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README.md

@@ -1,13 +1,6 @@
 ---
-title: Deep Research
-emoji: 💻
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 6.20.0
-python_version: '3.13'
+title: deep_research
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 6.14.0
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,38 @@
+import gradio as gr
+from dotenv import load_dotenv
+from research_manager import ResearchManager
+from styles import CSS, JS, EXAMPLES, HEADER_HTML
+
+load_dotenv(override=True)
+
+
+async def run(query: str):
+    async for status_update in ResearchManager().run(query):
+        yield status_update
+
+
+with gr.Blocks(title="Deep Research") as ui:
+    gr.HTML(HEADER_HTML)
+
+    with gr.Row(elem_classes="dr-query-row"):
+        query_textbox = gr.Textbox(
+            placeholder="Type a research question...",
+            show_label=False,
+            container=False,
+            autofocus=True,
+            elem_id="dr-query",
+            scale=5,
+        )
+        run_button = gr.Button("Investigate", variant="primary", elem_id="dr-run", scale=1)
+
+    gr.HTML('<div class="dr-examples-label">Try one</div>')
+    gr.Examples(examples=EXAMPLES, inputs=query_textbox, elem_id="dr-examples")
+
+    report = gr.Markdown(elem_id="dr-report")
+
+    run_button.click(run, inputs=query_textbox, outputs=report)
+    query_textbox.submit(run, inputs=query_textbox, outputs=report)
+
+
+if __name__ == "__main__":
+    ui.launch(css=CSS, js=JS, theme=gr.themes.Base())

email_agent.py

@@ -0,0 +1,34 @@
+from agents import Agent, function_tool, ModelSettings
+from messenger import send_email, push
+import os
+from dotenv import load_dotenv
+load_dotenv(override=True)
+
+MODEL_NAME = os.getenv("DEFAULT_MODEL_NAME", "gpt-5.4-mini")
+USE_EMAIL = os.getenv("USE_EMAIL", "true").lower() == "true"
+
+settings = ModelSettings(tool_choice="required")
+
+@function_tool
+def send_email_tool(subject: str, text_body: str, html_body: str) -> str:
+    """
+    Send out an email with the given subject and body
+    
+    Args:
+        subject: The subject of the email
+        text_body: The body of the email as plain text
+        html_body: The HTML body of the email
+    """
+    if USE_EMAIL:
+        send_email(subject, text_body, html_body)
+    else:
+        push(f"Subject: {subject}\n\n{text_body}")
+    return "Email sent successfully"
+
+
+INSTRUCTIONS = """
+You are provided with a detailed report. Use your tool to send an email, converting the report into
+a clean, well presented HTML email with an appropriate subject line.
+"""
+
+email_agent = Agent(name="Email Agent", instructions=INSTRUCTIONS, tools=[send_email_tool], model=MODEL_NAME, model_settings=settings)

messenger.py

@@ -0,0 +1,35 @@
+from dotenv import load_dotenv
+import requests
+import os
+import smtplib
+from email.message import EmailMessage
+load_dotenv(override=True)
+
+
+EMAIL_ADDRESS = os.getenv("EMAIL_ADDRESS")
+EMAIL_SMTP_SERVER = os.getenv("EMAIL_SMTP_SERVER")
+EMAIL_APP_PASSWORD = os.getenv("EMAIL_APP_PASSWORD")
+
+def send_email(subject, text_body, html_body):
+    msg = EmailMessage()
+    msg["From"] = EMAIL_ADDRESS
+    msg["To"] = EMAIL_ADDRESS
+    msg["Subject"] = subject
+    msg.set_content(text_body)
+    msg.add_alternative(html_body, subtype="html")
+
+    with smtplib.SMTP(EMAIL_SMTP_SERVER, 587) as server:
+        server.starttls()
+        server.login(EMAIL_ADDRESS, EMAIL_APP_PASSWORD)
+        server.send_message(msg)
+
+
+pushover_user = os.getenv("PUSHOVER_USER")
+pushover_token = os.getenv("PUSHOVER_TOKEN")
+pushover_url = "https://api.pushover.net/1/messages.json"
+
+def push(message):
+    print(f"Push: {message}")
+    payload = {"user": pushover_user, "token": pushover_token, "message": message}
+    requests.post(pushover_url, data=payload)
+

