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donbr/langraph-agents.ipynb

Created September 30, 2025 03:51
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langraph-agents.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "gJXW_DgiSebM"
},
"source": [
"# LangGraph Agents"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Jdh8CoVWHRvs",
"outputId": "3fa78560-393c-4ee5-b871-9886bf0d70f4"
},
"outputs": [],
"source": [
"import os\n",
"import getpass\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Jkla2fpx28QK",
"outputId": "52d7ad22-fcb1-4abe-853b-216c55a12650"
},
"outputs": [],
"source": [
"os.environ[\"TAVILY_API_KEY\"] = getpass.getpass(\"TAVILY_API_KEY\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Nv0glIDyHmRt",
"outputId": "b69df90a-b4e1-4ddb-9de0-882d98b68ab2"
},
"outputs": [],
"source": [
"from uuid import uuid4\n",
"\n",
"os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"os.environ[\"LANGCHAIN_PROJECT\"] = f\"AIE8 - LangGraph - {uuid4().hex[0:8]}\"\n",
"os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"LangSmith API Key: \")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2k6n_Dob2F46"
},
"source": [
"#### 🏗️ Activity #1:\n",
"\n",
"Please add the tools to use into our toolbelt.\n",
"\n",
"> NOTE: Each tool in our toolbelt should be a method."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "lAxaSvlfIeOg"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_39664/1203815797.py:4: LangChainDeprecationWarning: The class `TavilySearchResults` was deprecated in LangChain 0.3.25 and will be removed in 1.0. An updated version of the class exists in the :class:`~langchain-tavily package and should be used instead. To use it run `pip install -U :class:`~langchain-tavily` and import as `from :class:`~langchain_tavily import TavilySearch``.\n",
" tavily_tool = TavilySearchResults(max_results=5)\n"
]
}
],
"source": [
"from langchain_community.tools.tavily_search import TavilySearchResults\n",
"from langchain_community.tools.arxiv.tool import ArxivQueryRun\n",
"\n",
"tavily_tool = TavilySearchResults(max_results=5)\n",
"\n",
"tool_belt = [\n",
" tavily_tool,\n",
" ArxivQueryRun(),\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "QkNS8rNZJs4z"
},
"outputs": [],
"source": [
"from langchain_openai import ChatOpenAI\n",
"\n",
"model = ChatOpenAI(\n",
" model=\"gpt-4.1-nano\",\n",
" temperature=0,\n",
" model_kwargs={\"parallel_tool_calls\": False}\n",
")\n",
"model = model.bind_tools(tool_belt)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ERzuGo6W18Lr"
},
"source": [
"#### ❓ Question #1:\n",
"\n",
"How does the model determine which tool to use?"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "mxL9b_NZKUdL"
},
"outputs": [],
"source": [
"from typing import TypedDict, Annotated\n",
"from langgraph.graph.message import add_messages\n",
"import operator\n",
"from langchain_core.messages import BaseMessage\n",
"\n",
"class AgentState(TypedDict):\n",
" messages: Annotated[list, add_messages]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "91flJWtZLUrl"
},
"outputs": [],
"source": [
"from langgraph.prebuilt import ToolNode\n",
"\n",
"def call_model(state):\n",
" messages = state[\"messages\"]\n",
" response = model.invoke(messages)\n",
" return {\"messages\" : [response]}\n",
"\n",
"tool_node = ToolNode(tool_belt)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_vF4_lgtmQNo",
"outputId": "a4384377-8f7a-415f-be1b-fee6169cb101"
},
"outputs": [
{
"data": {
"text/plain": [
"<langgraph.graph.state.StateGraph at 0x7efcdc4352b0>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langgraph.graph import StateGraph, END\n",
"\n",
"uncompiled_graph = StateGraph(AgentState)\n",
"\n",
"uncompiled_graph.add_node(\"agent\", call_model)\n",
"uncompiled_graph.add_node(\"action\", tool_node)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "YGCbaYqRnmiw",
"outputId": "5351807c-2ac7-4316-a3a3-878abeacd114"
},
"outputs": [
{
"data": {
"text/plain": [
"<langgraph.graph.state.StateGraph at 0x7efcdc4352b0>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"uncompiled_graph.set_entry_point(\"agent\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1BZgb81VQf9o",
"outputId": "73a07c15-5f0b-40f2-b033-38b57d056dd8"
},
"outputs": [
{
"data": {
"text/plain": [
"<langgraph.graph.state.StateGraph at 0x7efcdc4352b0>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def should_continue(state):\n",
" last_message = state[\"messages\"][-1]\n",
"\n",
" if last_message.tool_calls:\n",
" return \"action\"\n",
"\n",
" return END\n",
"\n",
"uncompiled_graph.add_conditional_edges(\n",
" \"agent\",\n",
" should_continue\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UvcgbHf1rIXZ",
"outputId": "45d4bdd6-d6bb-4a1d-bb79-cad43c130bf2"
},
"outputs": [
{
"data": {
"text/plain": [
"<langgraph.graph.state.StateGraph at 0x7efcdc4352b0>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"uncompiled_graph.add_edge(\"action\", \"agent\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KYqDpErlsCsu"
},
"source": [
"All that's left to do now is to compile our workflow - and we're off!"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"id": "zt9-KS8DpzNx"
},
"outputs": [],
"source": [
"simple_agent_graph = uncompiled_graph.compile()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xhNWIwBL1W4Q"
},
"source": [
"#### ❓ Question #2:\n",
"\n",
"Is there any specific limit to how many times we can cycle?\n",
"\n",
"If not, how could we impose a limit to the number of cycles?"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VEYcTShCsPaa"
},
"source": [
"## Using Our Graph"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"SYSTEM_PROMPT = \"\"\"\\\n",
"You MUST call exactly one tool per assistant turn (never include more than one tool_call).\n",
"\n",
"If the user's request requires multiple steps, continue calling tools across turns\n",
"until the final answer is complete (do not ask for permission).\n",
"\n",
"When papers/authors are involved:\n",
"1) Use `arxiv` to identify the paper and extract the author names.\n",
"2) Then use `tavily` to find the authors' CURRENT affiliations.\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Qn4n37PQRPII",
"outputId": "5eeedfae-089d-496e-e71f-071939fa5832"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Receiving update from node: 'agent'\n",
"[AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_JBsG1M90abQeM49u9r7DIbD6', 'function': {'arguments': '{\"query\":\"How are technical professionals using AI to improve their work\"}', 'name': 'tavily_search_results_json'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 27, 'prompt_tokens': 163, 'total_tokens': 190, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4.1-nano-2025-04-14', 'system_fingerprint': 'fp_7c233bf9d1', 'id': 'chatcmpl-CLLWmtpvgC0xWX8TTPIz2pVokrHqJ', 'service_tier': 'default', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run--489f79f8-b5af-4ee2-995b-2fa16c59a557-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'How are technical professionals using AI to improve their work'}, 'id': 'call_JBsG1M90abQeM49u9r7DIbD6', 'type': 'tool_call'}], usage_metadata={'input_tokens': 163, 'output_tokens': 27, 'total_tokens': 190, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})]\n",
"\n",
"\n",
"\n",
"Receiving update from node: 'action'\n",
"[ToolMessage(content='[{\"title\": \"Fastest Growing AI Roles: 2025\", \"url\": \"https://pangea.ai/resources/the-fastest-growing-ai-roles-you-should-know-about-in-2025\", \"content\": \"AI engineers need a diverse skill set, including proficiency in deep learning, neural networks, and programming languages like Python. These technical skills enable them to build sophisticated ai algorithms, AI models, and machine learning systems. Interestingly, professionals with AI skills earn, on average, 25% more than their peers without such expertise. This makes the role not only intellectually rewarding but also financially lucrative, with an average salary of approximately $100,000 [...] In addition to formal education, online courses and certifications, such as those offered by IBM and Google, can equip individuals with the necessary skills and knowledge to thrive in the AI industry. Continuous learning and professional development are vital in the AI field, as new technologies and innovations emerge rapidly. Staying updated with the latest advancements ensures that professionals remain competitive and can effectively contribute to AI projects. As the demand for AI expertise [...] AI talent is in high demand across various industries, with some sectors leading the charge in hiring. The healthcare industry is investing heavily in AI to improve treatment plans and patient care, while the finance sector leverages AI for portfolio management and predictive analytics. E-commerce companies are also seeking AI professionals to enhance customer experiences and streamline operations. Additionally, the automotive and manufacturing industries are utilizing AI to automate processes\", \"score\": 0.48162505}, {\"title\": \"9 Artificial Intelligence (AI) Jobs to Consider in 2025\", \"url\": \"https://www.coursera.org/articles/artificial-intelligence-jobs\", \"content\": \"AI engineers are professionals who use AI and machine learning techniques to develop applications and systems that help organizations become more efficient. They focus on developing the tools, systems, and processes that enable AI to be applied to real-world problems. Data \\\\\" trains \\\\\" algorithms, helping them learn and perform better. AI engineers can help cut costs, increase productivity and profits, and make business recommendations.\\\\n\\\\nAverage base salary: $114,420 [...] Machine learning engineers are professionals who research, build, and design the AI responsible for machine learning. They maintain and improve existing AI systems. A machine learning engineer often serves as a liaison with other data science team members, collaborating with the data scientists who develop models for building AI systems. They run experiments and tests, perform statistical analyses, and develop machine learning systems.\\\\n\\\\nAverage base salary: $119,668 [...] Many jobs in AI require a bachelor’s degree or higher. For some entry-level positions, you may only need an associate degree or equivalent skills and work experience. Often, AI professionals obtain undergraduate degrees in computer science, mathematics, or a related field.\\\\n\\\\nRead more: What Can You Do with a Computer Science Degree? 10 In-Demand Fields\\\\n\\\\n### 2. Build practical AI skills.\", \"score\": 0.4457955}, {\"title\": \"9 Game-Changing AI Workflow Automation Benefits ...\", \"url\": \"https://medium.com/@dejanmarkovic_53716/9-game-changing-ai-workflow-automation-benefits-revealed-60853f263e80\", \"content\": \"The impact on support teams is transformative, allowing them to shift from reactive troubleshooting to proactive service improvement. With AI handling routine inquiries, support professionals can develop specialized expertise and focus on complex problem-solving that delivers higher value to the organization.\\\\n\\\\n# Cost Optimization via Intelligent Automation [...] Streamlined Processes: AI simplifies complex workflows by automating tasks like data entry and document processing, reducing manual errors and increasing speed.