Define LangGraph State and Agents

Initialize a LangGraph state with variables for `interaction_log`, `fluency_behaviors_identified`, `coaching_history`, and `user_feedback`. Define three LangChain agents: an `InteractionAgent` using OpenAI o4-mini to receive user input, a `BehaviorAnalyst` agent using Llama 4 Maverick to interpret interaction logs against the AI Fluency Index, and a `FluencyCoach` agent using OpenAI o4-mini to generate feedback. Ensure each agent has appropriate tools defined for their tasks. ```python
from typing import TypedDict, Annotated, List
from langchain_core.messages import HumanMessage
from langchain_openai import ChatOpenAI
from langchain_community.llms import LlamaCpp
from langgraph.graph import StateGraph, END # Define the state
class AgentState(TypedDict): interaction_log: Annotated[List[str], lambda x, y: x + y] fluency_behaviors_identified: Annotated[List[str], lambda x, y: x + y] coaching_history: Annotated[List[str], lambda x, y: x + y] user_feedback: str # Initialize LLMs
o4_mini_llm = ChatOpenAI(model='GPT-5 Pro-mini', temperature=0.2)
# Placeholder for Llama 4 Maverick, replace with actual loading if available
# Example for LlamaCpp assumes local .gguf file
llama_maverick_llm = LlamaCpp(model_path='/path/to/llama-4-maverick.gguf', temperature=0.1, n_ctx=2048) # Define agents (simplified for prompt, actual implementation would be more complex)
def interaction_agent(state: AgentState): print(f"Interaction Agent received: {state['user_feedback']}") new_log_entry = f"User: {state['user_feedback']}" # Logic to process and potentially generate AI response for next turn return {'interaction_log': [new_log_entry]} def behavior_analyst(state: AgentState): current_interaction = state['interaction_log'][-1] print(f"Behavior Analyst analyzing: {current_interaction}") # Simulate analysis against AI Fluency Index using llama_maverick_llm identified_behaviors = ['Context-setting', 'Iterative clarification'] # Example fluency_score = 7.5 # Example return {'fluency_behaviors_identified': identified_behaviors, 'fluency_score': fluency_score} # Build graph (simplified)
workflow = StateGraph(AgentState)
workflow.add_node('interaction', interaction_agent)
workflow.add_node('analyze', behavior_analyst)
workflow.add_edge('interaction', 'analyze')
# ... more nodes and edges ... # Example of how to add a conditional edge:
# workflow.add_conditional_edges(
# 'analyze',
# lambda state: 'coach' if state['fluency_score'] < 8 else END,
# {'coach': 'coach_node_name'}
# )
```