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"""Graph orchestrator for Phase 4.
Implements graph-based orchestration using Pydantic AI agents as nodes.
Supports both iterative and deep research patterns with parallel execution.
"""
import asyncio
from collections.abc import AsyncGenerator, Callable
from typing import TYPE_CHECKING, Any, Literal
import structlog
from src.agent_factory.agents import (
create_input_parser_agent,
create_knowledge_gap_agent,
create_long_writer_agent,
create_planner_agent,
create_thinking_agent,
create_tool_selector_agent,
create_writer_agent,
)
from src.agent_factory.graph_builder import (
AgentNode,
DecisionNode,
ParallelNode,
ResearchGraph,
StateNode,
create_deep_graph,
create_iterative_graph,
)
from src.middleware.budget_tracker import BudgetTracker
from src.middleware.state_machine import WorkflowState, init_workflow_state
from src.orchestrator.research_flow import DeepResearchFlow, IterativeResearchFlow
from src.utils.models import AgentEvent
if TYPE_CHECKING:
pass
logger = structlog.get_logger()
class GraphExecutionContext:
"""Context for managing graph execution state."""
def __init__(self, state: WorkflowState, budget_tracker: BudgetTracker) -> None:
"""Initialize execution context.
Args:
state: Current workflow state
budget_tracker: Budget tracker instance
"""
self.current_node: str = ""
self.visited_nodes: set[str] = set()
self.node_results: dict[str, Any] = {}
self.state = state
self.budget_tracker = budget_tracker
self.iteration_count = 0
def set_node_result(self, node_id: str, result: Any) -> None:
"""Store result from node execution.
Args:
node_id: The node ID
result: The execution result
"""
self.node_results[node_id] = result
def get_node_result(self, node_id: str) -> Any:
"""Get result from node execution.
Args:
node_id: The node ID
Returns:
The stored result, or None if not found
"""
return self.node_results.get(node_id)
def has_visited(self, node_id: str) -> bool:
"""Check if node was visited.
Args:
node_id: The node ID
Returns:
True if visited, False otherwise
"""
return node_id in self.visited_nodes
def mark_visited(self, node_id: str) -> None:
"""Mark node as visited.
Args:
node_id: The node ID
"""
self.visited_nodes.add(node_id)
def update_state(
self, updater: Callable[[WorkflowState, Any], WorkflowState], data: Any
) -> None:
"""Update workflow state.
Args:
updater: Function to update state
data: Data to pass to updater
"""
self.state = updater(self.state, data)
class GraphOrchestrator:
"""
Graph orchestrator using Pydantic AI Graphs.
Executes research workflows as graphs with nodes (agents) and edges (transitions).
Supports parallel execution, conditional routing, and state management.
"""
def __init__(
self,
mode: Literal["iterative", "deep", "auto"] = "auto",
max_iterations: int = 5,
max_time_minutes: int = 10,
use_graph: bool = True,
) -> None:
"""
Initialize graph orchestrator.
Args:
mode: Research mode ("iterative", "deep", or "auto" to detect)
max_iterations: Maximum iterations per loop
max_time_minutes: Maximum time per loop
use_graph: Whether to use graph execution (True) or agent chains (False)
"""
self.mode = mode
self.max_iterations = max_iterations
self.max_time_minutes = max_time_minutes
self.use_graph = use_graph
self.logger = logger
# Initialize flows (for backward compatibility)
self._iterative_flow: IterativeResearchFlow | None = None
self._deep_flow: DeepResearchFlow | None = None
# Graph execution components (lazy initialization)
self._graph: ResearchGraph | None = None
self._budget_tracker: BudgetTracker | None = None
async def run(self, query: str) -> AsyncGenerator[AgentEvent, None]:
"""
Run the research workflow.
