agents.py

from langchain.agents import create_agent
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from tools import web_search, scrape_url
import os
from dotenv import load_dotenv

load_dotenv()

#model setup
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)

#first_agent
def build_search_agent():
    return create_agent(
        model = llm,
        tools = [web_search]
    )

#second_agent
def build_reader_agent():
    return create_agent(
        model = llm,
        tools = [scrape_url]
    )

#writer_chain

write_prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "You are an expert research writer. Write clear, structured and insightful reports."),
        ("human", """Write a detailed research report on the topic below.
         Topic : {topic}
         Research Gathered: {research}

         Structure the report as : 
         - Introduction
         - Key Findings (minimu 3 well-explained points)
         - Conclusion
         - Source (list all URLs found in the research)

         Be detailed, factual and professional.
         """)
    ]
)

writer_chain = write_prompt | llm | StrOutputParser()

#crtic_chain

critic_prompt = ChatPromptTemplate.from_messages(
[
    ("system", "You are a sharp and constructive research critic. Be honest and specific."),
    ("human", """Review the research report below and evaluate it strictly.

Report:
{report}

Respond in this exact format:

Score: X/10

Strengths:
- ...
- ...

Areas to Improve:
- ...
- ...

One line verdict:
...""")
    ]
)

critic_chain = critic_prompt | llm | StrOutputParser()