import asyncio, os
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage
from langchain.tools import tool
from deepagents import create_deep_agent
from deepagents.backends import CompositeBackend, LocalShellBackend
from pydantic import BaseModel, Field
from langchain_core.output_parsers import PydanticOutputParser

# LLM configuration – always OpenRouter
llm = ChatOpenAI(
    model="openai/gpt-oss-20b:free",
    base_url="https://openrouter.ai/api/v1",
    api_key=os.getenv("OPENAI_API_KEY"),
    temperature=0.0,
)

# Backend – local shell in virtual mode
backend = CompositeBackend(
    default=LocalShellBackend(root_dir="./workspace", virtual_mode=True, inherit_env=True),
    routes={},
)

# Pydantic model for structured output
class ComparisonResult(BaseModel):
    entity: str = Field(description="Name of the entity")
    strengths: str = Field(description="Key strengths")
    weaknesses: str = Field(description="Key weaknesses")
    overall_score: int = Field(description="Overall score out of 10")

parser = PydanticOutputParser(pydantic_object=ComparisonResult)

# Tool that returns a mock comparison for three entities
@tool
def compare_entities(entities: str) -> str:
    """Return a JSON array with comparison of three entities.
    Expected input: comma separated list of three entity names.
    """
    names = [e.strip() for e in entities.split(',')]
    if len(names) != 3:
        return "Error: please provide exactly three entities separated by commas."
    results = []
    for name in names:
        results.append({
            "entity": name,
            "strengths": f"Strengths of {name}",
            "weaknesses": f"Weaknesses of {name}",
            "overall_score": 7,  # placeholder score
        })
    import json
    return json.dumps(results)

# Create the deep agent
agent = create_deep_agent(
    model=llm,
    tools=[compare_entities],
    backend=backend,
    system_prompt="You are a research assistant. Use the compare_entities tool to compare three entities and return the result in the specified JSON format.",
)

async def main():
    # Ask the agent to compare three entities
    user_query = "Compare Tavily, Google, and Bing."
    result = await agent.ainvoke(
        {"messages": [HumanMessage(content=user_query)]},
        {"configurable": {"thread_id": "session-compare-1"}},
    )
    # The agent will return the tool output; parse it into structured data
    tool_output = result["messages"][-1].content
    try:
        import json
        parsed = json.loads(tool_output)
        structured = [ComparisonResult(**item) for item in parsed]
        for item in structured:
            print(item.json(indent=2))
    except Exception as e:
        print("Failed to parse output:", e)
        print(tool_output)

if __name__ == "__main__":
    asyncio.run(main())