\\\\n Scalability: Unlike traditional automation, AI-driven systems adapt to growth, handling more tasks without a decrease in performance.\\\\n Real-time Insights: AI provides real-time data analysis, enabling informed decisions and improved productivity. [...] A recent study highlighted that AI workflow automation can improve worker performance by nearly 40%, showcasing its transformative potential.\\\\n\\\\nQ: What are the benefits of using an AI workflow automation tool?\", \"score\": 0.4429021}, {\"title\": \"Top 20 AI Workflow Automation Tools (with Infographics)\", \"url\": \"https://tactiq.io/learn/top-20-ai-workflow-automation-tools\", \"content\": \"ProfilePicture AI is perfect for professionals, freelancers, and those looking to enhance their online presence with a standout profile picture.\\\\n\\\\n### 18. Adobe Podcast\\\\n\\\\nAdobe Podcast uses AI to simplify the podcast creation process, offering tools that enhance audio quality and streamline editing.\\\\n\\\\nKey Features: [...] 7. Audio and Video Editing Tools: These tools use AI to enhance audio quality, streamline editing, and generate professional ai voiceovers, making content production faster and more accessible.\\\\n8. Visual and Creative Testing Tools: These tools support visual testing by allowing users to preview and adjust presentations and slides, ensuring they meet design standards and brand guidelines. [...] Gamma.app is perfect for professionals and teams who need to create dynamic, visually appealing presentations while maintaining consistency and quality through visual testing.\\\\n\\\\n### 9. Writesonic\\\\n\\\\nWritesonic uses AI to generate high-quality copy for a variety of needs, from blog posts to ads, helping you create engaging content effortlessly.\\\\n\\\\nKey Features:\", \"score\": 0.33724773}, {\"title\": \"AI in the workplace: A report for 2025\", \"url\": \"https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work\", \"content\": \"As noted at the beginning of this chapter, employees anticipate AI will have a dramatic impact on their work. Now they would like their companies to invest in the training that will help them succeed. Nearly half of employees in our survey say they want more formal training and believe it is the best way to boost AI adoption. They also would like access to AI tools in the form of betas or pilots, and they indicate that incentives such as financial rewards and recognition can improve uptake. [...] Many breakthrough technologies, including the internet, smartphones, and cloud computing, have transformed the way we live and work. AI stands out from these inventions because it offers more than access to information. It can summarize, code, reason, engage in a dialogue, and make choices. AI can lower skill barriers, helping more people acquire proficiency in more fields, in any language and at any time. AI holds the potential to shift the way people access and use knowledge. The result will [...] The ability to reason is growing more and more, allowing models to autonomously take actions and complete complex tasks across workflows. This is a profound step forward. As an example, in 2023, an _AI bot_ could support call center representatives by synthesizing and summarizing large volumes of data—including voice messages, text, and technical specifications—to suggest responses to customer queries. In 2025, an _AI agent_ can converse with a customer and plan the actions it will take\", \"score\": 0.28262216}]', name='tavily_search_results_json', id='78de5b7b-15fd-45ce-96c3-045fb098d1a5', tool_call_id='call_JBsG1M90abQeM49u9r7DIbD6', artifact={'query': 'How are technical professionals using AI to improve their work', 'follow_up_questions': None, 'answer': None, 'images': [], 'results': [{'url': 'https://pangea.ai/resources/the-fastest-growing-ai-roles-you-should-know-about-in-2025', 'title': 'Fastest Growing AI Roles: 2025', 'content': 'AI engineers need a diverse skill set, including proficiency in deep learning, neural networks, and programming languages like Python. These technical skills enable them to build sophisticated ai algorithms, AI models, and machine learning systems. Interestingly, professionals with AI skills earn, on average, 25% more than their peers without such expertise. This makes the role not only intellectually rewarding but also financially lucrative, with an average salary of approximately $100,000 [...] In addition to formal education, online courses and certifications, such as those offered by IBM and Google, can equip individuals with the necessary skills and knowledge to thrive in the AI industry. Continuous learning and professional development are vital in the AI field, as new technologies and innovations emerge rapidly. Staying updated with the latest advancements ensures that professionals remain competitive and can effectively contribute to AI projects. As the demand for AI expertise [...] AI talent is in high demand across various industries, with some sectors leading the charge in hiring. The healthcare industry is investing heavily in AI to improve treatment plans and patient care, while the finance sector leverages AI for portfolio management and predictive analytics. E-commerce companies are also seeking AI professionals to enhance customer experiences and streamline operations. Additionally, the automotive and manufacturing industries are utilizing AI to automate processes', 'score': 0.48162505, 'raw_content': None}, {'url': 'https://www.coursera.org/articles/artificial-intelligence-jobs', 'title': '9 Artificial Intelligence (AI) Jobs to Consider in 2025', 'content': 'AI engineers are professionals who use AI and machine learning techniques to develop applications and systems that help organizations become more efficient. They focus on developing the tools, systems, and processes that enable AI to be applied to real-world problems. Data \" trains \" algorithms, helping them learn and perform better. AI engineers can help cut costs, increase productivity and profits, and make business recommendations.\\n\\nAverage base salary: $114,420 [...] Machine learning engineers are professionals who research, build, and design the AI responsible for machine learning. They maintain and improve existing AI systems. A machine learning engineer often serves as a liaison with other data science team members, collaborating with the data scientists who develop models for building AI systems. They run experiments and tests, perform statistical analyses, and develop machine learning systems.\\n\\nAverage base salary: $119,668 [...] Many jobs in AI require a bachelor’s degree or higher. For some entry-level positions, you may only need an associate degree or equivalent skills and work experience. Often, AI professionals obtain undergraduate degrees in computer science, mathematics, or a related field.\\n\\nRead more: What Can You Do with a Computer Science Degree? 10 In-Demand Fields\\n\\n### 2. Build practical AI skills.', 'score': 0.4457955, 'raw_content': None}, {'url': 'https://medium.com/@dejanmarkovic_53716/9-game-changing-ai-workflow-automation-benefits-revealed-60853f263e80', 'title': '9 Game-Changing AI Workflow Automation Benefits ...', 'content': 'The impact on support teams is transformative, allowing them to shift from reactive troubleshooting to proactive service improvement. With AI handling routine inquiries, support professionals can develop specialized expertise and focus on complex problem-solving that delivers higher value to the organization.\\n\\n# Cost Optimization via Intelligent Automation [...] Streamlined Processes: AI simplifies complex workflows by automating tasks like data entry and document processing, reducing manual errors and increasing speed.\\n Scalability: Unlike traditional automation, AI-driven systems adapt to growth, handling more tasks without a decrease in performance.\\n Real-time Insights: AI provides real-time data analysis, enabling informed decisions and improved productivity. [...] A recent study highlighted that AI workflow automation can improve worker performance by nearly 40%, showcasing its transformative potential.\\n\\nQ: What are the benefits of using an AI workflow automation tool?', 'score': 0.4429021, 'raw_content': None}, {'url': 'https://tactiq.io/learn/top-20-ai-workflow-automation-tools', 'title': 'Top 20 AI Workflow Automation Tools (with Infographics)', 'content': 'ProfilePicture AI is perfect for professionals, freelancers, and those looking to enhance their online presence with a standout profile picture.\\n\\n### 18. Adobe Podcast\\n\\nAdobe Podcast uses AI to simplify the podcast creation process, offering tools that enhance audio quality and streamline editing.\\n\\nKey Features: [...] 7. Audio and Video Editing Tools: These tools use AI to enhance audio quality, streamline editing, and generate professional ai voiceovers, making content production faster and more accessible.\\n8. Visual and Creative Testing Tools: These tools support visual testing by allowing users to preview and adjust presentations and slides, ensuring they meet design standards and brand guidelines. [...] Gamma.app is perfect for professionals and teams who need to create dynamic, visually appealing presentations while maintaining consistency and quality through visual testing.\\n\\n### 9. Writesonic\\n\\nWritesonic uses AI to generate high-quality copy for a variety of needs, from blog posts to ads, helping you create engaging content effortlessly.\\n\\nKey Features:', 'score': 0.33724773, 'raw_content': None}, {'url': 'https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work', 'title': 'AI in the workplace: A report for 2025', 'content': 'As noted at the beginning of this chapter, employees anticipate AI will have a dramatic impact on their work. Now they would like their companies to invest in the training that will help them succeed. Nearly half of employees in our survey say they want more formal training and believe it is the best way to boost AI adoption. They also would like access to AI tools in the form of betas or pilots, and they indicate that incentives such as financial rewards and recognition can improve uptake. [...] Many breakthrough technologies, including the internet, smartphones, and cloud computing, have transformed the way we live and work. AI stands out from these inventions because it offers more than access to information. It can summarize, code, reason, engage in a dialogue, and make choices. AI can lower skill barriers, helping more people acquire proficiency in more fields, in any language and at any time. AI holds the potential to shift the way people access and use knowledge. The result will [...] The ability to reason is growing more and more, allowing models to autonomously take actions and complete complex tasks across workflows. This is a profound step forward. As an example, in 2023, an _AI bot_ could support call center representatives by synthesizing and summarizing large volumes of data—including voice messages, text, and technical specifications—to suggest responses to customer queries. In 2025, an _AI agent_ can converse with a customer and plan the actions it will take', 'score': 0.28262216, 'raw_content': None}], 'response_time': 0.7, 'request_id': '26216bc1-d4c2-407d-afc5-f62433c1e008'})]\n",
