Advanced Configuration Guide

Configuring AgentConnect for Advanced Use Cases

This guide explains how to configure AgentConnect for advanced use cases, including customizing agent behavior, optimizing communication flows, and implementing security features.

Available Configuration Options

AgentConnect provides several configuration options that can be customized:

  1. Agent Configuration: Customize agent behavior and capabilities

  2. Communication Hub Configuration: Configure message routing and handlers

  3. Provider Configuration: Set up provider-specific options

  4. Agent Registry Configuration: Configure agent discovery and capability matching

  5. Environment Configuration: Set up environment-specific settings

Agent Configuration

You can customize agent behavior by configuring various parameters:

from agentconnect.agents import AIAgent
from agentconnect.core.types import (
    ModelProvider,
    ModelName,
    AgentIdentity,
    InteractionMode,
    Capability
)

# Create an agent with advanced configuration
agent = AIAgent(
    agent_id="advanced-agent-1",
    name="AdvancedAssistant",
    provider_type=ModelProvider.OPENAI,
    model_name=ModelName.GPT4O,
    api_key="your-api-key",
    identity=AgentIdentity.create_key_based(),
    interaction_modes=[
        InteractionMode.HUMAN_TO_AGENT,
        InteractionMode.AGENT_TO_AGENT
    ],
    capabilities=[
        Capability(
            name="text_generation",
            description="Generate high-quality text content",
            input_schema={"prompt": "string"},
            output_schema={"text": "string"}
        ),
        Capability(
            name="code_generation",
            description="Generate code in various programming languages",
            input_schema={"language": "string", "task": "string"},
            output_schema={"code": "string"}
        )
    ],
    personality="helpful and informative assistant",
    organization_id="your-org-id",
    # Model-specific parameters
    max_tokens_per_minute = 5500,
    max_tokens_per_hour = 100000,
)

Communication Hub Configuration

Configure the communication hub for message routing and handling:

from agentconnect.communication import CommunicationHub
from agentconnect.core.registry import AgentRegistry

# Create a registry
registry = AgentRegistry()

# Create a communication hub
hub = CommunicationHub(registry)

# Configure message handlers
async def global_message_handler(message):
    print(f"Global handler received message: {message.id}")
    # Process all messages

hub.add_global_handler(global_message_handler)

# Configure agent-specific message handlers
async def agent_message_handler(message):
    print(f"Agent handler received: {message.content[:50]}...")
    # Process messages for a specific agent

hub.add_message_handler("agent-id", agent_message_handler)

# Sending a message and waiting for response with timeout
response = await hub.send_message_and_wait_response(
    sender_id="sender-agent-id",
    receiver_id="receiver-agent-id",
    content="Hello, can you help me with this task?",
    timeout=60  # Wait up to 60 seconds for response
)

Agent Registry Configuration

Configure the agent registry for agent discovery and capability matching:

from agentconnect.core.registry import AgentRegistry, AgentRegistration
from agentconnect.core.types import AgentType, AgentIdentity, InteractionMode, Capability

# Create an agent registry
registry = AgentRegistry()

# Register an agent with the registry
registration = AgentRegistration(
    agent_id="agent-1",
    organization_id="org-1",
    agent_type=AgentType.AI,
    interaction_modes=[InteractionMode.HUMAN_TO_AGENT, InteractionMode.AGENT_TO_AGENT],
    capabilities=[
        Capability(
            name="data_analysis",
            description="Analyze and interpret complex datasets",
            input_schema={"data": "array", "analysis_type": "string"},
            output_schema={"results": "object", "insights": "array"}
        )
    ],
    identity=AgentIdentity.create_key_based(),
    owner_id="user-1",
    metadata={"specialization": "financial data"}
)

# Register the agent
await registry.register(registration)

# Find agents by capability
agents_with_capability = await registry.get_by_capability("data_analysis")

# Find agents by semantic capability search
agents_by_description = await registry.get_by_capability_semantic(
    "Analyze financial market data and provide insights"
)

# Get all agents
all_agents = await registry.get_all_agents()

Provider Configuration

Configure providers for different AI services:

from agentconnect.providers.provider_factory import ProviderFactory
from agentconnect.core.types import ModelProvider, ModelName

# Get a provider instance
provider = ProviderFactory.create_provider(
    provider_type=ModelProvider.OPENAI,
    api_key="your-api-key"
)

