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Building an Autonomous AI Researcher with OpenAI and LangChain: A Complete Step-by-Step Guide

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Introduction

Research is an important part of almost every industry. Whether someone is working on a college project, preparing a technical document, studying a new technology, or analyzing a business idea, finding the right information takes time. The difficult part is not always finding information. It is reading multiple sources, understanding different viewpoints, checking whether the information is reliable, and bringing everything together into a useful report.

Building an Autonomous AI Researcher with OpenAI and LangChain: A Complete Step-by-Step Guide

Imagine having an assistant that can take a research topic, search the internet, find relevant articles and academic papers, extract important information, summarize the findings, and prepare a structured report. Instead of manually going through every step, you could give the assistant a topic and let it handle much of the initial research work.

This is the idea behind an autonomous AI researcher.

By combining OpenAI models with LangChain, developers can build an AI-powered research application that uses external tools, searches the web, reads documents, and organizes information into a final report. It is not simply a chatbot that generates answers. It is an application that can perform multiple connected tasks based on the research request.

For example, if someone asks the application to research the impact of artificial intelligence on healthcare, it can search for relevant sources, identify useful research papers, extract important findings, compare the available information, and prepare a report with references.

In this guide, we will build a practical AI research workflow using Python, OpenAI, and LangChain. We will cover environment setup, web search integration, academic paper discovery, PDF processing, research summarization, report generation, and some important practices for making the application more reliable.

1. What Is an Autonomous AI Researcher?

An autonomous AI researcher is an AI-powered application that can complete several research-related tasks with limited manual involvement. It receives a research topic, decides which information it needs, uses available tools to collect that information, and produces an organized result.

Traditional research usually involves opening search engines, visiting websites, downloading papers, reading documents, taking notes, and writing a report. An AI research agent can assist with many of these activities through a connected workflow.

For example, suppose you enter the following research request:

"Research how generative AI is changing software development. Find recent articles and academic papers, identify the major benefits and limitations, and prepare a report with references."

The research agent can break this request into smaller tasks. It may begin by searching for recent information, then look for academic publications, collect relevant content, summarize the important findings, and organize the results into sections.

The important point is that an agent does not need to perform every operation through the language model itself. It can use external tools to search the internet, retrieve documents, and process information.

What can the research agent do?

  • Web research: Search for relevant information from websites and online publications.
  • Academic research: Discover research papers from sources such as arXiv and other scholarly repositories.
  • Document reading: Extract text from PDF files and other supported documents.
  • Summarization: Convert lengthy documents into understandable summaries.
  • Information comparison: Identify similarities, differences, and conflicting findings across sources.
  • Report writing: Prepare a structured report with sections, conclusions, and references.

These capabilities can be useful for students, developers, researchers, analysts, content teams, and businesses that regularly work with large amounts of information.

2. Understanding the Technologies Used

Before writing the code, it is worth understanding the role of each technology in our project. OpenAI and LangChain work together, but they solve different parts of the problem.

OpenAI

OpenAI provides the language model that understands research requests, analyzes information, summarizes documents, and generates written content. The model acts as the reasoning and language-generation component of the application.

It can also work with tools when the application provides the required tool definitions. This allows the model to request a web search or another operation instead of trying to answer everything from its existing knowledge.

LangChain

LangChain is a framework for connecting language models with tools, prompts, and external data sources. It helps developers build applications in which a model can decide when to use a tool and how to work with the returned information.

For our research application, LangChain will help connect the OpenAI model to a web-search tool and custom research functions.

LangGraph and LangChain Agents

Modern LangChain agents use LangGraph-based execution to manage the tool-calling loop. This is useful when a task requires multiple steps rather than one model response.

For example, the research agent might search for a topic, inspect the search results, perform another search to fill an information gap, and then prepare the report.

We will use LangChain's create_agent approach for the basic implementation. This provides a practical starting point without requiring us to manually build every stage of the agent loop.

Technology comparison

Technology Purpose How We Use It
OpenAI Language model and reasoning Understand questions and generate research reports.
LangChain Agent and tool integration Connect the model with research tools.
LangGraph Agent workflow execution Manage multi-step agent interactions.
Tavily Web search Find relevant online sources.
arXiv Academic paper discovery Find research publications.
PyPDF PDF document processing Extract text from downloaded research papers.
Python Application development Connect all the components.