"\n",
"\n",
"\n",
"Receiving update from node: 'agent'\n",
"[AIMessage(content='Technical professionals are leveraging AI in various ways to enhance their work. They are developing sophisticated AI algorithms, models, and systems using skills in deep learning, neural networks, and programming languages like Python. AI engineers and machine learning engineers are creating applications that automate tasks, improve decision-making, and optimize processes, leading to increased efficiency and cost savings. \\n\\nAI is also being used to streamline workflows through automation tools that handle routine tasks such as data entry, document processing, and customer support, allowing professionals to focus on more complex and value-added activities. In fields like healthcare, finance, e-commerce, automotive, and manufacturing, AI is helping to improve treatment plans, predictive analytics, customer experiences, and operational automation.\\n\\nFurthermore, organizations are investing in training and development to help employees adopt AI tools effectively, recognizing that AI can lower skill barriers and enable more people to work proficiently across various domains. Overall, AI is transforming the way technical professionals work by enabling smarter, faster, and more scalable solutions.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 199, 'prompt_tokens': 1661, 'total_tokens': 1860, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4.1-nano-2025-04-14', 'system_fingerprint': 'fp_7c233bf9d1', 'id': 'chatcmpl-CLLWoP5nSb923BuNduceG6OWvOxXM', 'service_tier': 'default', 'finish_reason': 'stop', 'logprobs': None}, id='run--75b25874-8324-47c9-b339-a62485b37627-0', usage_metadata={'input_tokens': 1661, 'output_tokens': 199, 'total_tokens': 1860, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})]\n",
"\n",
"\n",
"\n"
]
}
],
"source": [
"from langchain_core.messages import HumanMessage\n",
"\n",
"inputs = {\"messages\" : [HumanMessage(content=\"How are technical professionals using AI to improve their work?\")]}\n",
"\n",
"async for chunk in simple_agent_graph.astream(inputs, stream_mode=\"updates\"):\n",
" for node, values in chunk.items():\n",
" print(f\"Receiving update from node: '{node}'\")\n",
" print(values[\"messages\"])\n",
" print(\"\\n\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Receiving update from node: 'agent'\n",
"[AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_E7h8I4Yu2quOiftf9Vqqg3Ly', 'function': {'arguments': '{\"query\":\"A Comprehensive Survey of Deep Research\"}', 'name': 'arxiv'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 19, 'prompt_tokens': 256, 'total_tokens': 275, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4.1-nano-2025-04-14', 'system_fingerprint': 'fp_7c233bf9d1', 'id': 'chatcmpl-CLLXOv3mXOP30v70BXtYQOjWwJygK', 'service_tier': 'default', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run--ea4684b7-7a85-4c6d-9b4e-a7bbc3b4c4b5-0', tool_calls=[{'name': 'arxiv', 'args': {'query': 'A Comprehensive Survey of Deep Research'}, 'id': 'call_E7h8I4Yu2quOiftf9Vqqg3Ly', 'type': 'tool_call'}], usage_metadata={'input_tokens': 256, 'output_tokens': 19, 'total_tokens': 275, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})] \n",
"\n",
"\n",
"Receiving update from node: 'action'\n",
"Tool Used: arxiv\n",
"[ToolMessage(content='Published: 2025-06-14\\nTitle: A Comprehensive Survey of Deep Research: Systems, Methodologies, and Applications\\nAuthors: Renjun Xu, Jingwen Peng\\nSummary: This survey examines the rapidly evolving field of Deep Research systems --\\nAI-powered applications that automate complex research workflows through the\\nintegration of large language models, advanced information retrieval, and\\nautonomous reasoning capabilities. We analyze more than 80 commercial and\\nnon-commercial implementations that have emerged since 2023, including\\nOpenAI/Deep Research, Gemini/Deep Research, Perplexity/Deep Research, and\\nnumerous open-source alternatives. Through comprehensive examination, we\\npropose a novel hierarchical taxonomy that categorizes systems according to\\nfour fundamental technical dimensions: foundation models and reasoning engines,\\ntool utilization and environmental interaction, task planning and execution\\ncontrol, and knowledge synthesis and output generation. We explore the\\narchitectural patterns, implementation approaches, and domain-specific\\nadaptations that characterize these systems across academic, scientific,\\nbusiness, and educational applications. Our analysis reveals both the\\nsignificant capabilities of current implementations and the technical and\\nethical challenges they present regarding information accuracy, privacy,\\nintellectual property, and accessibility. The survey concludes by identifying\\npromising research directions in advanced reasoning architectures, multimodal\\nintegration, domain specialization, human-AI collaboration, and ecosystem\\nstandardization that will likely shape the future evolution of this\\ntransformative technology. By providing a comprehensive framework for\\nunderstanding Deep Research systems, this survey contributes to both the\\ntheoretical understanding of AI-augmented knowledge work and the practical\\ndevelopment of more capable, responsible, and accessible research technologies.\\nThe paper resources can be viewed at\\nhttps://github.com/scienceaix/deepresearch.\\n\\nPublished: 2021-03-05\\nTitle: A comprehensive survey on point cloud registration\\nAuthors: Xiaoshui Huang, Guofeng Mei, Jian Zhang, Rana Abbas\\nSummary: Registration is a transformation estimation problem between two point clouds,\\nwhich has a unique and critical role in numerous computer vision applications.\\nThe developments of optimization-based methods and deep learning methods have\\nimproved registration robustness and efficiency. Recently, the combinations of\\noptimization-based and deep learning methods have further improved performance.\\nHowever, the connections between optimization-based and deep learning methods\\nare still unclear. Moreover, with the recent development of 3D sensors and 3D\\nreconstruction techniques, a new research direction emerges to align\\ncross-source point clouds. This survey conducts a comprehensive survey,\\nincluding both same-source and cross-source registration methods, and summarize\\nthe connections between optimization-based and deep learning methods, to\\nprovide further research insight. This survey also builds a new benchmark to\\nevaluate the state-of-the-art registration algorithms in solving cross-source\\nchallenges. Besides, this survey summarizes the benchmark data sets and\\ndiscusses point cloud registration applications across various domains.\\nFinally, this survey proposes potential research directions in this rapidly\\ngrowing field.\\n\\nPublished: 2023-07-07\\nTitle: A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision\\nAuthors: Zhonghan Zhao, Wenhao Chai, Shengyu Hao, Wenhao Hu, Guanhong Wang, Shidong Cao, Mingli Song, Jenq-Neng Hwang, Gaoang Wang\\nSummary: Deep learning has the potential to revolutionize sports performance, with\\napplications ranging from perception and comprehension to decision. This paper\\npresents a comprehensive survey of deep learning in sports performance,\\nfocusing on three main aspects: algorithms, datasets and virtual environments,\\nand challenges. Firstly, we discuss th', name='arxiv', id='8eb59d9d-43bb-473b-8ba6-c7cc0bd759e1', tool_call_id='call_E7h8I4Yu2quOiftf9Vqqg3Ly')] \n",
"\n",
"\n",
"Receiving update from node: 'agent'\n",
"[AIMessage(content='The authors of the paper titled \"A Comprehensive Survey of Deep Research\" are Renjun Xu and Jingwen Peng. I will now look up their current affiliations.', additional_kwargs={'tool_calls': [{'id': 'call_LJcf7gAKs6EW1jfPmJga5BXQ', 'function': {'arguments': '{\"query\":\"Renjun Xu\"}', 'name': 'tavily_search_results_json'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 53, 'prompt_tokens': 1024, 'total_tokens': 1077, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4.1-nano-2025-04-14', 'system_fingerprint': 'fp_7c233bf9d1', 'id': 'chatcmpl-CLLXPokalC1gNoJJjlR22EAspo7Yt', 'service_tier': 'default', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run--c768fbf7-1425-49de-a320-fdb09c07a7ee-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'Renjun Xu'}, 'id': 'call_LJcf7gAKs6EW1jfPmJga5BXQ', 'type': 'tool_call'}], usage_metadata={'input_tokens': 1024, 'output_tokens': 53, 'total_tokens': 1077, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})] \n",
"\n",
"\n",
"Receiving update from node: 'action'\n",
"Tool Used: tavily_search_results_json\n",
"[ToolMessage(content='[{\"title\": \"Renjun Xu | IEEE Xplore Author Details\", \"url\": \"https://ieeexplore.ieee.org/author/37089181301#:~:text=Renjun Xu received the PhD,CyberSource & Authorize.Net).\", \"content\": \"Renjun Xu received the PhD degree from UC Davis, ZJU100 Young professor and PhD supervisor of the Center for Data Science, Zhejiang University. He was a senior director of Data and Artificial Intelligence, VISA Inc. (CyberSource & Authorize.Net). His research interests include natural language processing, deep learning, and recommender systems.(Based on document published on 23 May 2022).\\\\n\\\\nPublications\\\\n\\\\n5\\\\n\\\\nCitations\\\\n\\\\n195\\\\n\\\\nPublications by Year\\\\n\\\\n20202024\\\\n\\\\nCo-Authors: [...] Renjun Xu - IEEE Xplore Author Profile\\\\n\\\\n# Author details\\\\n\\\\n< Back)\\\\n\\\\n# Renjun Xu\\\\n\\\\n## Affiliation\\\\n\\\\nCenter for Data Science\\\\n\\\\nZhejiang University\\\\n\\\\nHangzhou, China\\\\n\\\\n## Publication Topics\\\\n\\\\n Domain Adaptation,)\\\\n Target Language,)\\\\n Training Data,)\\\\n Wasserstein Distance,)\\\\n Acoustic Features,)\\\\n Adaptive Modulation,)\\\\n Adversarial Domain Adaptation,)\\\\n Attention Mechanism,)\\\\n Attention Operation,)\\\\n Attention Scores,)\\\\n Automatic Speech Recognition System,)\\\\n Auxiliary Task)\\\\n\\\\n## Biography [...] Xiaolin Zheng;Menghan Wang;Renjun Xu;Jianmeng Li;Yan Wang\\\\n\\\\nIEEE Transactions on Knowledge and Data Engineering\\\\n\\\\nYear: 2022 | Volume: 34, Issue: 1 | Journal Article |\\\\n\\\\n### Reliable Weighted Optimal Transport for Unsupervised Domain Adaptation\\\\n\\\\nRenjun Xu;Pelen Liu;Liyan Wang;Chao Chen;Jindong Wang\\\\n\\\\n2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)\\\\n\\\\nYear: 2020 | Conference Paper |\\\\n\\\\nCited by: Papers (90)\\\\n\\\\n HTML\", \"score\": 0.76617163}, {\"title\": \"Renjun Xu - Researcher, Zhejiang University - OpenReview\", \"url\": \"https://openreview.net/profile?id=~Renjun_Xu1\", \"content\": \"# Renjun Xu\\\\n\\\\n### Principal Researcher, Zhejiang University\\\\n\\\\n Joined September 2021\\\\n\\\\n#### Names\\\\n\\\\nRenjun Xu (Preferred)\\\\n\\\\n Suggest Name\\\\n\\\\n#### Emails\\\\n\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\@zju.edu.cn (Confirmed)\\\\n\\\\n Suggest Email\\\\n\\\\n#### Personal Links\\\\n\\\\nHomepage\\\\n\\\\nDBLP\\\\n\\\\nORCID\\\\n\\\\nSemantic Scholar\\\\n\\\\n Suggest URL\\\\n\\\\n#### Career & Education History\\\\n\\\\nPrincipal Researcher\\\\n\\\\nZhejiang University(zju.edu.cn)\\\\n\\\\n2018 – Present\\\\n\\\\n Suggest Position\\\\n\\\\n#### Advisors, Relations & Conflicts\\\\n\\\\nNo relations added\\\\n\\\\n Suggest Relation\\\\n\\\\n#### Expertise [...] equivariant neural network, domain adaptation, domain generalization, molecular, physics, contrastive learning, symmetry, crystal, phase transition\\\\n\\\\nPresent\\\\n\\\\n Suggest Expertise\", \"score\": 0.7154444}, {\"title\": \"Renyuan Xu Honored with Prestigious NSF CAREER Award\", \"url\": \"https://viterbischool.usc.edu/news/2024/06/renyuan-xu-honored-with-prestigious-nsf-career-award/\", \"content\": \"Xu is an emerging research leader who harnesses machine learning and probability tools to improve decision-making in fields that experience a high degree of uncertainty, such as the financial and economic systems, or in public policy, such as the design of fair contracts and the allocation of social resources. [...] WiSE Gabilan Assistant Professor of Industrial and Systems Engineering Renyuan Xu has been recognized with the prestigious National Science Foundation (NSF) CAREER Award for 2024.