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The Functional API allows you to add LangGraph’s key features — persistence, memory, human-in-the-loop, and streaming — to your applications with minimal changes to your existing code.
For conceptual information on the functional API, see Functional API.

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Creating a simple workflow

When defining an entrypoint, input is restricted to the first argument of the function. To pass multiple inputs, you can use a dictionary. 
@entrypoint(checkpointer=checkpointer) def my_workflow(inputs: dict) -> int:  value = inputs["value"]  another_value = inputs["another_value"]  ...  my_workflow.invoke({"value": 1, "another_value": 2}) 
import uuid from langgraph.func import entrypoint, task from langgraph.checkpoint.memory import InMemorySaver  # Task that checks if a number is even @task def is_even(number: int) -> bool:  return number % 2 == 0  # Task that formats a message @task def format_message(is_even: bool) -> str:  return "The number is even." if is_even else "The number is odd."  # Create a checkpointer for persistence checkpointer = InMemorySaver()  @entrypoint(checkpointer=checkpointer) def workflow(inputs: dict) -> str:  """Simple workflow to classify a number."""  even = is_even(inputs["number"]).result()  return format_message(even).result()  # Run the workflow with a unique thread ID config = {"configurable": {"thread_id": str(uuid.uuid4())}} result = workflow.invoke({"number": 7}, config=config) print(result)
This example demonstrates how to use the @task and @entrypoint decorators syntactically. Given that a checkpointer is provided, the workflow results will be persisted in the checkpointer.
import uuid from langchain.chat_models import init_chat_model from langgraph.func import entrypoint, task from langgraph.checkpoint.memory import InMemorySaver  model = init_chat_model('gpt-3.5-turbo')  # Task: generate essay using an LLM @task def compose_essay(topic: str) -> str:  """Generate an essay about the given topic."""  return model.invoke([  {"role": "system", "content": "You are a helpful assistant that writes essays."},  {"role": "user", "content": f"Write an essay about {topic}."}  ]).content  # Create a checkpointer for persistence checkpointer = InMemorySaver()  @entrypoint(checkpointer=checkpointer) def workflow(topic: str) -> str:  """Simple workflow that generates an essay with an LLM."""  return compose_essay(topic).result()  # Execute the workflow config = {"configurable": {"thread_id": str(uuid.uuid4())}} result = workflow.invoke("the history of flight", config=config) print(result)

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Parallel execution

Tasks can be executed in parallel by invoking them concurrently and waiting for the results. This is useful for improving performance in IO bound tasks (e.g., calling APIs for LLMs). 
@task def add_one(number: int) -> int:  return number + 1  @entrypoint(checkpointer=checkpointer) def graph(numbers: list[int]) -> list[str]:  futures = [add_one(i) for i in numbers]  return [f.result() for f in futures]
This example demonstrates how to run multiple LLM calls in parallel using @task. Each call generates a paragraph on a different topic, and results are joined into a single text output.
import uuid from langchain.chat_models import init_chat_model from langgraph.func import entrypoint, task from langgraph.checkpoint.memory import InMemorySaver  # Initialize the LLM model model = init_chat_model("gpt-3.5-turbo")  # Task that generates a paragraph about a given topic @task def generate_paragraph(topic: str) -> str:  response = model.invoke([  {"role": "system", "content": "You are a helpful assistant that writes educational paragraphs."},  {"role": "user", "content": f"Write a paragraph about {topic}."}  ])  return response.content  # Create a checkpointer for persistence checkpointer = InMemorySaver()  @entrypoint(checkpointer=checkpointer) def workflow(topics: list[str]) -> str:  """Generates multiple paragraphs in parallel and combines them."""  futures = [generate_paragraph(topic) for topic in topics]  paragraphs = [f.result() for f in futures]  return "\n\n".join(paragraphs)  # Run the workflow config = {"configurable": {"thread_id": str(uuid.uuid4())}} result = workflow.invoke(["quantum computing", "climate change", "history of aviation"], config=config) print(result)
This example uses LangGraph’s concurrency model to improve execution time, especially when tasks involve I/O like LLM completions.