3. Setting Up the Development Environment

We will use Python because it has a strong ecosystem for AI development, document processing, web requests, and research automation.

Before starting, make sure that Python is installed on your computer. You can download it from the official Python website.

Download Python

Create a new folder for the project.

mkdir autonomous-ai-researcher

cd autonomous-ai-researcher

It is a good idea to use a virtual environment so that the dependencies for this project remain separate from other Python applications.

python -m venv venv

# Windows
venv\Scripts\activate

# macOS and Linux
source venv/bin/activate

Next, install the packages required for our implementation.

pip install -U openai langchain langchain-openai langchain-tavily python-dotenv requests pypdf

These packages provide the OpenAI integration, LangChain agent framework, Tavily search integration, environment-variable management, HTTP requests, and PDF text extraction.

Configure your API keys

Our application requires an OpenAI API key and a Tavily API key.

The OpenAI key is used to access the model, while Tavily provides the web-search functionality.

Create a file named .env in your project directory.

OPENAI_API_KEY=your_openai_api_key
TAVILY_API_KEY=your_tavily_api_key
OPENAI_MODEL=your_supported_tool_calling_model

Replace these example values with your actual credentials and a model available to your API account.

Important: Never share your API keys publicly or include them directly in frontend code. Add your .env file to .gitignore before pushing the project to a repository.

4. Understanding the Research Workflow

Before writing the complete implementation, let's understand how the application will work.

We do not want the assistant to search randomly and immediately generate a report. A useful research workflow should have a clear sequence of activities.

For our project, we will use the following workflow:

  1. Receive the topic: The user provides a research question or subject.
  2. Understand the objective: The agent identifies what information needs to be collected.
  3. Search the web: The search tool finds relevant articles, documentation, and publications.
  4. Discover research papers: The application searches academic sources such as arXiv.
  5. Read relevant documents: Available PDF documents are processed to extract their contents.
  6. Summarize findings: The model identifies important information from the collected sources.
  7. Prepare the report: The assistant organizes the findings into a readable document.
  8. Review references: The final output includes source information so readers can verify the claims.

This workflow gives the research agent a clear purpose. It also makes debugging easier because each stage can be tested separately.

5. Creating a Web Search Tool with Tavily

One of the first capabilities our research agent needs is web search.

Without a search tool, the model is mainly working with information available through its existing knowledge and the content provided in the current conversation. That is not enough for research that requires recent publications or changing information.

Tavily is a search service designed for applications that use AI agents. It returns search results that can be passed to a language model for further analysis.

First, we will connect Tavily to our Python application using the LangChain integration.

Step 1: Import the required libraries

import os

from dotenv import load_dotenv
from langchain_tavily import TavilySearch

Step 2: Load environment variables

load_dotenv()

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is missing")

if not TAVILY_API_KEY:
    raise ValueError("TAVILY_API_KEY is missing")

This loads the API credentials from the .env file and checks that the required keys are available.

Step 3: Create the search tool

search_tool = TavilySearch(
    max_results=5,
    topic="general",
    include_raw_content=False
)

The search tool can now retrieve information based on a search query.

For example, a request such as latest research on large language models can return relevant pages and their available descriptions.

You can adjust the number of results depending on the size of the research task. However, collecting more results does not automatically make a report more accurate. Source quality and relevance are much more important than simply collecting a large number of pages.

Testing the search tool

results = search_tool.invoke({
    "query": "Recent research on generative AI in software development"
})

print(results)

Run this code independently before connecting it to the agent. If it returns useful search results, the web-search integration is working.

Developer Note: Search results are starting points for research, not automatically verified facts. The agent should distinguish between original research, official documentation, news articles, opinion pieces, and secondary summaries.

6. Finding Academic Research Papers

Web articles are useful, but a research application should also be able to discover academic papers.

Academic publications often provide a deeper explanation of a topic, including research methods, experiments, limitations, and conclusions. They are especially useful when the goal is to prepare a technical or educational report.

For this project, we will use arXiv as an example of an academic paper discovery source.

arXiv provides access to research papers across areas such as computer science, mathematics, physics, and related disciplines.

We can query its public API using Python's Requests library.