\\u202fThe award honors early-career faculty members with the potential to serve as academic role models in research and education to lead advances in their respective fields. The NSF selects CAREER Award recipients who are building a firm foundation for a lifetime of leadership in integrating education and research. [...] Xu joined the Daniel J. Epstein Department of Industrial and Systems Engineering in 2021 following a two-year role as a Hooke Research Fellow at Oxford University’s Mathematical Institute.\\\\n\\\\nShe completed her undergraduate studies in mathematics at the University of Science and Technology of China before moving to the U.S. for her Ph.D. at UC Berkeley in the Department of Industrial Engineering and Operations Research.\", \"score\": 0.6501347}, {\"title\": \"Renjun Xu - Center for Data Science, Zhejiang University | 人才画像\", \"url\": \"https://www.aminer.cn/profile/renjun-xu/53f42ceddabfaedd74d30355?source=bz1\", \"content\": \"Renjun Xu - Center for Data Science, Zhejiang University | 人才画像 - AMiner\\\\n\\\\n\\\\n\\\\nResearch\\\\n\\\\nCenter for Data Science Zhejiang University\\\\n\\\\n、《International Joint Conference on Artificial Intelligence》(IJCAI, CCF-A), 《IEEE Transactions on Knowledge and Data Engineering》(TKDE, CCF-A)交叉领域发表多篇国际顶尖期刊和会议文章，CVPR、AAAI、NIPS、TPAMI、TIP、TLT等顶级人工智能期刊和会议程序委员会委员，荣获2020年度世界人工智能大会青年优秀论文提名奖，指导并推荐的所有学生均已拿到麻省理工学院(MIT)、卡内基梅隆大学(CMU)等全球顶尖名校的offer！\\\\n\\\\nEducation\\\\n\\\\nSign in to view more\\\\n\\\\nExperience\\\\n\\\\nSign in to view more [...] Research Interests\\\\n\\\\n2012 2025\\\\n\\\\nPapers 共 39 篇 Patents 共 9 篇 Author Statistics Co-Author Similar Experts\\\\n\\\\nBy Year By Citation 主题筛选 期刊级别筛选 合作者筛选 合作机构筛选\\\\n\\\\n时间\\\\n\\\\n引用量\\\\n\\\\n主题\\\\n\\\\n期刊级别\\\\n\\\\n合作者\\\\n\\\\n合作机构\\\\n\\\\nAll 2025 2024 2023 2022 2021 2020 2015 2014 2013 2012 2010 2006\\\\n\\\\nDo PhD-level LLMs Truly Grasp Elementary Addition? Probing Rule Learning Vs. Memorization in Large Language Models\\\\n\\\\nYang Yan,Yu Lu,Renjun Xu,Zhenzhong Lan\\\\n\\\\narXiv · Computation and Language（2025）\\\\n\\\\nCited 0 Views 11 Bibtex\\\\n\\\\n0\\\\n\\\\n11 [...] The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: [email protected]\\\\n\\\\nSwipe to Fine Result\", \"score\": 0.60049355}, {\"title\": \"Renjun of NCT Dream, Chinese K-pop star who is ...\", \"url\": \"https://www.scmp.com/lifestyle/entertainment/article/3100230/renjun-nct-dream-chinese-k-pop-star-who-passionate-focused\", \"content\": \"Renjun was born Huang Ren-jun in Jilin province, northeast China, in March 2000. Korean is commonly spoken in the area because it is close to the Korean peninsula, so he grew up bilingual.\\\\n\\\\nA young Renjun was not just interested in studying the Korean language, but Korean music as well. He was inspired by the boy band Exo and, in particular, the international success of the band’s Chinese member Lay.\\\\n\\\\nAdvertisement [...] K-pop, Mandopop, other Asian pop\\\\n\\\\nLifestyleEntertainment\\\\n\\\\n# Renjun of NCT Dream, Chinese K-pop star who is passionate, focused and goal-driven\\\\n\\\\n### Renjun, born Huang Ren-jun, was so determined to become a K-pop singer that he travelled hours to go to an audition with less than a day’s notice The singer speaks fluent Korean, having grown up near the China-Korea border, and recently released his first solo cover, of Troye Sivan’s Fools, in English\\\\n\\\\nReading Time:3 minutes\\\\n\\\\nWhy you can trust SCMP [...] While his schoolmates set their sights on university, Renjun wanted to become an idol and elected to study at the Beijing Contemporary Music School (alongside NCT band member Chenle), from where he graduated with full marks.\\\\n\\\\nAdvertisement\\\\n\\\\nSelect Voice\\\\n\\\\nChoose your listening speed\\\\n\\\\nGet through articles 2x faster\\\\n\\\\n1.25x\\\\n\\\\n250WPM\\\\n\\\\n1.25x\", \"score\": 0.5976789}]', name='tavily_search_results_json', id='d266136a-8b29-4ccc-bbe7-0310d413c936', tool_call_id='call_LJcf7gAKs6EW1jfPmJga5BXQ', artifact={'query': 'Renjun Xu', 'follow_up_questions': None, 'answer': None, 'images': [], 'results': [{'url': 'https://ieeexplore.ieee.org/author/37089181301#:~:text=Renjun Xu received the PhD,CyberSource & Authorize.Net).', 'title': 'Renjun Xu | IEEE Xplore Author Details', 'content': 'Renjun Xu received the PhD degree from UC Davis, ZJU100 Young professor and PhD supervisor of the Center for Data Science, Zhejiang University. He was a senior director of Data and Artificial Intelligence, VISA Inc. (CyberSource & Authorize.Net). His research interests include natural language processing, deep learning, and recommender systems.(Based on document published on 23 May 2022).\\n\\nPublications\\n\\n5\\n\\nCitations\\n\\n195\\n\\nPublications by Year\\n\\n20202024\\n\\nCo-Authors: [...] Renjun Xu - IEEE Xplore Author Profile\\n\\n# Author details\\n\\n< Back)\\n\\n# Renjun Xu\\n\\n## Affiliation\\n\\nCenter for Data Science\\n\\nZhejiang University\\n\\nHangzhou, China\\n\\n## Publication Topics\\n\\n Domain Adaptation,)\\n Target Language,)\\n Training Data,)\\n Wasserstein Distance,)\\n Acoustic Features,)\\n Adaptive Modulation,)\\n Adversarial Domain Adaptation,)\\n Attention Mechanism,)\\n Attention Operation,)\\n Attention Scores,)\\n Automatic Speech Recognition System,)\\n Auxiliary Task)\\n\\n## Biography [...] Xiaolin Zheng;Menghan Wang;Renjun Xu;Jianmeng Li;Yan Wang\\n\\nIEEE Transactions on Knowledge and Data Engineering\\n\\nYear: 2022 | Volume: 34, Issue: 1 | Journal Article |\\n\\n### Reliable Weighted Optimal Transport for Unsupervised Domain Adaptation\\n\\nRenjun Xu;Pelen Liu;Liyan Wang;Chao Chen;Jindong Wang\\n\\n2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)\\n\\nYear: 2020 | Conference Paper |\\n\\nCited by: Papers (90)\\n\\n HTML', 'score': 0.76617163, 'raw_content': None}, {'url': 'https://openreview.net/profile?id=~Renjun_Xu1', 'title': 'Renjun Xu - Researcher, Zhejiang University - OpenReview', 'content': '# Renjun Xu\\n\\n### Principal Researcher, Zhejiang University\\n\\n Joined September 2021\\n\\n#### Names\\n\\nRenjun Xu (Preferred)\\n\\n Suggest Name\\n\\n#### Emails\\n\\n\\\\\\\\\\\\\\\\@zju.edu.cn (Confirmed)\\n\\n Suggest Email\\n\\n#### Personal Links\\n\\nHomepage\\n\\nDBLP\\n\\nORCID\\n\\nSemantic Scholar\\n\\n Suggest URL\\n\\n#### Career & Education History\\n\\nPrincipal Researcher\\n\\nZhejiang University(zju.edu.cn)\\n\\n2018 – Present\\n\\n Suggest Position\\n\\n#### Advisors, Relations & Conflicts\\n\\nNo relations added\\n\\n Suggest Relation\\n\\n#### Expertise [...] equivariant neural network, domain adaptation, domain generalization, molecular, physics, contrastive learning, symmetry, crystal, phase transition\\n\\nPresent\\n\\n Suggest Expertise', 'score': 0.7154444, 'raw_content': None}, {'url': 'https://viterbischool.usc.edu/news/2024/06/renyuan-xu-honored-with-prestigious-nsf-career-award/', 'title': 'Renyuan Xu Honored with Prestigious NSF CAREER Award', 'content': 'Xu is an emerging research leader who harnesses machine learning and probability tools to improve decision-making in fields that experience a high degree of uncertainty, such as the financial and economic systems, or in public policy, such as the design of fair contracts and the allocation of social resources. [...] WiSE Gabilan Assistant Professor of Industrial and Systems Engineering Renyuan Xu has been recognized with the prestigious National Science Foundation (NSF) CAREER Award for 2024.\\u202fThe award honors early-career faculty members with the potential to serve as academic role models in research and education to lead advances in their respective fields. The NSF selects CAREER Award recipients who are building a firm foundation for a lifetime of leadership in integrating education and research. [...] Xu joined the Daniel J. Epstein Department of Industrial and Systems Engineering in 2021 following a two-year role as a Hooke Research Fellow at Oxford University’s Mathematical Institute.\\n\\nShe completed her undergraduate studies in mathematics at the University of Science and Technology of China before moving to the U.S. for her Ph.D. at UC Berkeley in the Department of Industrial Engineering and Operations Research.', 'score': 0.6501347, 'raw_content': None}, {'url': 'https://www.aminer.cn/profile/renjun-xu/53f42ceddabfaedd74d30355?source=bz1', 'title': 'Renjun Xu - Center for Data Science, Zhejiang University | 人才画像', 'content': 'Renjun Xu - Center for Data Science, Zhejiang University | 人才画像 - AMiner\\n\\n\\n\\nResearch\\n\\nCenter for Data Science Zhejiang University\\n\\n、《International Joint Conference on Artificial Intelligence》(IJCAI, CCF-A), 《IEEE Transactions on Knowledge and Data Engineering》(TKDE, CCF-A)交叉领域发表多篇国际顶尖期刊和会议文章，CVPR、AAAI、NIPS、TPAMI、TIP、TLT等顶级人工智能期刊和会议程序委员会委员，荣获2020年度世界人工智能大会青年优秀论文提名奖，指导并推荐的所有学生均已拿到麻省理工学院(MIT)、卡内基梅隆大学(CMU)等全球顶尖名校的offer！\\n\\nEducation\\n\\nSign in to view more\\n\\nExperience\\n\\nSign in to view more [...] Research Interests\\n\\n2012 2025\\n\\nPapers 共 39 篇 Patents 共 9 篇 Author Statistics Co-Author Similar Experts\\n\\nBy Year By Citation 主题筛选 期刊级别筛选 合作者筛选 合作机构筛选\\n\\n时间\\n\\n引用量\\n\\n主题\\n\\n期刊级别\\n\\n合作者\\n\\n合作机构\\n\\nAll 2025 2024 2023 2022 2021 2020 2015 2014 2013 2012 2010 2006\\n\\nDo PhD-level LLMs Truly Grasp Elementary Addition? Probing Rule Learning Vs. Memorization in Large Language Models\\n\\nYang Yan,Yu Lu,Renjun Xu,Zhenzhong Lan\\n\\narXiv · Computation and Language（2025）\\n\\nCited 0 Views 11 Bibtex\\n\\n0\\n\\n11 [...] The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: [email protected]\\n\\nSwipe to Fine Result', 'score': 0.60049355, 'raw_content': None}, {'url': 'https://www.scmp.com/lifestyle/entertainment/article/3100230/renjun-nct-dream-chinese-k-pop-star-who-passionate-focused', 'title': 'Renjun of NCT Dream, Chinese K-pop star who is ...', 'content': 'Renjun was born Huang Ren-jun in Jilin province, northeast China, in March 2000. Korean is commonly spoken in the area because it is close to the Korean peninsula, so he grew up bilingual.\\n\\nA young Renjun was not just interested in studying the Korean language, but Korean music as well. He was inspired by the boy band Exo and, in particular, the international success of the band’s Chinese member Lay.\\n\\nAdvertisement [...] K-pop, Mandopop, other Asian pop\\n\\nLifestyleEntertainment\\n\\n# Renjun of NCT Dream, Chinese K-pop star who is passionate, focused and goal-driven\\n\\n### Renjun, born Huang Ren-jun, was so determined to become a K-pop singer that he travelled hours to go to an audition with less than a day’s notice The singer speaks fluent Korean, having grown up near the China-Korea border, and recently released his first solo cover, of Troye Sivan’s Fools, in English\\n\\nReading Time:3 minutes\\n\\nWhy you can trust SCMP [...] While his schoolmates set their sights on university, Renjun wanted to become an idol and elected to study at the Beijing Contemporary Music School (alongside NCT band member Chenle), from where he graduated with full marks.\\n\\nAdvertisement\\n\\nSelect Voice\\n\\nChoose your listening speed\\n\\nGet through articles 2x faster\\n\\n1.25x\\n\\n250WPM\\n\\n1.25x', 'score': 0.5976789, 'raw_content': None}], 'response_time': 1.44, 'request_id': 'e365ea89-e74e-43ae-8863-a2701785f929'})] \n",
