Added OpenAI Agents SDK demo
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ShubhamSaboo
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ai_agent_tutorials/opeani_research_agent/README.md
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ai_agent_tutorials/opeani_research_agent/README.md
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# OpenAI Researcher Agent
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A multi-agent research application built with OpenAI's Agents SDK and Streamlit. This application enables users to conduct comprehensive research on any topic by leveraging multiple specialized AI agents.
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### Features
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- Multi-Agent Architecture:
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- Triage Agent: Plans the research approach and coordinates the workflow
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- Research Agent: Searches the web and gathers relevant information
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- Editor Agent: Compiles collected facts into a comprehensive report
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- Automatic Fact Collection: Captures important facts from research with source attribution
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- Structured Report Generation: Creates well-organized reports with titles, outlines, and source citations
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- Interactive UI: Built with Streamlit for easy research topic input and results viewing
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- Tracing and Monitoring: Integrated tracing for the entire research workflow
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### How to get Started?
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1. Clone the GitHub repository
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```bash
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git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
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cd awesome-llm-apps/ai_agent_tutorials/openai_researcher_agent
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```
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2. Install the required dependencies:
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```bash
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cd awesome-llm-apps/ai_agent_tutorials/openai_researcher_agent
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pip install -r requirements.txt
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```
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3. Get your OpenAI API Key
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- - Sign up for an [OpenAI account](https://platform.openai.com/) and obtain your API key.
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- Set your OPENAI_API_KEY environment variable.
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```bash
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export OPENAI_API_KEY='your-api-key-here'
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```
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4. Run the team of AI Agents
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```bash
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python openai_researcher_agent.py
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```
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Then open your browser and navigate to the URL shown in the terminal (typically http://localhost:8501).
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### Research Process:
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- Enter a research topic in the sidebar or select one of the provided examples
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- Click "Start Research" to begin the process
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- View the research process in real-time on the "Research Process" tab
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- Once complete, switch to the "Report" tab to view and download the generated report
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openai-agents
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openai
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streamlit
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uuid
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pydantic
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python-dotenv
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asyncio
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331
ai_agent_tutorials/opeani_research_agent/research_agent.py
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ai_agent_tutorials/opeani_research_agent/research_agent.py
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import os
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import uuid
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import asyncio
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import streamlit as st
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from datetime import datetime
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from dotenv import load_dotenv
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from agents import (
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Agent,
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Runner,
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WebSearchTool,
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function_tool,
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handoff,
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trace,
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)
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from pydantic import BaseModel
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# Load environment variables
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load_dotenv()
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# Set up page configuration
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st.set_page_config(
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page_title="OpenAI Researcher Agent",
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page_icon="📰",
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layout="wide",
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initial_sidebar_state="expanded"
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)
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# Make sure API key is set
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if not os.environ.get("OPENAI_API_KEY"):
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st.error("Please set your OPENAI_API_KEY environment variable")
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st.stop()
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# App title and description
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st.title("📰 OpenAI Researcher Agent")
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st.subheader("Powered by OpenAI Agents SDK")
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st.markdown("""
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This app demonstrates the power of OpenAI's Agents SDK by creating a multi-agent system
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that researches news topics and generates comprehensive research reports.
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""")
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# Define data models
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class ResearchPlan(BaseModel):
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topic: str
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search_queries: list[str]
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focus_areas: list[str]
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class ResearchReport(BaseModel):
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title: str
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outline: list[str]
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report: str
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sources: list[str]
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word_count: int
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# Custom tool for saving facts found during research
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@function_tool
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def save_important_fact(fact: str, source: str = None) -> str:
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"""Save an important fact discovered during research.
