The installation guide for building limited limited social networking programs using the Agent Comstictocol (ACP)
In this lesson, we use the agent propicization Protocol (ACP) by creating a transverse, ACP corresponding system in Python, includes the Gemino's Gemino API for processing environmental processing. Since the installation and configuration of the Google-Generativeai Lesson introduces patriers, instruction types, operating materials, and acpmessage data class like ACPMSAGE, which must be related to ACPMSAGE. By describing Accigent classes and ACPMSAGEbroker, the guide shows how you can build, send, route, and process messaging messages between many independent agents. By using Examples of Clear Code, users learn to use ambassadors, request actions, and broadcast information, while storing chat strings, acceptance, and error management.
import google.generativeai as genai
import json
import time
import uuid
from enum import Enum
from typing import Dict, List, Any, Optional
from dataclasses import dataclass, asdict
GEMINI_API_KEY = "Use Your Gemini API Key"
genai.configure(api_key=GEMINI_API_KEY)
We import essential Python Modules, from JSON to manage unique storage and adjectives of the type, supporting the use of formal ACP. It also re-receives the user's main Gemino API owner and preparing the Google-Generativeai Cleric for the following telephones in the language language.
class ACPMessageType(Enum):
"""Standard ACP message types"""
REQUEST = "request"
RESPONSE = "response"
INFORM = "inform"
QUERY = "query"
SUBSCRIBE = "subscribe"
UNSUBSCRIBE = "unsubscribe"
ERROR = "error"
ACK = "acknowledge"ACPMSSAGETTEPE ENCREVEMENT IF THE BIBLE MARTICLES USE IN AGENT MARTERS USE IN AGENTS COMSTOCOLOCOL, including Applications, Discipline, Discipline, and Controls such subscriptions, error-signing, and approval. By including these forms of messages, protocol confirms consistent habitation and the interface of the alent in the entire system.
class ACPPerformative(Enum):
"""ACP speech acts (performatives)"""
TELL = "tell"
ASK = "ask"
REPLY = "reply"
REQUEST_ACTION = "request-action"
AGREE = "agree"
REFUSE = "refuse"
PROPOSE = "propose"
ACCEPT = "accept"
REJECT = "reject"AcpperformMorforforforvative Encheration captures a variety of exertes Exents can use when connecting under ACP framework, to comply with high-quality purposes, such as questions, or responding to the labels. This taxi clarification empowers the translation agents and answering messages in appropriate ways in content, confirming strong and rich communication.
@dataclass
class ACPMessage:
"""Agent Communication Protocol Message Structure"""
message_id: str
sender: str
receiver: str
performative: str
content: Dict[str, Any]
protocol: str = "ACP-1.0"
conversation_id: str = None
reply_to: str = None
language: str = "english"
encoding: str = "json"
timestamp: float = None
def __post_init__(self):
if self.timestamp is None:
self.timestamp = time.time()
if self.conversation_id is None:
self.conversation_id = str(uuid.uuid4())
def to_acp_format(self) -> str:
"""Convert to standard ACP message format"""
acp_msg = {
"message-id": self.message_id,
"sender": self.sender,
"receiver": self.receiver,
"performative": self.performative,
"content": self.content,
"protocol": self.protocol,
"conversation-id": self.conversation_id,
"reply-to": self.reply_to,
"language": self.language,
"encoding": self.encoding,
"timestamp": self.timestamp
}
return json.dumps(acp_msg, indent=2)
@classmethod
def from_acp_format(cls, acp_string: str) -> 'ACPMessage':
"""Parse ACP message from string format"""
data = json.loads(acp_string)
return cls(
message_id=data["message-id"],
sender=data["sender"],
receiver=data["receiver"],
performative=data["performative"],
content=data["content"],
protocol=data.get("protocol", "ACP-1.0"),
conversation_id=data.get("conversation-id"),
reply_to=data.get("reply-to"),
language=data.get("language", "english"),
encoding=data.get("encoding", "json"),
timestamp=data.get("timestamp", time.time())
)
ACPMESSAGE Data Category includes all the required exclusement categories of alignment, including indigenations, stakeholders, professional, and metadata such as the protocol version, language, and Timestamps. Its AT Post_init__ method of auto-populal auto-stamps lost time rates and conversation prices_id, to ensure that all messages are followed separately. Ways to use in_aacc_format and_affforp_format Bamber Servilization to and from a limited example of seamlessly.