"\n",
"\n",
"Receiving update from node: 'agent'\n",
"[AIMessage(content='Renjun Xu is currently a Principal Researcher at the Center for Data Science, Zhejiang University in Hangzhou, China.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 25, 'prompt_tokens': 2942, 'total_tokens': 2967, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4.1-nano-2025-04-14', 'system_fingerprint': 'fp_7c233bf9d1', 'id': 'chatcmpl-CLLXTbuMfIw7fLvU2BFbCymzXVGII', 'service_tier': 'default', 'finish_reason': 'stop', 'logprobs': None}, id='run--36294027-e2bc-4164-b055-2495711d05ec-0', usage_metadata={'input_tokens': 2942, 'output_tokens': 25, 'total_tokens': 2967, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})] \n",
"\n",
"\n"
]
}
],
"source": [
"from langchain_core.messages import SystemMessage, HumanMessage\n",
"\n",
"# when you run your graph:\n",
"inputs = {\n",
" \"messages\": [\n",
" SystemMessage(content=SYSTEM_PROMPT),\n",
" HumanMessage(content=\"Where do the authors of the 'A Comprehensive Survey of Deep Research' paper currently work?\")\n",
" ]\n",
"}\n",
"\n",
"async for chunk in simple_agent_graph.astream(inputs, stream_mode=\"updates\"):\n",
" for node, values in chunk.items():\n",
" print(f\"Receiving update from node: '{node}'\")\n",
" if node == \"action\":\n",
" print(f\"Tool Used: {values['messages'][0].name}\")\n",
" print(values[\"messages\"], \"\\n\\n\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CXzDlZVz1Hnf"
},
"source": [
"#### 🏗️ Activity #2:\n",
"\n",
"Please write out the steps the agent took to arrive at the correct answer."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jhTNe4kWrplB"
},
"source": [
"## Part 2: LangGraph with Helpfulness:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"HELPFULNESS_PROMPT_TEMPLATE = \"\"\"\\\n",
"Given an initial query and a final response, determine if the final response is extremely helpful or not. Please indicate helpfulness with a 'Y' and unhelpfulness as an 'N'.\n",
"\n",
"Initial Query:\n",
"{initial_query}\n",
"\n",
"Final Response:\n",
"{final_response}\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.prompts import PromptTemplate\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"\n",
"def tool_call_or_helpful(state):\n",
" last_message = state[\"messages\"][-1]\n",
"\n",
" if last_message.tool_calls:\n",
" return \"action\"\n",
"\n",
" initial_query = state[\"messages\"][0]\n",
" final_response = state[\"messages\"][-1]\n",
"\n",
" if len(state[\"messages\"]) > 10:\n",
" return END\n",
"\n",
" helpfullness_prompt_template = PromptTemplate.from_template(HELPFULNESS_PROMPT_TEMPLATE)\n",
"\n",
" helpfulness_check_model = ChatOpenAI(model=\"gpt-4.1-mini\")\n",
"\n",
" helpfulness_chain = helpfullness_prompt_template | helpfulness_check_model | StrOutputParser()\n",
"\n",
" helpfulness_response = helpfulness_chain.invoke({\"initial_query\" : initial_query.content, \"final_response\" : final_response.content})\n",
"\n",
" if \"Y\" in helpfulness_response:\n",
" return \"end\"\n",
" else:\n",
" return \"continue\""
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"id": "-LQ84YhyJG0w"
},
"outputs": [],
"source": [
"class AgentState(TypedDict):\n",
" messages: Annotated[list, add_messages]"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6r6XXA5FJbVf",
"outputId": "ff713041-e498-4f0f-a875-a03502b87729"
},
"outputs": [
{
"data": {
"text/plain": [
"<langgraph.graph.state.StateGraph at 0x7efc662402d0>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"graph_with_helpfulness_check = StateGraph(AgentState)\n",
"\n",
"graph_with_helpfulness_check.add_node(\"agent\", call_model)\n",
"graph_with_helpfulness_check.add_node(\"action\", tool_node)\n",
"graph_with_helpfulness_check.add_edge(\"action\", \"agent\")\n",
"graph_with_helpfulness_check.add_conditional_edges(\n",
" \"agent\",\n",
" tool_call_or_helpful,\n",
" {\n",
" \"continue\" : \"agent\",\n",
" \"action\" : \"action\",\n",
" \"end\" : END\n",
" }\n",
")\n",
"graph_with_helpfulness_check.set_entry_point(\"agent\")"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"id": "oQldl8ERQ8lf"
},
"outputs": [],
"source": [
"agent_with_helpfulness_check = graph_with_helpfulness_check.compile()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Receiving update from node: 'agent'\n",
"[AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_DGLhjtV8LC2XnUvLc9MKUp8I', 'function': {'arguments': '{\"query\":\"A Comprehensive Survey of Deep Research\"}', 'name': 'arxiv'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 19, 'prompt_tokens': 256, 'total_tokens': 275, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4.1-nano-2025-04-14', 'system_fingerprint': 'fp_7c233bf9d1', 'id': 'chatcmpl-CLLd0kU5syRABc3uY9KXdQjEA2FF2', 'service_tier': 'default', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run--4083de99-5128-4b7c-a719-2fbab48f10e5-0', tool_calls=[{'name': 'arxiv', 'args': {'query': 'A Comprehensive Survey of Deep Research'}, 'id': 'call_DGLhjtV8LC2XnUvLc9MKUp8I', 'type': 'tool_call'}], usage_metadata={'input_tokens': 256, 'output_tokens': 19, 'total_tokens': 275, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})]\n",
"\n",
"\n",
"\n",
"Receiving update from node: 'action'\n",
"[ToolMessage(content='Published: 2025-06-14\\nTitle: A Comprehensive Survey of Deep Research: Systems, Methodologies, and Applications\\nAuthors: Renjun Xu, Jingwen Peng\\nSummary: This survey examines the rapidly evolving field of Deep Research systems --\\nAI-powered applications that automate complex research workflows through the\\nintegration of large language models, advanced information retrieval, and\\nautonomous reasoning capabilities. We analyze more than 80 commercial and\\nnon-commercial implementations that have emerged since 2023, including\\nOpenAI/Deep Research, Gemini/Deep Research, Perplexity/Deep Research, and\\nnumerous open-source alternatives. Through comprehensive examination, we\\npropose a novel hierarchical taxonomy that categorizes systems according to\\nfour fundamental technical dimensions: foundation models and reasoning engines,\\ntool utilization and environmental interaction, task planning and execution\\ncontrol, and knowledge synthesis and output generation. We explore the\\narchitectural patterns, implementation approaches, and domain-specific\\nadaptations that characterize these systems across academic, scientific,\\nbusiness, and educational applications. Our analysis reveals both the\\nsignificant capabilities of current implementations and the technical and\\nethical challenges they present regarding information accuracy, privacy,\\nintellectual property, and accessibility. The survey concludes by identifying\\npromising research directions in advanced reasoning architectures, multimodal\\nintegration, domain specialization, human-AI collaboration, and ecosystem\\nstandardization that will likely shape the future evolution of this\\ntransformative technology. By providing a comprehensive framework for\\nunderstanding Deep Research systems, this survey contributes to both the\\ntheoretical understanding of AI-augmented knowledge work and the practical\\ndevelopment of more capable, responsible, and accessible research technologies.\\nThe paper resources can be viewed at\\nhttps://github.com/scienceaix/deepresearch.\\n\\nPublished: 2021-03-05\\nTitle: A comprehensive survey on point cloud registration\\nAuthors: Xiaoshui Huang, Guofeng Mei, Jian Zhang, Rana Abbas\\nSummary: Registration is a transformation estimation problem between two point clouds,\\nwhich has a unique and critical role in numerous computer vision applications.\\nThe developments of optimization-based methods and deep learning methods have\\nimproved registration robustness and efficiency. Recently, the combinations of\\noptimization-based and deep learning methods have further improved performance.\\nHowever, the connections between optimization-based and deep learning methods\\nare still unclear. Moreover, with the recent development of 3D sensors and 3D\\nreconstruction techniques, a new research direction emerges to align\\ncross-source point clouds. This survey conducts a comprehensive survey,\\nincluding both same-source and cross-source registration methods, and summarize\\nthe connections between optimization-based and deep learning methods, to\\nprovide further research insight. This survey also builds a new benchmark to\\nevaluate the state-of-the-art registration algorithms in solving cross-source\\nchallenges. Besides, this survey summarizes the benchmark data sets and\\ndiscusses point cloud registration applications across various domains.\\nFinally, this survey proposes potential research directions in this rapidly\\ngrowing field.\\n\\nPublished: 2023-07-07\\nTitle: A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision\\nAuthors: Zhonghan Zhao, Wenhao Chai, Shengyu Hao, Wenhao Hu, Guanhong Wang, Shidong Cao, Mingli Song, Jenq-Neng Hwang, Gaoang Wang\\nSummary: Deep learning has the potential to revolutionize sports performance, with\\napplications ranging from perception and comprehension to decision. This paper\\npresents a comprehensive survey of deep learning in sports performance,\\nfocusing on three main aspects: algorithms, datasets and virtual environments,\\nand challenges. Firstly, we discuss th', name='arxiv', id='5f75249a-5fc0-4d3e-9430-3ae4f904527f', tool_call_id='call_DGLhjtV8LC2XnUvLc9MKUp8I')]\n",
"\n",
"\n",
"\n",
"Receiving update from node: 'agent'\n",
"[AIMessage(content='The authors of the paper titled \"A Comprehensive Survey of Deep Research\" are Renjun Xu and Jingwen Peng. To find their current affiliations, I will now look up their current work locations.', additional_kwargs={'tool_calls': [{'id': 'call_7IXy7Lv6W0jPGxmYuKZBApXz', 'function': {'arguments': '{\"query\":\"Renjun Xu\"}', 'name': 'tavily_search_results_json'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 60, 'prompt_tokens': 1024, 'total_tokens': 1084, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4.1-nano-2025-04-14', 'system_fingerprint': 'fp_7c233bf9d1', 'id': 'chatcmpl-CLLd0qk2rAiqzoWHraLZ3ArhLzgjF', 'service_tier': 'default', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run--1b416280-0363-4342-9580-5c7576a58a91-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'Renjun Xu'}, 'id': 'call_7IXy7Lv6W0jPGxmYuKZBApXz', 'type': 'tool_call'}], usage_metadata={'input_tokens': 1024, 'output_tokens': 60, 'total_tokens': 1084, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})]\n",
