How to Build Your Own Deep Research

Imagine an AI assistant that can accomplish in 30 minutes what might take a human researcher 6–8 hours. That’s the promise of OpenAI’s Deep Research—and the best part is that you can build a version tailored to your own workflows. By examining Deep Research’s likely architecture, we’ll unpack how autonomous research agents work and why they matter for teams automating knowledge work.

What is Deep Research?

Deep Research is an autonomous AI agent that conducts multi-step internet research and produces comprehensive, cited reports. Unlike traditional chatbots that provide quick answers, Deep Research operates more like a diligent human researcher. It plans research strategies, searches and reads through dozens of web sources (including text, PDFs, and images), and synthesizes findings into structured reports with inline citations.

Read our full breakdown of it here.

The key capabilities that set Deep Research apart include:

• Strategic planning and task decomposition
• Iterative searching with progressive focusing
• Multi-modal analysis of HTML, PDFs, and images
• Code execution for data analysis and visualization
• Citation-driven synthesis with source verification

In real-world evaluations, professionals consistently rate Deep Research outputs as equivalent to experienced human researchers. Architects reviewing a 15,000-word building code checklist assembled from 21 sources deemed it "better than intern work"—something that would have taken 6-8 hours to produce manually. An antitrust lawyer compared an 8,000-word AI-generated legal memo favorably to what a junior attorney might deliver, estimating 15-20 hours of human labor saved.

Core Architecture

At the heart of Deep Research lies OpenAI's o3 reasoning model, an advanced successor to GPT-4 trained specifically via reinforcement learning on complex browsing and reasoning tasks. This isn't just a fine-tuned language model—it's a system optimized to maintain focus through lengthy reasoning chains without losing context or diverging from the goal.

The architecture follows a classic agent loop pattern:

Plan → Act → Observe → Update → Repeat

During training, the model was placed in simulated research environments with access to tools and given real-world tasks requiring multi-step problem solving. Through reinforcement learning with human feedback and automated reward signals, it learned to:

• Plan and execute multi-step search trajectories
• Backtrack when paths prove unfruitful
• Pivot strategies based on new information
• Maintain an internal monologue of reasoning

The tool integration is seamless. Deep Research can invoke:

• Web search for finding information
• Browser for reading and navigating pages
• Code interpreter for data analysis and visualization
• File parser for PDFs and images

Perhaps most importantly, the o3 model demonstrates an unprecedented ability to maintain focus through long reasoning chains. Where previous models might hallucinate or drift off-topic during extended tasks, Deep Research stays locked on target. It achieved 26.6% accuracy on the rigorous "Humanity's Last Exam" test—far surpassing earlier models like o1 (9.1%) on expert-level questions across domains from law to finance.

The Research Process Breakdown

When you give Deep Research a complex question, it launches into a sophisticated multi-phase process that mirrors how expert human researchers work.

Task Clarification and Query Refinement

Before diving into research, Deep Research often asks clarifying questions to ensure it truly understands your requirements. Users report that the agent proactively seeks details like "What timeframe should I focus on?" or "Do you prefer academic studies or industry reports?" This interactive clarification step—much like a human consultant would do—helps the AI form a concrete research plan aligned with your true intent.

Planning and Decomposition

Once the task is clear, the model internally maps out a research strategy. It breaks high-level queries into subtopics and sub-questions that need answering. For example, if asked about the economic impact of a new drug, it might decide it needs sections on clinical outcomes, cost effectiveness, and regional pricing. While this planning happens implicitly, we can infer it from the agent's systematic approach to gathering information.

Iterative Web Searching

The agent begins with broad searches, then progressively refines queries as it learns more. This iterative approach allows Deep Research to uncover niche information that single-pass searches would miss.

In one documented case, when confronted with a paywalled standards website, the model noted internally "Considering a non-ICC site might be a good move" and pivoted to search for public summaries on state government sites. A single Deep Research query can involve dozens of search queries and page fetches—one user's query led the agent to consult 21 different sources across nearly 30 minutes of analysis.

Multi-Modal Content Analysis

Deep Research doesn't just read text. It can:

• Parse HTML and navigate long pages using scrolling and search
• Extract content from PDFs, even when scholarly articles are only available in this format
• Interpret images and charts, leveraging the model's multimodal abilities
• Run Python code to analyze data, compute statistics, or generate visualizations

The code execution capability is particularly powerful. If the task requires crunching numbers or creating graphs, the model can write and run Python code on the fly in a sandboxed environment. The final reports can even embed these generated visualizations directly.