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Args:
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fact: The important fact to save
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source: Optional source of the fact
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Returns:
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Confirmation message
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"""
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if "collected_facts" not in st.session_state:
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st.session_state.collected_facts = []
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st.session_state.collected_facts.append({
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"fact": fact,
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"source": source or "Not specified",
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"timestamp": datetime.now().strftime("%H:%M:%S")
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})
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return f"Fact saved: {fact}"
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# Define the agents
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research_agent = Agent(
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name="Research Agent",
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instructions="You are a research assistant. Given a search term, you search the web for that term and"
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"produce a concise summary of the results. The summary must 2-3 paragraphs and less than 300"
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"words. Capture the main points. Write succintly, no need to have complete sentences or good"
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"grammar. This will be consumed by someone synthesizing a report, so its vital you capture the"
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"essence and ignore any fluff. Do not include any additional commentary other than the summary"
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"itself.",
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model="gpt-4o-mini",
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tools=[
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WebSearchTool(),
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save_important_fact
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],
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)
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editor_agent = Agent(
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name="Editor Agent",
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handoff_description="A senior researcher who writes comprehensive research reports",
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instructions="You are a senior researcher tasked with writing a cohesive report for a research query. "
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"You will be provided with the original query, and some initial research done by a research "
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"assistant.\n"
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"You should first come up with an outline for the report that describes the structure and "
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"flow of the report. Then, generate the report and return that as your final output.\n"
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"The final output should be in markdown format, and it should be lengthy and detailed. Aim "
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"for 5-10 pages of content, at least 1000 words.",
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model="gpt-4o-mini",
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output_type=ResearchReport,
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)
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triage_agent = Agent(
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name="Triage Agent",
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instructions="""You are the coordinator of this research operation. Your job is to:
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1. Understand the user's research topic
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2. Create a research plan with the following elements:
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- topic: A clear statement of the research topic
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- search_queries: A list of 3-5 specific search queries that will help gather information
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- focus_areas: A list of 3-5 key aspects of the topic to investigate
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3. Hand off to the Research Agent to collect information
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4. After research is complete, hand off to the Editor Agent who will write a comprehensive report
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Make sure to return your plan in the expected structured format with topic, search_queries, and focus_areas.
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""",
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handoffs=[
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handoff(research_agent),
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handoff(editor_agent)
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],
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model="gpt-4o-mini",
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output_type=ResearchPlan,
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)
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# Create sidebar for input and controls
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with st.sidebar:
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st.header("Research Topic")
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user_topic = st.text_input(
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"Enter a topic to research:",
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)
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start_button = st.button("Start Research", type="primary", disabled=not user_topic)
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st.divider()
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st.subheader("Example Topics")
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example_topics = [
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"What are the best cruise lines in USA for first-time travelers who have never been on a cruise?",
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"What are the best affordable espresso machines for someone upgrading from a French press?",
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"What are the best off-the-beaten-path destinations in India for a first-time solo traveler?"
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]
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for topic in example_topics:
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if st.button(topic):
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user_topic = topic
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start_button = True
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# Main content area with two tabs
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tab1, tab2 = st.tabs(["Research Process", "Report"])
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# Initialize session state for storing results
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if "conversation_id" not in st.session_state:
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st.session_state.conversation_id = str(uuid.uuid4().hex[:16])
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if "collected_facts" not in st.session_state:
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st.session_state.collected_facts = []
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if "research_done" not in st.session_state:
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st.session_state.research_done = False
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if "report_result" not in st.session_state:
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st.session_state.report_result = None
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# Main research function
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async def run_research(topic):
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# Reset state for new research
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st.session_state.collected_facts = []
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st.session_state.research_done = False
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st.session_state.report_result = None
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with tab1:
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message_container = st.container()
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# Create error handling container
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error_container = st.empty()
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# Create a trace for the entire workflow
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with trace("News Research", group_id=st.session_state.conversation_id):
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# Start with the triage agent
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with message_container:
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st.write("🔍 **Triage Agent**: Planning research approach...")
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triage_result = await Runner.run(
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triage_agent,
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f"Research this topic thoroughly: {topic}. This research will be used to create a comprehensive research report."
