LangGraph Supervisor

langgraph_supervisor.supervisor

FUNCTION	DESCRIPTION
create_supervisor	Create a multi-agent supervisor.

create_supervisor

create_supervisor(
    agents: list[Pregel],
    *,
    model: LanguageModelLike,
    tools: list[BaseTool | Callable] | ToolNode | None = None,
    prompt: Prompt | None = None,
    response_format: StructuredResponseSchema
    | tuple[str, StructuredResponseSchema]
    | None = None,
    pre_model_hook: RunnableLike | None = None,
    post_model_hook: RunnableLike | None = None,
    parallel_tool_calls: bool = False,
    state_schema: StateSchemaType | None = None,
    context_schema: Type[Any] | None = None,
    output_mode: OutputMode = "last_message",
    add_handoff_messages: bool = True,
    handoff_tool_prefix: str | None = None,
    add_handoff_back_messages: bool | None = None,
    supervisor_name: str = "supervisor",
    include_agent_name: AgentNameMode | None = None,
    **deprecated_kwargs: Unpack[DeprecatedKwargs],
) -> StateGraph

Create a multi-agent supervisor.

PARAMETER	DESCRIPTION
agents	List of agents to manage. An agent can be a LangGraph CompiledStateGraph, a functional API workflow, or any other Pregel object. TYPE: list[Pregel]
model	Language model to use for the supervisor TYPE: LanguageModelLike
tools	Tools to use for the supervisor TYPE: list[BaseTool | Callable] | ToolNode | None DEFAULT: None
prompt	Optional prompt to use for the supervisor. Can be one of: str: This is converted to a SystemMessage and added to the beginning of the list of messages in state["messages"]. SystemMessage: this is added to the beginning of the list of messages in state["messages"]. Callable: This function should take in full graph state and the output is then passed to the language model. Runnable: This runnable should take in full graph state and the output is then passed to the language model. TYPE: Prompt | None DEFAULT: None
response_format	An optional schema for the final supervisor output. If provided, output will be formatted to match the given schema and returned in the 'structured_response' state key. If not provided, structured_response will not be present in the output state. Can be passed in as: - An OpenAI function/tool schema, - A JSON Schema, - A TypedDict class, - A Pydantic class. - A tuple `(prompt, schema)`, where schema is one of the above. The prompt will be used together with the model that is being used to generate the structured response. Important response_format requires the model to support .with_structured_output Note response_format requires structured_response key in your state schema. You can use the prebuilt langgraph.prebuilt.chat_agent_executor.AgentStateWithStructuredResponse. TYPE: StructuredResponseSchema | tuple[str, StructuredResponseSchema] | None DEFAULT: None
pre_model_hook	An optional node to add before the LLM node in the supervisor agent (i.e., the node that calls the LLM). Useful for managing long message histories (e.g., message trimming, summarization, etc.). Pre-model hook must be a callable or a runnable that takes in current graph state and returns a state update in the form of # At least one of `messages` or `llm_input_messages` MUST be provided { # If provided, will UPDATE the `messages` in the state "messages": [RemoveMessage(id=REMOVE_ALL_MESSAGES), ...], # If provided, will be used as the input to the LLM, # and will NOT UPDATE `messages` in the state "llm_input_messages": [...], # Any other state keys that need to be propagated ... } Important At least one of messages or llm_input_messages MUST be provided and will be used as an input to the agent node. The rest of the keys will be added to the graph state. Warning If you are returning messages in the pre-model hook, you should OVERWRITE the messages key by doing the following: { "messages": [RemoveMessage(id=REMOVE_ALL_MESSAGES), *new_messages] ... } TYPE: RunnableLike | None DEFAULT: None
post_model_hook	An optional node to add after the LLM node in the supervisor agent (i.e., the node that calls the LLM). Useful for implementing human-in-the-loop, guardrails, validation, or other post-processing. Post-model hook must be a callable or a runnable that takes in current graph state and returns a state update. TYPE: RunnableLike | None DEFAULT: None
parallel_tool_calls	Whether to allow the supervisor LLM to call tools in parallel (only OpenAI and Anthropic). Use this to control whether the supervisor can hand off to multiple agents at once. If True, will enable parallel tool calls. If False, will disable parallel tool calls. Important This is currently supported only by OpenAI and Anthropic models. To control parallel tool calling for other providers, add explicit instructions for tool use to the system prompt. TYPE: bool DEFAULT: False
state_schema	State schema to use for the supervisor graph. TYPE: StateSchemaType | None DEFAULT: None
context_schema	Specifies the schema for the context object that will be passed to the workflow. TYPE: Type[Any] | None DEFAULT: None
output_mode	Mode for adding managed agents' outputs to the message history in the multi-agent workflow. Can be one of: full_history: Add the entire agent message history last_message: Add only the last message TYPE: OutputMode DEFAULT: 'last_message'
add_handoff_messages	Whether to add a pair of (AIMessage, ToolMessage) to the message history when a handoff occurs. TYPE: bool DEFAULT: True
handoff_tool_prefix	Optional prefix for the handoff tools (e.g., 'delegate_to_' or 'transfer_to_') If provided, the handoff tools will be named handoff_tool_prefix_agent_name. If not provided, the handoff tools will be named transfer_to_agent_name. TYPE: str | None DEFAULT: None
add_handoff_back_messages	Whether to add a pair of (AIMessage, ToolMessage) to the message history when returning control to the supervisor to indicate that a handoff has occurred. TYPE: bool | None DEFAULT: None
supervisor_name	Name of the supervisor node. TYPE: str DEFAULT: 'supervisor'
include_agent_name	Use to specify how to expose the agent name to the underlying supervisor LLM. None: Relies on the LLM provider using the name attribute on the AI message. Currently, only OpenAI supports this. 'inline': Add the agent name directly into the content field of the AI message using XML-style tags. Example: "How can I help you" -> "<name>agent_name</name><content>How can I help you?</content>" TYPE: AgentNameMode | None DEFAULT: None

