Universal Deep Research: A Flexible Framework for Customizable Research Agents

The Core Question This Article Answers

Can we build a research system that supports fully customizable strategies and works with any large language model, without requiring retraining or fine-tuning? Universal Deep Research (UDR) provides a definitive yes to this question, offering a groundbreaking approach to AI-powered research automation.

Deep research tools have become essential assistants for knowledge workers, automatically processing queries to search, analyze, and generate structured reports. However, existing solutions typically lock users into fixed strategies and predetermined models, severely limiting their adaptability for specialized professional use cases. UDR emerges as a transformative solution that fundamentally rethinks this paradigm by separating research strategy from execution, enabling unprecedented flexibility and control.

The Current Landscape of Deep Research Tools

How Existing DRT Systems Operate

Most deep research tools follow similar operational patterns: they take user queries, generate research plans, autonomously browse the web, perform iterative searches, and ultimately produce comprehensive reports. Major platforms like Gemini, Perplexity, and OpenAI Deep Research all employ some variation of this approach, with limited opportunities for user customization beyond the initial prompt.

Enterprise-focused DRTs have developed more specialized approaches. The NVIDIA AI-Q Research Assistant follows a structured five-step process: creating a report plan, searching data sources, writing the report, reflecting on gaps, and finishing with comprehensive source documentation. Similarly, SambaNova Deep Research operates through a document-oriented pipeline that parses prompts, generates detailed outlines, gathers information across sources, delegates to specialized agents, and synthesizes findings into formatted reports.

Perhaps the most unique approach comes from ERP AI Deep Research, which employs a “Graph-Based AI Architecture” that represents enterprise data through knowledge graphs accessed via Graph Neural Networks. This fundamentally different approach performs information aggregation across relevant graph elements rather than traditional document traversals combined with LLM text comprehension.

Three Fundamental Limitations in Current Systems

Through careful analysis of existing DRTs, the UDR team identified three critical limitations that hinder their effectiveness in professional contexts:

First, users cannot adequately enforce resource hierarchies, automate information validation against reputable sources, or control search-related expenses. This functionality gap between consumer and enterprise tools represents a significant barrier to adoption in professional settings.

Second, the creation of specialized document research strategies necessary in high-value industries isn’t possible within existing DRTs. This leaves substantial professional research workloads in fields like finance, legal, healthcare, and government to be automated by expensive custom-built agentic solutions.

Third, the models used in existing DRTs aren’t interchangeable. Users cannot freely combine the most powerful available models with their preferred research agents to create optimally performing systems.

Author’s reflection: Examining these limitations revealed that the core issue isn’t technological capability but design philosophy. Most DRT developers prioritize consistency and reliability over flexibility, making rational choices for mass-market products but creating significant constraints for professional users with specialized needs.

UDR’s Architectural Innovation: Separating Strategy from Execution

Core Design Philosophy

UDR introduces a fundamental architectural innovation by decoupling research strategy definition from execution. The system accepts two distinct inputs: a research strategy (described in natural language) and a research prompt (traditional query). By compiling strategies into executable code, UDR enables completely customizable research workflows without requiring model retraining.

This design delivers three significant advantages:

• Model agnosticism: Works with any language model without fine-tuning
• Strategy programmability: Users can define complex research processes using natural language
• Execution transparency: All intermediate states and decision processes remain fully traceable

System Workflow Explained

UDR operates through two primary phases that transform user intentions into actionable research:

Phase One: Strategy Processing
The natural language strategy is passed to a language model along with constraints on available functions and permitted code structures. The model converts this strategy into a single callable function that accepts the research prompt as input and continuously returns output notifications.

To ensure reliable code generation, UDR enforces a specific structure where each segment of generated code must be preceded by comments identifying the strategy step it implements. This approach significantly improves consistency between the intended strategy and actual execution.

