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Creates an agent graph that calls tools in a loop until a stopping condition is met.
This function is deprecated in favor of create_agent from the langchain package, which provides an equivalent agent factory with a flexible middleware system. For migration guidance, see Migrating from LangGraph v0.
Defined in: langgraph/prebuilt/chat_agent_executor.py:278

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Function Signature

def create_react_agent(
    model: str
    | LanguageModelLike
    | Callable[[StateSchema, Runtime[ContextT]], BaseChatModel]
    | Callable[[StateSchema, Runtime[ContextT]], Awaitable[BaseChatModel]]
    | Callable[
        [StateSchema, Runtime[ContextT]], Runnable[LanguageModelInput, BaseMessage]
    ]
    | Callable[
        [StateSchema, Runtime[ContextT]],
        Awaitable[Runnable[LanguageModelInput, BaseMessage]],
    ],
    tools: Sequence[BaseTool | Callable | dict[str, Any]] | ToolNode,
    *,
    prompt: Prompt | None = None,
    response_format: StructuredResponseSchema
    | tuple[str, StructuredResponseSchema]
    | None = None,
    pre_model_hook: RunnableLike | None = None,
    post_model_hook: RunnableLike | None = None,
    state_schema: StateSchemaType | None = None,
    context_schema: type[Any] | None = None,
    checkpointer: Checkpointer | None = None,
    store: BaseStore | None = None,
    interrupt_before: list[str] | None = None,
    interrupt_after: list[str] | None = None,
    debug: bool = False,
    version: Literal["v1", "v2"] = "v2",
    name: str | None = None,
) -> CompiledStateGraph

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Parameters

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model
str | LanguageModelLike | Callable
required
The language model for the agent. Supports static and dynamic model selection.Static model: A chat model instance (e.g., ChatOpenAI) or string identifier (e.g., "openai:gpt-4")Dynamic model: A callable with signature (state, runtime) -> BaseChatModel that returns different models based on runtime context. If the model has tools bound via bind_tools or other configurations, the return type should be a Runnable[LanguageModelInput, BaseMessage]. Coroutines are also supported, allowing for asynchronous model selection.Dynamic functions receive graph state and runtime, enabling context-dependent model selection. Must return a BaseChatModel instance. For tool calling, bind tools using .bind_tools(). Bound tools must be a subset of the tools parameter.
from dataclasses import dataclass

@dataclass
class ModelContext:
    model_name: str = "gpt-3.5-turbo"

# Instantiate models globally
gpt4_model = ChatOpenAI(model="gpt-4")
gpt35_model = ChatOpenAI(model="gpt-3.5-turbo")

def select_model(state: AgentState, runtime: Runtime[ModelContext]) -> ChatOpenAI:
    model_name = runtime.context.model_name
    model = gpt4_model if model_name == "gpt-4" else gpt35_model
    return model.bind_tools(tools)
Ensure returned models have appropriate tools bound via .bind_tools() and support required functionality. Bound tools must be a subset of those specified in the tools parameter.
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tools
Sequence[BaseTool | Callable | dict[str, Any]] | ToolNode
required
A list of tools or a ToolNode instance. If an empty list is provided, the agent will consist of a single LLM node without tool calling.
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prompt
str | SystemMessage | Callable | Runnable | None
default:"None"
An optional prompt for the LLM. Can take several forms:
  • str: Converted to a SystemMessage and added to the beginning of the list of messages in state["messages"]
  • SystemMessage: Added to the beginning of the list of messages in state["messages"]
  • Callable: Function that takes full graph state and the output is then passed to the language model
  • Runnable: Runnable that takes full graph state and the output is then passed to the language model
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response_format
StructuredResponseSchema | tuple[str, StructuredResponseSchema] | None
default:"None"
An optional schema for the final agent 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.
response_format requires the model to support .with_structured_output
The graph will make a separate call to the LLM to generate the structured response after the agent loop is finished. This is not the only strategy to get structured responses, see more options in this guide.
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pre_model_hook
RunnableLike | None
default:"None"
An optional node to add before the agent node (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]
    ...
}
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post_model_hook
RunnableLike | None
default:"None"
An optional node to add after the agent node (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.
Only available with version="v2".
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state_schema
StateSchemaType | None
default:"None"
An optional state schema that defines graph state. Must have messages and remaining_steps keys. Defaults to AgentState that defines those two keys.
remaining_steps is used to limit the number of steps the react agent can take. Calculated roughly as recursion_limit - total_steps_taken. If remaining_steps is less than 2 and tool calls are present in the response, the react agent will return a final AI Message with the content “Sorry, need more steps to process this request.”. No GraphRecursionError will be raised in this case.
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context_schema
type[Any] | None
default:"None"
An optional schema for runtime context.
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checkpointer
Checkpointer | None
default:"None"
An optional checkpoint saver object. This is used for persisting the state of the graph (e.g., as chat memory) for a single thread (e.g., a single conversation).
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store
BaseStore | None
default:"None"
An optional store object. This is used for persisting data across multiple threads (e.g., multiple conversations / users).
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interrupt_before
list[str] | None
default:"None"
An optional list of node names to interrupt before. Should be one of the following: "agent", "tools".This is useful if you want to add a user confirmation or other interrupt before taking an action.
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interrupt_after
list[str] | None
default:"None"
An optional list of node names to interrupt after. Should be one of the following: "agent", "tools".This is useful if you want to return directly or run additional processing on an output.
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debug
bool
default:"False"
A flag indicating whether to enable debug mode.
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version
Literal['v1', 'v2']
default:"'v2'"
Determines the version of the graph to create.Can be one of:
  • "v1": The tool node processes a single message. All tool calls in the message are executed in parallel within the tool node.
  • "v2": The tool node processes a tool call. Tool calls are distributed across multiple instances of the tool node using the Send API.
​
name
str | None
default:"None"
An optional name for the CompiledStateGraph. This name will be automatically used when adding ReAct agent graph to another graph as a subgraph node - particularly useful for building multi-agent systems.