Creating an arXiv search function

import requests
import xml.etree.ElementTree as ET

def search_arxiv(query, max_results=5):

    url = "https://export.arxiv.org/api/query"

    params = {
        "search_query": f"all:{query}",
        "start": 0,
        "max_results": max_results,
        "sortBy": "relevance",
        "sortOrder": "descending"
    }

    response = requests.get(
        url,
        params=params,
        timeout=20
    )

    response.raise_for_status()

    root = ET.fromstring(response.content)

    namespace = {
        "atom": "http://www.w3.org/2005/Atom"
    }

    papers = []

    for entry in root.findall("atom:entry", namespace):

        title = entry.findtext(
            "atom:title",
            default="",
            namespaces=namespace
        )

        summary = entry.findtext(
            "atom:summary",
            default="",
            namespaces=namespace
        )

        paper_url = entry.findtext(
            "atom:id",
            default="",
            namespaces=namespace
        )

        papers.append({
            "title": title.strip(),
            "abstract": summary.strip(),
            "url": paper_url
        })

    return papers

This function sends a search request to arXiv and extracts the title, abstract, and paper URL from the returned XML document.

Let's test it with a simple query.

papers = search_arxiv(
    "large language models",
    max_results=5
)

for paper in papers:

    print("Title:", paper["title"])
    print("Abstract:", paper["abstract"])
    print("URL:", paper["url"])
    print("-" * 60)

The output will contain relevant papers returned by the API. The actual results depend on the query and the publications available when the request is made.

One thing to remember is that arXiv contains preprints as well as research papers that may later appear in conferences or journals. A paper appearing on arXiv does not automatically mean that it has undergone peer review.

7. Reading Research Papers in PDF Format

Finding a paper is only one part of the research process. The next challenge is understanding its contents.

Many research papers are available as PDF documents. Reading every document manually can become time-consuming, particularly when the project involves several publications.

We can use PyPDFLoader to extract text from PDF files and prepare it for summarization.

Install the document-loading integration

pip install -U langchain-community pypdf

Create a PDF reading function

from langchain_community.document_loaders import PyPDFLoader

def read_research_pdf(pdf_path):

    loader = PyPDFLoader(pdf_path)

    documents = loader.load()

    full_text = "\n\n".join(
        document.page_content
        for document in documents
    )

    return full_text

Now provide the path to a research paper that you have downloaded and are permitted to process.

paper_text = read_research_pdf(
    "research_paper.pdf"
)

print(paper_text[:3000])

This example extracts text from the PDF and prints the first 3,000 characters.

For a longer document, it is better to divide the content into smaller sections instead of sending the entire paper to the model in one request. This helps manage context limits and makes it easier to summarize individual sections.

Splitting large research documents

from langchain_text_splitters import RecursiveCharacterTextSplitter

splitter = RecursiveCharacterTextSplitter(
    chunk_size=4000,
    chunk_overlap=400
)

chunks = splitter.split_text(paper_text)

print("Total chunks:", len(chunks))

for index, chunk in enumerate(chunks):

    print(f"Chunk {index + 1}")
    print(chunk[:500])
    print("-" * 50)

Each chunk contains a smaller portion of the original document. The overlap helps retain some context between neighboring sections.

For academic work, keep page numbers and document metadata wherever possible. They make it easier to trace a summary back to the original paper.

8. Connecting OpenAI with LangChain

Now we have the basic tools required for research. The next step is connecting them to an OpenAI model.

The model will understand the research request and decide when to use the available tools. LangChain manages the agent interaction and tool execution.

We will use the ChatOpenAI integration from the langchain-openai package.

Initialize the OpenAI model

import os

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model=os.getenv("OPENAI_MODEL"),
    temperature=0
)

We use a temperature of zero as a starting point for research-oriented tasks. This can help make responses more consistent, although it does not guarantee factual accuracy.

Make sure the model you configure supports the tool-calling capabilities required by your LangChain version.

9. Creating the Autonomous Research Agent

This is where the individual components come together.

We already have a search tool, an academic paper search function, and a method for reading PDFs. Now we need an agent that can use the research tools when needed.

LangChain provides a convenient way to define custom tools using the @tool decorator.

