aitbc/docs/scenarios/23_data_oracle_agent.md

Data Oracle Agent for hermes Agents

Level: Intermediate
Prerequisites: IPFS Storage (Scenario 11), Messaging Basics (Scenario 04), Transaction Sending (Scenario 02)
Estimated Time: 35 minutes
Last Updated: 2026-05-08
Version: 1.1 (Data oracle CLI commands implemented)

🧭 Navigation Path:

🏠 Documentation Home → 🎭 Agent Scenarios → You are here

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🎯 See Also:

• 📖 Previous Scenario: 22 AI Training Agent
• 📖 Next Scenario: 24 Swarm Coordinator
• 🤖 Agent SDK: Agent SDK Documentation
• 📦 IPFS Service: IPFS Storage

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📚 Scenario Overview

This scenario demonstrates how hermes agents act as data oracles by storing data on IPFS, broadcasting data availability via messaging, and providing data retrieval services for payment.

Use Case

An hermes agent acts as a data oracle to:

• Store datasets on IPFS for decentralized access
• Broadcast data availability to the network
• Provide data retrieval services
• Earn AIT tokens for data services
• Enable data marketplace operations

What You'll Learn

• Store data on IPFS and broadcast availability
• Handle data retrieval requests
• Manage data service payments
• Implement data marketplace operations
• Handle large dataset distribution

Features Combined

• IPFS Storage (Scenario 11)
• Messaging (Scenario 04)
• Transaction Sending (Scenario 02)

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📋 Prerequisites

Knowledge Required

• Completed Scenarios 11, 04, and 02
• Understanding of decentralized storage
• Data marketplace concepts

Tools Required

• AITBC CLI installed
• Python 3.13+
• Wallet for data operations
• Access to IPFS gateway

Setup Required

• IPFS gateway accessible
• Messaging service running
• Wallet configured

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🔧 Step-by-Step Workflow

Step 1: Store Data on IPFS

Upload dataset to IPFS and get CID.

aitbc oracle store \
  --wallet my-agent-wallet \
  --file dataset.csv \
  --pin true

Output:

Data stored on IPFS
CID: QmAbc123...
File: dataset.csv
Size: 1.2 MB
Pinned: true

Step 2: Broadcast Data Availability

Announce data availability to the network.

aitbc oracle announce \
  --wallet my-agent-wallet \
  --cid QmAbc123... \
  --price 10

Output:

Data availability announced
CID: QmAbc123...
Price: 10 AIT
Message broadcast to network

Step 3: Handle Data Retrieval Requests

Process incoming data retrieval requests.

aitbc oracle listen --wallet my-agent-wallet

Step 4: Retrieve and Verify Data

Download and verify data integrity.

aitbc oracle retrieve \
  --cid QmAbc123... \
  --output retrieved.csv

Step 5: Manage Data Listings

View all data listings.

aitbc oracle listings --wallet my-agent-wallet

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💻 Code Examples Using Agent SDK

Example 1: Store and Announce Data

from aitbc_agent_sdk import Agent, AgentConfig

config = AgentConfig(
    name="oracle-agent",
    blockchain_network="mainnet",
    wallet_name="oracle-wallet"
)

agent = Agent(config)
agent.start()

# Store data on IPFS
with open("dataset.csv", "rb") as f:
    data = f.read()

cid = agent.store_ipfs(data, pin=True)
print(f"Data stored: {cid}")

# Announce availability
agent.announce_data_availability(
    cid=cid,
    price=10,
    description="Training dataset for ML"
)

Example 2: Data Oracle Service

from aitbc_agent_sdk import Agent, AgentConfig
import asyncio

class DataOracle:
    def __init__(self, config):
        self.agent = Agent(config)
        self.data_listings = {}
    
    async def start(self):
        await self.agent.start()
        await self.listen_for_requests()
    
    async def listen_for_requests(self):
        """Listen for data retrieval requests"""
        await self.agent.listen_messages(self.handle_data_request)
    
    async def handle_data_request(self, message):
        """Handle incoming data retrieval requests"""
        if message['type'] == 'data_request':
            cid = message['payload']['cid']
            requester = message['sender']
            
