MultiAgentSupervisorResearcherCoder

import sys 
print("Python version")
print (sys. version) 


from typing import Annotated, Sequence, TypedDict
import operator
import functools

from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.messages import BaseMessage, HumanMessage, SystemMessage
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_experimental.tools import PythonREPLTool
from langchain.agents import create_openai_tools_agent
from langchain_huggingface import HuggingFacePipeline
from langgraph.graph import StateGraph, END

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

# SETUP: HuggingFace Model and Pipeline
#name = "meta-llama/Llama-3.2-1B"
#name="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"
#name="deepseek-ai/deepseek-llm-7b-chat"
name="openai-community/gpt2"
#name="deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
#name="microsoft/Phi-3.5-mini-instruct"
#name="Qwen/Qwen2.5-7B-Instruct-1M"

tokenizer = AutoTokenizer.from_pretrained(name,truncation=True)
tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained(name)

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    device_map="auto",
    max_new_tokens=500,  # text to generate for outputs
)
print ("pipeline is created")

# Wrap in LangChain's HuggingFacePipeline
llm = HuggingFacePipeline(pipeline=pipe)

# Members and Final Options
members = ["Researcher", "Coder"]
options = ["FINISH"] + members

# Supervisor prompt
system_prompt = (
    "You are a supervisor tasked with managing a conversation between the following workers: {members}."
    " Given the following user request, respond with the workers to act next. Each worker will perform a task"
    " and respond with their results and status. When all workers are finished, respond with FINISH."
)

# Prompt template required for the workflow
prompt = ChatPromptTemplate.from_messages(
    [
        ("system", system_prompt),
        MessagesPlaceholder(variable_name="messages"),
        ("system", "Given the conversation above, who should act next? Or Should we FINISH? Select one of: {options}"),
    ]
).partial(options=str(options), members=", ".join(members))

print ("Prompt Template created")

# Supervisor routing logic
def route_tool_response(llm_response):
    """
    Parse the LLM response to determine the next step based on routing logic.
    """
    if "FINISH" in llm_response:
        return "FINISH"
    for member in members:
        if member in llm_response:
            return member
    return "Unknown"

def supervisor_chain(state):
    """
    Supervisor logic to interact with HuggingFacePipeline and decide the next worker.
    """
    messages = state.get("messages", [])
    user_prompt = prompt.format(messages=messages)

    try:
        llm_response = pipe(user_prompt, max_new_tokens=500)[0]["generated_text"]
    except Exception as e:
        raise RuntimeError(f"LLM processing error: {e}")

    next_action = route_tool_response(llm_response)
    return {"next": next_action}

# AgentState definition
class AgentState(TypedDict):
    messages: Annotated[Sequence[BaseMessage], operator.add]
    next: str

# Create tools
tavily_tool = TavilySearchResults(max_results=5)
python_repl_tool = PythonREPLTool()

# Create agents with their respective prompts
research_agent = create_openai_tools_agent(
    llm=llm,
    tools=[tavily_tool],
    prompt=ChatPromptTemplate.from_messages(
        [
            SystemMessage(content="You are a web researcher."),
            MessagesPlaceholder(variable_name="messages"),
            MessagesPlaceholder(variable_name="agent_scratchpad"),  # Add required placeholder
        ]
    ),
)

print ("Created agents with their respective prompts")

code_agent = create_openai_tools_agent(
    llm=llm,
    tools=[python_repl_tool],
    prompt=ChatPromptTemplate.from_messages(
        [
            SystemMessage(content="You may generate safe Python code for analysis."),
            MessagesPlaceholder(variable_name="messages"),
            MessagesPlaceholder(variable_name="agent_scratchpad"),  # Add required placeholder
        ]
    ),
)


print ("create_openai_tools_agent")


# Create the workflow
workflow = StateGraph(AgentState)

# Nodes
workflow.add_node("Researcher", research_agent)  # Pass the agent directly (no .run required)
workflow.add_node("Coder", code_agent)          # Pass the agent directly
workflow.add_node("supervisor", supervisor_chain)

# Add edges for workflow transitions
for member in members:
    workflow.add_edge(member, "supervisor")

workflow.add_conditional_edges(
    "supervisor",
    lambda x: x["next"],
    {k: k for k in members} | {"FINISH": END}  # Dynamically map workers to their actions
)

# Define entry point
workflow.set_entry_point("supervisor")

print(workflow)

# Compile the workflow
graph = workflow.compile()

from IPython.display import display, Image
display(Image(graph.get_graph().draw_mermaid_png()))

# Properly formatted initial state
initial_state = {
    "messages": [
        #HumanMessage(content="Code hello world and print it to the terminal.")  # Correct format for user input
        HumanMessage(content="Write Code for printing \"hello world\" in Python. Keep it precise.")  # Correct format for user input
    ]
}

# Execute the workflow
result = graph.invoke(initial_state)
print("Workflow Result:", result)