class ACPAgent:
"""Agent implementing Agent Communication Protocol"""
def __init__(self, agent_id: str, name: str, capabilities: List[str]):
self.agent_id = agent_id
self.name = name
self.capabilities = capabilities
self.model = genai.GenerativeModel("gemini-1.5-flash")
self.message_queue: List[ACPMessage] = []
self.subscriptions: Dict[str, List[str]] = {}
self.conversations: Dict[str, List[ACPMessage]] = {}
def create_message(self, receiver: str, performative: str,
content: Dict[str, Any], conversation_id: str = None,
reply_to: str = None) -> ACPMessage:
"""Create a new ACP-compliant message"""
return ACPMessage(
message_id=str(uuid.uuid4()),
sender=self.agent_id,
receiver=receiver,
performative=performative,
content=content,
conversation_id=conversation_id,
reply_to=reply_to
)
def send_inform(self, receiver: str, fact: str, data: Any = None) -> ACPMessage:
"""Send an INFORM message (telling someone a fact)"""
content = {"fact": fact, "data": data}
return self.create_message(receiver, ACPPerformative.TELL.value, content)
def send_query(self, receiver: str, question: str, query_type: str = "yes-no") -> ACPMessage:
"""Send a QUERY message (asking for information)"""
content = {"question": question, "query-type": query_type}
return self.create_message(receiver, ACPPerformative.ASK.value, content)
def send_request(self, receiver: str, action: str, parameters: Dict = None) -> ACPMessage:
"""Send a REQUEST message (asking someone to perform an action)"""
content = {"action": action, "parameters": parameters or {}}
return self.create_message(receiver, ACPPerformative.REQUEST_ACTION.value, content)
def send_reply(self, original_msg: ACPMessage, response_data: Any) -> ACPMessage:
"""Send a REPLY message in response to another message"""
content = {"response": response_data, "original-question": original_msg.content}
return self.create_message(
original_msg.sender,
ACPPerformative.REPLY.value,
content,
conversation_id=original_msg.conversation_id,
reply_to=original_msg.message_id
)
def process_message(self, message: ACPMessage) -> Optional[ACPMessage]:
"""Process incoming ACP message and generate appropriate response"""
self.message_queue.append(message)
conv_id = message.conversation_id
if conv_id not in self.conversations:
self.conversations[conv_id] = []
self.conversations[conv_id].append(message)
if message.performative == ACPPerformative.ASK.value:
return self._handle_query(message)
elif message.performative == ACPPerformative.REQUEST_ACTION.value:
return self._handle_request(message)
elif message.performative == ACPPerformative.TELL.value:
return self._handle_inform(message)
return None
def _handle_query(self, message: ACPMessage) -> ACPMessage:
"""Handle incoming query messages"""
question = message.content.get("question", "")
prompt = f"As agent {self.name} with capabilities {self.capabilities}, answer: {question}"
try:
response = self.model.generate_content(prompt)
answer = response.text.strip()
except:
answer = "Unable to process query at this time"
return self.send_reply(message, {"answer": answer, "confidence": 0.8})
def _handle_request(self, message: ACPMessage) -> ACPMessage:
"""Handle incoming action requests"""
action = message.content.get("action", "")
parameters = message.content.get("parameters", {})
if any(capability in action.lower() for capability in self.capabilities):
result = f"Executing {action} with parameters {parameters}"
status = "agreed"
else:
result = f"Cannot perform {action} - not in my capabilities"
status = "refused"
return self.send_reply(message, {"status": status, "result": result})
def _handle_inform(self, message: ACPMessage) -> Optional[ACPMessage]:
"""Handle incoming information messages"""
fact = message.content.get("fact", "")
print(f"[{self.name}] Received information: {fact}")
ack_content = {"status": "received", "fact": fact}
return self.create_message(message.sender, "acknowledge", ack_content,
conversation_id=message.conversation_id)
ACPAGent section includes an independent business capabilities, accepting, and processing ACP-compliance attachments using Gemini Language model. It regulates its message line, chat history, and subscription-provider, and provides assistance in Help (Send_inform, Send_Reply, Send_reply) Creating ACPMSSAGE DEPTS. The incoming messages are distributed through process_message, special relatives for special questions, deed requests, and informative messages.