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Returns

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return
CompiledStateGraph
A compiled LangChain Runnable that can be used for chat interactions.The “agent” node calls the language model with the messages list (after applying the prompt). If the resulting AIMessage contains tool_calls, the graph will then call the “tools” node. The “tools” node executes the tools (1 tool per tool_call) and adds the responses to the messages list as ToolMessage objects. The agent node then calls the language model again. The process repeats until no more tool_calls are present in the response. The agent then returns the full list of messages as a dictionary containing the key 'messages'.

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How It Works

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Usage Example

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Basic Usage

from langgraph.prebuilt import create_react_agent

def check_weather(location: str) -> str:
    '''Return the weather forecast for the specified location.'''
    return f"It's always sunny in {location}"

graph = create_react_agent(
    "anthropic:claude-3-7-sonnet-latest",
    tools=[check_weather],
    prompt="You are a helpful assistant",
)
inputs = {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
for chunk in graph.stream(inputs, stream_mode="updates"):
    print(chunk)

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With Dynamic Model Selection

from dataclasses import dataclass
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
from langgraph.runtime import Runtime

@dataclass
class ModelContext:
    model_name: str = "gpt-3.5-turbo"

gpt4_model = ChatOpenAI(model="gpt-4")
gpt35_model = ChatOpenAI(model="gpt-3.5-turbo")

def select_model(state, runtime: Runtime[ModelContext]):
    model_name = runtime.context.model_name
    model = gpt4_model if model_name == "gpt-4" else gpt35_model
    return model.bind_tools(tools)

graph = create_react_agent(
    select_model,
    tools=[check_weather],
    context_schema=ModelContext,
)

​
With Structured Output

from pydantic import BaseModel
from langgraph.prebuilt import create_react_agent

class WeatherReport(BaseModel):
    location: str
    temperature: int
    conditions: str

graph = create_react_agent(
    "anthropic:claude-3-7-sonnet-latest",
    tools=[check_weather],
    response_format=WeatherReport,
)

result = graph.invoke({
    "messages": [{"role": "user", "content": "what is the weather in sf"}]
})
print(result["structured_response"])  # WeatherReport instance

​
With Pre-Model Hook (Message Trimming)

from langchain_core.messages import RemoveMessage
from langgraph.prebuilt import create_react_agent
from langgraph.graph.message import REMOVE_ALL_MESSAGES

def trim_messages(state):
    # Keep only the last 10 messages
    messages = state["messages"]
    if len(messages) > 10:
        return {
            "messages": [
                RemoveMessage(id=REMOVE_ALL_MESSAGES),
                *messages[-10:]
            ]
        }
    return {}

graph = create_react_agent(
    "anthropic:claude-3-7-sonnet-latest",
    tools=[check_weather],
    pre_model_hook=trim_messages,
)

​
With Post-Model Hook (Validation)

from langgraph.prebuilt import create_react_agent

def validate_response(state):
    last_message = state["messages"][-1]
    if last_message.content and "unsafe" in last_message.content.lower():
        return {"messages": [{"role": "assistant", "content": "I cannot provide that information."}]}
    return {}

graph = create_react_agent(
    "anthropic:claude-3-7-sonnet-latest",
    tools=[check_weather],
    post_model_hook=validate_response,
    version="v2",
)

​
See Also

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