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Calling graphs

The Functional API and the Graph API can be used together in the same application as they share the same underlying runtime. 
from langgraph.func import entrypoint from langgraph.graph import StateGraph  builder = StateGraph() ... some_graph = builder.compile()  @entrypoint() def some_workflow(some_input: dict) -> int:  # Call a graph defined using the graph API  result_1 = some_graph.invoke(...)  # Call another graph defined using the graph API  result_2 = another_graph.invoke(...)  return {  "result_1": result_1,  "result_2": result_2  } 
import uuid from typing import TypedDict from langgraph.func import entrypoint from langgraph.checkpoint.memory import InMemorySaver from langgraph.graph import StateGraph  # Define the shared state type class State(TypedDict):  foo: int  # Define a simple transformation node def double(state: State) -> State:  return {"foo": state["foo"] * 2}  # Build the graph using the Graph API builder = StateGraph(State) builder.add_node("double", double) builder.set_entry_point("double") graph = builder.compile()  # Define the functional API workflow checkpointer = InMemorySaver()  @entrypoint(checkpointer=checkpointer) def workflow(x: int) -> dict:  result = graph.invoke({"foo": x})  return {"bar": result["foo"]}  # Execute the workflow config = {"configurable": {"thread_id": str(uuid.uuid4())}} print(workflow.invoke(5, config=config)) # Output: {'bar': 10}

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Call other entrypoints

You can call other entrypoints from within an entrypoint or a task. 
@entrypoint() # Will automatically use the checkpointer from the parent entrypoint def some_other_workflow(inputs: dict) -> int:  return inputs["value"]  @entrypoint(checkpointer=checkpointer) def my_workflow(inputs: dict) -> int:  value = some_other_workflow.invoke({"value": 1})  return value 
import uuid from langgraph.func import entrypoint from langgraph.checkpoint.memory import InMemorySaver  # Initialize a checkpointer checkpointer = InMemorySaver()  # A reusable sub-workflow that multiplies a number @entrypoint() def multiply(inputs: dict) -> int:  return inputs["a"] * inputs["b"]  # Main workflow that invokes the sub-workflow @entrypoint(checkpointer=checkpointer) def main(inputs: dict) -> dict:  result = multiply.invoke({"a": inputs["x"], "b": inputs["y"]})  return {"product": result}  # Execute the main workflow config = {"configurable": {"thread_id": str(uuid.uuid4())}} print(main.invoke({"x": 6, "y": 7}, config=config)) # Output: {'product': 42}

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Streaming

The Functional API uses the same streaming mechanism as the Graph API. Please read the streaming guide section for more details. Example of using the streaming API to stream both updates and custom data. 
from langgraph.func import entrypoint from langgraph.checkpoint.memory import InMemorySaver from langgraph.config import get_stream_writer   checkpointer = InMemorySaver()  @entrypoint(checkpointer=checkpointer) def main(inputs: dict) -> int:  writer = get_stream_writer()   writer("Started processing")   result = inputs["x"] * 2  writer(f"Result is {result}")   return result  config = {"configurable": {"thread_id": "abc"}}  for mode, chunk in main.stream(   {"x": 5},  stream_mode=["custom", "updates"],   config=config ):  print(f"{mode}: {chunk}")
  1. Import get_stream_writer from langgraph.config.
  2. Obtain a stream writer instance within the entrypoint.
  3. Emit custom data before computation begins.
  4. Emit another custom message after computing the result.
  5. Use .stream() to process streamed output.
  6. Specify which streaming modes to use.
('updates', {'add_one': 2}) ('updates', {'add_two': 3}) ('custom', 'hello') ('custom', 'world') ('updates', {'main': 5})
Async with Python < 3.11 If using Python < 3.11 and writing async code, using get_stream_writer will not work. Instead please use the StreamWriter class directly. See Async with Python < 3.11 for more details.
from langgraph.types import StreamWriter  @entrypoint(checkpointer=checkpointer) async def main(inputs: dict, writer: StreamWriter) -> int:  ...

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Retry policy

from langgraph.checkpoint.memory import InMemorySaver from langgraph.func import entrypoint, task from langgraph.types import RetryPolicy  # This variable is just used for demonstration purposes to simulate a network failure. # It's not something you will have in your actual code. attempts = 0  # Let's configure the RetryPolicy to retry on ValueError. # The default RetryPolicy is optimized for retrying specific network errors. retry_policy = RetryPolicy(retry_on=ValueError)  @task(retry_policy=retry_policy) def get_info():  global attempts  attempts += 1   if attempts < 2:  raise ValueError('Failure')  return "OK"  checkpointer = InMemorySaver()  @entrypoint(checkpointer=checkpointer) def main(inputs, writer):  return get_info().result()  config = {  "configurable": {  "thread_id": "1"  } }  main.invoke({'any_input': 'foobar'}, config=config) 
'OK'

