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from agents import Runner, trace, gen_trace_id | |
from search_agent import search_agent | |
from planner_agent import planner_agent, WebSearchItem, WebSearchPlan | |
from writer_agent import writer_agent, ReportData | |
from email_agent import email_agent | |
from clarify_agent import clarify_agent | |
import asyncio | |
class ResearchManager: | |
async def conduct_research(self, query: str): | |
""" Run the deep research process, yielding the status updates and the final report""" | |
trace_id = gen_trace_id() | |
with trace("Research trace", trace_id=trace_id): | |
print(f"View trace: https://platform.openai.com/traces/trace?trace_id={trace_id}") | |
yield f"View trace: https://platform.openai.com/traces/trace?trace_id={trace_id}" | |
print("Starting research...") | |
search_plan = await self.plan_searches(query) | |
yield "Searches planned, starting to search..." | |
search_results = await self.perform_searches(search_plan) | |
yield "Searches complete, writing report..." | |
report = await self.write_report(query, search_results) | |
# yield "Report written, sending email..." | |
# await self.send_email(report) | |
yield "Research complete" | |
yield report.markdown_report | |
async def plan_searches(self, query: str) -> WebSearchPlan: | |
""" Plan the searches to perform for the query """ | |
print("Planning searches...") | |
result = await Runner.run( | |
planner_agent, | |
f"Query: {query}", | |
) | |
print(f"Will perform {len(result.final_output.searches)} searches") | |
return result.final_output_as(WebSearchPlan) | |
async def perform_searches(self, search_plan: WebSearchPlan) -> list[str]: | |
""" Perform the searches to perform for the query """ | |
print("Searching...") | |
num_completed = 0 | |
tasks = [asyncio.create_task(self.search(item)) for item in search_plan.searches] | |
results = [] | |
for task in asyncio.as_completed(tasks): | |
result = await task | |
if result is not None: | |
results.append(result) | |
num_completed += 1 | |
print(f"Searching... {num_completed}/{len(tasks)} completed") | |
print("Finished searching") | |
return results | |
async def search(self, item: WebSearchItem) -> str | None: | |
""" Perform a search for the query """ | |
input = f"Search term: {item.query}\nReason for searching: {item.reason}" | |
try: | |
result = await Runner.run( | |
search_agent, | |
input, | |
) | |
return str(result.final_output) | |
except Exception: | |
return None | |
async def write_report(self, query: str, search_results: list[str]) -> ReportData: | |
""" Write the report for the query """ | |
print("Thinking about report...") | |
input = f"Original query: {query}\nSummarized search results: {search_results}" | |
result = await Runner.run( | |
writer_agent, | |
input, | |
) | |
print("Finished writing report") | |
return result.final_output_as(ReportData) | |
async def send_email(self, report: ReportData) -> None: | |
print("Writing email...") | |
result = await Runner.run( | |
email_agent, | |
report.markdown_report, | |
) | |
print("Email sent") | |
return report | |
async def generate_clarification_questions(self, query: str) -> str: | |
""" Generate clarification questions based on the user's query """ | |
print("Generating clarification questions...") | |
input = f"Please analyze this research query and generate 3 clarifying questions that would help focus the research: {query}" | |
try: | |
questions = await Runner.run( | |
clarify_agent, | |
input | |
) | |
print("Generated clarification questions") | |
return questions.final_output | |
except Exception as e: | |
print(f"Error generating questions: {e}") | |
return "" | |