planner_agent.py

@@ -0,0 +1,24 @@
+from pydantic import BaseModel, Field
+from agents import Agent
+import os
+from dotenv import load_dotenv
+load_dotenv(override=True)
+
+MODEL_NAME = os.getenv("DEFAULT_MODEL_NAME", "gpt-5.4-mini")
+HOW_MANY_SEARCHES = int(os.getenv("HOW_MANY_SEARCHES", 5))
+
+
+INSTRUCTIONS = f"""
+You are a research assistant. Given a user query, come up with a set of web searches
+to perform to best answer the query. Output {HOW_MANY_SEARCHES} terms to query for.
+"""
+
+class WebSearchItem(BaseModel):
+    reason: str = Field(description="Your reasoning for why this search is important to the query.")
+    query: str = Field(description="The search term to use for the web search.")
+
+
+class WebSearchPlan(BaseModel):
+    searches: list[WebSearchItem] = Field(description="A list of web searches to perform to best answer the query.")
+    
+planner_agent = Agent(name="Planner Agent", instructions=INSTRUCTIONS, model=MODEL_NAME, output_type=WebSearchPlan)

requirements.txt

@@ -0,0 +1,6 @@
+pydantic
+python-dotenv
+openai
+openai-agents
+gradio
+requests

research_manager.py

@@ -0,0 +1,48 @@
+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
+import asyncio
+
+class ResearchManager:
+
+    async def run(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):
+            yield f"Starting research. Trace: https://platform.openai.com/traces/trace?trace_id={trace_id}"
+            search_plan = await self.plan_searches(query)
+            yield f"Searches planned, starting {len(search_plan.searches)} searches..."     
+            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 "Email sent, research complete"
+            yield report.markdown_report
+
+    async def plan_searches(self, query: str) -> WebSearchPlan:
+        """ Plan the searches to perform for the query """
+        result = await Runner.run(planner_agent, f"Query: {query}")
+        return result.final_output
+
+    async def perform_searches(self, search_plan: WebSearchPlan) -> list[str]:
+        """ Perform the searches to perform for the query """
+        tasks = [self.search(item) for item in search_plan.searches]
+        return await asyncio.gather(*tasks)
+
+    async def search(self, item: WebSearchItem) -> str | None:
+        """ Perform a search for the query """
+        input_message = f"Search term: {item.query}\nReason for searching: {item.reason}"
+        result = await Runner.run(search_agent, input_message)
+        return result.final_output
+
+    async def write_report(self, query: str, search_results: list[str]) -> ReportData:
+        """ Write the report for the query """
+        input_message = f"Original query: {query}\nSummarized search results: {search_results}"
+        result = await Runner.run(writer_agent, input_message)
+        return result.final_output
+    
+    async def send_email(self, report: ReportData) -> None:
+        await Runner.run(email_agent, report.markdown_report)

search_agent.py

@@ -0,0 +1,17 @@
+from agents import Agent, WebSearchTool, ModelSettings
+from dotenv import load_dotenv
+import os
+
+load_dotenv(override=True)
+MODEL_NAME = os.getenv("DEFAULT_MODEL_NAME", "gpt-5.4-mini")
+
+INSTRUCTIONS = """
+You are a research assistant. Given a search term, you search the web for that term and 
+produce a concise summary of the results. The summary must 2-3 paragraphs and less than 300 words.
+Capture the main points and be succinct. Reply only with the summary.
+"""
+
+settings = ModelSettings(tool_choice="required")
+tools = [WebSearchTool()]
+
+search_agent = Agent(name="Search Agent", instructions=INSTRUCTIONS, tools=tools, model=MODEL_NAME, model_settings=settings)

simple.py

@@ -0,0 +1,22 @@
+import gradio as gr
+from dotenv import load_dotenv
+from research_manager import ResearchManager
+
+load_dotenv(override=True)
+
+
+async def run(query: str):
+    async for status_update in ResearchManager().run(query):
+        yield status_update
+
+
+with gr.Blocks() as ui:
+    query_textbox = gr.Textbox(label="What topic would you like to research?")
+    run_button = gr.Button("Run", variant="primary")
+    report = gr.Markdown(label="Report")
+    
+    run_button.click(run, inputs=query_textbox, outputs=report)
+    query_textbox.submit(run, inputs=query_textbox, outputs=report)
+
+ui.launch(theme=gr.themes.Default(primary_hue="sky"))
+

