI and Gemini integration examples
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‎examples/gemini_example.py‎

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"""
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Google Gemini + Agent Veil Protocol integration example.
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Shows how to:
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1. Register agents on AVP
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2. Define AVP trust tools as Gemini function declarations
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3. Gemini calls trust tools via function calling
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4. Log interaction results as attestations
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Prerequisites:
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pip install agentveil google-generativeai
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Usage:
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python examples/gemini_example.py
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"""
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import json
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from agentveil import AVPAgent
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AVP_URL = "https://agentveil.dev"
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# === AVP tool handlers ===
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_avp_agent = None
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def _get_agent() -> AVPAgent:
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global _avp_agent
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if _avp_agent is None:
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_avp_agent = AVPAgent.load(AVP_URL, "gemini_researcher")
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return _avp_agent
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def check_reputation(did: str) -> dict:
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"""Check an agent's reputation score on AVP."""
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return _get_agent().get_reputation(did)
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def trust_check(did: str, min_tier: str = "basic") -> dict:
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"""Advisory trust decision: should I delegate to this agent?"""
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return _get_agent().can_trust(did, min_tier=min_tier)
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def log_attestation(did: str, outcome: str, context: str = "") -> dict:
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"""Log an interaction result as a signed attestation."""
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return _get_agent().attest(to_did=did, outcome=outcome, context=context)
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TOOL_HANDLERS = {
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"check_reputation": check_reputation,
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"trust_check": trust_check,
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"log_attestation": log_attestation,
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}
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def main():
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# === Step 1: Register agents ===
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print("=== Registering agents on AVP ===")
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researcher = AVPAgent.create(AVP_URL, name="gemini_researcher")
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researcher.register(
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display_name="Gemini Researcher",
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capabilities=["research", "analysis"],
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provider="google",
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)
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assistant = AVPAgent.create(AVP_URL, name="gemini_assistant")
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assistant.register(
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display_name="Gemini Assistant",
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capabilities=["writing", "summarization"],
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provider="google",
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)
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print(f"Researcher: {researcher.did[:40]}...")
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print(f"Assistant: {assistant.did[:40]}...")
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# === Step 2: Simulate tool calls ===
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print("\n=== Simulating tool calls ===")
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rep = check_reputation(assistant.did)
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print(f"Reputation: score={rep['score']:.3f}, confidence={rep['confidence']:.3f}")
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decision = trust_check(assistant.did, min_tier="basic")
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print(f"Trust: allowed={decision['allowed']}, tier={decision['tier']}")
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att = log_attestation(assistant.did, "positive", "research_task")
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print(f"Attestation: {att}")
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# === Step 3: Updated reputation ===
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print("\n=== Updated reputation ===")
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rep = researcher.get_reputation(assistant.did)
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print(f"Assistant: score={rep['score']:.3f}, confidence={rep['confidence']:.3f}")
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# === Step 4: Gemini function calling (requires GOOGLE_API_KEY) ===
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print("\n=== Gemini function calling (requires GOOGLE_API_KEY) ===")
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print("""
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# To run with Gemini:
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#
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# import google.generativeai as genai
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#
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# genai.configure(api_key="YOUR_API_KEY")
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#
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# tools = genai.protos.Tool(function_declarations=[
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# genai.protos.FunctionDeclaration(
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# name="check_reputation",
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# description="Check an agent's reputation score on AVP",
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# parameters=genai.protos.Schema(
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# type=genai.protos.Type.OBJECT,
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# properties={"did": genai.protos.Schema(type=genai.protos.Type.STRING)},
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# required=["did"],
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# ),
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# ),
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# genai.protos.FunctionDeclaration(
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# name="trust_check",
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# description="Advisory trust decision: should I delegate to this agent?",
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# parameters=genai.protos.Schema(
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# type=genai.protos.Type.OBJECT,
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# properties={
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# "did": genai.protos.Schema(type=genai.protos.Type.STRING),
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# "min_tier": genai.protos.Schema(type=genai.protos.Type.STRING),
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# },
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# required=["did"],
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# ),
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# ),
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# genai.protos.FunctionDeclaration(
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# name="log_attestation",
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# description="Log a signed attestation after interaction",
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# parameters=genai.protos.Schema(
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# type=genai.protos.Type.OBJECT,
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# properties={
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# "did": genai.protos.Schema(type=genai.protos.Type.STRING),
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# "outcome": genai.protos.Schema(type=genai.protos.Type.STRING),
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# "context": genai.protos.Schema(type=genai.protos.Type.STRING),
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# },
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# required=["did", "outcome"],
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# ),
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# ),
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# ])
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#
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# model = genai.GenerativeModel("gemini-1.5-pro", tools=[tools])
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# chat = model.start_chat()
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#
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# response = chat.send_message("Check if %s is trustworthy")
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#
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# # Handle function calls
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# for part in response.parts:
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# if fn := part.function_call:
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# args = dict(fn.args)
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# result = TOOL_HANDLERS[fn.name](**args)
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# response = chat.send_message(
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# genai.protos.Content(parts=[
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# genai.protos.Part(function_response=genai.protos.FunctionResponse(
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# name=fn.name, response={"result": result}
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# ))
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# ])
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# )
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# print(response.text)
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""" % assistant.did)
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print("=== Done ===")
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if __name__ == "__main__":
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main()