Args:
query: The user's research query
Yields:
AgentEvent objects for real-time UI updates
"""
self.logger.info(
"Starting graph orchestrator",
query=query[:100],
mode=self.mode,
use_graph=self.use_graph,
)
yield AgentEvent(
type="started",
message=f"Starting research ({self.mode} mode): {query}",
iteration=0,
)
try:
# Determine research mode
research_mode = self.mode
if research_mode == "auto":
research_mode = await self._detect_research_mode(query)
# Use graph execution if enabled, otherwise fall back to agent chains
if self.use_graph:
async for event in self._run_with_graph(query, research_mode):
yield event
else:
async for event in self._run_with_chains(query, research_mode):
yield event
except Exception as e:
self.logger.error("Graph orchestrator failed", error=str(e), exc_info=True)
yield AgentEvent(
type="error",
message=f"Research failed: {e!s}",
iteration=0,
)
async def _run_with_graph(
self, query: str, research_mode: Literal["iterative", "deep"]
) -> AsyncGenerator[AgentEvent, None]:
"""Run workflow using graph execution.
Args:
query: The research query
research_mode: The research mode
Yields:
AgentEvent objects
"""
# Initialize state and budget tracker
from src.services.embeddings import get_embedding_service
embedding_service = get_embedding_service()
state = init_workflow_state(embedding_service=embedding_service)
budget_tracker = BudgetTracker()
budget_tracker.create_budget(
loop_id="graph_execution",
tokens_limit=100000,
time_limit_seconds=self.max_time_minutes * 60,
iterations_limit=self.max_iterations,
)
budget_tracker.start_timer("graph_execution")
context = GraphExecutionContext(state, budget_tracker)
# Build graph
self._graph = await self._build_graph(research_mode)
# Execute graph
async for event in self._execute_graph(query, context):
yield event
async def _run_with_chains(
self, query: str, research_mode: Literal["iterative", "deep"]
) -> AsyncGenerator[AgentEvent, None]:
"""Run workflow using agent chains (backward compatibility).
Args:
query: The research query
research_mode: The research mode
Yields:
AgentEvent objects
"""
if research_mode == "iterative":
yield AgentEvent(
type="searching",
message="Running iterative research flow...",
iteration=1,
)
if self._iterative_flow is None:
self._iterative_flow = IterativeResearchFlow(
max_iterations=self.max_iterations,
max_time_minutes=self.max_time_minutes,
)
try:
final_report = await self._iterative_flow.run(query)
except Exception as e:
self.logger.error("Iterative flow failed", error=str(e), exc_info=True)
# Yield error event - outer handler will also catch and yield error event
yield AgentEvent(
type="error",
message=f"Iterative research failed: {e!s}",
iteration=1,
)
# Re-raise so outer handler can also yield error event for consistency
raise
yield AgentEvent(
type="complete",
message=final_report,
data={"mode": "iterative"},
iteration=1,
)
elif research_mode == "deep":
yield AgentEvent(
type="searching",
message="Running deep research flow...",
iteration=1,
)
if self._deep_flow is None:
self._deep_flow = DeepResearchFlow(
max_iterations=self.max_iterations,
max_time_minutes=self.max_time_minutes,
)
try:
final_report = await self._deep_flow.run(query)
except Exception as e:
self.logger.error("Deep flow failed", error=str(e), exc_info=True)
# Yield error event before re-raising so test can capture it
yield AgentEvent(
type="error",
message=f"Deep research failed: {e!s}",
iteration=1,
)
raise
yield AgentEvent(
type="complete",
message=final_report,
data={"mode": "deep"},
iteration=1,
)
async def _build_graph(self, mode: Literal["iterative", "deep"]) -> ResearchGraph:
"""Build graph for the specified mode.
Args:
mode: Research mode
Returns:
Constructed ResearchGraph
"""
if mode == "iterative":
# Get agents
knowledge_gap_agent = create_knowledge_gap_agent()
tool_selector_agent = create_tool_selector_agent()
thinking_agent = create_thinking_agent()
writer_agent = create_writer_agent()
# Create graph
graph = create_iterative_graph(
knowledge_gap_agent=knowledge_gap_agent.agent,
tool_selector_agent=tool_selector_agent.agent,
thinking_agent=thinking_agent.agent,
writer_agent=writer_agent.agent,
)
else: # deep
# Get agents
planner_agent = create_planner_agent()
knowledge_gap_agent = create_knowledge_gap_agent()
tool_selector_agent = create_tool_selector_agent()
thinking_agent = create_thinking_agent()
writer_agent = create_writer_agent()
long_writer_agent = create_long_writer_agent()
# Create graph
graph = create_deep_graph(
planner_agent=planner_agent.agent,
knowledge_gap_agent=knowledge_gap_agent.agent,
tool_selector_agent=tool_selector_agent.agent,
thinking_agent=thinking_agent.agent,
writer_agent=writer_agent.agent,
long_writer_agent=long_writer_agent.agent,
)
return graph
def _emit_start_event(
self, node: Any, current_node_id: str, iteration: int, context: GraphExecutionContext
) -> AgentEvent:
"""Emit start event for a node.