styles.py

@@ -0,0 +1,364 @@
+EXAMPLES = [
+    "Most popular AI Agent frameworks in 2026",
+    "Most commercially successful Agentic AI implementations in 2026",
+    "Celebrities who don't like cheese",
+]
+
+HEADER_HTML = """
+<div class="dr-brand">
+    <div class="dr-mark">
+        <span class="dr-bar dr-bar-1"></span>
+        <span class="dr-bar dr-bar-2"></span>
+        <span class="dr-bar dr-bar-3"></span>
+    </div>
+    <div class="dr-titles">
+        <h1>Deep<span class="dr-sep">/</span>Research</h1>
+        <p>Multi-search web investigation</p>
+    </div>
+</div>
+"""
+
+CSS = """
+.gradio-container {
+    --dr-bg: #fafaf7;
+    --dr-surface: #ffffff;
+    --dr-line: #0c0c0d;
+    --dr-line-soft: #e1e1da;
+    --dr-text: #0c0c0d;
+    --dr-muted: #6f6f72;
+    --dr-amber: #ecad0a;
+    --dr-blue: #209dd7;
+    --dr-purple: #753991;
+
+    max-width: 1080px !important;
+    margin: 0 auto !important;
+    padding: 2.5rem 2rem 4rem !important;
+    background: var(--dr-bg) !important;
+    color: var(--dr-text) !important;
+    font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif !important;
+}
+
+.gradio-container.dark,
+.dark .gradio-container,
+body.dark .gradio-container,
+html.dark .gradio-container {
+    --dr-bg: #0b0b0c;
+    --dr-surface: #161618;
+    --dr-line: #f1f1ec;
+    --dr-line-soft: #2a2a2d;
+    --dr-text: #f1f1ec;
+    --dr-muted: #8a8a8e;
+}
+
+body { background: var(--dr-bg, #fafaf7); }
+
+/* === HEADER === */
+.dr-brand {
+    display: grid;
+    grid-template-columns: auto 1fr;
+    align-items: center;
+    gap: 1.4rem;
+    padding-bottom: 1.25rem;
+    border-bottom: 3px solid var(--dr-line);
+    margin-bottom: 2.5rem;
+}
+
+.dr-mark {
+    display: flex;
+    flex-direction: column;
+    gap: 5px;
+    width: 38px;
+}
+
+.dr-bar { height: 7px; display: block; }
+.dr-bar-1 { background: var(--dr-amber);  width: 100%; }
+.dr-bar-2 { background: var(--dr-blue);   width: 70%;  }
+.dr-bar-3 { background: var(--dr-purple); width: 45%;  }
+
+.dr-titles h1 {
+    font-size: clamp(1.8rem, 4vw, 2.6rem);
+    font-weight: 900;
+    letter-spacing: -0.045em;
+    margin: 0;
+    line-height: 0.95;
+    text-transform: uppercase;
+    color: var(--dr-text);
+}
+
+.dr-sep {
+    color: var(--dr-amber);
+    font-weight: 300;
+    margin: 0 0.04em;
+}
+
+.dr-titles p {
+    font-family: ui-monospace, SFMono-Regular, "SF Mono", Menlo, monospace;
+    font-size: 0.7rem;
+    letter-spacing: 0.22em;
+    text-transform: uppercase;
+    margin: 0.55rem 0 0;
+    color: var(--dr-muted);
+}
+
+/* === QUERY ROW === */
+.dr-query-row {
+    gap: 0 !important;
+    align-items: stretch !important;
+}
+
+#dr-query, #dr-query > div, #dr-query .wrap, #dr-query .form, #dr-query .block {
+    background: transparent !important;
+    border: none !important;
+    box-shadow: none !important;
+    padding: 0 !important;
+    border-radius: 0 !important;
+}
+
+#dr-query textarea, #dr-query input {
+    background: var(--dr-surface) !important;
+    color: var(--dr-text) !important;
+    border: 2px solid var(--dr-line) !important;
+    border-radius: 0 !important;
+    padding: 1.05rem 1.2rem !important;
+    font-size: 1.05rem !important;
+    font-family: inherit !important;
+    box-shadow: none !important;
+    line-height: 1.45 !important;
+    resize: none !important;
+    min-height: 56px !important;
+    transition: border-color 0.15s, box-shadow 0.15s !important;
+}
+
+#dr-query textarea:focus, #dr-query input:focus {
