create_supervisor(
agents: list[Pregel],
*,
model: LanguageModelLike| Name | Type | Description |
|---|---|---|
agents* | list[Pregel] | List of agents to manage. An agent can be a LangGraph |
model* | LanguageModelLike | Language model to use for the supervisor |
tools | list[BaseTool | Callable] | ToolNode | None | Default: None |
prompt | Prompt | None | Default: None |
response_format | Optional[Union[StructuredResponseSchema, tuple[str, StructuredResponseSchema]]] | Default: None |
pre_model_hook | Optional[RunnableLike] | Default: None |
post_model_hook | Optional[RunnableLike] | Default: None |
parallel_tool_calls | bool | Default: False |
state_schema | StateSchemaType | None | Default: None |
context_schema | Type[Any] | None | Default: None |
output_mode | OutputMode | Default: 'last_message' |
add_handoff_messages | bool | Default: True |
handoff_tool_prefix | Optional[str] | Default: None |
add_handoff_back_messages | Optional[bool] | Default: None |
supervisor_name | str | Default: 'supervisor' |
include_agent_name | AgentNameMode | None | Default: None |
Create a multi-agent supervisor.
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?"
}
]
})Tools to use for the supervisor
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.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:
(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.response_format requires the model to support .with_structured_output
response_format requires structured_response key in your state schema.
You can use the prebuilt langgraph.prebuilt.chat_agent_executor.AgentStateWithStructuredResponse.
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.
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.
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.
State schema to use for the supervisor graph.
Specifies the schema for the context object that will be passed to the workflow.
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 historylast_message: Add only the last messageWhether to add a pair of (AIMessage, ToolMessage) to the message history
when a handoff occurs.
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.
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.
Name of the supervisor node.
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>"
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
...
}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.
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]
...
}