mrhavens
40378ad65e
- LangGraph workflow orchestration - CrewAI agent crews (Fiction Fortress & Nonfiction Fortress) - PydanticAI schema validation - Fiction agents: Architect, Worldsmith, Character Lead, Voice, Editor - Nonfiction agents: Researcher, Analyst, Writer, Fact-Checker, Editor - Complete schema definitions for books, chapters, critiques - Configuration management - Basic test suite
107 lines
2.5 KiB
Python
107 lines
2.5 KiB
Python
"""Base agent class for Opus Orchestrator."""
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from abc import ABC, abstractmethod
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from typing import Any, Generic, TypeVar
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from pydantic import BaseModel
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from opus_orchestrator.config import AgentConfig, get_config
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T = TypeVar("T", bound=BaseModel)
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class AgentResponse(BaseModel):
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"""Standard response from an agent."""
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success: bool
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output: Any
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error: Optional[str] = None
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metadata: dict[str, Any] = {}
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class Config:
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arbitrary_types_allowed = True
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from typing import Optional
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class BaseAgent(ABC, Generic[T]):
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"""Base class for all Opus agents.
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Each agent has:
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- A specific role (from Fortress documentation)
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- System prompts derived from Fortress methodologies
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- Input/output schemas
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- Execution logic
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"""
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def __init__(
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self,
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role: str,
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description: str,
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system_prompt: str,
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output_schema: type[T] | None = None,
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config: Optional[AgentConfig] = None,
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):
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self.role = role
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self.description = description
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self.system_prompt = system_prompt
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self.output_schema = output_schema
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self.config = config or get_config().agent
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@abstractmethod
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async def execute(self, input_data: Any, context: dict[str, Any]) -> AgentResponse:
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"""Execute the agent's task.
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Args:
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input_data: The input data for this agent
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context: Additional context from the orchestrator
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Returns:
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AgentResponse with output and metadata
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"""
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pass
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def build_system_prompt(self, context: dict[str, Any]) -> str:
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"""Build the full system prompt with context.
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Args:
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context: Additional context to inject
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Returns:
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Complete system prompt
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"""
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base = self.system_prompt
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if context:
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context_str = "\n\n## Context\n"
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for key, value in context.items():
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context_str += f"- **{key}**: {value}\n"
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return base + context_str
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return base
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def build_user_prompt(self, task: str, input_data: Any) -> str:
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"""Build the user prompt for a specific task.
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Args:
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task: Description of the task
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input_data: Input data formatted for the task
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Returns:
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Complete user prompt
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"""
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return f"""## Task
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{task}
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## Input
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{input_data}
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## Instructions
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Please complete this task following the methodology specified in your system prompt.
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"""
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