+    outline: none !important;
+    border-color: var(--dr-blue) !important;
+    box-shadow: 6px 6px 0 0 var(--dr-blue) !important;
+}
+
+#dr-query textarea::placeholder, #dr-query input::placeholder {
+    color: var(--dr-muted) !important;
+    opacity: 1 !important;
+}
+
+#dr-run {
+    background: var(--dr-amber) !important;
+    color: #0c0c0d !important;
+    border: 2px solid var(--dr-line) !important;
+    border-left: none !important;
+    border-radius: 0 !important;
+    font-weight: 800 !important;
+    text-transform: uppercase !important;
+    letter-spacing: 0.14em !important;
+    font-size: 0.85rem !important;
+    box-shadow: none !important;
+    transition: background 0.15s, color 0.15s, transform 0.08s !important;
+    min-width: 150px !important;
+    padding: 1rem 1.5rem !important;
+}
+
+#dr-run:hover {
+    background: var(--dr-purple) !important;
+    color: #ffffff !important;
+}
+
+#dr-run:active { transform: translate(2px, 2px) !important; }
+
+/* === EXAMPLES === */
+.dr-examples-label {
+    font-family: ui-monospace, SFMono-Regular, monospace;
+    font-size: 0.65rem;
+    letter-spacing: 0.28em;
+    color: var(--dr-muted);
+    text-transform: uppercase;
+    margin: 2rem 0 0.85rem 0;
+    display: flex;
+    align-items: center;
+    gap: 0.85rem;
+}
+
+.dr-examples-label::after {
+    content: "";
+    flex: 1;
+    height: 1px;
+    background: var(--dr-line-soft);
+}
+
+#dr-examples, #dr-examples > div, #dr-examples .wrap, #dr-examples .block {
+    background: transparent !important;
+    border: none !important;
+    padding: 0 !important;
+    box-shadow: none !important;
+}
+
+#dr-examples label, #dr-examples .label-wrap, #dr-examples > div > .label-wrap {
+    display: none !important;
+}
+
+#dr-examples table {
+    border-collapse: separate !important;
+    border-spacing: 0 !important;
+    width: auto !important;
+    background: transparent !important;
+    border: none !important;
+}
+
+#dr-examples thead { display: none !important; }
+
+#dr-examples tbody { background: transparent !important; }
+
+#dr-examples tr {
+    background: transparent !important;
+    display: flex !important;
+    flex-wrap: wrap !important;
+    gap: 8px !important;
+    border: none !important;
+}
+
+#dr-examples td, #dr-examples button {
+    background: var(--dr-surface) !important;
+    border: 1.5px solid var(--dr-line-soft) !important;
+    padding: 0.7rem 1.05rem !important;
+    cursor: pointer !important;
+    transition: border-color 0.15s, color 0.15s, transform 0.1s !important;
+    font-size: 0.9rem !important;
+    color: var(--dr-text) !important;
+    border-radius: 0 !important;
+    margin: 0 !important;
+    text-align: left !important;
+    box-shadow: none !important;
+}
+
+#dr-examples td:hover, #dr-examples button:hover {
+    border-color: var(--dr-purple) !important;
+    color: var(--dr-purple) !important;
+    transform: translateY(-1px);
+}
+
+/* === REPORT === */
+#dr-report {
+    margin-top: 2.5rem !important;
+    padding: 0 !important;
+    background: transparent !important;
+    border: none !important;
+    box-shadow: none !important;
+    color: var(--dr-text) !important;
+    min-height: 40px;
+}
+
+#dr-report > div, #dr-report .prose {
+    background: transparent !important;
+    color: var(--dr-text) !important;
+}
+
+#dr-report:not(:empty) {
+    border-top: 1px solid var(--dr-line-soft) !important;
+    padding-top: 1.75rem !important;