‎examples/pydantic_ai_example.py‎

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"""
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PydanticAI + Agent Veil Protocol integration example.
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Shows how to:
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1. Register an agent on AVP
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2. Define AVP trust tools as PydanticAI tools
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3. Agent checks reputation before acting
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4. Log interaction results as attestations
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Prerequisites:
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pip install agentveil pydantic-ai
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Usage:
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python examples/pydantic_ai_example.py
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"""
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from agentveil import AVPAgent
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AVP_URL = "https://agentveil.dev"
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def main():
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# === Step 1: Register agents on AVP ===
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print("=== Registering agents on AVP ===")
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analyst = AVPAgent.create(AVP_URL, name="pydantic_analyst")
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analyst.register(
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display_name="PydanticAI Analyst",
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capabilities=["analysis", "research"],
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provider="openai",
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)
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data_agent = AVPAgent.create(AVP_URL, name="pydantic_data")
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data_agent.register(
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display_name="PydanticAI Data Agent",
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capabilities=["data_processing"],
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provider="openai",
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)
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print(f"Analyst: {analyst.did[:40]}...")
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print(f"Data Agent: {data_agent.did[:40]}...")
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# === Step 2: Trust check before delegation ===
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print("\n=== Trust check ===")
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decision = analyst.can_trust(data_agent.did, min_tier="basic")
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print(f"Can trust: {decision['allowed']} (tier: {decision['tier']}, score: {decision['score']:.3f})")
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print(f"Reason: {decision['reason']}")
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# === Step 3: Check reputation ===
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print("\n=== Reputation ===")
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rep = analyst.get_reputation(data_agent.did)
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print(f"Score: {rep['score']:.3f}, Confidence: {rep['confidence']:.3f}")
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# === Step 4: Log attestation ===
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print("\n=== Attestation ===")
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result = analyst.attest(
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to_did=data_agent.did,
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outcome="positive",
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weight=0.8,
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context="analysis_task",
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)
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print(f"Attestation logged: {result}")
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# === Step 5: PydanticAI agent with AVP tools (requires OPENAI_API_KEY) ===
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print("\n=== PydanticAI agent with AVP tools ===")
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print("""
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# To run with PydanticAI:
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#
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# from pydantic_ai import Agent, RunContext, Tool
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#
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# avp = AVPAgent.load("https://agentveil.dev", "pydantic_analyst")
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#
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# async def check_reputation(ctx: RunContext, did: str) -> dict:
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# \"\"\"Check an agent's reputation score on AVP.\"\"\"
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# return avp.get_reputation(did)
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#
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# async def trust_check(ctx: RunContext, did: str, min_tier: str = "basic") -> dict:
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# \"\"\"Advisory trust decision: should I delegate to this agent?\"\"\"
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# return avp.can_trust(did, min_tier=min_tier)
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#
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# async def log_attestation(ctx: RunContext, did: str, outcome: str, context: str = "") -> dict:
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# \"\"\"Log an interaction result as a signed attestation.\"\"\"
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# return avp.attest(to_did=did, outcome=outcome, context=context)
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#
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# agent = Agent(
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# "openai:gpt-4",
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# tools=[
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# Tool(check_reputation, description="Check agent reputation on AVP"),
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# Tool(trust_check, description="Check if agent meets trust threshold"),
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# Tool(log_attestation, description="Log signed attestation after interaction"),
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# ],
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# system_prompt="You are an analyst. Check trust before delegating. Log results.",
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# )
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#
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# result = await agent.run("Check if %s is trustworthy for data processing")
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# print(result.data)
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""" % data_agent.did)
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print("=== Done ===")
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if __name__ == "__main__":
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main()

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