Args:
node: The node being executed
current_node_id: Current node ID
iteration: Current iteration number
context: Execution context
Returns:
AgentEvent for the start of node execution
"""
if node and node.node_id == "planner":
return AgentEvent(
type="searching",
message="Creating report plan...",
iteration=iteration,
)
elif node and node.node_id == "parallel_loops":
# Get report plan to show section count
report_plan = context.get_node_result("planner")
if report_plan and hasattr(report_plan, "report_outline"):
section_count = len(report_plan.report_outline)
return AgentEvent(
type="looping",
message=f"Running parallel research loops for {section_count} sections...",
iteration=iteration,
data={"sections": section_count},
)
return AgentEvent(
type="looping",
message="Running parallel research loops...",
iteration=iteration,
)
elif node and node.node_id == "synthesizer":
return AgentEvent(
type="synthesizing",
message="Synthesizing final report from section drafts...",
iteration=iteration,
)
return AgentEvent(
type="looping",
message=f"Executing node: {current_node_id}",
iteration=iteration,
)
def _emit_completion_event(
self, node: Any, current_node_id: str, result: Any, iteration: int
) -> AgentEvent:
"""Emit completion event for a node.
Args:
node: The node that was executed
current_node_id: Current node ID
result: Node execution result
iteration: Current iteration number
Returns:
AgentEvent for the completion of node execution
"""
if not node:
return AgentEvent(
type="looping",
message=f"Completed node: {current_node_id}",
iteration=iteration,
)
if node.node_id == "planner":
if isinstance(result, dict) and "report_outline" in result:
section_count = len(result["report_outline"])
return AgentEvent(
type="search_complete",
message=f"Report plan created with {section_count} sections",
iteration=iteration,
data={"sections": section_count},
)
return AgentEvent(
type="search_complete",
message="Report plan created",
iteration=iteration,
)
elif node.node_id == "parallel_loops":
if isinstance(result, list):
return AgentEvent(
type="search_complete",
message=f"Completed parallel research for {len(result)} sections",
iteration=iteration,
data={"sections_completed": len(result)},
)
return AgentEvent(
type="search_complete",
message="Parallel research loops completed",
iteration=iteration,
)
elif node.node_id == "synthesizer":
return AgentEvent(
type="synthesizing",
message="Final report synthesis completed",
iteration=iteration,
)
return AgentEvent(
type="searching" if node.node_type == "agent" else "looping",
message=f"Completed {node.node_type} node: {current_node_id}",
iteration=iteration,
)
async def _execute_graph(
self, query: str, context: GraphExecutionContext
) -> AsyncGenerator[AgentEvent, None]:
"""Execute the graph from entry node.