+}
+
+#dr-report h1 {
+    font-size: 1.85rem;
+    font-weight: 900;
+    color: var(--dr-blue);
+    border-bottom: 2px solid var(--dr-line);
+    padding-bottom: 0.45rem;
+    margin: 1.5rem 0 1rem;
+    letter-spacing: -0.025em;
+}
+
+#dr-report h2 {
+    font-size: 1.35rem;
+    color: var(--dr-purple);
+    font-weight: 800;
+    margin-top: 1.75rem;
+    letter-spacing: -0.015em;
+}
+
+#dr-report h3 {
+    font-size: 1.1rem;
+    color: var(--dr-text);
+    font-weight: 800;
+    margin-top: 1.5rem;
+}
+
+#dr-report p { line-height: 1.7; }
+
+#dr-report a {
+    color: var(--dr-blue);
+    text-decoration: underline;
+    text-decoration-thickness: 2px;
+    text-underline-offset: 3px;
+}
+
+#dr-report a:hover { color: var(--dr-amber); }
+
+#dr-report code {
+    background: var(--dr-surface);
+    border: 1px solid var(--dr-line-soft);
+    padding: 0.1rem 0.4rem;
+    font-size: 0.92em;
+    border-radius: 0;
+}
+
+#dr-report pre {
+    background: var(--dr-surface);
+    border: 1.5px solid var(--dr-line-soft);
+    border-radius: 0;
+    padding: 1rem 1.25rem;
+}
+
+#dr-report blockquote {
+    border-left: none !important;
+    background: var(--dr-surface);
+    padding: 1rem 1.25rem;
+    margin: 1rem 0;
+    color: var(--dr-text);
+}
+
+#dr-report ul, #dr-report ol { padding-left: 1.5rem; }
+#dr-report li { margin: 0.3rem 0; line-height: 1.6; }
+
+#dr-report table {
+    border-collapse: collapse;
+    border: 1.5px solid var(--dr-line);
+}
+
+#dr-report th, #dr-report td {
+    border: 1px solid var(--dr-line-soft);
+    padding: 0.5rem 0.85rem;
+    text-align: left;
+}
+
+#dr-report th {
+    background: var(--dr-surface);
+    font-weight: 800;
+    color: var(--dr-blue);
+}
+
+footer { display: none !important; }
+
+@media (max-width: 700px) {
+    .gradio-container { padding: 1.5rem 1rem 3rem !important; }
+    .dr-query-row { flex-direction: column !important; }
+    #dr-run {
+        border-left: 2px solid var(--dr-line) !important;
+        border-top: none !important;
+        width: 100% !important;
+    }
+}
+"""
+
+JS = """
+() => {
+    const focus = () => {
+        const el = document.querySelector("#dr-query textarea, #dr-query input");
+        if (el) { el.focus(); return true; }
+        return false;
+    };
+    if (!focus()) {
+        let tries = 0;
+        const i = setInterval(() => {
+            if (focus() || ++tries > 20) clearInterval(i);
+        }, 100);
+    }
+}
+"""

writer_agent.py

@@ -0,0 +1,24 @@
+from pydantic import BaseModel, Field
+from agents import Agent
+from dotenv import load_dotenv
+import os
+
+load_dotenv(override=True)
+MODEL_NAME = os.getenv("DEFAULT_MODEL_NAME", "gpt-5.4-mini")
+
+INSTRUCTIONS = """
+You are a senior researcher tasked with writing a cohesive report for a research query.
+You will be provided with the original query, and some research.
+Generate a comprehensive report based on the research and the query.
+The final output should be in markdown format, and it should be lengthy and detailed. Aim 
+for 5-10 pages of content, at least 1000 words.
+"""
+
+
+class ReportData(BaseModel):
+    short_summary: str = Field(description="A short 2-3 sentence summary of the findings.")
+    markdown_report: str = Field(description="The final report")
+    follow_up_questions: list[str] = Field(description="Suggested topics to research further")
+
+
+writer_agent = Agent(name="Writer Agent", instructions=INSTRUCTIONS, model=MODEL_NAME, output_type=ReportData)