Initial commit: Opus Orchestrator AI - Full-flow book generation

- 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

opus_orchestrator/agents/base.py

@@ -0,0 +1,106 @@
+"""Base agent class for Opus Orchestrator."""
+
+from abc import ABC, abstractmethod
+from typing import Any, Generic, TypeVar
+
+from pydantic import BaseModel
+
+from opus_orchestrator.config import AgentConfig, get_config
+
+
+T = TypeVar("T", bound=BaseModel)
+
+
+class AgentResponse(BaseModel):
+    """Standard response from an agent."""
+
+    success: bool
+    output: Any
+    error: Optional[str] = None
+    metadata: dict[str, Any] = {}
+
+    class Config:
+        arbitrary_types_allowed = True
+
+
+from typing import Optional
+
+
+class BaseAgent(ABC, Generic[T]):
+    """Base class for all Opus agents.
+
+    Each agent has:
+    - A specific role (from Fortress documentation)
+    - System prompts derived from Fortress methodologies
+    - Input/output schemas
+    - Execution logic
+    """
+
+    def __init__(
+        self,
+        role: str,
+        description: str,
+        system_prompt: str,
+        output_schema: type[T] | None = None,
+        config: Optional[AgentConfig] = None,
+    ):
+        self.role = role
+        self.description = description
+        self.system_prompt = system_prompt
+        self.output_schema = output_schema
+        self.config = config or get_config().agent
+
+    @abstractmethod
+    async def execute(self, input_data: Any, context: dict[str, Any]) -> AgentResponse:
+        """Execute the agent's task.
+
+        Args:
+            input_data: The input data for this agent
+            context: Additional context from the orchestrator
+
+        Returns:
+            AgentResponse with output and metadata
+        """
+        pass
+
+    def build_system_prompt(self, context: dict[str, Any]) -> str:
+        """Build the full system prompt with context.
+
+        Args:
+            context: Additional context to inject
+
+        Returns:
+            Complete system prompt
+        """
+        base = self.system_prompt
+
+        if context:
+            context_str = "\n\n## Context\n"
+            for key, value in context.items():
+                context_str += f"- **{key}**: {value}\n"
+            return base + context_str
+
+        return base
+
+    def build_user_prompt(self, task: str, input_data: Any) -> str:
+        """Build the user prompt for a specific task.
+
+        Args:
+            task: Description of the task
+            input_data: Input data formatted for the task
+
+        Returns:
+            Complete user prompt
+        """
+        return f"""## Task
+
+{task}
+
+## Input
+
+{input_data}
+
+## Instructions
+
+Please complete this task following the methodology specified in your system prompt.
+"""