class ACPMessageBroker:
"""Message broker implementing ACP routing and delivery"""
def __init__(self):
self.agents: Dict[str, ACPAgent] = {}
self.message_log: List[ACPMessage] = []
self.routing_table: Dict[str, str] = {}
def register_agent(self, agent: ACPAgent):
"""Register an agent with the message broker"""
self.agents[agent.agent_id] = agent
self.routing_table[agent.agent_id] = "local"
print(f"✓ Registered agent: {agent.name} ({agent.agent_id})")
def route_message(self, message: ACPMessage) -> bool:
"""Route ACP message to appropriate recipient"""
if message.receiver not in self.agents:
print(f"✗ Receiver {message.receiver} not found")
return False
print(f"n📨 ACP MESSAGE ROUTING:")
print(f"From: {message.sender} → To: {message.receiver}")
print(f"Performative: {message.performative}")
print(f"Content: {json.dumps(message.content, indent=2)}")
receiver_agent = self.agents[message.receiver]
response = receiver_agent.process_message(message)
self.message_log.append(message)
if response:
print(f"n📤 GENERATED RESPONSE:")
print(f"From: {response.sender} → To: {response.receiver}")
print(f"Content: {json.dumps(response.content, indent=2)}")
if response.receiver in self.agents:
self.agents[response.receiver].process_message(response)
self.message_log.append(response)
return True
def broadcast_message(self, message: ACPMessage, recipients: List[str]):
"""Broadcast message to multiple recipients"""
for recipient in recipients:
msg_copy = ACPMessage(
message_id=str(uuid.uuid4()),
sender=message.sender,
receiver=recipient,
performative=message.performative,
content=message.content.copy(),
conversation_id=message.conversation_id
)
self.route_message(msg_copy)ACPMSAGEBROKER is acting as an ACP between ACP messages, store registering agents and the message log. It gives ways to registering agents, which has brought each messages for a rount_message, handling, logging, and feeding, and sending the same recipient to many_message broadcasts.
def demonstrate_acp():
"""Comprehensive demonstration of Agent Communication Protocol"""
print("🤖 AGENT COMMUNICATION PROTOCOL (ACP) DEMONSTRATION")
print("=" * 60)
broker = ACPMessageBroker()
researcher = ACPAgent("agent-001", "Dr. Research", ["analysis", "research", "data-processing"])
assistant = ACPAgent("agent-002", "AI Assistant", ["information", "scheduling", "communication"])
calculator = ACPAgent("agent-003", "MathBot", ["calculation", "mathematics", "computation"])
broker.register_agent(researcher)
broker.register_agent(assistant)
broker.register_agent(calculator)
print(f"n📋 REGISTERED AGENTS:")
for agent_id, agent in broker.agents.items():
print(f" • {agent.name} ({agent_id}): {', '.join(agent.capabilities)}")
print(f"n🔬 SCENARIO 1: Information Query (ASK performative)")
query_msg = assistant.send_query("agent-001", "What are the key factors in AI research?")
broker.route_message(query_msg)
print(f"n🔢 SCENARIO 2: Action Request (REQUEST-ACTION performative)")
calc_request = researcher.send_request("agent-003", "calculate", {"expression": "sqrt(144) + 10"})
broker.route_message(calc_request)
print(f"n📢 SCENARIO 3: Information Sharing (TELL performative)")
info_msg = researcher.send_inform("agent-002", "New research paper published on quantum computing")
broker.route_message(info_msg)
print(f"n📊 PROTOCOL STATISTICS:")
print(f" • Total messages processed: {len(broker.message_log)}")
print(f" • Active conversations: {len(set(msg.conversation_id for msg in broker.message_log))}")
print(f" • Message types used: {len(set(msg.performative for msg in broker.message_log))}")
print(f"n📋 SAMPLE ACP MESSAGE FORMAT:")
sample_msg = assistant.send_query("agent-001", "Sample question for format demonstration")
print(sample_msg.to_acp_format())Coll_acp's Works Hands of ACP: Storing a Deliverer and Three different agencies (researcher, ALIs, requesting the formula, which shares the message. how under the protocol.
def setup_guide():
print("""
🚀 GOOGLE COLAB SETUP GUIDE:
1. Get Gemini API Key:
2. Replace: GEMINI_API_KEY = "YOUR_ACTUAL_API_KEY"
3. Run: demonstrate_acp()
🔧 ACP PROTOCOL FEATURES:
• Standardized message format with required fields
• Speech act performatives (TELL, ASK, REQUEST-ACTION, etc.)
• Conversation tracking and message threading
• Error handling and acknowledgments
• Message routing and delivery confirmation
📝 EXTEND THE PROTOCOL:
```python
# Create custom agent
my_agent = ACPAgent("my-001", "CustomBot", ["custom-capability"])
broker.register_agent(my_agent)
# Send custom message
msg = my_agent.send_query("agent-001", "Your question here")
broker.route_message(msg)
```
""")
if __name__ == "__main__":
setup_guide()
demonstrate_acp()
Finally, the Setup Work_Guide provides a quick reference to use ACP Demo on Google Colab, explaining how to get your Gemini API method and officed your Gemini API process and requested the CollTini process. It also reaches the important protocol features, such as the correct order format, transactions, and a route route. It provides a brief code snippet indicating how you can register customized agents and send compatible messages.
In conclusion, this tutorials used for acp-based multi-based multi-agents are able to do research, integration, and cooperatives. The sample conditions showing regular use charges, information questions, futational questions, and factual sharing, while the seller confirms reliable messaging. Students are encouraged to extend the framework by adding new ebeliefs, including Domain's capacity, or additional subscriptions and notice processes.
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