# Example: Generated code for minimal research strategy
def execute_research_strategy(research_prompt):
    # Step 1: Send prompt received notification
    yield {"type": "prompt_received", "description": f"Received research request: {research_prompt}"}
    
    # Step 2: Begin prompt analysis
    yield {"type": "prompt_analysis_started", "description": "Analyzing research request"}
    
    # Step 3: Generate search phrases
    search_phrases = generate_search_phrases(research_prompt)
    
    # Additional steps...

Phase Two: Strategy Execution
The generated code executes in an isolated environment with several distinctive characteristics:

• State management: All intermediate information stores as code variables rather than growing context windows, enabling complex workflows within small context limits (8K tokens)
• Tool usage: Tools accessed via synchronous function calls ensuring transparent and deterministic behavior
• LM reasoning: Language models invoked only for specific tasks (summarization, ranking, extraction) as directed by the research strategy
• Notification mechanism: Structured progress updates delivered through yield statements, with users controlling update granularity and content

UDR system architecture: Accepts both research strategy and prompt as inputs, outputs structured reports and real-time notifications

Author’s reflection: Implementing this two-phase approach required rethinking traditional assumptions about AI system design. The breakthrough realization was that code generation could serve as an intermediate representation that balances flexibility with reliability, allowing natural language strategies to become executable plans without sacrificing determinism.

Practical Application: Three Research Strategy Paradigms

Minimal Research Strategy

The minimal strategy employs a straightforward linear workflow ideal for simple, focused research tasks. It prioritizes efficiency over comprehensiveness, following a clean sequence: receive prompt → generate search phrases → execute searches → produce report.

Typical application scenario: Quick fact verification, simple concept explanation, or preliminary information gathering. For example, queries like “airspeed velocity of an unladen swallow” that require synthesizing information from multiple sources but don’t need deep analysis.

Operational workflow:

1. Receive research request and send notification
2. Analyze prompt and generate 3 search phrases
3. Execute search for each phrase and process results
4. Generate Markdown-formatted report based on collected content

Expansive Research Strategy

The expansive strategy uses a two-stage approach that first identifies broad topics before generating specific search phrases for each topic. This method suits complex subjects requiring multi-perspective examination.

Typical application scenario: Market research, competitive analysis, or comprehensive technical assessment. For example, “analyze AI applications in medical diagnosis for 2025” type queries that benefit from examining multiple facets of a topic.

Unique advantage: By conducting topic-level analysis first, this strategy ensures research breadth while maintaining logical separation between different aspects of the subject matter.

Intensive Research Strategy

The intensive strategy employs an iterative approach that deepens understanding through multiple search and analysis cycles. The system maintains two context stores: “supercontext” preserving all resource content and “subcontext” containing only single-iteration material.

Typical application scenario: Academic research, investment decision support, or policy analysis. For example, “research Ulysses S. Grant’s Civil War leadership and Reconstruction impact” type queries requiring deep analysis and multi-source validation.

Iteration process:

1. Initial search phrase generation and execution
2. New search phrase generation based on preliminary results
3. Multiple iterations until strategy conditions satisfied
4. Final report generation

Author’s reflection: Testing these strategies revealed that there’s no “best” approach overall—only the most appropriate strategy for a specific context. The minimal strategy delivers speed but limited depth, while the intensive strategy provides comprehensiveness at higher computational cost. UDR’s real value lies in letting users match strategy to their specific requirements.

UDR User Interface and Interaction Experience

UDR features a comprehensive web interface that supports complete research workflows:

• Search bar: For inputting research prompts specifying topics, questions, and desired output formats
• Strategy selection list: Displays previously saved research strategies for quick application
• Strategy editing area: Allows writing, editing, and refining custom strategies using natural language
• Research progress notifications: Shows real-time updates and notifications from the executing system
• Report viewer: Renders final research reports in user-friendly format with proper structural element visualization

UDR strategy editing interface: Search bar (top), strategy selection list (middle), strategy editing area (bottom)

UDR research workflow interface: Showing search bar, strategy selection, progress notifications, and report viewer