Step 1: Define the academic search tool

from langchain.tools import tool

@tool
def academic_search(query: str) -> str:
    """
    Search arXiv for academic research papers
    related to the provided topic.
    """

    papers = search_arxiv(
        query,
        max_results=5
    )

    if not papers:
        return "No matching papers were found."

    output = []

    for paper in papers:

        output.append(
            f"Title: {paper['title']}\n"
            f"Abstract: {paper['abstract']}\n"
            f"URL: {paper['url']}"
        )

    return "\n\n".join(output)

This function makes academic paper discovery available as an agent tool. The model can request it when the research question requires academic sources.

Step 2: Configure the agent instructions

A research agent needs more guidance than a general-purpose assistant. It should know how to search, what kind of sources to prioritize, and how to handle information that cannot be verified.

RESEARCH_PROMPT = """
You are an AI research assistant.

Your responsibility is to investigate research topics
and prepare clear, evidence-based reports.

Research guidelines:

1. Understand the user's research question.

2. Use the web search tool to find relevant information.

3. Use academic_search when academic publications
   would improve the research.

4. Prefer primary research, official documentation,
   reputable institutions, and original sources.

5. Compare information from multiple sources.

6. Do not invent statistics, quotations, paper titles,
   publication dates, or references.

7. Clearly distinguish verified information from
   interpretations and uncertain findings.

8. Mention limitations and conflicting evidence.

9. Include source titles and URLs where available.

10. Prepare the final report using clear headings,
    readable paragraphs, and useful conclusions.

Never claim to have read a full paper when you have
only inspected its abstract or search-result summary.
"""

These instructions establish the research standards that the agent should follow. They also reduce the risk of producing a report that sounds convincing but contains unsupported information.

Step 3: Create the LangChain agent

from langchain.agents import create_agent

research_agent = create_agent(
    model=llm,
    tools=[
        search_tool,
        academic_search
    ],
    system_prompt=RESEARCH_PROMPT
)

The agent now has access to the OpenAI model, the Tavily web-search tool, and the custom arXiv search tool.

When it receives a research question, it can decide which available tools are relevant and use their results as part of the conversation.

10. Running Your First Research Task

Let's test the agent with a realistic research topic.

For this example, we will research the role of generative AI in software development.

research_topic = """
Research the impact of generative AI on software development.

Find relevant online sources and academic papers.
Explain the main benefits, limitations, and risks.

Prepare a structured report with:
- Introduction
- Current applications
- Benefits
- Challenges
- Research findings
- Future opportunities
- Conclusion
- References

Use source links wherever available.
Do not invent research findings.
"""

result = research_agent.invoke({
    "messages": [
        {
            "role": "user",
            "content": research_topic
        }
    ]
})

print(result["messages"][-1].content)

When the application runs, the agent receives the topic and can use the tools provided to it. It may perform web searches, retrieve academic paper information, and use the returned content to prepare its response.

The exact tool sequence is determined by the model and the agent configuration. It is therefore important to test the workflow with different research topics rather than assuming that every request will follow exactly the same sequence.

11. Summarizing Research Papers with OpenAI

Collecting documents is useful, but the real value comes from understanding what those documents contain.

A research paper may have an abstract, introduction, methodology, results, discussion, and conclusion. Simply shortening the abstract is not enough when the objective is to understand the actual research.

We can create a separate summarization function that processes extracted paper text.

from openai import OpenAI

client = OpenAI()

def summarize_research(text):

    response = client.responses.create(
        model=os.getenv("OPENAI_MODEL"),

        instructions=(
            "You are a research paper summarization assistant. "
            "Summarize only the information provided. "
            "Do not invent findings or statistics. "
            "Clearly mention limitations."
        ),

        input=f"""
        Analyze the following research paper content.

        Provide:

        1. Research objective
        2. Problem being addressed
        3. Methodology
        4. Main findings
        5. Important limitations
        6. Practical implications

        Paper content:

        {text[:20000]}
        """
    )

    return response.output_text

This function sends extracted paper content to the OpenAI Responses API and requests a structured summary.

For longer papers, process the chunks individually and then combine the summaries into a paper-level overview. The 20,000-character limit in this example is simply a demonstration choice, not a universal model context limit.

Also remember that PDF extraction can miss information in scanned pages, charts, mathematical expressions, and complex tables. Important findings should be checked against the original paper.

12. Generating a Complete Research Report

After collecting information and preparing summaries, the next step is generating the final report.

