A Practical Guide to Building a Scalable Multi-Agent Communication System
In today’s rapidly evolving technological landscape, implementing an efficient communication system between agents is crucial for businesses looking to leverage artificial intelligence. This guide outlines how to use the Agent Communication Protocol (ACP) to create a scalable messaging system using Python and Google’s Gemini API.
Core Components of ACP
Message Types
The ACP defines several core message categories essential for communication:
- REQUEST: A request for information or action.
- RESPONSE: A reply to a previous request.
- INFORM: Sharing information.
- QUERY: Seeking specific information.
- SUBSCRIBE: Requesting to receive updates.
- UNSUBSCRIBE: Stopping updates.
- ERROR: Indicating a problem occurred.
- ACK: Acknowledging receipt of a message.
Speech Acts
Agents use various speech acts to interact under the ACP framework, which includes:
- TELL: Informing another agent.
- ASK: Requesting information.
- REPLY: Responding to requests.
- REQUEST-ACTION: Asking for an action to be taken.
- AGREE: Confirming acceptance.
- REFUSE: Declining a request.
- PROPOSE: Suggesting an action or information.
- ACCEPT: Agreeing to a proposal.
- REJECT: Denying a proposal.
Message Structure
The ACPMessage class includes all necessary fields for structured communication, such as:
- Identifiers for each agent.
- Participants involved in the exchange.
- Performative indicating the action type.
- Payload containing the actual message content.
- Metadata like protocol version and timestamps.
Agent Implementation
The ACPAgent class represents an autonomous entity capable of sending and receiving messages. Key functionalities include:
- Create messages: Use create_message to draft messages.
- Send messages: Employ methods like send_inform and send_query.
- Process incoming messages: Use process_message to handle responses.
Message Broker
The ACPMessageBroker acts as the central router for messages, offering functionalities to:
- Register agents: Use register_agent to add new agents to the system.
- Route messages: Employ route_message to send messages to the right recipient.
- Broadcast messages: Use broadcast_message to send information to multiple agents simultaneously.
Demonstration of ACP
The demonstrate_acp function provides a practical overview of how the ACP functions by initializing a broker and three agents: Researcher, AI Assistant, and MathBot. It showcases various interaction scenarios, including:
- Information Query using the ASK performative.
- Action Request with the REQUEST-ACTION performative.
- Information Sharing through the TELL performative.
Setup Guide
To run the demonstration in Google Colab:
- Obtain your Gemini API Key.
- Replace
GEMINI_API_KEY = "YOUR_ACTUAL_API_KEY"in the code. - Run
demonstrate_acp().
Conclusion
This guide provides a comprehensive overview of implementing an ACP-based multi-agent system that can perform various tasks such as research, computation, and collaboration. By understanding the key components and functionalities of the ACP, businesses can effectively harness the power of AI to improve their operations.
For further assistance or to explore how AI can impact your business, please reach out to us at hello@itinai.ru or connect with us through our social media platforms.
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