Args:
query: The research query
context: Execution context
Yields:
AgentEvent objects
"""
if not self._graph:
raise ValueError("Graph not built")
current_node_id = self._graph.entry_node
iteration = 0
while current_node_id and current_node_id not in self._graph.exit_nodes:
# Check budget
if not context.budget_tracker.can_continue("graph_execution"):
self.logger.warning("Budget exceeded, exiting graph execution")
break
# Execute current node
iteration += 1
context.current_node = current_node_id
node = self._graph.get_node(current_node_id)
# Emit start event
yield self._emit_start_event(node, current_node_id, iteration, context)
try:
result = await self._execute_node(current_node_id, query, context)
context.set_node_result(current_node_id, result)
context.mark_visited(current_node_id)
# Yield completion event
yield self._emit_completion_event(node, current_node_id, result, iteration)
except Exception as e:
self.logger.error("Node execution failed", node_id=current_node_id, error=str(e))
yield AgentEvent(
type="error",
message=f"Node {current_node_id} failed: {e!s}",
iteration=iteration,
)
break
# Get next node(s)
next_nodes = self._get_next_node(current_node_id, context)
if not next_nodes:
# No more nodes, check if we're at exit
if current_node_id in self._graph.exit_nodes:
break
# Otherwise, we've reached a dead end
self.logger.warning("Reached dead end in graph", node_id=current_node_id)
break
current_node_id = next_nodes[0] # For now, take first next node (handle parallel later)
# Final event
final_result = context.get_node_result(current_node_id) if current_node_id else None
yield AgentEvent(
type="complete",
message=final_result if isinstance(final_result, str) else "Research completed",
data={"mode": self.mode, "iterations": iteration},
iteration=iteration,
)
async def _execute_node(self, node_id: str, query: str, context: GraphExecutionContext) -> Any:
"""Execute a single node.
Args:
node_id: The node ID
query: The research query
context: Execution context
Returns:
Node execution result
"""
if not self._graph:
raise ValueError("Graph not built")
node = self._graph.get_node(node_id)
if not node:
raise ValueError(f"Node {node_id} not found")
if isinstance(node, AgentNode):
return await self._execute_agent_node(node, query, context)
elif isinstance(node, StateNode):
return await self._execute_state_node(node, query, context)
elif isinstance(node, DecisionNode):
return await self._execute_decision_node(node, query, context)
elif isinstance(node, ParallelNode):
return await self._execute_parallel_node(node, query, context)
else:
raise ValueError(f"Unknown node type: {type(node)}")
async def _execute_agent_node(
self, node: AgentNode, query: str, context: GraphExecutionContext
) -> Any:
"""Execute an agent node.
Special handling for deep research nodes:
- "planner": Takes query string, returns ReportPlan
- "synthesizer": Takes query + ReportPlan + section drafts, returns final report
Args:
node: The agent node
query: The research query
context: Execution context
Returns:
Agent execution result
"""
# Special handling for synthesizer node
if node.node_id == "synthesizer":
# Call LongWriterAgent.write_report() directly instead of using agent.run()
from src.agent_factory.agents import create_long_writer_agent
from src.utils.models import ReportDraft, ReportDraftSection, ReportPlan
report_plan = context.get_node_result("planner")
section_drafts = context.get_node_result("parallel_loops") or []
if not isinstance(report_plan, ReportPlan):
raise ValueError("ReportPlan not found for synthesizer")
if not section_drafts:
raise ValueError("Section drafts not found for synthesizer")
# Create ReportDraft from section drafts
report_draft = ReportDraft(
sections=[
ReportDraftSection(
section_title=section.title,
section_content=draft,
)
for section, draft in zip(
report_plan.report_outline, section_drafts, strict=False
)
]
)
# Get LongWriterAgent instance and call write_report directly
long_writer_agent = create_long_writer_agent()
final_report = await long_writer_agent.write_report(
original_query=query,
report_title=report_plan.report_title,
report_draft=report_draft,
)
# Estimate tokens (rough estimate)
estimated_tokens = len(final_report) // 4 # Rough token estimate
context.budget_tracker.add_tokens("graph_execution", estimated_tokens)
return final_report
# Standard agent execution
# Prepare input based on node type
if node.node_id == "planner":
# Planner takes the original query
input_data = query
else:
# Standard: use previous node result or query
prev_result = context.get_node_result(context.current_node)
input_data = prev_result if prev_result is not None else query
# Apply input transformer if provided
if node.input_transformer:
input_data = node.input_transformer(input_data)
# Execute agent
result = await node.agent.run(input_data)
# Transform output if needed
output = result.output
if node.output_transformer:
output = node.output_transformer(output)
# Estimate and track tokens
if hasattr(result, "usage") and result.usage:
tokens = result.usage.total_tokens if hasattr(result.usage, "total_tokens") else 0
context.budget_tracker.add_tokens("graph_execution", tokens)
return output
async def _execute_state_node(
self, node: StateNode, query: str, context: GraphExecutionContext
) -> Any:
"""Execute a state node.
