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Deep research

Classes:

Name Description
DeepResearchAgent

Deep Research Agent: PLAN → RESEARCH → SYNTHESIZE → CRITIQUE loop.

Attributes:

Name Type Description
StateT

Attributes

StateT module-attribute 

StateT = TypeVar('StateT', bound=AgentState)

Classes

DeepResearchAgent

Deep Research Agent: PLAN → RESEARCH → SYNTHESIZE → CRITIQUE loop.

This agent mirrors modern deep-research patterns inspired by DeerFlow and Tongyi DeepResearch: plan tasks, use tools to research, synthesize findings, critique gaps and iterate a bounded number of times.

Nodes: - PLAN: Decompose problem, propose search/tool actions; may include tool calls - RESEARCH: ToolNode executes search/browse/calc/etc tools - SYNTHESIZE: Aggregate and draft a coherent report or partial answer - CRITIQUE: Identify gaps, contradictions, or follow-ups; can request more tools

Routing:
- PLAN -> conditional(_route_after_plan):
    {"RESEARCH": RESEARCH, "SYNTHESIZE": SYNTHESIZE, END: END}
  • RESEARCH -> SYNTHESIZE
  • SYNTHESIZE -> CRITIQUE
  • CRITIQUE -> conditional(_route_after_critique): {"RESEARCH": RESEARCH, END: END}

Iteration Control: - Uses execution_meta.internal_data keys: dr_max_iters (int): maximum critique→research loops (default 2) dr_iters (int): current loop count (auto-updated) dr_heavy_mode (bool): if True, bias towards one more loop when critique suggests

Methods:

Name Description
__init__
compile
Source code in pyagenity/prebuilt/agent/deep_research.py 
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class DeepResearchAgent[StateT: AgentState]:
    """Deep Research Agent: PLAN → RESEARCH → SYNTHESIZE → CRITIQUE loop.

    This agent mirrors modern deep-research patterns inspired by DeerFlow and
    Tongyi DeepResearch: plan tasks, use tools to research, synthesize findings,
    critique gaps and iterate a bounded number of times.

    Nodes:
    - PLAN: Decompose problem, propose search/tool actions; may include tool calls
    - RESEARCH: ToolNode executes search/browse/calc/etc tools
    - SYNTHESIZE: Aggregate and draft a coherent report or partial answer
    - CRITIQUE: Identify gaps, contradictions, or follow-ups; can request more tools

        Routing:
        - PLAN -> conditional(_route_after_plan):
            {"RESEARCH": RESEARCH, "SYNTHESIZE": SYNTHESIZE, END: END}
    - RESEARCH -> SYNTHESIZE
    - SYNTHESIZE -> CRITIQUE
    - CRITIQUE -> conditional(_route_after_critique): {"RESEARCH": RESEARCH, END: END}

    Iteration Control:
    - Uses execution_meta.internal_data keys:
        dr_max_iters (int): maximum critique→research loops (default 2)
        dr_iters (int): current loop count (auto-updated)
        dr_heavy_mode (bool): if True, bias towards one more loop when critique suggests
    """

    def __init__(
        self,
        state: StateT | None = None,
        context_manager: BaseContextManager[StateT] | None = None,
        publisher: BasePublisher | None = None,
        id_generator: BaseIDGenerator = DefaultIDGenerator(),
        container: InjectQ | None = None,
        max_iters: int = 2,
        heavy_mode: bool = False,
    ):
        # initialize graph
        self._graph = StateGraph[StateT](
            state=state,
            context_manager=context_manager,
            publisher=publisher,
            id_generator=id_generator,
            container=container,
        )
        # seed default internal config on prototype state
        # Note: These values will be copied to new state at invoke time.
        exec_meta: ExecutionState = self._graph._state.execution_meta
        exec_meta.internal_data.setdefault("dr_max_iters", max(0, int(max_iters)))
        exec_meta.internal_data.setdefault("dr_iters", 0)
        exec_meta.internal_data.setdefault("dr_heavy_mode", bool(heavy_mode))

    def compile(  # noqa: PLR0912
        self,
        plan_node: Callable | tuple[Callable, str],
        research_tool_node: ToolNode | tuple[ToolNode, str],
        synthesize_node: Callable | tuple[Callable, str],
        critique_node: Callable | tuple[Callable, str],
        checkpointer: BaseCheckpointer[StateT] | None = None,
        store: BaseStore | None = None,
        interrupt_before: list[str] | None = None,
        interrupt_after: list[str] | None = None,
        callback_manager: CallbackManager = CallbackManager(),
    ) -> CompiledGraph:
        # Handle plan_node
        if isinstance(plan_node, tuple):
            plan_func, plan_name = plan_node
            if not callable(plan_func):
                raise ValueError("plan_node[0] must be callable")
        else:
            plan_func = plan_node
            plan_name = "PLAN"
            if not callable(plan_func):
                raise ValueError("plan_node must be callable")

        # Handle research_tool_node
        if isinstance(research_tool_node, tuple):
            research_func, research_name = research_tool_node
            if not isinstance(research_func, ToolNode):
                raise ValueError("research_tool_node[0] must be a ToolNode")
        else:
            research_func = research_tool_node
            research_name = "RESEARCH"
            if not isinstance(research_func, ToolNode):
                raise ValueError("research_tool_node must be a ToolNode")

        # Handle synthesize_node
        if isinstance(synthesize_node, tuple):
            synthesize_func, synthesize_name = synthesize_node
            if not callable(synthesize_func):
                raise ValueError("synthesize_node[0] must be callable")
        else:
            synthesize_func = synthesize_node
            synthesize_name = "SYNTHESIZE"
            if not callable(synthesize_func):
                raise ValueError("synthesize_node must be callable")