A useful research report should not simply place several summaries next to each other. It should explain how the findings relate to one another, identify common themes, and point out where the available evidence disagrees.

We can create a separate report-generation function for this purpose.

def generate_research_report(research_material):

    response = client.responses.create(
        model=os.getenv("OPENAI_MODEL"),

        instructions=(
            "You are a professional research report writer. "
            "Use only the supplied research material. "
            "Do not invent sources or citations. "
            "Identify unsupported claims and evidence gaps."
        ),

        input=f"""
        Prepare a detailed research report using
        the following collected material.

        Include:

        1. Executive Summary
        2. Introduction
        3. Background
        4. Key Research Findings
        5. Comparison of Sources
        6. Benefits and Challenges
        7. Research Limitations
        8. Future Opportunities
        9. Conclusion
        10. References

        Collected Research Material:

        {research_material}
        """
    )

    return response.output_text

Here, we ask the model to organize the collected information into a readable document. The report-writing stage should receive the actual research material and source metadata, rather than only a broad topic.

For a larger project, you can save individual paper summaries and web sources to files, then generate the final report from those saved research materials.

13. Creating a cURL Request for OpenAI Research

Python is convenient for building the complete application, but developers may also want to test the OpenAI API directly.

The following cURL example demonstrates how to send a research request using the Responses API with OpenAI's built-in web-search tool.

curl https://api.openai.com/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "YOUR_SUPPORTED_MODEL",
    "tools": [
      {
        "type": "web_search"
      }
    ],
    "input": "Research the recent applications of generative AI in software development. Summarize the findings and include source citations."
  }'

This example uses OpenAI's hosted web-search capability directly. It is an alternative to the Tavily search integration used in our LangChain example.

The two approaches serve a similar purpose but have different execution arrangements. Tavily is an external search provider that can be exposed as a LangChain tool. OpenAI's built-in web search is configured directly in a Responses API request.

For a LangChain-based project, choose the approach that best fits your architecture rather than adding multiple search providers without a clear reason.

14. Improving the Agent with Multiple Research Stages

The basic implementation is a useful starting point, but a serious research application needs more structure.

For example, imagine that you want to research a topic containing several different areas. A single search query may not cover all of them.

Instead, the application could divide the research into smaller subjects and investigate each one separately.

For a topic such as AI in healthcare, the research stages could include:

  1. AI applications in medical imaging.
  2. AI-assisted clinical decision support.
  3. Patient data privacy and security.
  4. Clinical research and regulatory challenges.
  5. Future opportunities in healthcare technology.

Each area can have its own search queries and collected sources. The results can then be brought together during report generation.

LangGraph is particularly useful when you want more control over this process. It allows developers to design workflows with separate nodes for planning, searching, document processing, review, and report writing.

Example of a structured research workflow

Research Request
       |
       v
Topic Analysis
       |
       v
Research Planning
       |
       v
Web Search and Paper Discovery
       |
       v
Document Collection
       |
       v
Paper Summarization
       |
       v
Evidence Comparison
       |
       v
Report Generation
       |
       v
Citation and Quality Review
       |
       v
Final Research Report

This approach is more reliable than asking one model call to perform every activity at once. Each stage has a clear responsibility, making it easier to inspect the results and correct problems.

15. Adding Source Verification and Citation Management

One of the biggest challenges in AI-generated research is source accuracy.

An assistant may produce a well-written paragraph but attach a reference that does not actually support the statement. It may also confuse the publication date with the date when a web page was updated.

For this reason, source verification should be a separate part of the research workflow.

For every source, try to collect the following information:

  • Source title.
  • Author or organization, where available.
  • Publication date.
  • Original URL.
  • Source type, such as academic paper, official documentation, or news article.
  • The specific findings or claims supported by that source.

It is also useful to store the source information alongside the extracted text instead of keeping only the summary.

For example, a research source record could look like this:

{
    "title": "Example Research Paper",
    "authors": ["Author One", "Author Two"],
    "source_type": "Academic Paper",
    "url": "https://example.org/research-paper",
    "publication_year": 2026,
    "summary": "A summary of the paper's findings.",
    "verified": false
}

The verified field should only be changed after an actual review. It should not be automatically set to true simply because the model produced a summary.

For academic reports, it is also important to distinguish between a paper's abstract and its full text. If only the abstract was available, the report should say so rather than suggesting that the entire paper was reviewed.

