Do markdown

README.md

@@ -59,7 +59,7 @@ from langchain_core.messages import HumanMessage
 from langchain_anthropic import ChatAnthropic
 from langchain_core.tools import tool
 from langgraph.checkpoint.memory import MemorySaver
-from langgraph.graph import END, StateGraph, MessagesState
+from langgraph.graph import END, START, StateGraph, MessagesState
 from langgraph.prebuilt import ToolNode
 
 
@@ -107,7 +107,7 @@ workflow.add_node("tools", tool_node)
 
 # Set the entrypoint as `agent`
 # This means that this node is the first one called
-workflow.set_entry_point("agent")
+workflow.add_edge(START, "agent")
 
 # We now add a conditional edge
 workflow.add_conditional_edges(

docs/docs/cloud/deployment/graph_rebuild.md

@@ -28,7 +28,7 @@ In the standard LangGraph API configuration, the server uses the compiled graph
 
 ```python
 from langchain_openai import ChatOpenAI
-from langgraph.graph import END, MessageGraph
+from langgraph.graph import END, START, MessageGraph
 
 model = ChatOpenAI(temperature=0)
 
@@ -36,7 +36,7 @@ graph_workflow = MessageGraph()
 
 graph_workflow.add_node("agent", model)
 graph_workflow.add_edge("agent", END)
-graph_workflow.set_entry_point("agent")
+graph_workflow.add_edge(START, "agent")
 
 agent = graph_workflow.compile()
 ```
@@ -60,7 +60,7 @@ To make your graph rebuild on each new run with custom configuration, you need t
 ```python
 from typing import Annotated, TypedDict
 from langchain_openai import ChatOpenAI
-from langgraph.graph import END, MessageGraph
+from langgraph.graph import END, START, MessageGraph
 from langgraph.graph.state import StateGraph
 from langgraph.graph.message import add_messages
 from langgraph.prebuilt import ToolNode
@@ -83,7 +83,7 @@ def make_default_graph():
 
     graph_workflow.add_node("agent", call_model)
     graph_workflow.add_edge("agent", END)
-    graph_workflow.set_entry_point("agent")
+    graph_workflow.add_edge(START, "agent")
 
     agent = graph_workflow.compile()
     return agent
@@ -113,7 +113,7 @@ def make_alternative_graph():
     graph_workflow.add_node("agent", call_model)
     graph_workflow.add_node("tools", tool_node)
     graph_workflow.add_edge("tools", "agent")
-    graph_workflow.set_entry_point("agent")
+    graph_workflow.add_edge(START, "agent")
     graph_workflow.add_conditional_edges("agent", should_continue)
 
     agent = graph_workflow.compile()

docs/docs/cloud/deployment/setup.md

@@ -99,7 +99,7 @@ Example `agent.py` file, which shows how to import from other modules you define
 # my_agent/agent.py
 from typing import TypedDict, Literal
 
-from langgraph.graph import StateGraph, END
+from langgraph.graph import StateGraph, END, START
 from my_agent.utils.nodes import call_model, should_continue, tool_node # import nodes
 from my_agent.utils.state import AgentState # import state
 
@@ -110,7 +110,7 @@ class GraphConfig(TypedDict):
 workflow = StateGraph(AgentState, config_schema=GraphConfig)
 workflow.add_node("agent", call_model)
 workflow.add_node("action", tool_node)
-workflow.set_entry_point("agent")
+workflow.add_edge(START, "agent")
 workflow.add_conditional_edges(
     "agent",
     should_continue,

docs/docs/cloud/deployment/setup_pyproject.md

@@ -109,7 +109,7 @@ Example `agent.py` file, which shows how to import from other modules you define
 # my_agent/agent.py
 from typing import TypedDict, Literal
 
-from langgraph.graph import StateGraph, END
+from langgraph.graph import StateGraph, END, START
 from my_agent.utils.nodes import call_model, should_continue, tool_node # import nodes
 from my_agent.utils.state import AgentState # import state
 
@@ -120,7 +120,7 @@ class GraphConfig(TypedDict):
 workflow = StateGraph(AgentState, config_schema=GraphConfig)
 workflow.add_node("agent", call_model)
 workflow.add_node("action", tool_node)
-workflow.set_entry_point("agent")
+workflow.add_edge(START, "agent")
 workflow.add_conditional_edges(
     "agent",
     should_continue,

libs/langgraph/README.md

@@ -59,7 +59,7 @@ from langchain_core.messages import HumanMessage
 from langchain_anthropic import ChatAnthropic
 from langchain_core.tools import tool
 from langgraph.checkpoint.memory import MemorySaver
-from langgraph.graph import END, StateGraph, MessagesState
+from langgraph.graph import END, START, StateGraph, MessagesState
 from langgraph.prebuilt import ToolNode
 
 
@@ -107,7 +107,7 @@ workflow.add_node("tools", tool_node)
 
 # Set the entrypoint as `agent`
 # This means that this node is the first one called
-workflow.set_entry_point("agent")
+workflow.add_edge(START, "agent")
 
 # We now add a conditional edge
 workflow.add_conditional_edges(