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Caching Tasks

import time from langgraph.cache.memory import InMemoryCache from langgraph.func import entrypoint, task from langgraph.types import CachePolicy   @task(cache_policy=CachePolicy(ttl=120))  def slow_add(x: int) -> int:  time.sleep(1)  return x * 2   @entrypoint(cache=InMemoryCache()) def main(inputs: dict) -> dict[str, int]:  result1 = slow_add(inputs["x"]).result()  result2 = slow_add(inputs["x"]).result()  return {"result1": result1, "result2": result2}   for chunk in main.stream({"x": 5}, stream_mode="updates"):  print(chunk)  #> {'slow_add': 10} #> {'slow_add': 10, '__metadata__': {'cached': True}} #> {'main': {'result1': 10, 'result2': 10}}
  1. ttl is specified in seconds. The cache will be invalidated after this time.

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Resuming after an error

import time from langgraph.checkpoint.memory import InMemorySaver from langgraph.func import entrypoint, task from langgraph.types import StreamWriter  # This variable is just used for demonstration purposes to simulate a network failure. # It's not something you will have in your actual code. attempts = 0  @task() def get_info():  """  Simulates a task that fails once before succeeding.  Raises an exception on the first attempt, then returns "OK" on subsequent tries.  """  global attempts  attempts += 1   if attempts < 2:  raise ValueError("Failure") # Simulate a failure on the first attempt  return "OK"  # Initialize an in-memory checkpointer for persistence checkpointer = InMemorySaver()  @task def slow_task():  """  Simulates a slow-running task by introducing a 1-second delay.  """  time.sleep(1)  return "Ran slow task."  @entrypoint(checkpointer=checkpointer) def main(inputs, writer: StreamWriter):  """  Main workflow function that runs the slow_task and get_info tasks sequentially.   Parameters:  - inputs: Dictionary containing workflow input values.  - writer: StreamWriter for streaming custom data.   The workflow first executes `slow_task` and then attempts to execute `get_info`,  which will fail on the first invocation.  """  slow_task_result = slow_task().result() # Blocking call to slow_task  get_info().result() # Exception will be raised here on the first attempt  return slow_task_result  # Workflow execution configuration with a unique thread identifier config = {  "configurable": {  "thread_id": "1" # Unique identifier to track workflow execution  } }  # This invocation will take ~1 second due to the slow_task execution try:  # First invocation will raise an exception due to the `get_info` task failing  main.invoke({'any_input': 'foobar'}, config=config) except ValueError:  pass # Handle the failure gracefully
When we resume execution, we won’t need to re-run the slow_task as its result is already saved in the checkpoint. 
main.invoke(None, config=config) 
'Ran slow task.'

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Human-in-the-loop

The functional API supports human-in-the-loop workflows using the interrupt function and the Command primitive.

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Basic human-in-the-loop workflow

We will create three tasks:
  1. Append "bar".
  2. Pause for human input. When resuming, append human input.
  3. Append "qux".
from langgraph.func import entrypoint, task from langgraph.types import Command, interrupt   @task def step_1(input_query):  """Append bar."""  return f"{input_query} bar"   @task def human_feedback(input_query):  """Append user input."""  feedback = interrupt(f"Please provide feedback: {input_query}")  return f"{input_query} {feedback}"   @task def step_3(input_query):  """Append qux."""  return f"{input_query} qux"
We can now compose these tasks in an entrypoint: 
from langgraph.checkpoint.memory import InMemorySaver  checkpointer = InMemorySaver()   @entrypoint(checkpointer=checkpointer) def graph(input_query):  result_1 = step_1(input_query).result()  result_2 = human_feedback(result_1).result()  result_3 = step_3(result_2).result()   return result_3
interrupt() is called inside a task, enabling a human to review and edit the output of the previous task. The results of prior tasks— in this case step_1— are persisted, so that they are not run again following the interrupt. Let’s send in a query string: 
config = {"configurable": {"thread_id": "1"}}  for event in graph.stream("foo", config):  print(event)  print("\n")
Note that we’ve paused with an interrupt after step_1. The interrupt provides instructions to resume the run. To resume, we issue a Command containing the data expected by the human_feedback task. 
# Continue execution for event in graph.stream(Command(resume="baz"), config):  print(event)  print("\n")
After resuming, the run proceeds through the remaining step and terminates as expected.