16. Important Challenges When Building an AI Researcher

Although an autonomous research application can save time, it also introduces some challenges that developers should understand before using it for important work.

Information quality

Not every website provides reliable information. Search results may include outdated pages, promotional content, opinion articles, or material that does not directly answer the research question.

To improve quality, prioritize original studies, official documentation, established research institutions, and primary sources whenever possible.

Hallucinated findings

Language models can generate information that sounds reasonable but is not supported by the collected material. This is especially problematic when a report contains technical claims, research statistics, or academic references.

Use clear instructions, preserve source metadata, and require the final report to identify claims that cannot be verified.

Large documents and context limits

Research papers can be lengthy. Sending multiple complete papers in a single request may exceed practical context limits or increase the cost of processing.

Document chunking, staged summaries, and retrieval-based workflows help manage large collections of research material.

Search limitations

A search tool does not guarantee that every relevant paper or publication will be discovered. Search providers have their own indexing, ranking, and access limitations.

For important projects, combine multiple discovery methods and check the original publication pages.

Copyright and access restrictions

Not every research paper is freely available for downloading or redistribution. Respect publisher terms, licensing conditions, website access restrictions, and the copyright requirements of the documents you process.

17. Best Practices for Building a Reliable Research Agent

If you want to move beyond a demonstration and build an application that people can actually use, spend time improving its reliability and usability.

Start by defining the type of research the application is expected to perform. An assistant designed for technical documentation may need different source priorities from one designed for medical or financial research.

Next, keep the research process traceable. Store the search queries, source URLs, retrieval dates, document metadata, and summaries. This makes it easier to review where a particular conclusion came from.

You should also introduce limits on tool usage. An agent should not perform an unlimited number of searches just because it has access to a search tool. Set maximum search counts, timeouts, and request budgets.

Another useful improvement is to include a review stage before generating the final report. This stage can check whether the major claims have supporting sources, whether the references are accessible, and whether the report clearly explains any missing information.

Finally, keep a human involved in important research decisions. An AI researcher can make the first round of investigation much faster, but a person should review critical findings before using them in academic submissions, business decisions, or professional publications.

18. Future Improvements and Advanced Features

Once the basic research agent is working, there are several ways to expand it.

You could introduce a research-planning component that creates a list of questions before searching. This helps ensure that the application investigates the topic from multiple perspectives.

You could also add a vector database to store collected research papers and summaries. This would allow users to search previously collected information without downloading and processing the same documents repeatedly.

Another useful addition is a report export feature. The application could generate Markdown, HTML, or PDF reports and save them for later use.

For larger projects, you may want to introduce specialized agents. One agent could discover academic papers, another could analyze technical findings, and a final agent could prepare the report. These agents should share structured evidence rather than simply passing unsupported summaries to one another.

LangGraph can help coordinate these stages, while LangSmith can help developers trace and evaluate agent executions.

It is worth remembering that more agents do not automatically produce better research. A carefully designed workflow with a few well-defined tools is often easier to maintain than a large collection of agents with overlapping responsibilities.

19. Conclusion

Building an autonomous AI researcher with OpenAI and LangChain is a practical way to combine language models with real research tools.

Instead of relying on a chatbot to generate an answer from a single prompt, developers can create an application that searches the web, discovers academic publications, processes research documents, summarizes findings, and prepares structured reports.

In this guide, we explored the complete process, starting with Python environment setup and API configuration. We then connected Tavily for web search, created an arXiv paper discovery function, extracted PDF content, integrated an OpenAI model with LangChain, and built an agent that can use research tools.

We also discussed how to summarize papers, generate reports, test OpenAI requests with cURL, and improve the workflow through citation management and source verification.

The most important lesson is that a good research agent is not simply an AI model with access to a search engine. It is a structured application that collects information, preserves its sources, checks the quality of its findings, and presents the results in a useful format.

Start with a small research workflow, test it with different topics, and gradually add document processing, source verification, and report generation. With the right design, you can build a useful research assistant that reduces repetitive work while keeping the final research process transparent and reviewable.

References and Official Documentation

If you want to continue developing this project, the following documentation and resources are useful starting points. They cover the OpenAI API, LangChain agents, web search, document loading, and academic paper discovery.

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