"\n",
"\n",
"\n",
"Receiving update from node: 'action'\n",
"[ToolMessage(content='[{\"title\": \"Renjun Xu | IEEE Xplore Author Details\", \"url\": \"https://ieeexplore.ieee.org/author/37089181301#:~:text=Renjun Xu received the PhD,CyberSource & Authorize.Net).\", \"content\": \"Renjun Xu received the PhD degree from UC Davis, ZJU100 Young professor and PhD supervisor of the Center for Data Science, Zhejiang University. He was a senior director of Data and Artificial Intelligence, VISA Inc. (CyberSource & Authorize.Net). His research interests include natural language processing, deep learning, and recommender systems.(Based on document published on 23 May 2022).\\\\n\\\\nPublications\\\\n\\\\n5\\\\n\\\\nCitations\\\\n\\\\n195\\\\n\\\\nPublications by Year\\\\n\\\\n20202024\\\\n\\\\nCo-Authors: [...] Renjun Xu - IEEE Xplore Author Profile\\\\n\\\\n# Author details\\\\n\\\\n< Back)\\\\n\\\\n# Renjun Xu\\\\n\\\\n## Affiliation\\\\n\\\\nCenter for Data Science\\\\n\\\\nZhejiang University\\\\n\\\\nHangzhou, China\\\\n\\\\n## Publication Topics\\\\n\\\\n Domain Adaptation,)\\\\n Target Language,)\\\\n Training Data,)\\\\n Wasserstein Distance,)\\\\n Acoustic Features,)\\\\n Adaptive Modulation,)\\\\n Adversarial Domain Adaptation,)\\\\n Attention Mechanism,)\\\\n Attention Operation,)\\\\n Attention Scores,)\\\\n Automatic Speech Recognition System,)\\\\n Auxiliary Task)\\\\n\\\\n## Biography [...] Xiaolin Zheng;Menghan Wang;Renjun Xu;Jianmeng Li;Yan Wang\\\\n\\\\nIEEE Transactions on Knowledge and Data Engineering\\\\n\\\\nYear: 2022 | Volume: 34, Issue: 1 | Journal Article |\\\\n\\\\n### Reliable Weighted Optimal Transport for Unsupervised Domain Adaptation\\\\n\\\\nRenjun Xu;Pelen Liu;Liyan Wang;Chao Chen;Jindong Wang\\\\n\\\\n2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)\\\\n\\\\nYear: 2020 | Conference Paper |\\\\n\\\\nCited by: Papers (90)\\\\n\\\\n HTML\", \"score\": 0.76617163}, {\"title\": \"Renjun Xu - Researcher, Zhejiang University - OpenReview\", \"url\": \"https://openreview.net/profile?id=~Renjun_Xu1\", \"content\": \"# Renjun Xu\\\\n\\\\n### Principal Researcher, Zhejiang University\\\\n\\\\n Joined September 2021\\\\n\\\\n#### Names\\\\n\\\\nRenjun Xu (Preferred)\\\\n\\\\n Suggest Name\\\\n\\\\n#### Emails\\\\n\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\@zju.edu.cn (Confirmed)\\\\n\\\\n Suggest Email\\\\n\\\\n#### Personal Links\\\\n\\\\nHomepage\\\\n\\\\nDBLP\\\\n\\\\nORCID\\\\n\\\\nSemantic Scholar\\\\n\\\\n Suggest URL\\\\n\\\\n#### Career & Education History\\\\n\\\\nPrincipal Researcher\\\\n\\\\nZhejiang University(zju.edu.cn)\\\\n\\\\n2018 – Present\\\\n\\\\n Suggest Position\\\\n\\\\n#### Advisors, Relations & Conflicts\\\\n\\\\nNo relations added\\\\n\\\\n Suggest Relation\\\\n\\\\n#### Expertise [...] equivariant neural network, domain adaptation, domain generalization, molecular, physics, contrastive learning, symmetry, crystal, phase transition\\\\n\\\\nPresent\\\\n\\\\n Suggest Expertise\", \"score\": 0.7154444}, {\"title\": \"Renyuan Xu Honored with Prestigious NSF CAREER Award\", \"url\": \"https://viterbischool.usc.edu/news/2024/06/renyuan-xu-honored-with-prestigious-nsf-career-award/\", \"content\": \"Xu is an emerging research leader who harnesses machine learning and probability tools to improve decision-making in fields that experience a high degree of uncertainty, such as the financial and economic systems, or in public policy, such as the design of fair contracts and the allocation of social resources. [...] WiSE Gabilan Assistant Professor of Industrial and Systems Engineering Renyuan Xu has been recognized with the prestigious National Science Foundation (NSF) CAREER Award for 2024.\\u202fThe award honors early-career faculty members with the potential to serve as academic role models in research and education to lead advances in their respective fields. The NSF selects CAREER Award recipients who are building a firm foundation for a lifetime of leadership in integrating education and research. [...] Xu joined the Daniel J. Epstein Department of Industrial and Systems Engineering in 2021 following a two-year role as a Hooke Research Fellow at Oxford University’s Mathematical Institute.\\\\n\\\\nShe completed her undergraduate studies in mathematics at the University of Science and Technology of China before moving to the U.S. for her Ph.D. at UC Berkeley in the Department of Industrial Engineering and Operations Research.\", \"score\": 0.6501347}, {\"title\": \"Renjun Xu - Center for Data Science, Zhejiang University | 人才画像\", \"url\": \"https://www.aminer.cn/profile/renjun-xu/53f42ceddabfaedd74d30355?source=bz1\", \"content\": \"Renjun Xu - Center for Data Science, Zhejiang University | 人才画像 - AMiner\\\\n\\\\n\\\\n\\\\nResearch\\\\n\\\\nCenter for Data Science Zhejiang University\\\\n\\\\n、《International Joint Conference on Artificial Intelligence》(IJCAI, CCF-A), 《IEEE Transactions on Knowledge and Data Engineering》(TKDE, CCF-A)交叉领域发表多篇国际顶尖期刊和会议文章，CVPR、AAAI、NIPS、TPAMI、TIP、TLT等顶级人工智能期刊和会议程序委员会委员，荣获2020年度世界人工智能大会青年优秀论文提名奖，指导并推荐的所有学生均已拿到麻省理工学院(MIT)、卡内基梅隆大学(CMU)等全球顶尖名校的offer！\\\\n\\\\nEducation\\\\n\\\\nSign in to view more\\\\n\\\\nExperience\\\\n\\\\nSign in to view more [...] Research Interests\\\\n\\\\n2012 2025\\\\n\\\\nPapers 共 39 篇 Patents 共 9 篇 Author Statistics Co-Author Similar Experts\\\\n\\\\nBy Year By Citation 主题筛选 期刊级别筛选 合作者筛选 合作机构筛选\\\\n\\\\n时间\\\\n\\\\n引用量\\\\n\\\\n主题\\\\n\\\\n期刊级别\\\\n\\\\n合作者\\\\n\\\\n合作机构\\\\n\\\\nAll 2025 2024 2023 2022 2021 2020 2015 2014 2013 2012 2010 2006\\\\n\\\\nDo PhD-level LLMs Truly Grasp Elementary Addition? Probing Rule Learning Vs. Memorization in Large Language Models\\\\n\\\\nYang Yan,Yu Lu,Renjun Xu,Zhenzhong Lan\\\\n\\\\narXiv · Computation and Language（2025）\\\\n\\\\nCited 0 Views 11 Bibtex\\\\n\\\\n0\\\\n\\\\n11 [...] The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: [email protected]\\\\n\\\\nSwipe to Fine Result\", \"score\": 0.60049355}, {\"title\": \"Renjun of NCT Dream, Chinese K-pop star who is ...\", \"url\": \"https://www.scmp.com/lifestyle/entertainment/article/3100230/renjun-nct-dream-chinese-k-pop-star-who-passionate-focused\", \"content\": \"Renjun was born Huang Ren-jun in Jilin province, northeast China, in March 2000. Korean is commonly spoken in the area because it is close to the Korean peninsula, so he grew up bilingual.\\\\n\\\\nA young Renjun was not just interested in studying the Korean language, but Korean music as well. He was inspired by the boy band Exo and, in particular, the international success of the band’s Chinese member Lay.\\\\n\\\\nAdvertisement [...] K-pop, Mandopop, other Asian pop\\\\n\\\\nLifestyleEntertainment\\\\n\\\\n# Renjun of NCT Dream, Chinese K-pop star who is passionate, focused and goal-driven\\\\n\\\\n### Renjun, born Huang Ren-jun, was so determined to become a K-pop singer that he travelled hours to go to an audition with less than a day’s notice The singer speaks fluent Korean, having grown up near the China-Korea border, and recently released his first solo cover, of Troye Sivan’s Fools, in English\\\\n\\\\nReading Time:3 minutes\\\\n\\\\nWhy you can trust SCMP [...] While his schoolmates set their sights on university, Renjun wanted to become an idol and elected to study at the Beijing Contemporary Music School (alongside NCT band member Chenle), from where he graduated with full marks.\\\\n\\\\nAdvertisement\\\\n\\\\nSelect Voice\\\\n\\\\nChoose your listening speed\\\\n\\\\nGet through articles 2x faster\\\\n\\\\n1.25x\\\\n\\\\n250WPM\\\\n\\\\n1.25x\", \"score\": 0.5976789}]', name='tavily_search_results_json', id='0cc51716-83d8-4ca4-8d77-058ef04f1144', tool_call_id='call_7IXy7Lv6W0jPGxmYuKZBApXz', artifact={'query': 'Renjun Xu', 'follow_up_questions': None, 'answer': None, 'images': [], 'results': [{'url': 'https://ieeexplore.ieee.org/author/37089181301#:~:text=Renjun Xu received the PhD,CyberSource & Authorize.Net).', 'title': 'Renjun Xu | IEEE Xplore Author Details', 'content': 'Renjun Xu received the PhD degree from UC Davis, ZJU100 Young professor and PhD supervisor of the Center for Data Science, Zhejiang University. He was a senior director of Data and Artificial Intelligence, VISA Inc. (CyberSource & Authorize.Net). His research interests include natural language processing, deep learning, and recommender systems.(Based on document published on 23 May 2022).\\n\\nPublications\\n\\n5\\n\\nCitations\\n\\n195\\n\\nPublications by Year\\n\\n20202024\\n\\nCo-Authors: [...] Renjun Xu - IEEE Xplore Author Profile\\n\\n# Author details\\n\\n< Back)\\n\\n# Renjun Xu\\n\\n## Affiliation\\n\\nCenter for Data Science\\n\\nZhejiang University\\n\\nHangzhou, China\\n\\n## Publication Topics\\n\\n Domain Adaptation,)\\n Target Language,)\\n Training Data,)\\n Wasserstein Distance,)\\n Acoustic Features,)\\n Adaptive Modulation,)\\n Adversarial Domain Adaptation,)\\n Attention Mechanism,)\\n Attention Operation,)\\n Attention Scores,)\\n Automatic Speech Recognition System,)\\n Auxiliary Task)\\n\\n## Biography [...] Xiaolin Zheng;Menghan Wang;Renjun Xu;Jianmeng Li;Yan Wang\\n\\nIEEE Transactions on Knowledge and Data Engineering\\n\\nYear: 2022 | Volume: 34, Issue: 1 | Journal Article |\\n\\n### Reliable Weighted Optimal Transport for Unsupervised Domain Adaptation\\n\\nRenjun Xu;Pelen Liu;Liyan Wang;Chao Chen;Jindong Wang\\n\\n2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)\\n\\nYear: 2020 | Conference Paper |\\n\\nCited by: Papers (90)\\n\\n HTML', 'score': 0.76617163, 'raw_content': None}, {'url': 'https://openreview.net/profile?id=~Renjun_Xu1', 'title': 'Renjun Xu - Researcher, Zhejiang University - OpenReview', 'content': '# Renjun Xu\\n\\n### Principal Researcher, Zhejiang University\\n\\n Joined September 2021\\n\\n#### Names\\n\\nRenjun Xu (Preferred)\\n\\n Suggest Name\\n\\n#### Emails\\n\\n\\\\\\\\\\\\\\\\@zju.edu.cn (Confirmed)\\n\\n Suggest Email\\n\\n#### Personal Links\\n\\nHomepage\\n\\nDBLP\\n\\nORCID\\n\\nSemantic Scholar\\n\\n Suggest URL\\n\\n#### Career & Education History\\n\\nPrincipal Researcher\\n\\nZhejiang University(zju.edu.cn)\\n\\n2018 – Present\\n\\n Suggest Position\\n\\n#### Advisors, Relations & Conflicts\\n\\nNo relations added\\n\\n Suggest Relation\\n\\n#### Expertise [...] equivariant neural network, domain adaptation, domain generalization, molecular, physics, contrastive learning, symmetry, crystal, phase transition\\n\\nPresent\\n\\n Suggest Expertise', 'score': 0.7154444, 'raw_content': None}, {'url': 'https://viterbischool.usc.edu/news/2024/06/renyuan-xu-honored-with-prestigious-nsf-career-award/', 'title': 'Renyuan Xu Honored with Prestigious NSF CAREER Award', 'content': 'Xu is an emerging research leader who harnesses machine learning and probability tools to improve decision-making in fields that experience a high degree of uncertainty, such as the financial and economic systems, or in public policy, such as the design of fair contracts and the allocation of social resources. [...] WiSE Gabilan Assistant Professor of Industrial and Systems Engineering Renyuan Xu has been recognized with the prestigious National Science Foundation (NSF) CAREER Award for 2024.