Design LangGraph Agent Workflow for Content Review

Using LangGraph, design a stateful multi-agent workflow for autonomous content review. Define four key nodes: 'Content Ingester', 'Bias Analyzer', 'Fact Checker', and 'Compliance Reporter'. The 'Bias Analyzer' node should primarily use GPT-5 Pro for nuanced language interpretation and bias detection. The 'Fact Checker' node should leverage Gemini 3 Flash for quick factual verification, potentially using external tool calls. The 'Compliance Reporter' aggregates findings and generates a final compliance report. Define the edges for sequential processing and conditional routing based on detected issues (e.g., if high bias is detected, route to a human review node; if factual errors, re-route for a second check).
```python
from langgraph.graph import StateGraph, START, END
from langchain_core.messages import BaseMessage
from typing import TypedDict, Annotated, List, Union # Define the state for the LangGraph workflow
class AgentState(TypedDict): messages: Annotated[List[BaseMessage], lambda x, y: x + y] # Conversation history content_to_review: str # The article text being reviewed bias_analysis: str # Output from Bias Analyzer fact_check_results: List[dict] # Output from Fact Checker compliance_report: str # Final report has_issues: bool # Flag for conditional routing # Initialize the LangGraph
graph_builder = StateGraph(AgentState) # Define placeholder nodes (these will be implemented as functions or LangChain Runnables)
# def content_ingester_node(state: AgentState): ...
# def bias_analyzer_node(state: AgentState): ... # Will use GPT-5 Pro
# def fact_checker_node(state: AgentState): ... # Will use Gemini 3 Flash
# def compliance_reporter_node(state: AgentState): ... # Add nodes to the graph
# graph_builder.add_node("ingester", content_ingester_node)
# graph_builder.add_node("bias_analyzer", bias_analyzer_node)
# graph_builder.add_node("fact_checker", fact_checker_node)
# graph_builder.add_node("reporter", compliance_reporter_node) # Set entry point
# graph_builder.set_entry_point("ingester") # Add edges
# graph_builder.add_edge("ingester", "bias_analyzer") # Define conditional edge (example: based on 'has_issues' flag from bias analyzer)
# graph_builder.add_conditional_edges(
# "bias_analyzer",
# lambda state: "fact_checker" if not state.get("has_issues", False) else "human_review_escalation",
# {"fact_checker": "fact_checker", "human_review_escalation": "human_review_node"}
# ) # ... continue defining edges ... # graph_builder.set_finish_point("reporter") # Compile the graph
# app = graph_builder.compile()
```