Agentic Workflow Showdown: GPT-Researcher vs DeerFlow

Building AI agents that can research, reason, and produce quality reports is one of the hottest challenges in 2026. Two frameworks have emerged as leaders: GPT-Researcher and DeerFlow (by ByteDance). Both use LangGraph for orchestration, but their architectural approaches couldn't be more different.

I spent a week diving deep into both codebases. Here's what I found.

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Table of Contents

1. TL;DR
2. The LangGraph Foundation
3. GPT-Researcher: The Parallel Machine
4. DeerFlow: The Collaborative Researcher
5. Head-to-Head Comparison
6. When to Use Which
7. Architectural Lessons

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TL;DR

| Aspect | GPT-Researcher | DeerFlow | |--------|----------------|----------| | Pattern | Nested parallel graphs | Flat dispatcher | | Speed | ~3 min (parallel) | Variable (sequential) | | Human Loop | Minimal | Deep integration | | Best For | Fast autonomous research | Collaborative exploration |

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The LangGraph Foundation

Both frameworks build on LangGraph, LangChain's extension for stateful multi-agent applications. The core concepts:

graph TB
    subgraph LangGraph Core
        SG[StateGraph] --> |defines| S[State TypedDict]
        SG --> |contains| N[Nodes - Python Functions]
        SG --> |connects via| E[Edges - Static/Conditional]
        SG --> |produces| C[Compiled Executable Graph]
    end

But how each framework uses these primitives reveals fundamentally different philosophies.

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GPT-Researcher: The Parallel Machine

Architecture: Graph-Within-Graph

GPT-Researcher implements a nested graph pattern — a main graph spawns sub-graphs for parallel section research:

flowchart LR
    subgraph Main["MAIN GRAPH (ResearchState)"]
        B[Browser] --> P[Planner]
        P --> R[Researcher]
        R --> W[Writer]
        W --> E((END))

        R --> S1[Sub-Graph\nSection 1]
        R --> S2[Sub-Graph\nSection 2]
        R --> SN[Sub-Graph\nSection N]
    end

    subgraph Sub["SUB-GRAPH (DraftState)"]
        RS[Research] --> RV[Reviewer]
        RV -->|approved| END2((END))
        RV -->|revise| REV[Reviser]
        REV --> RV
    end

Each sub-graph has its own DraftState with a review-revise loop:

# Sub-graph with conditional edges
sub_workflow.add_conditional_edges(
    "reviewer",
    lambda state: "accept" if state["review"] == "approved" else "revise",
    {"accept": END, "revise": "reviser"}
)
sub_workflow.add_edge("reviser", "reviewer")  # Loop back

Why This Works

1. Isolation — Each section gets its own state, preventing race conditions
2. Parallelism — All sub-graphs run concurrently
3. Quality Control — Built-in review cycle catches issues early
4. Speed — ~3 minutes for a full research report

The Agent Lineup

| Agent | Role | Superpower | |-------|------|------------| | GPT-Researcher | Deep research | Web scraping, source aggregation | | Editor | Planning | Outline structure | | Reviewer | QA | Validates against guidelines | | Reviser | Refinement | Incorporates feedback | | Writer | Compilation | Introduction, conclusion | | Publisher | Export | PDF, Docx, Markdown |

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DeerFlow: The Collaborative Researcher

Architecture: Flat Graph with Dispatcher

DeerFlow takes the opposite approach — a single flat graph with a central dispatcher routing to specialists:

flowchart TB
    subgraph DeerFlow["DEERFLOW WORKFLOW"]
        CO[Coordinator] <-->|multi-turn| CL[Clarification Loop]
        CO --> PL[Planner]
        PL --> HF[Human Feedback]
        HF -->|interrupt| RD[Research Dispatcher]

        RD --> RS[Researcher]
        RD --> AN[Analyst]
        RD --> CD[Coder]

        RS --> RP[Reporter]
        AN --> RP
        CD --> RP

        RP --> END((END))
    end

The Command Pattern

DeerFlow uses LangGraph's Command objects instead of conditional edges:

from langgraph.types import Command

def coordinator_node(state: State) -> Command:
    if needs_clarification(state):
        return Command(
            update={"clarification_round": state["clarification_round"] + 1},
            goto="coordinator"  # Loop back
        )
    elif is_research_request(state):
        return Command(
            update={"research_topic": extract_topic(state)},
            goto="planner"
        )
    else:
        return Command(goto=END)

This gives explicit, readable control flow versus implicit conditional edge logic.

