Landscape Survey

Deep Research Agent Landscape: Architectural Innovations for DeepDelve

Reviews four open source deep research systems for patterns worth adapting: GPT Researcher, STORM, Tongyi DeepResearch, and Open Deep Research.

GPT Researcher

Parallelizes crawling instead of sequential search then fetch, and prunes HTML boilerplate before synthesis. Actionable: run WebSearcher tasks fully concurrently, and add a context pruning utility before writing fetched content to the workspace.

Stanford STORM

Generates 3 to 5 diverse personas before planning (avoiding consensus bias), and drafts a stable outline before any text, writing section by section. Actionable: a persona brainstorming step before write_todos, and sectional drafting instead of one massive final_report.md call that risks running out of output tokens.

Tongyi DeepResearch

Scales inference compute on complex queries, expanding into multiple search strings and fetching deeper into results. Actionable: a search_mode: "heavy" config that expands one query into three and fetches the top result of each.

Open Deep Research

Pauses for human approval of the plan before consuming any search quota, and integrates external tools via MCP. Actionable: a human_in_the_loop gate pausing after write_todos, and a dynamic MCP tool loader.

Proposed evolutionary path

flowchart TD
    A[User Query] --> B[Phase 1: Multi-Perspective Persona Generation]
    B --> C[Phase 2: Bounded Outline Planning]
    C -->|HITL Gate: User Approval| D[Phase 3: Test-Time Search Scaling / Heavy Mode]
    D --> E[Phase 4: Concurrency-Optimized Scraping & Pruning]
    E --> F[Phase 5: Section-by-Section Drafting & Peer Critique]
    F --> G[Final Report]

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A sketch of the human in the loop gate, the concrete new check that would go in engine/tui.py:

if config.cfg.get("settings", {}).get("human_in_the_loop", False):
    await self.wait_for_user_plan_approval()