Simple Deepresearch Framework

• Simple Deepresearch Framework is a modular, prompt-based scaffold that enables LLMs to perform iterative, transparent deep research with minimal development friction.
• It leverages a standardized JSON interface to capture thought, action, and observation steps, facilitating fine-grained agent evaluation and debugging.
• The framework supports rapid prototyping and benchmarking through detailed process logging and human feedback, ensuring reproducible and extensible research agent development.

A simple deep research framework refers to the class of modular, end-to-end agent scaffolds designed to transform LLMs into autonomous deep research agents capable of multi-step information retrieval, structured reasoning, and detailed reporting. These frameworks aim to minimize development friction for researchers, ensure fair agent evaluation, and enable fine-grained process transparency. The Simple Deepresearch framework described in the context of Deep Research Comparator is emblematic of this design philosophy, providing a minimal yet robust baseline for integrating and benchmarking LLM-based research agents (Chandrahasan et al., 7 Jul 2025).

1. Modular Integration of LLMs via Prompt-Based Scaffold

The Simple Deepresearch framework is architected as a prompt-centric agent scaffold. Any LLM—such as one accessed via the OpenAI API—can be embedded within the agent structure by responding to standardized, structured prompts. The key technical abstraction is a JSON interface, which encapsulates both intermediate reasoning (“intermediate_steps”) and final outputs (“final_report”). This enables direct, model-agnostic swapping of the LLM core or upgrading to new architectures without framework rewrites.

The agent’s operational cycle is explicitly defined:

• At each step, the agent receives a user query and a running “history” $h_k$, which is the ordered log of all previous steps.
• The agent produces a “thought” $t_k$ (an explicit description of internal reasoning) and selects an “action” $a_k$ from a restricted set.

Typical action types include $\langle$plan$\rangle$ (laying out a high-level research plan), $\langle$search$\rangle$ (query execution via a designated search API and ingestion of retrieved content), $\langle$scripts$\rangle$ (drafting or revising the report), $\langle$summary$\rangle$ (summarizing the process for context management), and $\langle$answer$\rangle$ (final report production). The structured format and restricted action space support fine-grained analysis of agent decision-making.

2. Iterative Reasoning and Search Process

The agent scaffold is governed by a strict, stepwise iterative loop, in which the update rule for agent state is defined as follows (in LaTeX notation):

$$h_{k+1} = \begin{cases} h_k + t_k + a_k + obs_k, & \text{if } a_k = \text{search} \ h_k + t_k + a_k, & \text{if } a_k \in \{\text{plan}, \text{scripts}\} \ a_k, & \text{if } a_k = \text{summary} \end{cases}$$

Here, $obs_k$ refers to the observation (e.g., snippets or full documents) returned from the search API after a “search” action. The explicit stepwise record of “thought–action–observation” couples introspective self-critique (“thought”) with tool usage, facilitating transparent debugging and auditing of the agent’s process.

This “ReAct-like” separation of reasoning/planning from external tool invocation is essential for evaluating and improving the agent’s strategic capabilities, as well as for systematic benchmarking.

3. Transparent Evaluation through Intermediate Steps

A core aim of the Simple Deepresearch framework is process-level transparency, supporting not only outcome-based but also process-based human evaluation. Integration with the Deep Research Comparator platform ensures that both the full, final report and every intermediate step (reasoning, search, scripting, summarizing) are surfaced in the user interface. Annotators may thus:

• Provide outcome-based preferences (side-by-side report comparison)
• Annotate at the step level (upvote or downvote reasoning steps)
• Highlight and evaluate specific text spans within the long-form report

This collection of fine-grained annotation data (e.g., thousands of upvotes/downvotes on steps and text spans) is vital for the development of more interpretable and responsive research agents and for constructing datasets suitable for advanced RLHF or alignment initiatives.

4. Implementation Simplicity and Model-Agnosticism

The framework’s design choices prioritize ease of LLM integration and low engineering overhead. Any model capable of producing text compliant with the agent’s output schema can be mounted as a research agent in the evaluation ecosystem. A uniform JSON protocol ensures compatibility between agent outputs and comparator system, further supporting prompt-based customization of intermediate action types or output formatting.

This model-agnosticism reflects a deliberate “baseline” philosophy—the scaffold is intended not as a prescription of state-of-the-art methods, but as a minimal, reproducible, and extensible template for progress tracking and ablation studies.

5. Benchmarking, Human Feedback, and Iterative Improvement

The Simple Deepresearch scaffold is specifically crafted for robust comparative evaluation in multi-agent platforms such as Deep Research Comparator (Chandrahasan et al., 7 Jul 2025). The system logs all intermediate steps, captures annotator preferences via outcome-based and process-based votes, and supports automatic side-by-side ranking (e.g., via Bradley-Terry models). This comprehensive annotation regime not only enables more granular diagnostics but also supports the emergence of detailed process-level datasets.

Such structured feedback can be invaluable for:

• Isolating failure modes in reasoning or search (e.g., premature search, shallow synthesis, poor context management)
• Quantifying agent improvement over time through human preference learning
• Benchmarking against other agent designs (including non-LLM baselines or more advanced multi-agent systems)

6. Applications and Extensibility

The Simple Deepresearch framework is broadly applicable wherever interpretable, iterative research agents are required. Example use cases include literature review synthesis, policy report drafting, and analysis of scientific controversies, especially in settings where prototype LLM agents are in active development.

By providing a lightweight, modular baseline, the scaffold supports rapid experimentation, ablation of reasoning modules, or integration of supplementary capabilities (such as summarization or sub-agent delegation) without compromising evaluation comparability. This approach supports both academic inquiry and industrial prototyping in the evolving field of autonomous research agents.

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In summary, the Simple Deepresearch framework exemplifies a prompt-based, modular, and process-transparent agent scaffold for deep research. Its emphasis on iterative reasoning steps, modular LLM integration, and fine-grained human annotation underpins both systematic benchmarking and the reproducible, incremental improvement of LLM-driven research agents (Chandrahasan et al., 7 Jul 2025).