Human-in-the-Loop Done Right

DeerFlow's killer feature is deep human integration:

sequenceDiagram
    participant U as User
    participant C as Coordinator
    participant P as Planner
    participant R as Research Team

    U->>C: Research request
    C->>U: Clarification question
    U->>C: Additional context
    C->>P: Create plan
    P->>U: Review plan?
    U->>P: [EDIT_PLAN] Add section on X
    P->>P: Revise plan
    P->>U: Review updated plan?
    U->>P: [ACCEPTED]
    P->>R: Execute research
    R->>U: Final report

1. Clarification Loop — "What specifically about X do you want to know?"
2. Plan Approval — Human reviews the research plan before execution
3. Plan Editing — Natural language edits: "Add a section on Y"
4. Tool Oversight — Approval for sensitive operations

from langgraph.types import interrupt

def human_feedback_node(state: State) -> Command:
    feedback = interrupt({
        "plan": state["current_plan"],
        "message": "Please review the research plan"
    })

    if feedback.startswith("[ACCEPTED]"):
        return Command(goto="research_team")
    elif feedback.startswith("[EDIT_PLAN]"):
        return Command(goto="planner")

The Agent Lineup

| Agent | Role | Tools | |-------|------|-------| | Coordinator | Entry/routing | Intent classification | | Planner | Task decomposition | None (pure LLM) | | Researcher | Information gathering | web_search, crawl, RAG | | Analyst | Reasoning/synthesis | None (pure LLM) | | Coder | Data processing | python_repl | | Reporter | Report generation | None (pure LLM) |

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Head-to-Head Comparison

State Management

# GPT-Researcher: Dual state, isolated sub-graphs
class ResearchState(TypedDict):    # Main graph
    task: dict
    sections: List[str]
    report: str

class DraftState(TypedDict):       # Sub-graph (per section)
    topic: str
    draft: dict
    review: str

# DeerFlow: Single unified state
class State(MessagesState):
    messages: Annotated[list, add_messages]  # Automatic history
    research_topic: str
    current_plan: Optional[Plan]
    observations: List[str]
    final_report: str

GPT-Researcher's dual state enables parallelism but adds complexity. DeerFlow's unified state is simpler but requires careful preservation.

Parallelism vs Sequential

| Aspect | GPT-Researcher | DeerFlow | |--------|----------------|----------| | Approach | Multiple sub-graphs | Single dispatcher | | Concurrent Agents | Yes | No | | Race Conditions | Prevented by isolation | N/A (sequential) | | Time to Complete | ~3 min | Variable (human dependent) |

LangGraph Feature Usage

| Feature | GPT-Researcher | DeerFlow | |---------|----------------|----------| | StateGraph | Yes | Yes | | Nested Graphs | Yes | No | | Conditional Edges | Yes Primary | Yes Secondary | | Command Objects | No | Yes Primary | | MessagesState | No | Yes | | Interrupt (HITL) | No | Yes | | Checkpointing | No | Yes |

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When to Use Which

flowchart TD
    Q1{Need human oversight?}
    Q1 -->|Yes| DF[DeerFlow]
    Q1 -->|No| Q2{Speed critical?}
    Q2 -->|Yes| GPT[GPT-Researcher]
    Q2 -->|No| Q3{Need code execution?}
    Q3 -->|Yes| DF
    Q3 -->|No| Q4{Budget constrained?}
    Q4 -->|Yes| GPT
    Q4 -->|No| BOTH[Either works!]

Choose GPT-Researcher when:

• Speed matters (~3 min vs variable)
• Cost is a concern (~$0.10/research)
• You want autonomous execution
• You need parallel processing
• Simple integration is preferred

Choose DeerFlow when:

• Research requires human guidance
• Multi-turn clarification is needed
• Code execution is required
• You have existing RAG infrastructure
• You need TTS/PPT generation
• Extensive customization matters

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Architectural Lessons

Building these systems taught me several principles:

1. State isolation enables parallelism — GPT-Researcher's sub-graph pattern is elegant
2. Commands > conditional edges for complex routing — DeerFlow's explicit goto is more readable
3. Human-in-the-loop is a feature, not a fallback — DeerFlow's interrupt points add real value
4. MessagesState simplifies conversation tracking — Automatic history management is worth adopting

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What's Next

Both frameworks continue to evolve. GPT-Researcher just added support for local LLMs, while DeerFlow is expanding its MCP protocol integration. The agentic research space is heating up.

For our projects at BlestLabs, we're experimenting with a hybrid approach — using GPT-Researcher's parallel pattern for bulk research, with DeerFlow-style human checkpoints for quality-critical sections.

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Sources

• GPT-Researcher Documentation
• GPT-Researcher LangGraph Blog
• DeerFlow System Architecture
• LangGraph Documentation

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Have questions or want to discuss multi-agent architectures? Reach out on Twitter @aceism_ or check out our other research posts.

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About the Author

BlestLabs builds AI-powered tools and agents. We run multiple AI agents 24/7 including Alfred (Mac Mini) and Pip (Raspberry Pi) for research, automation, and monitoring. Follow our journey on Twitter.