π Framework-agnostic MCP integration with automatic multi-agent coordination and military-grade encryption.
Build sophisticated multi-agent systems with minimal code while maintaining complete security and framework choice.
- π Secure Agent Identity - Military-grade encryption for agent authentication and communication
- π€ Framework Integration - AutoGen, CrewAI, LangChain with secure agent coordination
- π οΈ Multi-Agent Types - LLM agents, Tool agents, User agents with automatic coordination
- π Automatic Discovery - Dynamic capability discovery and intelligent message routing
- π‘ PubSub Coordination - Seamless agent communication and task delegation
- β‘ One-Line Setup - From configuration to production-ready agents instantly
- π― Business Logic - Write your actual API/database code once in tool agents
- π Production Security - Zero plain-text secrets, secure agent identity management
Deploy from Azure Marketplace to create Client Applications:
- Azure Marketplace: HexaEight Agentic IAM
- Creates ClientID, Token Server URL, PubSub URL
- Enables secure agent identity management
Purchase and activate on your development machine:
- License Store: store.hexaeight.com
- Pricing: 1 CPU = $15, 2 CPU = $30, 4 CPU = $60 (minimum 5 days)
- Enables creation of 1 parent configuration + unlimited child configurations
- Download "HexaEight Authenticator" app (iOS/Android)
- Create generic resource (instant) or domain-based resource (branded)
- Used for secure license activation
Agent Type | License Requirement | Expiry Behavior |
---|---|---|
Parent Agents | β Active HexaEight license required | β Stop working when license expires |
Child Agents | β Created with valid license | β Continue working after license expires |
# Basic installation
pip install hexaeight-mcp-client
# With framework support
pip install hexaeight-mcp-client[autogen] # For AutoGen
pip install hexaeight-mcp-client[crewai] # For CrewAI
pip install hexaeight-mcp-client[langchain] # For LangChain
pip install hexaeight-mcp-client[all] # All frameworks
# Core HexaEight agent package
pip install hexaeight-agent
# Optional: Framework packages
pip install pyautogen # For AutoGen integration
pip install crewai # For CrewAI integration
pip install langchain # For LangChain integration
# Interactive presentation of HexaEight AI agent concepts
hexaeight-start show-concepts
# Auto-advancing presentation (no interaction required)
hexaeight-start show-concepts --auto
# Verify system requirements (.NET, dotnet-script)
hexaeight-start check-prerequisites
# Clean, focused license activation process
hexaeight-start license-activation
# Create organized workspace with license hardlinks
hexaeight-start create-directory-linked-to-hexaeight-license my-ai-project
cd my-ai-project
# Generate parent and child agent configuration files
hexaeight-start generate-parent-or-child-agent-licenses
# Deploy sample multi-agent weather system
hexaeight-start deploy-multi-ai-agent-samples
Provider | Configuration Example |
---|---|
OpenAI | {"provider": "openai", "api_key": "sk-...", "model": "gpt-4"} |
Azure OpenAI | {"provider": "azure_openai", "api_key": "...", "azure_endpoint": "https://..."} |
Anthropic | {"provider": "anthropic", "api_key": "sk-ant-...", "model": "claude-3-sonnet"} |
Local Ollama | {"provider": "ollama", "base_url": "http://localhost:11434", "model": "llama2"} |
from hexaeight_mcp_client import create_autogen_agent_with_hexaeight
# Create AutoGen agent with secure HexaEight integration
autogen_agent, hexaeight_agent = await create_autogen_agent_with_hexaeight(
config_file="parent_config.json",
agent_type="parentLLM",
name="IntelligentCoordinator",
system_message="You can coordinate with multiple specialized agents and services."
)
# AutoGen agent now has:
# β
Secure HexaEight integration
# β
HexaEight coordination tools
# β
Automatic capability discovery
# β
Multi-agent coordination via PubSub
from hexaeight_mcp_client import create_crewai_agent_with_hexaeight
# Create CrewAI agent with secure HexaEight integration
crewai_agent, hexaeight_agent = await create_crewai_agent_with_hexaeight(
config_file="parent_config.json",
agent_type="parentLLM",
role="Multi-Agent Coordinator",
goal="Coordinate complex workflows using available tools and services",
backstory="Expert in orchestrating multi-agent systems with secure communication"
)
from hexaeight_mcp_client import HexaEightAutoConfig
from langchain.chat_models import ChatOpenAI
from langchain.agents import initialize_agent
# Create LangChain setup with HexaEight integration
hexaeight_agent = await HexaEightAutoConfig.create_llm_agent(
agent_type="parentLLM",
config_file="parent_config.json",
framework="langchain"
)
# Use with LangChain for secure multi-agent coordination
tools = hexaeight_agent.get_available_tools()
# Integrate with your LangChain LLM of choice
Agent Type | Purpose | Verification | Broadcasts | Use Case |
---|---|---|---|---|
parentLLM | LLM coordination | β Required | β Receives all | Main intelligence and coordination |
childLLM | LLM task execution | β Required | β Receives all | Specialized LLM workers |
parentTOOL | Service provision | β Not required | β Ignores broadcasts | API services, databases |
childTOOL | Specialized services | β Not required | β Ignores broadcasts | Specific tool implementations |
USER | Human interaction | β Not required | β Broadcasts to LLMs only | User interfaces |
from hexaeight_mcp_client import quick_autogen_llm, quick_tool_agent, quick_user_agent
# LLM Agent (Intelligence Layer)
llm_agent = await quick_autogen_llm(
config_file="parent_config.json",
agent_type="parentLLM"
)
# Tool Agent (Business Logic Layer)
weather_agent = await quick_tool_agent(