Citation-Driven Synthesis

As Deep Research gathers material, it gradually forms a comprehensive answer. The final synthesis phase produces a well-structured report with:

• Clear sections with descriptive headings
• Data-rich insights with specific figures
• Inline citations linking every claim to its source
• Tables, bullet points, or visualizations as appropriate

Every factual claim includes a citation that users can click to verify the source. This citation practice means the final output is fully traceable—addressing trust issues that plague typical AI-generated content.

Budget and Stop Conditions

Understanding how Deep Research manages its resources is crucial for building your own version. While OpenAI doesn't publish exact limits, we can infer the control mechanisms from documentation and user reports.

Resource Budgets

Deep Research tracks multiple resource types:

• Wall-clock time: Typically 15-30 minutes, with background processing to avoid timeouts
• Search calls: Each search has a cost (around $0.01), with practical limits of 20-60 searches per run
• Page fetches: Usually 60-200 pages per research task
• Reasoning loops: Hundreds of internal reasoning steps
• Code executions: 5-10 Python cells with 30-60 second timeouts
• Token usage: Large context windows handling 20,000-50,000 output tokens

Stop Conditions

The system uses a two-tier stopping strategy:

Coverage-driven early stop triggers when:

• Sufficient sources cover each sub-question (typically ≥2 independent sources)
• No novel findings emerge from recent searches
• Contradictions are resolved or documented
• Confidence thresholds are met

Hard budget stops enforce limits on:

• Total runtime
• Number of tool calls
• Token consumption
• Computational resources

When hitting budget limits, Deep Research gracefully produces a "partial report" noting what's missing and suggesting next steps—ensuring users always receive value even from incomplete runs.

Building Your Own Version

Creating your own Deep Research clone requires assembling several key components with the right training approach.

Required Components

1. Strong Language Model

• Use GPT-4 or equivalent with chain-of-thought capabilities
• Fine-tune specifically for multi-step reasoning if possible
• Ensure support for function calling/tool use

2. Tool Interfaces

• Web search API (mandatory—Deep Research won't work without it)
• Web browser/parser for content extraction
• Code interpreter (sandboxed Python environment)
• File handlers for PDFs and images

3. Controller Loop

• Implement the Plan → Act → Observe → Update cycle
• Track state across multiple iterations
• Manage budgets and stop conditions

Key Training Insights

OpenAI's breakthrough came from reinforcement learning on actual web tasks. Key lessons:

• Train on real browsing tasks, not just Q&A
• Reward successful information gathering and synthesis
• Teach the model to backtrack and pivot strategies
• Emphasize citation accuracy and source verification

The model must learn to ask clarifying questions upfront—this dramatically improves task success rates.

Implementation Essentials

Background Processing: Design for long-running tasks (15-30 minutes) using webhooks or polling rather than synchronous requests.

Structured Citation Tracking: Store source metadata with every extracted fact. Map text spans to their origin URLs for inline citations.

Safety Rails:

• Filter harmful content from search results
• Prevent arbitrary URL construction
• Sandbox code execution with no network access
• Resist prompt injections found on web pages

Cost Considerations

Deep Research is computationally expensive. The o3-deep-research model costs approximately:

• $10 per million input tokens
• $40 per million output tokens
• $0.01 per web search

Budget carefully—some developers report spending $100 on just a handful of queries during testing. This is a tool for high-value research questions where depth matters, not casual browsing.

Conclusion

Deep Research demonstrates true autonomous task completion beyond simple chat interactions. By combining a reasoning-optimized language model with tool-use capabilities in a controlled iterative loop, it approaches research tasks the way a human expert would—asking clarifying questions, searching widely, reading voluminously, and constructing detailed answers backed by evidence.

The architecture principles are reproducible. With proper tool integration and training, any developer can build an AI system that doesn't just answer questions but genuinely researches them. The key is equipping the AI with the ability to reason in steps, use external tools, verify information, and remain grounded with sources.

This represents a significant step toward AI that can generate new knowledge, not just regurgitate training data. As we stand at this inflection point, Deep Research offers a glimpse of a future where every knowledge worker has a tireless, capable research assistant—one that can handle everything from finding niche technical details to compiling competitive intelligence in a single, comprehensive report.

The ability to synthesize existing knowledge is, after all, the prerequisite to creating original insights. And that's exactly what Deep Research—and your own implementation—can deliver.