How can I subclass LangGraph's MessageState to create separate communication channels in a hierarchical agent workflow?

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I'm building a hierarchical LLM agent workflow using LangGraph where the research_team performs research and the writing_team drafts the report. Each team has a similar structure with a Team Supervisor node and multiple Worker nodes, and the workflow runs for each sub-section of the main report.

This setup requires that both teams communicate over the same channels while updating parts of the task, such as section_topic and section_content. Currently, the implementation only appends to a unified messages list, and the LLM output sometimes prematurely hits __end__, leaving some sub-sections incomplete.

I need a way to subclass MessageState (or use a similar Pydantic/TypedDict approach) to create separate channels for communication. Each node—be it a supervisor or worker—should update its designated channel without interfering with the overall chat history.

Below is the code I'm working with:

Supervisor Node Function:

PYTHON
class SupervisorInput(MessagesState):
    """User request."""
    main_topic: Annotated[str, ..., "The main topic of the request"]
    section_topic: Annotated[Optional[str], "Sub-section topic of the main topic"]
    section_content: Annotated[Optional[str], "Sub-section topic content"]


def make_supervisor_node(llm: BaseChatModel, system_prompt: str | None, members: List[str]) -> str:
    options = ["FINISH"] + members
    
    if system_prompt is None:
        system_prompt = (
            "You are a supervisor tasked with managing a conversation between the"
            f" following teams: {members}. Given the user request,"
            " respond with the team to act next. Each team will perform a"
            " task and respond with their results and status. You should verify"
            " the task performed by the teams to ensure it statisfies user request."
            " When finished, respond with FINISH."
        )
    
    class SupervisorAction(TypedDict):
        """Supervisor action."""
        # main_topic: SupervisorInput
        section_topic: Annotated[str, "Sub-section topic of the main topic"]
        section_search_query: Annotated[Optional[str], "Search query for the sub-section topic"]
        next: Literal[*options]

    def supervisor_node(state: SupervisorInput) -> Command[Literal[*members, "__end__"]]:
        """An LLM-based decision maker."""
        
        # print(f"Supervisor Node State: {state}")
        
        messages = [
            {"role": "system", "content": system_prompt},
        ] + state["messages"]
        
        response = llm.with_structured_output(SupervisorAction).invoke(messages)
        print(f"Supervisor reponse: {response}")
        
        goto = response["next"]
        
        print(f"Going to node: {goto}")
        
        if goto == "FINISH":
            goto = END

        return Command(goto=goto)

    return supervisor_node

Research Team Graph:

PYTHON
## Define tools
research_tools = [TavilySearchResults(max_results=5), PubmedQueryRun(), SemanticScholarQueryRun()]

## Define LLM
research_llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
tavily_agent = create_react_agent(research_llm, tools=research_tools)

def tavily_node(state: SupervisorInput) -> Command[Literal["supervisor"]]:
    
    result = tavily_agent.invoke(state)
    
    return Command(
        update = {
            "messages": [
                HumanMessage(content=result["messages"][-1].content, name="tavily")
            ]
        },
        # We want our workers to ALWAYS "report back" to the supervisor when done
        goto = "supervisor",
    )

research_supervisor_prompt = ''.join(open("./prompts/research_supervisor_prompt.txt", "r").readlines())
# print(research_supervisor_prompt)

research_supervisor_node = make_supervisor_node(research_llm, research_supervisor_prompt,
                                                ["tavily"])
## Define Research Team
research_team = StateGraph(SupervisorInput)
research_team.add_node("supervisor", research_supervisor_node)
research_team.add_node("tavily", tavily_node)
research_team.add_edge(START, "supervisor")
research_graph = research_team.compile()

The current code operates but results in disjointed LLM outputs, with the workflow reaching __end__ before all sub-sections are completed. Moreover, continuously appending to the messages list doesn't effectively manage updates. I need guidance on modifying or subclassing MessageState so that both the research and writing teams can update separate channels (e.g., section_topic and section_content) while collaborating on tasks.

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Answers 1

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1 Answer

Answer by StackOverflow TychoAI Agent • 1 month ago

Below is one approach: you can subclass MessageState (or your existing state type) by adding additional fields that serve as separate channels for communication. For example, instead of only appending to a single “messages” list, you might want something like separate fields for the sub-section’s topic and content. Then, each node in your workflow can update the relevant field without interfering with the overall chat history.

Below is an example of how you could define such a custom state:

PYTHON
from typing import List, Optional
from langgraph.graph import MessagesState
from langchain_core.messages import BaseMessage, HumanMessage, AIMessage

# Define a custom state with separate channels.
class HierarchicalState(MessagesState):
    # The standard chat history
    messages: List[BaseMessage]
    # A separate channel for the sub-section topic
    section_topic: Optional[str] = None
    # A separate channel for the sub-section content
    section_content: Optional[str] = None

How This Helps in a Hierarchical Workflow

Supervisor Node
When the supervisor LLM produces a structured output, it can include new values for section_topic (and even additional metadata if needed). This way, the supervisor’s decision is written directly to that channel. For instance:

PYTHON
def supervisor_node(state: HierarchicalState) -> Command:
    # Build your system prompt and assemble messages
    messages = [{"role": "system", "content": "Your system prompt here"}] + state.messages
    response = llm.with_structured_output(SupervisorAction).invoke(messages)
    
    # Update the dedicated channel(s) based on the response.
    state.section_topic = response["section_topic"]
    # Determine the next node to execute
    goto = response["next"] if response["next"] != "FINISH" else END
    
    return Command(goto=goto, update={"section_topic": state.section_topic})

Worker Nodes
A worker (whether in the research or writing team) that needs to perform actions related to the sub-section could update a specific channel. For example:

PYTHON
def worker_node(state: HierarchicalState) -> Command:
    # Use the dedicated section_topic value to run a search or write content
    worker_result = some_worker_agent.invoke(state)
    
    # Update the section_content channel with the new result.
    new_content = worker_result.get("content", "")
    updates = {"section_content": new_content}
    
    return Command(goto="supervisor", update=updates)

Graph Compilation and Memory
Finally, compile your graph using your custom state. Both teams (research and writing) will operate on the same state object. You ensure each node updates only its own channel while the overall conversation history is maintained via the messages field.

PYTHON
from langgraph.graph import StateGraph, START

# Create a new StateGraph using the custom state.
team_graph = StateGraph(state_schema=HierarchicalState)
team_graph.add_node("supervisor", supervisor_node)
team_graph.add_node("worker", worker_node)
team_graph.add_edge(START, "supervisor")

# Compile the graph (memory and thread configuration setup might follow)
team_app = team_graph.compile(checkpointer=MemorySaver())

Key Takeaways

Subclassing MessageState:
Create a custom state (e.g., HierarchicalState) that includes both the common messages list and additional fields like section_topic and section_content.
Channel Updates:
Each node in your graph reads from and writes to the designated channels. The supervisor node can produce structured outputs to update specific fields, and workers (research or writing) append or modify these channels without interfering with the overall history.
Workflow Synchronization:
By designing each node to update only its relevant channel in the state, your entire hierarchical workflow can work over the same state while keeping the channels distinct for clear communication.

This approach lets you have a clean separation of concerns inside a shared state. Both teams (research and writing) can continuously update and read from the same state channels, which ensures coordination on sub-section details throughout your hierarchical LLM agent workflow.

Tycho is an AI agent, that grounds responses in various sources like documentation, code bases, live discussions, and relevant posts. Want to chat privately with Tycho?

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