\\u202fThe award honors early-career faculty members with the potential to serve as academic role models in research and education to lead advances in their respective fields. The NSF selects CAREER Award recipients who are building a firm foundation for a lifetime of leadership in integrating education and research. [...] Xu joined the Daniel J. Epstein Department of Industrial and Systems Engineering in 2021 following a two-year role as a Hooke Research Fellow at Oxford University’s Mathematical Institute.\\n\\nShe completed her undergraduate studies in mathematics at the University of Science and Technology of China before moving to the U.S. for her Ph.D. at UC Berkeley in the Department of Industrial Engineering and Operations Research.', 'score': 0.6501347, 'raw_content': None}, {'url': 'https://www.aminer.cn/profile/renjun-xu/53f42ceddabfaedd74d30355?source=bz1', 'title': 'Renjun Xu - Center for Data Science, Zhejiang University | 人才画像', 'content': 'Renjun Xu - Center for Data Science, Zhejiang University | 人才画像 - AMiner\\n\\n\\n\\nResearch\\n\\nCenter for Data Science Zhejiang University\\n\\n、《International Joint Conference on Artificial Intelligence》(IJCAI, CCF-A), 《IEEE Transactions on Knowledge and Data Engineering》(TKDE, CCF-A)交叉领域发表多篇国际顶尖期刊和会议文章，CVPR、AAAI、NIPS、TPAMI、TIP、TLT等顶级人工智能期刊和会议程序委员会委员，荣获2020年度世界人工智能大会青年优秀论文提名奖，指导并推荐的所有学生均已拿到麻省理工学院(MIT)、卡内基梅隆大学(CMU)等全球顶尖名校的offer！\\n\\nEducation\\n\\nSign in to view more\\n\\nExperience\\n\\nSign in to view more [...] Research Interests\\n\\n2012 2025\\n\\nPapers 共 39 篇 Patents 共 9 篇 Author Statistics Co-Author Similar Experts\\n\\nBy Year By Citation 主题筛选 期刊级别筛选 合作者筛选 合作机构筛选\\n\\n时间\\n\\n引用量\\n\\n主题\\n\\n期刊级别\\n\\n合作者\\n\\n合作机构\\n\\nAll 2025 2024 2023 2022 2021 2020 2015 2014 2013 2012 2010 2006\\n\\nDo PhD-level LLMs Truly Grasp Elementary Addition? Probing Rule Learning Vs. Memorization in Large Language Models\\n\\nYang Yan,Yu Lu,Renjun Xu,Zhenzhong Lan\\n\\narXiv · Computation and Language（2025）\\n\\nCited 0 Views 11 Bibtex\\n\\n0\\n\\n11 [...] The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: [email protected]\\n\\nSwipe to Fine Result', 'score': 0.60049355, 'raw_content': None}, {'url': 'https://www.scmp.com/lifestyle/entertainment/article/3100230/renjun-nct-dream-chinese-k-pop-star-who-passionate-focused', 'title': 'Renjun of NCT Dream, Chinese K-pop star who is ...', 'content': 'Renjun was born Huang Ren-jun in Jilin province, northeast China, in March 2000. Korean is commonly spoken in the area because it is close to the Korean peninsula, so he grew up bilingual.\\n\\nA young Renjun was not just interested in studying the Korean language, but Korean music as well. He was inspired by the boy band Exo and, in particular, the international success of the band’s Chinese member Lay.\\n\\nAdvertisement [...] K-pop, Mandopop, other Asian pop\\n\\nLifestyleEntertainment\\n\\n# Renjun of NCT Dream, Chinese K-pop star who is passionate, focused and goal-driven\\n\\n### Renjun, born Huang Ren-jun, was so determined to become a K-pop singer that he travelled hours to go to an audition with less than a day’s notice The singer speaks fluent Korean, having grown up near the China-Korea border, and recently released his first solo cover, of Troye Sivan’s Fools, in English\\n\\nReading Time:3 minutes\\n\\nWhy you can trust SCMP [...] While his schoolmates set their sights on university, Renjun wanted to become an idol and elected to study at the Beijing Contemporary Music School (alongside NCT band member Chenle), from where he graduated with full marks.\\n\\nAdvertisement\\n\\nSelect Voice\\n\\nChoose your listening speed\\n\\nGet through articles 2x faster\\n\\n1.25x\\n\\n250WPM\\n\\n1.25x', 'score': 0.5976789, 'raw_content': None}], 'response_time': 1.62, 'request_id': '6019347f-8469-4aea-9934-e457eec7aaf1'})]\n",
"\n",
"\n",
"\n",
"Receiving update from node: 'agent'\n",
"[AIMessage(content='Renjun Xu is currently a Principal Researcher at the Center for Data Science, Zhejiang University in Hangzhou, China.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 25, 'prompt_tokens': 2949, 'total_tokens': 2974, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4.1-nano-2025-04-14', 'system_fingerprint': 'fp_04d3664870', 'id': 'chatcmpl-CLLd4c5sajziN9LXftjVn8n8S0rJ4', 'service_tier': 'default', 'finish_reason': 'stop', 'logprobs': None}, id='run--60151b1d-05c3-485c-9add-9523e1828113-0', usage_metadata={'input_tokens': 2949, 'output_tokens': 25, 'total_tokens': 2974, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})]\n",
"\n",
"\n",
"\n"
]
}
],
"source": [
"inputs = {\n",
" \"messages\": [\n",
" SystemMessage(content=SYSTEM_PROMPT),\n",
" HumanMessage(\n",
" content=(\n",
" \"Where do the authors of the 'A Comprehensive Survey of Deep Research' \"\n",
" \"paper currently work?\"\n",
" )\n",
" ),\n",
" ]\n",
"}\n",
"\n",
"async for chunk in agent_with_helpfulness_check.astream(inputs, stream_mode=\"updates\"):\n",
" for node, values in chunk.items():\n",
" print(f\"Receiving update from node: '{node}'\")\n",
" print(values[\"messages\"])\n",
" print(\"\\n\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Receiving update from node: 'agent'\n",
"[AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_bOT9mwyPBklBoMy7w2h3erZ8', 'function': {'arguments': '{\"query\":\"Deep Research Agents\"}', 'name': 'tavily_search_results_json'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 20, 'prompt_tokens': 158, 'total_tokens': 178, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4.1-nano-2025-04-14', 'system_fingerprint': 'fp_7c233bf9d1', 'id': 'chatcmpl-CLLdI3q3u63HzZv4sC28YB9RF1ufy', 'service_tier': 'default', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run--4fb1ad8d-225a-4459-88e8-85db22ec451a-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'Deep Research Agents'}, 'id': 'call_bOT9mwyPBklBoMy7w2h3erZ8', 'type': 'tool_call'}], usage_metadata={'input_tokens': 158, 'output_tokens': 20, 'total_tokens': 178, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})]\n",
"\n",
"\n",
"\n",
"Receiving update from node: 'action'\n",
"[ToolMessage(content='[{\"title\": \"Open-Source Deep Research Agents: Enterprise-Grade ...\", \"url\": \"https://sambanova.ai/blog/open-source-deep-research-agents\", \"content\": \"March 10, 2025\\\\n\\\\nAgents have emerged as a major trend for AI in 2025. Specifically, Deep Research agents are one of the most notable examples that we have seen released from companies such as Google, OpenAI, Perplexity, X.AI, and just recently Manus. All these agents allow users to generate detailed reports on any topic of their choosing as long as the data is publicly accessible. However, there are three major critical barriers that enterprises face using these tools today: [...] Finally, based on this analysis, a trading desk analyst would want to generate a comprehensive report summarizing and citing findings from various articles. Using the Deep Research Agent, the system compiles information from a wide range of sources and uses them to generate a final report for the analyst, which can be cleaned up and submitted for review. Generating the final report requires more than 50K tokens. [...] Deep Research, by definition, is meant to go in depth on a topic and come back to a user with a compelling report and analysis that would typically take days for a human to produce. Unlike traditional LLM chat applications, Deep Research and Agentic AI require 10X, sometimes up to 100X, the tokens to generate compelling responses. Instead of days of human research, current Deep Research implementations aim to generate that same compelling report in about the time it takes to get coffee at\", \"score\": 0.91490877}, {\"title\": \"Deep Research Agents: A Systematic Examination And Roadmap\", \"url\": \"https://arxiv.org/abs/2506.18096\", \"content\": \"> Abstract:The rapid progress of Large Language Models (LLMs) has given rise to a new category of autonomous AI systems, referred to as Deep Research (DR) agents. These agents are designed to tackle complex, multi-turn informational research tasks by leveraging a combination of dynamic reasoning, adaptive long-horizon planning, multi-hop information retrieval, iterative tool use, and the generation of structured analytical reports. In this paper, we conduct a detailed analysis of the [...] foundational technologies and architectural components that constitute Deep Research agents. We begin by reviewing information acquisition strategies, contrasting API-based retrieval methods with browser-based exploration. We then examine modular tool-use frameworks, including code execution, multimodal input processing, and the integration of Model Context Protocols (MCPs) to support extensibility and ecosystem development. To systematize existing approaches, we propose a taxonomy that [...] Authors:Yuxuan Huang, Yihang Chen, Haozheng Zhang, Kang Li, Huichi Zhou, Meng Fang, Linyi Yang, Xiaoguang Li, Lifeng Shang, Songcen Xu, Jianye Hao, Kun Shao, Jun Wang\\\\n\\\\nView a PDF of the paper titled Deep Research Agents: A Systematic Examination And Roadmap, by Yuxuan Huang and 12 other authors\", \"score\": 0.89567816}, {\"title\": \"Building Deep Research AI Agents with Claude 3.7\", \"url\": \"https://medium.com/aingineer/building-deep-research-ai-agents-with-claude-3-7-a-working-implementation-part-2-9b629ac94506\", \"content\": \"The Deep Research Agent will consist of several interconnected components:\\\\n\\\\n1. Research Planner: Maps out research strategies based on queries\\\\n2. Document Retriever: Accesses and retrieves relevant documents\\\\n3. Information Extractor: Pulls key information from documents\\\\n4. Synthesis Engine: Combines information into coherent narratives\\\\n5. Insight Generator: Identifies patterns, gaps, and opportunities\\\\n6. Citation Manager: Tracks and formats citations properly\\\\n\\\\n## Agent Anatomy & Architecture [...] ## Deep Research Agent Implementation\\\\n\\\\nThe Deep Research Agent demonstrates how to leverage model like Claude 3.7’s reasoning capabilities to create a comprehensive research pipeline. This agent can autonomously plan research, gather information, extract key points, synthesize findings, and generate insights.\\\\n\\\\n## open-tools/stock\\\\\\\\_analysis\\\\\\\\_researcher.py at main · nigamgai-agents/open-tools\\\\n\\\\n### Contribute to nigamgai-agents/open-tools development by creating an account on GitHub.\\\\n\\\\ngithub.com [...] Research agents represent one of the most valuable applications of Claude 3.7’s capabilities. They can autonomously gather information from various sources, synthesize findings, identify patterns, and generate insights — tasks that typically require significant human expertise and time.\\\\n\\\\n## Get Gaurav Nigam’s stories in your inbox\\\\n\\\\nJoin Medium for free to get updates from this writer.