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from langgraph.graph import StateGraph
from langchain_core.runnables import RunnableLambda
from src.state import AgentState
from src.nodes.researcher import researcher_node
from src.nodes.analyzer import analyzer_node
from src.nodes.critic import critic_node
from src.utils.conditions import should_continue
from langsmith import traceable
# Create the cyclic workflow
workflow = StateGraph(AgentState)
# Add nodes to the workflow (Analyzer handles both initial generation and revisions)
workflow.add_node("Researcher", RunnableLambda(researcher_node))
workflow.add_node("Analyzer", RunnableLambda(analyzer_node))
workflow.add_node("Critic", RunnableLambda(critic_node))
# Define the workflow edges
workflow.set_entry_point("Researcher")
workflow.add_edge("Researcher", "Analyzer")
workflow.add_edge("Analyzer", "Critic")
# Add conditional edges for the self-correcting loop
# Analyzer now handles revisions directly (no separate Editor node)
workflow.add_conditional_edges(
"Critic",
should_continue,
{
"exit": "__end__",
"retry": "Analyzer" # Route back to Analyzer for revisions
}
)
# Set the finish point
workflow.set_finish_point("Critic")
# Enhanced configuration for better tracing
workflow.config = {
"project_name": "AI-strategy-agent-cyclic",
"tags": ["self-correcting", "quality-loop", "swot-analysis"],
"metadata": {
"version": "2.0",
"environment": "development",
"workflow_type": "researcher-analyzer-critic"
}
}
# Compile the workflow
app = workflow.compile()
# Wrapped execution with enhanced tracing
@traceable(name="Run - Self-Correcting SWOT Analysis", tags=["cyclic", "quality-control", "demo"], metadata={"purpose": "iterative_improvement"})
def run_self_correcting_workflow(company_name="Tesla", strategy_focus="Cost Leadership", workflow_id=None, progress_store=None):
"""Execute the complete self-correcting SWOT analysis workflow"""
# Initialize state with default values
initial_state = {
"company_name": company_name,
"strategy_focus": strategy_focus,
"raw_data": None,
"draft_report": None,
"critique": None,
"revision_count": 0,
"messages": [],
"score": 0,
"data_source": "live",
"provider_used": None,
"sources_failed": [],
"workflow_id": workflow_id,
"progress_store": progress_store,
"error": None # Set when LLM providers fail
}
# Execute the workflow
output = app.invoke(initial_state, config={
"configurable": {
"workflow_id": workflow_id,
"progress_store": progress_store
}
})
return output
# Main execution
if __name__ == "__main__":
# Test with Tesla as the default company
target_company = "Tesla"
print(f"🔍 Running Self-Correcting SWOT Analysis for {target_company}...")
print("📝 This workflow includes: Researcher → Analyzer → Critic → Analyzer (revision loop)")
print("🎯 Loop continues until score ≥ 7 or 3 revisions attempted\n")
# Execute the workflow
result = run_self_correcting_workflow(target_company)
# Display results (with safe fallbacks)
print(f"🏁 Analysis completed for {target_company}!")
final_score = result.get('score', 'N/A')
final_revision_count = result.get('revision_count', 0)
final_critique = result.get('critique', 'No critique available')
print(f"📊 Final Score: {final_score}/10")
print(f"🔄 Revision Count: {final_revision_count}")
print(f"💬 Critique: {final_critique}")
print(f"\n📄 Final SWOT Analysis:")
print(result['draft_report'])
# Summary
print(f"\n✅ Self-Correcting Workflow Summary:")
print(f" - Company: {target_company}")
print(f" - Initial Quality: Improved from unknown to {final_score}/10")
print(f" - Revisions Made: {final_revision_count}")
print(f" - Final Report Length: {len(result['draft_report'])} characters")
print(f" - Workflow: Researcher → Analyzer → Critic → Analyzer (revision loop)")
print(f" - Tracing: Enhanced LangSmith traces available")
# Quality assessment
print(f" - Quality Score: {final_score}/10") |