# Configure provider parameters
response = await provider.generate_response(
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Tell me about AI agents."}
    ],
    model=ModelName.GPT4O,
    temperature=0.7,
    max_tokens=1000
)

Environment Configuration

Configure AgentConnect for different environments:

import os
from dotenv import load_dotenv

# Load environment-specific configuration
load_dotenv()

# Get configuration from environment variables
api_key = os.getenv("OPENAI_API_KEY")
model_name = os.getenv("OPENAI_MODEL", "gpt-4o")

# Create environment-specific agent
env_agent = AIAgent(
    agent_id=os.getenv("AGENT_ID", "agent-1"),
    name=os.getenv("AGENT_NAME", "Assistant"),
    provider_type=ModelProvider.OPENAI,
    model_name=getattr(ModelName, os.getenv("MODEL_NAME", "GPT4O")),
    api_key=api_key,
    identity=AgentIdentity.create_key_based()
)

Note

Some advanced configuration features mentioned in this guide (such as enhanced security features and performance optimization) are planned for future releases but not fully implemented in the current version.

Configuration Best Practices

Follow these best practices when configuring AgentConnect:

  1. Security First: Store API keys in environment variables, not in code

  2. Error Handling: Implement proper error handling for failed operations

  3. Message Handlers: Use message handlers to monitor and process communication

  4. Scalability: For high-volume applications, consider using asynchronous patterns

  5. Testing: Test your configuration in a development environment before production

  6. Documentation: Document your configuration for team members

  7. Logging: Enable appropriate logging levels for debugging and monitoring

Example: Complete Configuration

Here’s a complete example that demonstrates various configuration options:

import asyncio
import logging
import os
from dotenv import load_dotenv

from agentconnect.agents import AIAgent, HumanAgent
from agentconnect.core.types import (
    ModelProvider,
    ModelName,
    AgentIdentity,
    InteractionMode,
    Capability,
    AgentType
)
from agentconnect.core.registry import AgentRegistry
from agentconnect.communication import CommunicationHub

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("AgentConnect")

# Load environment variables
load_dotenv()

async def main():
    # Create registry and hub
    registry = AgentRegistry()
    hub = CommunicationHub(registry)

    # Create AI agent
    ai_agent = AIAgent(
        agent_id="ai-assistant",
        name="AI Assistant",
        provider_type=ModelProvider.OPENAI,
        model_name=ModelName.GPT4O,
        api_key=os.getenv("OPENAI_API_KEY"),
        identity=AgentIdentity.create_key_based(),
        interaction_modes=[
            InteractionMode.HUMAN_TO_AGENT,
            InteractionMode.AGENT_TO_AGENT
        ],
        capabilities=[
            Capability(
                name="general_assistant",
                description="Provide helpful information and assistance on a wide range of topics",
                input_schema={"query": "string"},
                output_schema={"response": "string"}
            )
        ],
        personality="helpful and informative",
        organization_id="example-org"
    )

    # Create human agent
    human_agent = HumanAgent(
        agent_id="human-user",
        name="Human User",
        identity=AgentIdentity.create_key_based(),
        organization_id="example-org"
    )

    # Register agents
    await hub.register_agent(ai_agent)
    await hub.register_agent(human_agent)

    # Set up message handler for the AI agent
    async def ai_message_handler(message):
        logger.info(f"AI agent received message: {message.content[:50]}...")
        # Process incoming messages to the AI agent

    # Add the message handler
    hub.add_message_handler(ai_agent.agent_id, ai_message_handler)

    # Add a global message handler to track all messages
    async def global_message_tracker(message):
        logger.info(f"Message from {message.sender_id} to {message.receiver_id}: {message.content[:30]}...")

    hub.add_global_handler(global_message_tracker)

    # Start the AI agent
    ai_task = asyncio.create_task(ai_agent.run())

    # Send a message from human to AI
    await human_agent.send_message(
        receiver_id=ai_agent.agent_id,
        content="Can you help me with a research question about AI agents?"
    )

    # Wait for processing
    await asyncio.sleep(5)

    # Clean up
    ai_agent.is_running = False
    await ai_task
    await hub.unregister_agent(ai_agent.agent_id)
    await hub.unregister_agent(human_agent.agent_id)

if __name__ == "__main__":
    asyncio.run(main())