Notable features:

• Stop research button: Allows users to interrupt ongoing research at any point, controlling resource usage
• Generate report button: Enabled after manual research stoppage, produces preliminary reports based on collected information
• Real-time notifications: Each notification includes icons, descriptive messages, and timestamps for complete workflow transparency

Real-World Examples and Output Analysis

Using the Llama 3.3 70B model with the minimal research strategy, UDR successfully processed diverse queries producing high-quality reports:

Example 1: Cultural Reference Query

Input: “What is the airspeed velocity of an unladen swallow? Produce a detailed report including popular culture occurrences. Condense into three sections.”

Output report contained three well-structured sections: origins and popular culture significance, technical analysis and accuracy, and enduring cultural impact. The report correctly identified the Monty Python and the Holy Grail origin, provided scientific estimates (20.1 mph or 24 mph), and discussed the phrase’s ongoing cultural relevance.

Example 2: Historical Event Research

Input: “Produce a report on the most significant events that occurred on May 1, 2025. Write 3 sections.”

Output report accurately identified International Labour Day, Maharashtra Day and Gujarat Day in India, and discussed the day’s dual significance for workers’ rights and regional heritage celebrations.

Example 3: Stock Market Analysis

Input: “Produce a detailed report on US stock movements on Thursday April 24, 2025. Note opening and closing prices. Format with three sections: opening conditions, closing conditions, and wider perspective.”

Output report provided detailed market analysis including opening prices (S&P 500: 5395.92, Dow: 39815.01, Nasdaq: 16880.29), closing prices with percentage changes, and broader market trend context.

Example 4: Historical Figure Research

Input: “Produce a research report on General Ulysses S. Grant focusing on Civil War military leadership, key battles, strategic approach, and Reconstruction influence as president. Structure into five sections: introduction, military career, leadership style, political legacy, conclusion. Use at least three scholarly sources with citations and clear section headings.”

Output report featured proper academic structure, complete with sections on Grant’s military contributions (including Vicksburg campaign) and political legacy during Reconstruction, all properly sourced and cited.

Author’s reflection: These examples demonstrate UDR’s versatility across different research domains. What impressed me most was how the same underlying system could produce academically rigorous reports on historical figures while also handling precise financial data requests—all using the same minimal research strategy adapted through different prompts.

System Limitations and Future Directions

Current Constraints

As a research prototype, UDR has several important limitations:

First, the system’s faithfulness to intended research strategies depends heavily on the quality of code generated by the underlying language model. While the comment-code structure improves consistency, semantic drift or hallucinated logic can still occur, particularly with ambiguous or underspecified strategies.

Second, UDR assumes that user-authored research strategies are logically sound, safe, and fit for purpose. The system doesn’t validate whether specified steps produce coherent or meaningful workflows beyond basic syntactic and execution checks, meaning poorly designed strategies may yield ineffective or incomplete results.

Third, despite providing real-time progress notifications, UDR doesn’t support mid-execution user intervention (beyond stopping the workflow) or dynamic branching based on real-time feedback. All decision logic must be encoded upfront in the research strategy.

Development Recommendations

Based on the UDR development experience, the team proposes three key recommendations for future development:

First, consumer-facing systems similar to UDR should include libraries of research strategies for modification and customization rather than requiring users to create strategies from scratch.

Second, researchers should further explore methods for giving users control over language models’ free reasoning processes (sometimes called “thinking”).

Third, the field should investigate how large sets of user prompts could be automatically converted into deterministically controlled agents performing complex action sequences on users’ behalf.

Author’s reflection: Working with UDR highlighted the delicate balance between flexibility and usability. While advanced users appreciate the control UDR provides, less technical users often prefer pre-optimized strategies. The ideal system might offer a smooth progression from “out-of-the-box” solutions to “fully customizable” approaches based on user expertise and needs.

