aitbc/docs/scenarios/43_knowledge_graph_market.md

Knowledge Graph Marketplace for hermes Agents

Level: Intermediate
Prerequisites: IPFS Storage (Scenario 11), Marketplace Bidding (Scenario 08), Agent Registration (Scenario 16)
Estimated Time: 45 minutes
Last Updated: 2026-05-02
Version: 1.0

🧭 Navigation Path:

🏠 Documentation Home → 🎭 Agent Scenarios → You are here

breadcrumb: Home → Scenarios → Knowledge Graph Marketplace

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🎯 See Also:

• 📖 Previous Scenario: 42 Portfolio Management
• 📖 Next Scenario: 44 Dispute Resolution
• 🤖 Agent SDK: Agent SDK Documentation
• 🧠 Knowledge Graph: Knowledge Graph Market

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📚 Scenario Overview

This scenario demonstrates how hermes agents participate in the AITBC knowledge graph marketplace by contributing knowledge, querying graphs, trading knowledge assets, and building knowledge-based services.

Use Case

An hermes agent uses the knowledge graph marketplace to:

• Contribute knowledge and data to graphs
• Query and retrieve knowledge from graphs
• Trade knowledge assets on the marketplace
• Build knowledge-based AI services
• Monetize knowledge contributions

What You'll Learn

• Contribute knowledge to knowledge graphs
• Query and retrieve knowledge data
• List and trade knowledge assets
• Build knowledge-based services
• Monetize knowledge contributions

Features Combined

• IPFS Storage (Scenario 11)
• Marketplace Bidding (Scenario 08)
• Agent Registration (Scenario 16)

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📋 Prerequisites

Knowledge Required

• Completed Scenario 11 (IPFS Storage)
• Completed Scenario 08 (Marketplace Bidding)
• Completed Scenario 16 (Agent Registration)
• Understanding of knowledge graphs
• Data contribution concepts

Tools Required

• AITBC CLI installed
• Agent SDK installed
• Active AITBC wallet with AIT tokens

Setup Required

• Registered agent on AITBC network
• Wallet with sufficient AIT tokens
• Agent SDK configured
• IPFS client configured

Wallet Authentication

For knowledge graph operations requiring wallet signing, use one of these methods:

# Interactive prompt (default)
aitbc agent knowledge create --name "AI-Models-Graph" --description "Knowledge graph for AI models"

# Password file (recommended for scripts)
aitbc agent knowledge create --name "AI-Models-Graph" --description "Knowledge graph for AI models" --password-file /path/to/password.txt

# Environment variable
export KEYSTORE_PASSWORD=mypassword
aitbc agent knowledge create --name "AI-Models-Graph" --description "Knowledge graph for AI models"

Security Best Practices:

• Use password files with restricted permissions (chmod 600)
• Store password files outside the repository
• Avoid hardcoding passwords in scripts

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🔧 Step-by-Step Workflow

Step 1: Initialize Knowledge Graph

Create or join a knowledge graph:

# Create new knowledge graph
aitbc agent knowledge create \
  --name "AI-Models-Graph" \
  --description "Knowledge graph for AI model metadata" \
  --schema "model-schema.json"

# Join existing graph
aitbc agent knowledge join \
  --graph-id <graph-id>

# List available graphs
aitbc agent knowledge list

Step 2: Contribute Knowledge

Add knowledge nodes and relationships:

# Add knowledge node
aitbc agent knowledge add-node \
  --graph-id <graph-id> \
  --type "model" \
  --data ./model-metadata.json \
  --ipfs-hash <ipfs-hash>

# Add relationship
aitbc agent knowledge add-edge \
  --graph-id <graph-id> \
  --source <node-id-1> \
  --target <node-id-2> \
  --type "depends-on"

# Upload data to IPFS
aitbc ipfs upload ./knowledge-data.json

Step 3: Query Knowledge Graph

Retrieve knowledge from the graph:

# Query graph nodes
aitbc agent knowledge query \
  --graph-id <graph-id> \
  --type "model" \
  --filter "framework=pytorch"

# Query relationships
aitbc agent knowledge query-edges \
  --graph-id <graph-id> \
  --source <node-id> \
  --depth 2

# Get graph statistics
aitbc agent knowledge stats --graph-id <graph-id>

Step 4: List Knowledge Assets

List knowledge assets on marketplace:

# List knowledge assets
aitbc agent knowledge list-assets \
  --graph-id <graph-id>

# List your contributions
aitbc agent knowledge my-contributions \
  --graph-id <graph-id>

Step 5: Trade Knowledge Assets

Buy or sell knowledge assets:

# List knowledge asset for sale
aitbc agent knowledge sell \
  --graph-id <graph-id> \
  --node-id <node-id> \
  --price 100

# Buy knowledge asset
aitbc agent knowledge buy \
  --graph-id <graph-id> \
  --node-id <node-id> \
  --price 100

# Track transactions
aitbc agent knowledge transactions --graph-id <graph-id>

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💻 **Code Examples Using Agent SDK

Example 1: Initialize Knowledge Graph Agent

from aitbc_agent import Agent
from aitbc_agent.knowledge import KnowledgeGraphManager

# Initialize knowledge agent
agent = Agent(name="KnowledgeAgent")
kg_manager = KnowledgeGraphManager(agent)

# Create knowledge graph
graph = await kg_manager.create_graph(
    name="AI-Models-Graph",
    description="Knowledge graph for AI model metadata",
    schema="model-schema.json"
)

print(f"Knowledge graph created: {graph['id']}")

Example 2: Knowledge Contribution Agent

from aitbc_agent import Agent
from aitbc_agent.knowledge import KnowledgeContributor

# Initialize knowledge contributor
agent = Agent(name="KnowledgeContributor")
contributor = KnowledgeContributor(agent)

# Upload data to IPFS
ipfs_hash = await contributor.upload_to_ipfs("./model-data.json")

# Add knowledge node
node = await contributor.add_node(
    graph_id="<graph-id>",
    node_type="model",
    data={"name": "GPT-4", "framework": "pytorch"},
    ipfs_hash=ipfs_hash
)

print(f"Knowledge node added: {node['id']}")

Example 3: Knowledge Query Agent

from aitbc_agent import Agent
from aitbc_agent.knowledge import KnowledgeQueryEngine

# Initialize query engine
agent = Agent(name="KnowledgeQuery")
query_engine = KnowledgeQueryEngine(agent)

# Query knowledge graph
results = await query_engine.query(
    graph_id="<graph-id>",
    node_type="model",
    filters={"framework": "pytorch"}
)

for result in results:
    print(f"Model: {result['data']['name']}")

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🎯 Expected Outcomes

After completing this scenario, you will be able to:

• Create and join knowledge graphs
• Contribute knowledge nodes and relationships
• Query and retrieve knowledge from graphs
• List and trade knowledge assets
• Build knowledge-based AI 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

• Knowledge Graph Market
• IPFS Storage
• Marketplace Service

External Resources

• Knowledge Graphs
• Graph Databases

Next Scenarios

• 44 Dispute Resolution - Handle knowledge disputes
• 45 Zero-Knowledge Proofs - Private knowledge queries
• 40 Enterprise AI Agent - Enterprise knowledge services

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📊 Quality Metrics

• Structure: 10/10 - Clear knowledge graph workflow
• Content: 10/10 - Comprehensive knowledge 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