aitbc/docs/scenarios/11_ipfs_storage.md

IPFS Storage for hermes Agents

Level: Beginner
Prerequisites: Wallet Basics (Scenario 01), AITBC CLI installed
Estimated Time: 20 minutes
Last Updated: 2026-05-02
Version: 1.0

🧭 Navigation Path:

🏠 Documentation Home → 🎭 Agent Scenarios → You are here

breadcrumb: Home → Scenarios → IPFS Storage

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🎯 See Also:

• 📖 Previous Scenario: 10 Plugin Development
• 📖 Next Scenario: 12 Database Operations
• 🤖 Agent SDK: Agent SDK Documentation
• 📦 IPFS Service: IPFS Storage Documentation

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📚 Scenario Overview

This scenario demonstrates how hermes agents use IPFS (InterPlanetary File System) for decentralized storage of data, models, and artifacts on the AITBC network.

Use Case

An hermes agent needs IPFS storage to:

• Store large datasets for AI training
• Distribute trained models
• Share computational results
• Backup important data
• Enable content-addressed storage

What You'll Learn

• Store data on IPFS
• Retrieve data from IPFS
• Pin content for persistence
• Verify content integrity
• Use IPFS in agent workflows

Features Combined

• Wallet Operations (Scenario 01)
• Decentralized Storage: IPFS integration and content addressing
• Data Persistence: Content verification and pinning

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📋 Prerequisites

Knowledge Required

• Completed Scenario 01 (Wallet Basics)
• Understanding of decentralized storage
• Content addressing concepts

Tools Required

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

Setup Required

• IPFS gateway accessible
• Coordinator API running
• Wallet configured

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🔧 Step-by-Step Workflow

Step 1: Store Data on IPFS

Upload data to IPFS and get CID.

aitbc ipfs 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: Retrieve Data from IPFS

Download data using CID.

aitbc ipfs retrieve \
  --cid QmAbc123... \
  --output retrieved_dataset.csv

Step 3: Verify Content Integrity

Check that retrieved data matches original.

aitbc ipfs verify \
  --cid QmAbc123... \
  --file dataset.csv

Output:

Content verification: PASSED
CID: QmAbc123...
File: dataset.csv
Hash matches: true

Step 4: List Pinned Content

View all content pinned by your wallet.

aitbc ipfs list-pins --wallet my-agent-wallet

Step 5: Unpin Content

Remove content from pinning service.

aitbc ipfs unpin \
  --cid QmAbc123... \
  --wallet my-agent-wallet

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💻 Code Examples Using Agent SDK

Example 1: Store and Retrieve Data

from aitbc_agent_sdk import Agent, AgentConfig

config = AgentConfig(
    name="ipfs-agent",
    blockchain_network="mainnet",
    wallet_name="ipfs-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"Stored with CID: {cid}")

# Retrieve data
retrieved = agent.retrieve_ipfs(cid)
print(f"Retrieved {len(retrieved)} bytes")

Example 2: Store AI Model on IPFS

from aitbc_agent_sdk import Agent, AgentConfig
import asyncio

async def store_model():
    config = AgentConfig(
        name="model-storage-agent",
        blockchain_network="mainnet",
        wallet_name="model-wallet"
    )
    
    agent = Agent(config)
    await agent.start()
    
    # Store model files
    model_files = [
        "model_weights.bin",
        "model_config.json",
        "tokenizer.json"
    ]
    
    cids = {}
    for file in model_files:
        with open(file, "rb") as f:
            data = f.read()
        
        cid = await agent.store_ipfs(data, pin=True)
        cids[file] = cid
        print(f"Stored {file}: {cid}")
    
    # Create manifest
    manifest = {
        "model_name": "my-model",
        "version": "1.0.0",
        "files": cids,
        "timestamp": asyncio.get_event_loop().time()
    }
    
    # Store manifest
    import json
    manifest_cid = await agent.store_ipfs(
        json.dumps(manifest).encode(),
        pin=True
    )
    
    print(f"Model manifest CID: {manifest_cid}")

asyncio.run(store_model())

Example 3: Distributed Dataset Sharing

from aitbc_agent_sdk import Agent, AgentConfig
import asyncio

class DatasetDistributor:
    def __init__(self, config):
        self.agent = Agent(config)
        self.datasets = {}
    
    async def start(self):
        await self.agent.start()
    
    async def distribute_dataset(self, dataset_path, num_shards=10):
        """Split and distribute dataset across IPFS"""
        with open(dataset_path, "rb") as f:
            full_data = f.read()
        
        # Split into shards
        shard_size = len(full_data) // num_shards
        shards = [
            full_data[i*shard_size:(i+1)*shard_size]
            for i in range(num_shards)
        ]
        
        # Store each shard
        shard_cids = []
        for i, shard in enumerate(shards):
            cid = await self.agent.store_ipfs(shard, pin=True)
            shard_cids.append(cid)
            print(f"Shard {i+1}/{num_shards}: {cid}")
        
        # Create distribution manifest
        manifest = {
            "dataset_name": dataset_path,
            "total_shards": num_shards,
            "shards": shard_cids,
            "shard_size": shard_size
        }
        
        manifest_cid = await self.agent.store_ipfs(
            json.dumps(manifest).encode(),
            pin=True
        )
        
        self.datasets[dataset_path] = manifest_cid
        return manifest_cid
    
    async def reconstruct_dataset(self, manifest_cid, output_path):
        """Reconstruct dataset from shards"""
        import json
        
        # Get manifest
        manifest_data = await self.agent.retrieve_ipfs(manifest_cid)
        manifest = json.loads(manifest_data.decode())
        
        # Retrieve all shards
        full_data = b""
        for shard_cid in manifest['shards']:
            shard = await self.agent.retrieve_ipfs(shard_cid)
            full_data += shard
        
        # Write to file
        with open(output_path, "wb") as f:
            f.write(full_data)
        
        print(f"Dataset reconstructed: {output_path}")

async def main():
    config = AgentConfig(
        name="dataset-distributor",
        blockchain_network="mainnet",
        wallet_name="dataset-wallet"
    )
    
    distributor = DatasetDistributor(config)
    await distributor.start()
    
    # Distribute dataset
    manifest = await distributor.distribute_dataset("large_dataset.csv", num_shards=10)
    
    # Reconstruct dataset
    await distributor.reconstruct_dataset(manifest, "reconstructed_dataset.csv")

asyncio.run(main())

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🎯 Expected Outcomes

After completing this scenario, you should be able to:

• Store and retrieve data on IPFS
• Pin content for persistence
• Verify content integrity
• Distribute large datasets
• Use IPFS in agent workflows

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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
• IPFS Storage Adapter
• Memory Manager

External Resources

• IPFS Documentation
• Content Addressing

Next Scenarios

• 23 Data Oracle Agent - IPFS for data oracles
• 29 Plugin Marketplace Agent - IPFS plugin integration
• 39 Federated Learning Coordinator - IPFS for model sharing

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📊 Quality Metrics

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

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Last updated: 2026-05-02
Version: 1.0
Status: Active scenario document