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)
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# Check if the result is a ResearchPlan object or a string
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if hasattr(triage_result.final_output, 'topic'):
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research_plan = triage_result.final_output
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plan_display = {
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"topic": research_plan.topic,
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"search_queries": research_plan.search_queries,
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"focus_areas": research_plan.focus_areas
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}
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else:
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# Fallback if we don't get the expected output type
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research_plan = {
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"topic": topic,
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"search_queries": ["Researching " + topic],
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"focus_areas": ["General information about " + topic]
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}
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plan_display = research_plan
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with message_container:
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st.write("📋 **Research Plan**:")
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st.json(plan_display)
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# Display facts as they're collected
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fact_placeholder = message_container.empty()
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# Check for new facts periodically
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previous_fact_count = 0
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for i in range(15): # Check more times to allow for more comprehensive research
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current_facts = len(st.session_state.collected_facts)
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if current_facts > previous_fact_count:
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with fact_placeholder.container():
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st.write("📚 **Collected Facts**:")
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for fact in st.session_state.collected_facts:
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st.info(f"**Fact**: {fact['fact']}\n\n**Source**: {fact['source']}")
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previous_fact_count = current_facts
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await asyncio.sleep(1)
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# Editor Agent phase
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with message_container:
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st.write("📝 **Editor Agent**: Creating comprehensive research report...")
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try:
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report_result = await Runner.run(
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editor_agent,
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triage_result.to_input_list()
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)
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st.session_state.report_result = report_result.final_output
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with message_container:
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st.write("✅ **Research Complete! Report Generated.**")
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# Preview a snippet of the report
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if hasattr(report_result.final_output, 'report'):
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report_preview = report_result.final_output.report[:300] + "..."
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else:
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report_preview = str(report_result.final_output)[:300] + "..."
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st.write("📄 **Report Preview**:")
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st.markdown(report_preview)
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st.write("*See the Report tab for the full document.*")
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except Exception as e:
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st.error(f"Error generating report: {str(e)}")
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# Fallback to display raw agent response
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if hasattr(triage_result, 'new_items'):
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messages = [item for item in triage_result.new_items if hasattr(item, 'content')]
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if messages:
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raw_content = "\n\n".join([str(m.content) for m in messages if m.content])
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st.session_state.report_result = raw_content
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with message_container:
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st.write("⚠️ **Research completed but there was an issue generating the structured report.**")
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st.write("Raw research results are available in the Report tab.")
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st.session_state.research_done = True
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# Run the research when the button is clicked
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if start_button:
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with st.spinner(f"Researching: {user_topic}"):
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try:
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asyncio.run(run_research(user_topic))
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except Exception as e:
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st.error(f"An error occurred during research: {str(e)}")
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# Set a basic report result so the user gets something
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st.session_state.report_result = f"# Research on {user_topic}\n\nUnfortunately, an error occurred during the research process. Please try again later or with a different topic.\n\nError details: {str(e)}"
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st.session_state.research_done = True
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# Display results in the Report tab
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with tab2:
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if st.session_state.research_done and st.session_state.report_result:
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report = st.session_state.report_result
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# Handle different possible types of report results
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if hasattr(report, 'title'):
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# We have a properly structured ResearchReport object
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title = report.title
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# Display outline if available
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if hasattr(report, 'outline') and report.outline:
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with st.expander("Report Outline", expanded=True):
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for i, section in enumerate(report.outline):
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st.markdown(f"{i+1}. {section}")
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# Display word count if available
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if hasattr(report, 'word_count'):
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st.info(f"Word Count: {report.word_count}")
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# Display the full report in markdown
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if hasattr(report, 'report'):
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report_content = report.report
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st.markdown(report_content)
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else:
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report_content = str(report)
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st.markdown(report_content)
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# Display sources if available
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if hasattr(report, 'sources') and report.sources:
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with st.expander("Sources"):
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for i, source in enumerate(report.sources):
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st.markdown(f"{i+1}. {source}")
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# Add download button for the report
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st.download_button(
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label="Download Report",
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data=report_content,
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file_name=f"{title.replace(' ', '_')}.md",
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mime="text/markdown"
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)
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else:
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# Handle string or other type of response
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report_content = str(report)
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title = user_topic.title()
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st.title(f"{title}")
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st.markdown(report_content)
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# Add download button for the report
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st.download_button(
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label="Download Report",
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data=report_content,
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file_name=f"{title.replace(' ', '_')}.md",
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mime="text/markdown"
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)
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