Special handling for deep research state nodes:
- "store_plan": Stores ReportPlan in context for parallel loops
- "collect_drafts": Stores section drafts in context for synthesizer
Args:
node: The state node
query: The research query
context: Execution context
Returns:
State update result
"""
# Get previous result for state update
# For "store_plan", get from planner node
# For "collect_drafts", get from parallel_loops node
if node.node_id == "store_plan":
prev_result = context.get_node_result("planner")
elif node.node_id == "collect_drafts":
prev_result = context.get_node_result("parallel_loops")
else:
prev_result = context.get_node_result(context.current_node)
# Update state
updated_state = node.state_updater(context.state, prev_result)
context.state = updated_state
# Store result in context for next nodes to access
context.set_node_result(node.node_id, prev_result)
# Read state if needed
if node.state_reader:
return node.state_reader(context.state)
return prev_result # Return the stored result for next nodes
async def _execute_decision_node(
self, node: DecisionNode, query: str, context: GraphExecutionContext
) -> str:
"""Execute a decision node.
Args:
node: The decision node
query: The research query
context: Execution context
Returns:
Next node ID
"""
# Get previous result for decision
prev_result = context.get_node_result(context.current_node)
# Make decision
next_node_id = node.decision_function(prev_result)
# Validate decision
if next_node_id not in node.options:
self.logger.warning(
"Decision function returned invalid node",
node_id=node.node_id,
returned=next_node_id,
options=node.options,
)
# Default to first option
next_node_id = node.options[0]
return next_node_id
async def _execute_parallel_node(
self, node: ParallelNode, query: str, context: GraphExecutionContext
) -> list[Any]:
"""Execute a parallel node.
Special handling for deep research "parallel_loops" node:
- Extracts report plan from previous node result
- Creates IterativeResearchFlow instances for each section
- Executes them in parallel
- Returns section drafts
Args:
node: The parallel node
query: The research query
context: Execution context
Returns:
List of results from parallel nodes
"""
# Special handling for deep research parallel_loops node
if node.node_id == "parallel_loops":
return await self._execute_deep_research_parallel_loops(node, query, context)
# Standard parallel node execution
# Execute all parallel nodes concurrently
tasks = [
self._execute_node(parallel_node_id, query, context)
for parallel_node_id in node.parallel_nodes
]
results = await asyncio.gather(*tasks, return_exceptions=True)
# Handle exceptions
for i, result in enumerate(results):
if isinstance(result, Exception):
self.logger.error(
"Parallel node execution failed",
node_id=node.parallel_nodes[i] if i < len(node.parallel_nodes) else "unknown",
error=str(result),
)
results[i] = None
# Aggregate if needed
if node.aggregator:
aggregated = node.aggregator(results)
# Type cast: aggregator returns Any, but we expect list[Any]
return list(aggregated) if isinstance(aggregated, list) else [aggregated]
return results
async def _execute_deep_research_parallel_loops(
self, node: ParallelNode, query: str, context: GraphExecutionContext
) -> list[str]:
"""Execute parallel iterative research loops for deep research.
Args:
node: The parallel node (should be "parallel_loops")
query: The research query
context: Execution context
Returns:
List of section draft strings
"""
from src.agent_factory.judges import create_judge_handler
from src.orchestrator.research_flow import IterativeResearchFlow
from src.utils.models import ReportPlan
# Get report plan from previous node (store_plan)
# The plan should be stored in context.node_results from the planner node
planner_result = context.get_node_result("planner")
if not isinstance(planner_result, ReportPlan):
self.logger.error(
"Planner result is not a ReportPlan",
type=type(planner_result),
)
raise ValueError("Planner must return ReportPlan for deep research")
report_plan: ReportPlan = planner_result
self.logger.info(
"Executing parallel loops for deep research",
sections=len(report_plan.report_outline),
)
# Create judge handler for iterative flows
judge_handler = create_judge_handler()
# Create and execute iterative research flows for each section
async def run_section_research(section_index: int) -> str:
"""Run iterative research for a single section."""