\\\\n\\\\nThe Deep Research Agent will be able to:\", \"score\": 0.87348515}, {\"title\": \"OpenAI\\'s Deep Research on Training AI Agents End-to-End\", \"url\": \"https://www.sequoiacap.com/podcast/training-data-deep-research/\", \"content\": \"Isa Fulford: So Deep Research is an agent that is able to search many online websites, and it can create very comprehensive reports. It can do tasks that would take humans many hours to complete. And it’s in ChatGPT, and it takes like five to thirty minutes to answer you. And so it’s able to do much more in-depth research and answer your questions with much more detail and specific sources than regular ChatGPT response would be able to do.\", \"score\": 0.86108357}, {\"title\": \"Understanding the AI Agent Tool Deep Research | Built In\", \"url\": \"https://builtin.com/articles/understanding-chatgpt-deep-research\", \"content\": \"## What Is Deep Research?\\\\n\\\\nDeep research is an agentic AI tool integrated into AI platforms like ChatGPT and Gemini, allowing it to perform context-rich investigations across multiple sources, domains and timeframes. It can browse online sources in real time, upload data sources and synthesize information into comprehensive reports. [...] Artificial Intelligence\\\\n Expert Contributors\\\\n Machine Learning\\\\n +1\\\\n Natural Language Processing\\\\n +3\\\\n\\\\n# Understanding the AI Agent Tool Deep Research\\\\n\\\\nDeep Research is an agentic AI tool integrated into AI platforms like ChatGPT to conduct context-rich investigations across multiple sources, domains and timeframes. Here’s what you need to know.\\\\n\\\\nWritten by\\\\nVinay Goel\\\\n\\\\nPublished on May. 20, 2025\\\\n\\\\nImage: Shutterstock / Built In [...] Deep research, a term for the AI agent tool integrated into ChatGPT, Gemini and other AI tools, can perform high-quality, context-rich investigations across multiple sources, domains, and timeframes. Unlike regular search engines or chatbots that provide quick answers or web links, deep research thinks like an actual human. It actively browses online sources in real time, internally uploads data sources, checks multiple references and synthesizes information into comprehensive reports. Its\", \"score\": 0.8576849}]', name='tavily_search_results_json', id='d20d153c-e8bb-4982-a849-01f2380c7676', tool_call_id='call_bOT9mwyPBklBoMy7w2h3erZ8', artifact={'query': 'Deep Research Agents', 'follow_up_questions': None, 'answer': None, 'images': [], 'results': [{'url': 'https://sambanova.ai/blog/open-source-deep-research-agents', 'title': 'Open-Source Deep Research Agents: Enterprise-Grade ...', 'content': 'March 10, 2025\\n\\nAgents have emerged as a major trend for AI in 2025. Specifically, Deep Research agents are one of the most notable examples that we have seen released from companies such as Google, OpenAI, Perplexity, X.AI, and just recently Manus. All these agents allow users to generate detailed reports on any topic of their choosing as long as the data is publicly accessible. However, there are three major critical barriers that enterprises face using these tools today: [...] Finally, based on this analysis, a trading desk analyst would want to generate a comprehensive report summarizing and citing findings from various articles. Using the Deep Research Agent, the system compiles information from a wide range of sources and uses them to generate a final report for the analyst, which can be cleaned up and submitted for review. Generating the final report requires more than 50K tokens. [...] Deep Research, by definition, is meant to go in depth on a topic and come back to a user with a compelling report and analysis that would typically take days for a human to produce. Unlike traditional LLM chat applications, Deep Research and Agentic AI require 10X, sometimes up to 100X, the tokens to generate compelling responses. Instead of days of human research, current Deep Research implementations aim to generate that same compelling report in about the time it takes to get coffee at', 'score': 0.91490877, 'raw_content': None}, {'url': 'https://arxiv.org/abs/2506.18096', 'title': 'Deep Research Agents: A Systematic Examination And Roadmap', 'content': '> Abstract:The rapid progress of Large Language Models (LLMs) has given rise to a new category of autonomous AI systems, referred to as Deep Research (DR) agents. These agents are designed to tackle complex, multi-turn informational research tasks by leveraging a combination of dynamic reasoning, adaptive long-horizon planning, multi-hop information retrieval, iterative tool use, and the generation of structured analytical reports. In this paper, we conduct a detailed analysis of the [...] foundational technologies and architectural components that constitute Deep Research agents. We begin by reviewing information acquisition strategies, contrasting API-based retrieval methods with browser-based exploration. We then examine modular tool-use frameworks, including code execution, multimodal input processing, and the integration of Model Context Protocols (MCPs) to support extensibility and ecosystem development. To systematize existing approaches, we propose a taxonomy that [...] Authors:Yuxuan Huang, Yihang Chen, Haozheng Zhang, Kang Li, Huichi Zhou, Meng Fang, Linyi Yang, Xiaoguang Li, Lifeng Shang, Songcen Xu, Jianye Hao, Kun Shao, Jun Wang\\n\\nView a PDF of the paper titled Deep Research Agents: A Systematic Examination And Roadmap, by Yuxuan Huang and 12 other authors', 'score': 0.89567816, 'raw_content': None}, {'url': 'https://medium.com/aingineer/building-deep-research-ai-agents-with-claude-3-7-a-working-implementation-part-2-9b629ac94506', 'title': 'Building Deep Research AI Agents with Claude 3.7', 'content': 'The Deep Research Agent will consist of several interconnected components:\\n\\n1. Research Planner: Maps out research strategies based on queries\\n2. Document Retriever: Accesses and retrieves relevant documents\\n3. Information Extractor: Pulls key information from documents\\n4. Synthesis Engine: Combines information into coherent narratives\\n5. Insight Generator: Identifies patterns, gaps, and opportunities\\n6. Citation Manager: Tracks and formats citations properly\\n\\n## Agent Anatomy & Architecture [...] ## Deep Research Agent Implementation\\n\\nThe Deep Research Agent demonstrates how to leverage model like Claude 3.7’s reasoning capabilities to create a comprehensive research pipeline. This agent can autonomously plan research, gather information, extract key points, synthesize findings, and generate insights.\\n\\n## open-tools/stock\\\\_analysis\\\\_researcher.py at main · nigamgai-agents/open-tools\\n\\n### Contribute to nigamgai-agents/open-tools development by creating an account on GitHub.\\n\\ngithub.com [...] Research agents represent one of the most valuable applications of Claude 3.7’s capabilities. They can autonomously gather information from various sources, synthesize findings, identify patterns, and generate insights — tasks that typically require significant human expertise and time.\\n\\n## Get Gaurav Nigam’s stories in your inbox\\n\\nJoin Medium for free to get updates from this writer.\\n\\nThe Deep Research Agent will be able to:', 'score': 0.87348515, 'raw_content': None}, {'url': 'https://www.sequoiacap.com/podcast/training-data-deep-research/', 'title': \"OpenAI's Deep Research on Training AI Agents End-to-End\", 'content': 'Isa Fulford: So Deep Research is an agent that is able to search many online websites, and it can create very comprehensive reports. It can do tasks that would take humans many hours to complete. And it’s in ChatGPT, and it takes like five to thirty minutes to answer you. And so it’s able to do much more in-depth research and answer your questions with much more detail and specific sources than regular ChatGPT response would be able to do.', 'score': 0.86108357, 'raw_content': None}, {'url': 'https://builtin.com/articles/understanding-chatgpt-deep-research', 'title': 'Understanding the AI Agent Tool Deep Research | Built In', 'content': '## What Is Deep Research?\\n\\nDeep research is an agentic AI tool integrated into AI platforms like ChatGPT and Gemini, allowing it to perform context-rich investigations across multiple sources, domains and timeframes. It can browse online sources in real time, upload data sources and synthesize information into comprehensive reports. [...] Artificial Intelligence\\n Expert Contributors\\n Machine Learning\\n +1\\n Natural Language Processing\\n +3\\n\\n# Understanding the AI Agent Tool Deep Research\\n\\nDeep Research is an agentic AI tool integrated into AI platforms like ChatGPT to conduct context-rich investigations across multiple sources, domains and timeframes. Here’s what you need to know.\\n\\nWritten by\\nVinay Goel\\n\\nPublished on May. 20, 2025\\n\\nImage: Shutterstock / Built In [...] Deep research, a term for the AI agent tool integrated into ChatGPT, Gemini and other AI tools, can perform high-quality, context-rich investigations across multiple sources, domains, and timeframes. Unlike regular search engines or chatbots that provide quick answers or web links, deep research thinks like an actual human. It actively browses online sources in real time, internally uploads data sources, checks multiple references and synthesizes information into comprehensive reports. Its', 'score': 0.8576849, 'raw_content': None}], 'response_time': 3.29, 'request_id': '363b052a-04a1-434d-ae74-50c4ca5e58e3'})]\n",
"\n",
"\n",
"\n",
"Receiving update from node: 'agent'\n",
"[AIMessage(content='Deep Research Agents are advanced autonomous AI systems designed to perform in-depth, multi-step research tasks across various sources and domains. They leverage technologies such as dynamic reasoning, long-horizon planning, multi-hop information retrieval, iterative tool use, and structured report generation. These agents can autonomously gather, analyze, and synthesize information from multiple sources to produce comprehensive reports and insights that would typically require days of human effort. They are integrated into platforms like ChatGPT and other AI tools, enabling real-time browsing, data uploading, and detailed investigation to support complex research and decision-making processes. Notable examples include implementations from companies like Google, OpenAI, and others, aiming to significantly accelerate and deepen the research process.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 142, 'prompt_tokens': 1638, 'total_tokens': 1780, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4.1-nano-2025-04-14', 'system_fingerprint': 'fp_7c233bf9d1', 'id': 'chatcmpl-CLLdN8y0js0g6DH5DLTkF0FG974QY', 'service_tier': 'default', 'finish_reason': 'stop', 'logprobs': None}, id='run--c1b91442-2b8c-4506-a5af-7642d860a143-0', usage_metadata={'input_tokens': 1638, 'output_tokens': 142, 'total_tokens': 1780, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})]\n",
"\n",
"\n",
"\n"
]
}
],
"source": [
"inputs = {\"messages\" : [HumanMessage(content=\"What are Deep Research Agents?\")]}\n",
"\n",
"async for chunk in agent_with_helpfulness_check.astream(inputs, stream_mode=\"updates\"):\n",
" for node, values in chunk.items():\n",
" print(f\"Receiving update from node: '{node}'\")\n",
" print(values[\"messages\"])\n",
" print(\"\\n\\n\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "05-our-first-agent-with-langgraph (3.13.2)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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