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Review tool calls

To review tool calls before execution, we add a review_tool_call function that calls interrupt. When this function is called, execution will be paused until we issue a command to resume it. Given a tool call, our function will interrupt for human review. At that point we can either:
  • Accept the tool call
  • Revise the tool call and continue
  • Generate a custom tool message (e.g., instructing the model to re-format its tool call)
from typing import Union  def review_tool_call(tool_call: ToolCall) -> Union[ToolCall, ToolMessage]:  """Review a tool call, returning a validated version."""  human_review = interrupt(  {  "question": "Is this correct?",  "tool_call": tool_call,  }  )  review_action = human_review["action"]  review_data = human_review.get("data")  if review_action == "continue":  return tool_call  elif review_action == "update":  updated_tool_call = {**tool_call, **{"args": review_data}}  return updated_tool_call  elif review_action == "feedback":  return ToolMessage(  content=review_data, name=tool_call["name"], tool_call_id=tool_call["id"]  )
We can now update our entrypoint to review the generated tool calls. If a tool call is accepted or revised, we execute in the same way as before. Otherwise, we just append the ToolMessage supplied by the human. The results of prior tasks — in this case the initial model call — are persisted, so that they are not run again following the interrupt. 
from langgraph.checkpoint.memory import InMemorySaver from langgraph.graph.message import add_messages from langgraph.types import Command, interrupt   checkpointer = InMemorySaver()   @entrypoint(checkpointer=checkpointer) def agent(messages, previous):  if previous is not None:  messages = add_messages(previous, messages)   model_response = call_model(messages).result()  while True:  if not model_response.tool_calls:  break   # Review tool calls  tool_results = []  tool_calls = []  for i, tool_call in enumerate(model_response.tool_calls):  review = review_tool_call(tool_call)  if isinstance(review, ToolMessage):  tool_results.append(review)  else: # is a validated tool call  tool_calls.append(review)  if review != tool_call:  model_response.tool_calls[i] = review # update message   # Execute remaining tool calls  tool_result_futures = [call_tool(tool_call) for tool_call in tool_calls]  remaining_tool_results = [fut.result() for fut in tool_result_futures]   # Append to message list  messages = add_messages(  messages,  [model_response, *tool_results, *remaining_tool_results],  )   # Call model again  model_response = call_model(messages).result()   # Generate final response  messages = add_messages(messages, model_response)  return entrypoint.final(value=model_response, save=messages)

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Short-term memory

Short-term memory allows storing information across different invocations of the same thread id. See short-term memory for more details.

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Manage checkpoints

You can view and delete the information stored by the checkpointer.

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View thread state

config = {  "configurable": {  "thread_id": "1",   # optionally provide an ID for a specific checkpoint,  # otherwise the latest checkpoint is shown  # "checkpoint_id": "1f029ca3-1f5b-6704-8004-820c16b69a5a" #   } } graph.get_state(config)  
StateSnapshot(  values={'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today?), HumanMessage(content="what's my name?"), AIMessage(content='Your name is Bob.')]}, next=(),  config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1f5b-6704-8004-820c16b69a5a'}},  metadata={  'source': 'loop',  'writes': {'call_model': {'messages': AIMessage(content='Your name is Bob.')}},  'step': 4,  'parents': {},  'thread_id': '1'  },  created_at='2025-05-05T16:01:24.680462+00:00',  parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1790-6b0a-8003-baf965b6a38f'}},  tasks=(),  interrupts=() )

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View the history of the thread

