Delete agent.py

agent.py

@@ -1,180 +0,0 @@
-"""
-LangGraph Agent Implementation
-This file contains the build_graph() function that creates your agent.
-Customize this with your own tools, logic, and LLM integration.
-"""
-
-from langgraph.graph import StateGraph, START, END
-from langchain_core.messages import BaseMessage, AIMessage
-from typing import Annotated
-from typing_extensions import TypedDict
-import operator
-
-
-# Define the state schema for your agent
-class AgentState(TypedDict):
-    """State passed through the agent graph."""
-    messages: Annotated[list[BaseMessage], operator.add]
-
-
-def build_graph():
-    """
-    Build and compile your LangGraph agent graph.
-    
-    Returns:
-        A compiled LangGraph graph that can process messages.
-        
-    The graph should:
-    - Accept input: {"messages": [HumanMessage(...)]}
-    - Return output: {"messages": [HumanMessage(...), AIMessage(...)]}
-    """
-    
-    # Create the state graph
-    graph = StateGraph(AgentState)
-    
-    # ============================================
-    # DEFINE YOUR NODES HERE
-    # ============================================
-    
-    def agent_node(state: AgentState) -> dict:
-        """
-        Main agent processing node.
-        This is where your agent logic goes.
-        
-        Args:
-            state: Current conversation state with messages
-            
-        Returns:
-            Updated state with agent's response
-        """
-        messages = state["messages"]
-        last_message = messages[-1]
-        
-        # TODO: Replace this with your actual agent logic
-        # Examples:
-        # - Call an LLM
-        # - Process the question
-        # - Use tools
-        # - Return a response
-        
-        response_text = f"Echo: {last_message.content}"
-        
-        # Create the agent's response
-        agent_response = AIMessage(content=response_text)
-        
-        return {"messages": [agent_response]}
-    
-    
-    # ============================================
-    # OPTIONAL: ADD MORE NODES FOR COMPLEX LOGIC
-    # ============================================
-    
-    # Example: Tool execution node
-    def tool_node(state: AgentState) -> dict:
-        """
-        Optional node for executing tools.
-        Implement if your agent uses external tools.
-        """
-        # Placeholder for tool execution
-        return {"messages": []}
-    
-    
-    # ============================================
-    # BUILD THE GRAPH
-    # ============================================
-    
-    # Add nodes to the graph
-    graph.add_node("agent", agent_node)
-    # graph.add_node("tools", tool_node)  # Uncomment if using tools
-    
-    # Define edges (connections between nodes)
-    graph.add_edge(START, "agent")
-    graph.add_edge("agent", END)
-    # graph.add_edge("agent", "tools")  # Uncomment if using tools
-    # graph.add_edge("tools", "agent")
-    
-    # Compile the graph
-    return graph.compile()
-
-
-# ============================================
-# EXAMPLE IMPLEMENTATIONS (uncomment to use)
-# ============================================
-
-def build_graph_with_llm():
-    """
-    Example: Agent with LLM integration.
-    Requires: pip install langchain-openai (or another LLM provider)
-    """
-    from langchain_openai import ChatOpenAI
-    from langchain_core.prompts import ChatPromptTemplate
-    
-    llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
-    
-    graph = StateGraph(AgentState)
-    
-    def llm_agent(state: AgentState) -> dict:
-        prompt = ChatPromptTemplate.from_messages([
-            ("system", "You are a helpful assistant."),
-            ("user", "{input}"),
-        ])
-        
-        chain = prompt | llm
-        last_message = state["messages"][-1]
-        
-        response = chain.invoke({"input": last_message.content})
-        
-        return {"messages": [response]}
-    
-    graph.add_node("llm", llm_agent)
-    graph.add_edge(START, "llm")
-    graph.add_edge("llm", END)
-    
-    return graph.compile()
-
-
-def build_graph_with_tools():
-    """
-    Example: Agent with tool use.
-    Requires: pip install langchain-openai
-    """
-    from langchain_openai import ChatOpenAI
-    from langchain_core.tools import tool
-    from langgraph.prebuilt import create_react_agent
-    
-    llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
-    
-    @tool
-    def add(a: int, b: int) -> int:
-        """Add two numbers."""
-        return a + b
-    
-    @tool
-    def multiply(a: int, b: int) -> int:
-        """Multiply two numbers."""
-        return a * b
-    
-    tools = [add, multiply]
-    
-    # Use the pre-built ReAct agent
-    return create_react_agent(llm, tools)
-
-
-# ============================================
-# FOR TESTING (run this file directly)
-# ============================================
-
-if __name__ == "__main__":
-    from langchain_core.messages import HumanMessage
-    
-    # Build your graph
-    graph = build_graph()
-    
-    # Test with a sample question
-    test_question = "What is 2 + 2?"
-    messages = [HumanMessage(content=test_question)]
-    
-    result = graph.invoke({"messages": messages})
-    
-    print(f"Question: {test_question}")
-    print(f"Agent Response: {result['messages'][-1].content}")