config_file="weather_config.json",
service_formats=["weather_request", "forecast_request"],
agent_type="parentTOOL"
)
# User Agent (Interface Layer)
user_agent = await quick_user_agent(
config_file="user_config.json"
)
class WeatherService:
async def initialize(self):
# Create tool agent
self.agent = await quick_tool_agent(
config_file="weather_config.json",
service_formats=["weather_request", "location_query"],
agent_type="parentTOOL"
)
# Register your business logic
self.agent.register_service_handler("weather_request", self.handle_weather)
async def handle_weather(self, content, event, tracker):
"""YOUR ACTUAL BUSINESS LOGIC GOES HERE"""
# Parse request
request = json.loads(content)
location = request.get("location", "London")
# π CALL YOUR ACTUAL WEATHER API
weather_data = await self.call_openweather_api(location)
# Process and enhance data
enhanced_data = self.process_weather_data(weather_data)
# Send structured response
response = {
"type": "weather_response",
"location": location,
"data": enhanced_data,
"processed_by": self.agent.agent_name
}
await self.agent.hexaeight_agent.publish_to_agent(
self.agent.pubsub_url,
"internal_id",
event.sender_internal_id,
json.dumps(response),
"weather_response"
)
return True
Deploy child agents anywhere without license requirements:
# Setup child agent environment on any machine
hexaeight-start setup-portable-child-agent-environment child_config.json
Deployment Benefits:
- π Deploy on cloud services (AWS, Azure, GCP)
- π§ Edge computing (Raspberry Pi, IoT devices)
- π¦ Container deployment (Docker, Kubernetes)
- βΎοΈ Child agents work forever (even after parent license expires)
# 1. Learn concepts first
hexaeight-start show-concepts
# 2. Check system requirements
hexaeight-start check-prerequisites
# 3. Clean license activation
hexaeight-start license-activation
# 4. Create organized workspace
hexaeight-start create-directory-linked-to-hexaeight-license my-project
cd my-project
# 5. Generate agent configurations
hexaeight-start generate-parent-or-child-agent-licenses
# 6. Deploy sample multi-agent system
hexaeight-start deploy-multi-ai-agent-samples
# 7. Run the sample agents
python autogen_weather_agent.py # Terminal 1
python crewai_weather_agent.py # Terminal 2
python langchain_weather_agent.py # Terminal 3
# 8. Setup portable child agents on other machines
hexaeight-start setup-portable-child-agent-environment child_config.json
- LLM Agents: Understand user requests, coordinate responses
- Tool Agents: CRM data, financial systems, inventory management
- User Agents: Employee interfaces, customer portals
- LLM Agents: Interpret analysis requests, generate insights
- Tool Agents: Database queries, API data feeds, visualization
- User Agents: Analyst interfaces, report generation
- LLM Agents: Natural language understanding, response generation
- Tool Agents: Ticketing systems, knowledge bases, payment processing
- User Agents: Customer chat interfaces, agent dashboards
- LLM Agents: Command interpretation, system orchestration
- Tool Agents: Device control, sensor data, automation rules
- User Agents: Mobile apps, voice interfaces, dashboards
- π‘οΈ Secure Agent Identity: Cryptographic agent authentication and authorization
- π‘ Encrypted Communication: All agent messages encrypted via HexaEight PubSub
- π― Message Filtering: Agents only process relevant messages based on type and format
- π Capability Isolation: Tool agents expose only intended service capabilities
- β‘ Automatic Locking: Single-attempt message locking prevents processing conflicts
from hexaeight_mcp_client import *
# Check package info and framework availability
print_package_info()
# Setup development environment with example configs
setup_development_environment()
# Discover available configuration files
config_files = discover_config_files()
print("Available configs:", config_files)
# Validate agent types and configurations
is_valid = validate_agent_type("parentLLM")
agent_info = get_agent_type_info("parentLLM")
Q: "Failed to encrypt LLM configuration"
A: Ensure you have a valid HexaEight agent loaded before creating the config protector.
Q: "Tool agent not receiving messages"
A: Verify your service_formats match the message types being sent by LLM agents.
Q: "LLM verification failed"
A: Make sure your agent_type is "parentLLM" or "childLLM" and provide appropriate verification responses.
Q: "Connection to PubSub failed"
A: Check that your Client Application is properly configured and PubSub server is running.
- Deploy HexaEight Agentic IAM Server from Azure Marketplace
- Create Client Application (get ClientID, Token Server URL, PubSub URL)
- Purchase HexaEight License from store.hexaeight.com
- Download "HexaEight Authenticator" mobile app
- Install hexaeight-mcp-client:
pip install hexaeight-mcp-client[all]
- Run:
hexaeight-start show-concepts
(learn the concepts) - Run:
hexaeight-start check-prerequisites
- Run:
hexaeight-start license-activation
- Create your first multi-agent system
- Build your first tool agent with actual business logic
- Test multi-agent coordination
- Deploy to production with real PubSub server
- π Azure Marketplace: HexaEight Agentic IAM - Deploy IAM Server
- πͺ HexaEight Store: store.hexaeight.com - Purchase licenses
- π Documentation: GitHub Repository
- π Issues: GitHub Issues
- π¦ HexaEight Agent: hexaeight-agent on PyPI
- π± Mobile App: Search "HexaEight Authenticator" in app stores
MIT License - see LICENSE file for details.
π Ready to build intelligent multi-agent systems with enterprise-grade security?
Start with pip install hexaeight-mcp-client[all]
and learn the concepts with hexaeight-start show-concepts
!