        # Handle critique_node
        if isinstance(critique_node, tuple):
            critique_func, critique_name = critique_node
            if not callable(critique_func):
                raise ValueError("critique_node[0] must be callable")
        else:
            critique_func = critique_node
            critique_name = "CRITIQUE"
            if not callable(critique_func):
                raise ValueError("critique_node must be callable")

        # Add nodes
        self._graph.add_node(plan_name, plan_func)
        self._graph.add_node(research_name, research_func)
        self._graph.add_node(synthesize_name, synthesize_func)
        self._graph.add_node(critique_name, critique_func)

        # Edges
        self._graph.add_conditional_edges(
            plan_name,
            _route_after_plan,
            {research_name: research_name, synthesize_name: synthesize_name, END: END},
        )
        self._graph.add_edge(research_name, synthesize_name)
        self._graph.add_edge(synthesize_name, critique_name)
        self._graph.add_conditional_edges(
            critique_name,
            _route_after_critique,
            {research_name: research_name, END: END},
        )

        # Entry
        self._graph.set_entry_point(plan_name)

        return self._graph.compile(
            checkpointer=checkpointer,
            store=store,
            interrupt_before=interrupt_before,
            interrupt_after=interrupt_after,
            callback_manager=callback_manager,
        )

Functions

__init__ 
__init__(state=None, context_manager=None, publisher=None, id_generator=DefaultIDGenerator(), container=None, max_iters=2, heavy_mode=False)
Source code in pyagenity/prebuilt/agent/deep_research.py 
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def __init__(
    self,
    state: StateT | None = None,
    context_manager: BaseContextManager[StateT] | None = None,
    publisher: BasePublisher | None = None,
    id_generator: BaseIDGenerator = DefaultIDGenerator(),
    container: InjectQ | None = None,
    max_iters: int = 2,
    heavy_mode: bool = False,
):
    # initialize graph
    self._graph = StateGraph[StateT](
        state=state,
        context_manager=context_manager,
        publisher=publisher,
        id_generator=id_generator,
        container=container,
    )
    # seed default internal config on prototype state
    # Note: These values will be copied to new state at invoke time.
    exec_meta: ExecutionState = self._graph._state.execution_meta
    exec_meta.internal_data.setdefault("dr_max_iters", max(0, int(max_iters)))
    exec_meta.internal_data.setdefault("dr_iters", 0)
    exec_meta.internal_data.setdefault("dr_heavy_mode", bool(heavy_mode))
compile 
compile(plan_node, research_tool_node, synthesize_node, critique_node, checkpointer=None, store=None, interrupt_before=None, interrupt_after=None, callback_manager=CallbackManager())
Source code in pyagenity/prebuilt/agent/deep_research.py 
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def compile(  # noqa: PLR0912
    self,
    plan_node: Callable | tuple[Callable, str],
    research_tool_node: ToolNode | tuple[ToolNode, str],
    synthesize_node: Callable | tuple[Callable, str],
    critique_node: Callable | tuple[Callable, str],
    checkpointer: BaseCheckpointer[StateT] | None = None,
    store: BaseStore | None = None,
    interrupt_before: list[str] | None = None,
    interrupt_after: list[str] | None = None,
    callback_manager: CallbackManager = CallbackManager(),
) -> CompiledGraph:
    # Handle plan_node
    if isinstance(plan_node, tuple):
        plan_func, plan_name = plan_node
        if not callable(plan_func):
            raise ValueError("plan_node[0] must be callable")
    else:
        plan_func = plan_node
        plan_name = "PLAN"
        if not callable(plan_func):
            raise ValueError("plan_node must be callable")

    # Handle research_tool_node
    if isinstance(research_tool_node, tuple):
        research_func, research_name = research_tool_node
        if not isinstance(research_func, ToolNode):
            raise ValueError("research_tool_node[0] must be a ToolNode")
    else:
        research_func = research_tool_node
        research_name = "RESEARCH"
        if not isinstance(research_func, ToolNode):
            raise ValueError("research_tool_node must be a ToolNode")

    # Handle synthesize_node
    if isinstance(synthesize_node, tuple):
        synthesize_func, synthesize_name = synthesize_node
        if not callable(synthesize_func):
            raise ValueError("synthesize_node[0] must be callable")
    else:
        synthesize_func = synthesize_node
        synthesize_name = "SYNTHESIZE"
        if not callable(synthesize_func):
            raise ValueError("synthesize_node must be callable")

    # Handle critique_node
    if isinstance(critique_node, tuple):
        critique_func, critique_name = critique_node
        if not callable(critique_func):
            raise ValueError("critique_node[0] must be callable")
    else:
        critique_func = critique_node
        critique_name = "CRITIQUE"
        if not callable(critique_func):
            raise ValueError("critique_node must be callable")

    # Add nodes
    self._graph.add_node(plan_name, plan_func)
    self._graph.add_node(research_name, research_func)
    self._graph.add_node(synthesize_name, synthesize_func)
    self._graph.add_node(critique_name, critique_func)

    # Edges
    self._graph.add_conditional_edges(
        plan_name,
        _route_after_plan,
        {research_name: research_name, synthesize_name: synthesize_name, END: END},
    )
    self._graph.add_edge(research_name, synthesize_name)
    self._graph.add_edge(synthesize_name, critique_name)
    self._graph.add_conditional_edges(
        critique_name,
        _route_after_critique,
        {research_name: research_name, END: END},
    )

    # Entry
    self._graph.set_entry_point(plan_name)

    return self._graph.compile(
        checkpointer=checkpointer,
        store=store,
        interrupt_before=interrupt_before,
        interrupt_after=interrupt_after,
        callback_manager=callback_manager,
    )