section = report_plan.report_outline[section_index]
try:
# Create iterative research flow
flow = IterativeResearchFlow(
max_iterations=self.max_iterations,
max_time_minutes=self.max_time_minutes,
verbose=False, # Less verbose in parallel execution
use_graph=False, # Use agent chains for section research
judge_handler=judge_handler,
)
# Run research for this section
section_draft = await flow.run(
query=section.key_question,
background_context=report_plan.background_context,
)
self.logger.info(
"Section research completed",
section_index=section_index,
section_title=section.title,
draft_length=len(section_draft),
)
return section_draft
except Exception as e:
self.logger.error(
"Section research failed",
section_index=section_index,
section_title=section.title,
error=str(e),
)
# Return empty string for failed sections
return f"# {section.title}\n\n[Research failed: {e!s}]"
# Execute all sections in parallel
section_drafts = await asyncio.gather(
*(run_section_research(i) for i in range(len(report_plan.report_outline))),
return_exceptions=True,
)
# Handle exceptions and filter None results
filtered_drafts: list[str] = []
for i, draft in enumerate(section_drafts):
if isinstance(draft, Exception):
self.logger.error(
"Section research exception",
section_index=i,
error=str(draft),
)
filtered_drafts.append(
f"# {report_plan.report_outline[i].title}\n\n[Research failed: {draft!s}]"
)
elif draft is not None:
# Type narrowing: after Exception check, draft is str | None
assert isinstance(draft, str), "Expected str after Exception check"
filtered_drafts.append(draft)
self.logger.info(
"Parallel loops completed",
sections=len(filtered_drafts),
total_sections=len(report_plan.report_outline),
)
return filtered_drafts
def _get_next_node(self, node_id: str, context: GraphExecutionContext) -> list[str]:
"""Get next node(s) from current node.
Args:
node_id: Current node ID
context: Execution context
Returns:
List of next node IDs
"""
if not self._graph:
return []
# Get node result for condition evaluation
node_result = context.get_node_result(node_id)
# Get next nodes
next_nodes = self._graph.get_next_nodes(node_id, context=node_result)
# If this was a decision node, use its result
node = self._graph.get_node(node_id)
if isinstance(node, DecisionNode):
decision_result = node_result
if isinstance(decision_result, str):
return [decision_result]
# Return next node IDs
return [next_node_id for next_node_id, _ in next_nodes]
async def _detect_research_mode(self, query: str) -> Literal["iterative", "deep"]:
"""
Detect research mode from query using input parser agent.
Uses input parser agent to analyze query and determine research mode.
Falls back to heuristic if parser fails.
Args:
query: The research query
Returns:
Detected research mode
"""
try:
# Use input parser agent for intelligent mode detection
input_parser = create_input_parser_agent()
parsed_query = await input_parser.parse(query)
self.logger.info(
"Research mode detected by input parser",
mode=parsed_query.research_mode,
query=query[:100],
)
return parsed_query.research_mode
except Exception as e:
# Fallback to heuristic if parser fails
self.logger.warning(
"Input parser failed, using heuristic",
error=str(e),
query=query[:100],
)
query_lower = query.lower()
if any(
keyword in query_lower
for keyword in [
"section",
"sections",
"report",
"outline",
"structure",
"comprehensive",
"analyze",
"analysis",
]
):
return "deep"
return "iterative"
def create_graph_orchestrator(
mode: Literal["iterative", "deep", "auto"] = "auto",
max_iterations: int = 5,
max_time_minutes: int = 10,
use_graph: bool = True,
) -> GraphOrchestrator:
"""
Factory function to create a graph orchestrator.
Args:
mode: Research mode
max_iterations: Maximum iterations per loop
max_time_minutes: Maximum time per loop
use_graph: Whether to use graph execution (True) or agent chains (False)
Returns:
Configured GraphOrchestrator instance
"""
return GraphOrchestrator(
mode=mode,
max_iterations=max_iterations,
max_time_minutes=max_time_minutes,
use_graph=use_graph,
)
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