Example

from langchain_openai import ChatOpenAI

from langgraph_supervisor import create_supervisor
from langgraph.prebuilt import create_react_agent

# Create specialized agents

def add(a: float, b: float) -> float:
    '''Add two numbers.'''
    return a + b

def web_search(query: str) -> str:
    '''Search the web for information.'''
    return 'Here are the headcounts for each of the FAANG companies in 2024...'

math_agent = create_react_agent(
    model="openai:gpt-4o",
    tools=[add],
    name="math_expert",
)

research_agent = create_react_agent(
    model="openai:gpt-4o",
    tools=[web_search],
    name="research_expert",
)

# Create supervisor workflow
workflow = create_supervisor(
    [research_agent, math_agent],
    model=ChatOpenAI(model="gpt-4o"),
)

# Compile and run
app = workflow.compile()
result = app.invoke({
    "messages": [
        {
            "role": "user",
            "content": "what's the combined headcount of the FAANG companies in 2024?"
        }
    ]
})

langgraph_supervisor.handoff

FUNCTION	DESCRIPTION
create_handoff_tool	Create a tool that can handoff control to the requested agent.
create_forward_message_tool	Create a tool the supervisor can use to forward a worker message by name.

create_handoff_tool

create_handoff_tool(
    *,
    agent_name: str,
    name: str | None = None,
    description: str | None = None,
    add_handoff_messages: bool = True,
) -> BaseTool

Create a tool that can handoff control to the requested agent.

PARAMETER	DESCRIPTION
agent_name	The name of the agent to handoff control to, i.e. the name of the agent node in the multi-agent graph. Agent names should be simple, clear and unique, preferably in snake_case, although you are only limited to the names accepted by LangGraph nodes as well as the tool names accepted by LLM providers (the tool name will look like this: transfer_to_<agent_name>). TYPE: str
name	Optional name of the tool to use for the handoff. If not provided, the tool name will be transfer_to_<agent_name>. TYPE: str | None DEFAULT: None
description	Optional description for the handoff tool. If not provided, the description will be Ask agent <agent_name> for help. TYPE: str | None DEFAULT: None
add_handoff_messages	Whether to add handoff messages to the message history. If False, the handoff messages will be omitted from the message history. TYPE: bool DEFAULT: True

create_forward_message_tool

create_forward_message_tool(supervisor_name: str = 'supervisor') -> BaseTool

Create a tool the supervisor can use to forward a worker message by name.

This helps avoid information loss any time the supervisor rewrites a worker query to the user and also can save some tokens.

PARAMETER	DESCRIPTION
supervisor_name	The name of the supervisor node (used for namespacing the tool). TYPE: str DEFAULT: 'supervisor'

RETURNS	DESCRIPTION
BaseTool	The 'forward_message' tool. TYPE: BaseTool