Implementation Guide and Technical Details

System Requirements and Setup

UDR requires several technical components:

• Python 3.8+ (backend)
• Node.js 18+ (frontend)
• API keys for LLM providers (NVIDIA NGC, OpenAI, etc.)
• Tavily API key for web search functionality

Project Structure

/
├── backend/          # FastAPI backend service
│   ├── main.py       # FastAPI application
│   ├── scan_research.py  # Core research logic
│   ├── frame/        # Advanced reliability framework
│   └── ...
├── frontend/         # Next.js frontend application
│   ├── src/          # React components and pages
│   └── ...
└── README.md         # Project documentation

Running the Prototype

1. Start backend server (typically port 8000):

   cd backend
   launch_server.sh
   

2. Start frontend development server (typically port 3000):

   cd frontend
   npm run dev
   

3. Access the application:
   Open browser and navigate to http://localhost:3000

Conclusion: The Future of Autonomous Research Agents

Universal Deep Research represents a significant directional shift for autonomous research agents: from closed, fixed-strategy systems toward open, programmable platforms. By separating research strategy from control logic, UDR achieves unprecedented flexibility and model independence.

While the current prototype primarily targets developers and researchers, its core concept—user-programmable research agents—has important implications for future DRT development. As language model capabilities advance and code generation reliability improves, UDR-like systems could become standard tools for professional research and knowledge work.

Ultimately, UDR’s success stems not just from its technical innovations but from its philosophical stance: believing users should have complete control over their research processes rather than passively accepting outputs from black-box systems. This principle may influence the entire field of AI-assisted research moving forward.

Practical Summary and Action Checklist

Core Value Proposition

• Enables custom research strategies without additional training or fine-tuning
• Works with any large language model, avoiding vendor lock-in
• Provides completely transparent and traceable research processes
• Supports diverse research scenarios from simple to complex

Quick Start Checklist

1. Select or create research strategy matching your needs (minimal, expansive, or intensive)
2. Prepare research prompt clearly specifying topic, content, and format requirements
3. Configure API keys (LLM and search services)
4. Launch backend and frontend services
5. Submit strategy and prompt via web interface
6. Monitor real-time progress notifications
7. Review and export final research report

One-Page Overview

Aspect	UDR Approach	Traditional DRT Approach
Strategy flexibility	Fully customizable	Fixed, hardcoded
Model support	Any language model	Typically model-specific
Control granularity	User controls each step	System controls most processes
Transparency	Fully traceable	Typically black-box
Usage complexity	Higher (strategy definition)	Lower (prompt only)
Suitable scenarios	Professional, high-value research	General, everyday queries

Frequently Asked Questions (FAQ)

Do I need programming knowledge to use UDR?
No. Users can define research strategies using natural language, which UDR automatically converts to executable code. However, users with programming experience can create more sophisticated and precise strategies.

Can UDR work with internal corporate document repositories?
Yes. UDR’s search functionality can be configured to work with any data source, including internal document repositories and databases, by adapting the search tool implementation accordingly.

Is it safe to execute user-generated code?
UDR is designed to execute generated code in a sandboxed environment that prevents access to the host system. Isolation is enforced by default to eliminate potential side effects beyond the execution context.

How does UDR differ from using a regular language model?
Standard language models only respond to prompts, while UDR combines prompts with complete research strategies to execute multi-step research processes including search, analysis, validation, and report generation.

Can I save and reuse custom research strategies?
Yes. The UDR interface includes a strategy selection list that allows users to save and quickly apply previously created research strategies.

Does UDR support user interaction during research execution?
The current implementation doesn’t support mid-execution user intervention (beyond stopping the workflow). All decision logic must be pre-encoded in the research strategy.

How does UDR handle inaccurate information in research results?
UDR relies on user-defined research strategies to include verification and cross-checking steps. The system itself doesn’t automatically validate information accuracy—this depends on strategy design.

Can UDR integrate with other AI tools and services?
Yes. UDR’s tool usage architecture allows integration with any service accessible via API, including specialized analysis tools, database systems, and other AI services.