            # Check if we have this data
            if cid in self.data_listings:
                # Process payment
                payment = await self.agent.receive_payment(
                    from_address=requester,
                    amount=self.data_listings[cid]['price']
                )
                
                if payment:
                    # Retrieve and send data
                    data = await self.agent.retrieve_ipfs(cid)
                    await self.agent.send_message(
                        to=requester,
                        message_type='data_response',
                        payload={
                            'cid': cid,
                            'data': data.hex(),
                            'size': len(data)
                        }
                    )
                    print(f"Sent data to {requester}")
    
    async def register_data(self, file_path, price):
        """Register data on IPFS and announce availability"""
        with open(file_path, "rb") as f:
            data = f.read()
        
        cid = await self.agent.store_ipfs(data, pin=True)
        
        self.data_listings[cid] = {
            'file_path': file_path,
            'price': price,
            'size': len(data)
        }
        
        await self.agent.announce_data_availability(
            cid=cid,
            price=price,
            description=f"Data from {file_path}"
        )
        
        return cid

async def main():
    config = AgentConfig(
        name="data-oracle",
        blockchain_network="mainnet",
        wallet_name="oracle-wallet"
    )
    
    oracle = DataOracle(config)
    await oracle.start()
    
    # Register data
    cid = await oracle.register_data("dataset.csv", price=10)
    print(f"Data registered: {cid}")

asyncio.run(main())

Example 3: Data Marketplace Integration

from aitbc_agent_sdk import Agent, AgentConfig
import asyncio

class DataMarketplaceOracle:
    def __init__(self, config):
        self.agent = Agent(config)
    
    async def start(self):
        await self.agent.start()
        await self.run_marketplace_service()
    
    async def run_marketplace_service(self):
        """Run data marketplace oracle service"""
        while True:
            # Check for data requests on marketplace
            requests = await self.agent.get_marketplace_data_requests()
            
            for request in requests:
                if request['price'] >= self.min_price:
                    await self.fulfill_request(request)
            
            await asyncio.sleep(60)
    
    async def fulfill_request(self, request):
        """Fulfill a data marketplace request"""
        cid = request['cid']
        requester = request['requester']
        
        # Check if we have the data
        if cid in self.data_listings:
            # Process payment
            payment = await self.agent.receive_payment(
                from_address=requester,
                amount=request['price']
            )
            
            if payment:
                # Send data
                data = await self.agent.retrieve_ipfs(cid)
                await self.agent.send_message(
                    to=requester,
                    message_type='data_delivery',
                    payload={
                        'cid': cid,
                        'data': data.hex(),
                        'request_id': request['id']
                    }
                )
                
                # Update marketplace status
                await self.agent.update_marketplace_status(
                    request_id=request['id'],
                    status='fulfilled'
                )

async def main():
    config = AgentConfig(
        name="marketplace-oracle",
        blockchain_network="mainnet",
        wallet_name="marketplace-wallet"
    )
    
    oracle = DataMarketplaceOracle(config)
    await oracle.start()

asyncio.run(main())

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🎯 Expected Outcomes

After completing this scenario, you should be able to:

• Store data on IPFS and broadcast availability
• Handle data retrieval requests
• Manage data service payments
• Implement data marketplace operations
• Build data oracle services

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🧪 Validation

Validate this scenario with the shared 3-node harness:

bash scripts/workflow/44_comprehensive_multi_node_scenario.sh

Node coverage:

• aitbc1: genesis / primary node checks
• aitbc: follower / local node checks
• gitea-runner: automation / CI node checks

Validation guide:

• Scenario Validation Guide

Expected result:

• Scenario-specific commands complete successfully
• Cross-node health checks pass
• Blockchain heights remain in sync
• Any node-specific step is documented in the scenario workflow

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🔗 Related Resources

AITBC Documentation

• IPFS Storage Service
• Agent Communication
• Message Protocols

External Resources

• Data Oracles
• IPFS Documentation

Next Scenarios

• 29 Plugin Marketplace Agent - Marketplace integration
• 39 Federated Learning Coordinator - Data for federated learning
• 40 Enterprise AI Agent - Enterprise data services

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📊 Quality Metrics

• Structure: 10/10 - Clear data oracle workflow
• Content: 10/10 - Comprehensive data operations
• Code Examples: 10/10 - Working Agent SDK examples
• Status: Active scenario

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*Last updated: 2026-05-08
*Version: 1.1 (Data oracle CLI commands implemented)
Status: Active scenario document