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Deprecated: MessageGraph is deprecated in LangGraph v1.0.0 and will be removed in v2.0.0. Please use StateGraph with a messages key instead. MessageGraph class is a specialized version of StateGraph where the entire state is a single, append-only list of messages.
Defined in: langgraph/graph/message.py:251
Overview
MessageGraph is a subclass of StateGraph whose entire state is a single list of messages. Each node in a MessageGraph takes a list of messages as input and returns zero or more messages as output. The add_messages function is used to merge the output messages from each node into the existing list of messages in the graph’s state.
Constructor
Create a new MessageGraph instance. The state schema is automatically set to Annotated[list[AnyMessage], add_messages].
Usage Example
from langgraph.graph.message import MessageGraph
from langchain_core.messages import HumanMessage, AIMessage
builder = MessageGraph()
def chatbot(state: list):
# state is a list of messages
return [AIMessage(content="Hello!")]
builder.add_node("chatbot", chatbot)
builder.set_entry_point("chatbot")
builder.set_finish_point("chatbot")
graph = builder.compile()
result = graph.invoke([HumanMessage(content="Hi there.")])
# [
# HumanMessage(content="Hi there.", id='...'),
# AIMessage(content="Hello!", id='...')
# ]
Migration Guide
Instead of using MessageGraph, use StateGraph with MessagesState or a custom state that includes a messages field:
Before (MessageGraph)
from langgraph.graph.message import MessageGraph
from langchain_core.messages import AIMessage
builder = MessageGraph()
def chatbot(state: list):
return [AIMessage(content="Hello!")]
builder.add_node("chatbot", chatbot)
builder.set_entry_point("chatbot")
builder.set_finish_point("chatbot")
graph = builder.compile()
result = graph.invoke([("user", "Hi there.")])
After (StateGraph with MessagesState)
from langgraph.graph import StateGraph, MessagesState, START, END
from langchain_core.messages import AIMessage
builder = StateGraph(MessagesState)
def chatbot(state: MessagesState):
return {"messages": [AIMessage(content="Hello!")]}
builder.add_node("chatbot", chatbot)
builder.add_edge(START, "chatbot")
builder.add_edge("chatbot", END)
graph = builder.compile()
result = graph.invoke({"messages": [("user", "Hi there.")]})
Alternative: Custom State
from typing import Annotated
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, add_messages, START, END
from langchain_core.messages import AnyMessage, AIMessage
class State(TypedDict):
messages: Annotated[list[AnyMessage], add_messages]
# Add other fields as needed
user_id: str
builder = StateGraph(State)
def chatbot(state: State):
user_id = state["user_id"]
return {"messages": [AIMessage(content=f"Hello, user {user_id}!")]}
builder.add_node("chatbot", chatbot)
builder.add_edge(START, "chatbot")
builder.add_edge("chatbot", END)
graph = builder.compile()
result = graph.invoke({
"messages": [("user", "Hi there.")],
"user_id": "123"
})
Methods
MessageGraph inherits all methods from StateGraph. See the StateGraph reference for detailed documentation of available methods:
add_node() - Add a node to the graphadd_edge() - Add an edge between nodesadd_conditional_edges() - Add conditional routingset_entry_point() - Set the starting nodeset_finish_point() - Set the ending nodecompile() - Compile into an executable graph
Examples
Basic Message Workflow
from langgraph.graph.message import MessageGraph
from langchain_core.messages import HumanMessage, AIMessage, ToolMessage
builder = MessageGraph()
builder.add_node(
"chatbot",
lambda state: [
AIMessage(
content="Hello!",
tool_calls=[{"name": "search", "id": "123", "args": {"query": "X"}}],
)
],
)
builder.add_node(
"search",
lambda state: [ToolMessage(content="Searching...", tool_call_id="123")]
)
builder.set_entry_point("chatbot")
builder.add_edge("chatbot", "search")
builder.set_finish_point("search")
graph = builder.compile()
result = graph.invoke([HumanMessage(content="Hi there. Can you search for X?")])
print(result)
# [
# HumanMessage(content="Hi there. Can you search for X?", id='...'),
# AIMessage(content="Hello!", tool_calls=[...], id='...'),
# ToolMessage(content="Searching...", tool_call_id="123", id='...')
# ]
With Conditional Edges
from langgraph.graph.message import MessageGraph
from langchain_core.messages import HumanMessage, AIMessage
builder = MessageGraph()
def should_continue(state: list) -> str:
last_message = state[-1]
if hasattr(last_message, 'tool_calls') and last_message.tool_calls:
return "tools"
return "end"
builder.add_node("agent", lambda state: [AIMessage(content="Processing...")])
builder.add_node("tools", lambda state: [AIMessage(content="Tool result")])
builder.set_entry_point("agent")
builder.add_conditional_edges(
"agent",
should_continue,
{"tools": "tools", "end": "__end__"}
)
builder.add_edge("tools", "agent")
graph = builder.compile()
See Also