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"""
Base Agent for SPARKNET
Defines the core agent interface and functionality
"""
from abc import ABC, abstractmethod
from typing import List, Dict, Optional, Any
from dataclasses import dataclass
from datetime import datetime
from loguru import logger
import json
from ..llm.ollama_client import OllamaClient
from ..tools.base_tool import BaseTool, ToolRegistry, ToolResult
@dataclass
class Message:
"""Message for agent communication."""
role: str # 'system', 'user', 'assistant', 'agent'
content: str
sender: Optional[str] = None
timestamp: Optional[datetime] = None
metadata: Optional[Dict[str, Any]] = None
def __post_init__(self):
if self.timestamp is None:
self.timestamp = datetime.now()
def to_dict(self) -> Dict[str, str]:
"""Convert to dictionary for Ollama API."""
return {
"role": "user" if self.role == "agent" else self.role,
"content": self.content,
}
@dataclass
class Task:
"""Task for agent execution."""
id: str
description: str
priority: int = 0
status: str = "pending" # pending, in_progress, completed, failed
result: Optional[Any] = None
error: Optional[str] = None
metadata: Optional[Dict[str, Any]] = None
def __post_init__(self):
if self.metadata is None:
self.metadata = {}
class BaseAgent(ABC):
"""Base class for all SPARKNET agents."""
def __init__(
self,
name: str,
description: str,
llm_client: OllamaClient,
model: str,
system_prompt: str,
tools: Optional[List[BaseTool]] = None,
temperature: float = 0.7,
max_tokens: Optional[int] = None,
):
"""
Initialize agent.
Args:
name: Agent name
description: Agent description
llm_client: Ollama client instance
model: Model to use
system_prompt: System prompt for the agent
tools: List of available tools
temperature: LLM temperature
max_tokens: Max tokens to generate
"""
self.name = name
self.description = description
self.llm_client = llm_client
self.model = model
self.system_prompt = system_prompt
self.tools = {tool.name: tool for tool in (tools or [])}
self.temperature = temperature
self.max_tokens = max_tokens
# Message history
self.messages: List[Message] = []
# Tool registry
self.tool_registry: Optional[ToolRegistry] = None
logger.info(f"Initialized agent: {self.name} with model {self.model}")
def add_tool(self, tool: BaseTool):
"""
Add a tool to the agent's toolbox.
Args:
tool: Tool to add
"""
self.tools[tool.name] = tool
logger.info(f"Agent {self.name} added tool: {tool.name}")
def remove_tool(self, tool_name: str):
"""
Remove a tool from the agent's toolbox.
Args:
tool_name: Name of tool to remove
"""
if tool_name in self.tools:
del self.tools[tool_name]
logger.info(f"Agent {self.name} removed tool: {tool_name}")
def set_tool_registry(self, registry: ToolRegistry):
"""
Set the tool registry for accessing shared tools.
Args:
registry: Tool registry instance
"""
self.tool_registry = registry
async def call_llm(
self,
prompt: Optional[str] = None,
messages: Optional[List[Message]] = None,
temperature: Optional[float] = None,
) -> str:
"""
Call the LLM with a prompt or messages.
Args:
prompt: Single prompt string
messages: List of messages
temperature: Override temperature
Returns:
LLM response
"""
temp = temperature if temperature is not None else self.temperature
if prompt:
# Single prompt
response = self.llm_client.generate(
prompt=prompt,
model=self.model,
system=self.system_prompt,
temperature=temp,
max_tokens=self.max_tokens,
)
elif messages:
# Chat with messages
# Add system message
chat_messages = [
{"role": "system", "content": self.system_prompt}
]
# Add conversation messages
chat_messages.extend([msg.to_dict() for msg in messages])
response = self.llm_client.chat(
messages=chat_messages,
model=self.model,
temperature=temp,
)
else:
raise ValueError("Either prompt or messages must be provided")
logger.debug(f"Agent {self.name} received LLM response: {len(response)} chars")
return response
async def execute_tool(self, tool_name: str, **kwargs) -> ToolResult:
"""
Execute a tool by name.
Args:
tool_name: Name of tool to execute
**kwargs: Tool parameters
Returns:
ToolResult from tool execution
"""
# Try agent's tools first
tool = self.tools.get(tool_name)
# If not found, try tool registry
if tool is None and self.tool_registry:
tool = self.tool_registry.get_tool(tool_name)
if tool is None:
logger.error(f"Tool not found: {tool_name}")
return ToolResult(
success=False,
output=None,
error=f"Tool not found: {tool_name}",
)
logger.info(f"Agent {self.name} executing tool: {tool_name}")
result = await tool.safe_execute(**kwargs)
return result
def add_message(self, message: Message):
"""
Add a message to the agent's history.
Args:
message: Message to add
"""
self.messages.append(message)
async def receive_message(self, message: Message) -> Optional[str]:
"""
Receive and process a message from another agent or user.
Args:
message: Incoming message
Returns:
Response or None
"""
logger.info(f"Agent {self.name} received message from {message.sender}")
self.add_message(message)
# Process message (can be overridden by subclasses)
return await self.process_message(message)
async def process_message(self, message: Message) -> Optional[str]:
"""
Process an incoming message. Can be overridden by subclasses.
Args:
message: Message to process
Returns:
Response or None
"""
# Default: generate a response using LLM
response = await self.call_llm(messages=self.messages)
# Add response to history
self.add_message(
Message(
role="assistant",
content=response,
sender=self.name,
)
)
return response
@abstractmethod
async def process_task(self, task: Task) -> Task:
"""
Process a task. Must be implemented by subclasses.
Args:
task: Task to process
Returns:
Updated task with results
"""
pass
async def send_message(self, recipient: "BaseAgent", content: str) -> Optional[str]:
"""
Send a message to another agent.
Args:
recipient: Recipient agent
content: Message content
Returns:
Response from recipient
"""
message = Message(
role="agent",
content=content,
sender=self.name,
)
logger.info(f"Agent {self.name} sending message to {recipient.name}")
response = await recipient.receive_message(message)
return response
def get_available_tools(self) -> List[str]:
"""
Get list of available tool names.
Returns:
List of tool names
"""
tool_names = list(self.tools.keys())
if self.tool_registry:
tool_names.extend(self.tool_registry.list_tools())
return list(set(tool_names)) # Remove duplicates
def get_tool_schemas(self) -> List[Dict[str, Any]]:
"""
Get schemas for all available tools.
Returns:
List of tool schemas
"""
schemas = [tool.get_schema() for tool in self.tools.values()]
if self.tool_registry:
schemas.extend(self.tool_registry.get_schemas())
return schemas
def clear_history(self):
"""Clear message history."""
self.messages.clear()
logger.info(f"Agent {self.name} cleared message history")
def get_stats(self) -> Dict[str, Any]:
"""
Get agent statistics.
Returns:
Dictionary with agent stats
"""
return {
"name": self.name,
"model": self.model,
"messages_count": len(self.messages),
"tools_count": len(self.tools),
}
def __repr__(self) -> str:
return f"<Agent: {self.name} (model={self.model}, tools={len(self.tools)})>"
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