config = {  "configurable": {  "thread_id": "1"  } } list(graph.get_state_history(config))  
[  StateSnapshot(  values={'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?'), HumanMessage(content="what's my name?"), AIMessage(content='Your name is Bob.')]},  next=(),  config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1f5b-6704-8004-820c16b69a5a'}},  metadata={'source': 'loop', 'writes': {'call_model': {'messages': AIMessage(content='Your name is Bob.')}}, 'step': 4, 'parents': {}, 'thread_id': '1'},  created_at='2025-05-05T16:01:24.680462+00:00',  parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1790-6b0a-8003-baf965b6a38f'}},  tasks=(),  interrupts=()  ),  StateSnapshot(  values={'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?'), HumanMessage(content="what's my name?")]},  next=('call_model',),  config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1790-6b0a-8003-baf965b6a38f'}},  metadata={'source': 'loop', 'writes': None, 'step': 3, 'parents': {}, 'thread_id': '1'},  created_at='2025-05-05T16:01:23.863421+00:00',  parent_config={...}  tasks=(PregelTask(id='8ab4155e-6b15-b885-9ce5-bed69a2c305c', name='call_model', path=('__pregel_pull', 'call_model'), error=None, interrupts=(), state=None, result={'messages': AIMessage(content='Your name is Bob.')}),),  interrupts=()  ),  StateSnapshot(  values={'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?')]},  next=('__start__',),  config={...},  metadata={'source': 'input', 'writes': {'__start__': {'messages': [{'role': 'user', 'content': "what's my name?"}]}}, 'step': 2, 'parents': {}, 'thread_id': '1'},  created_at='2025-05-05T16:01:23.863173+00:00',  parent_config={...}  tasks=(PregelTask(id='24ba39d6-6db1-4c9b-f4c5-682aeaf38dcd', name='__start__', path=('__pregel_pull', '__start__'), error=None, interrupts=(), state=None, result={'messages': [{'role': 'user', 'content': "what's my name?"}]}),),  interrupts=()  ),  StateSnapshot(  values={'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?')]},  next=(),  config={...},  metadata={'source': 'loop', 'writes': {'call_model': {'messages': AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?')}}, 'step': 1, 'parents': {}, 'thread_id': '1'},  created_at='2025-05-05T16:01:23.862295+00:00',  parent_config={...}  tasks=(),  interrupts=()  ),  StateSnapshot(  values={'messages': [HumanMessage(content="hi! I'm bob")]},  next=('call_model',),  config={...},  metadata={'source': 'loop', 'writes': None, 'step': 0, 'parents': {}, 'thread_id': '1'},  created_at='2025-05-05T16:01:22.278960+00:00',  parent_config={...}  tasks=(PregelTask(id='8cbd75e0-3720-b056-04f7-71ac805140a0', name='call_model', path=('__pregel_pull', 'call_model'), error=None, interrupts=(), state=None, result={'messages': AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?')}),),  interrupts=()  ),  StateSnapshot(  values={'messages': []},  next=('__start__',),  config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-0870-6ce2-bfff-1f3f14c3e565'}},  metadata={'source': 'input', 'writes': {'__start__': {'messages': [{'role': 'user', 'content': "hi! I'm bob"}]}}, 'step': -1, 'parents': {}, 'thread_id': '1'},  created_at='2025-05-05T16:01:22.277497+00:00',  parent_config=None,  tasks=(PregelTask(id='d458367b-8265-812c-18e2-33001d199ce6', name='__start__', path=('__pregel_pull', '__start__'), error=None, interrupts=(), state=None, result={'messages': [{'role': 'user', 'content': "hi! I'm bob"}]}),),  interrupts=()  ) ]

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Decouple return value from saved value

Use entrypoint.final to decouple what is returned to the caller from what is persisted in the checkpoint. This is useful when:
  • You want to return a computed result (e.g., a summary or status), but save a different internal value for use on the next invocation.
  • You need to control what gets passed to the previous parameter on the next run.
from langgraph.func import entrypoint from langgraph.checkpoint.memory import InMemorySaver  checkpointer = InMemorySaver()  @entrypoint(checkpointer=checkpointer) def accumulate(n: int, *, previous: int | None) -> entrypoint.final[int, int]:  previous = previous or 0  total = previous + n  # Return the *previous* value to the caller but save the *new* total to the checkpoint.  return entrypoint.final(value=previous, save=total)  config = {"configurable": {"thread_id": "my-thread"}}  print(accumulate.invoke(1, config=config)) # 0 print(accumulate.invoke(2, config=config)) # 1 print(accumulate.invoke(3, config=config)) # 3

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Chatbot example

An example of a simple chatbot using the functional API and the InMemorySaver checkpointer. The bot is able to remember the previous conversation and continue from where it left off. 
from langchain.messages import BaseMessage from langgraph.graph import add_messages from langgraph.func import entrypoint, task from langgraph.checkpoint.memory import InMemorySaver from langchain_anthropic import ChatAnthropic  model = ChatAnthropic(model="claude-sonnet-4-5-20250929")  @task def call_model(messages: list[BaseMessage]):  response = model.invoke(messages)  return response  checkpointer = InMemorySaver()  @entrypoint(checkpointer=checkpointer) def workflow(inputs: list[BaseMessage], *, previous: list[BaseMessage]):  if previous:  inputs = add_messages(previous, inputs)   response = call_model(inputs).result()  return entrypoint.final(value=response, save=add_messages(inputs, response))  config = {"configurable": {"thread_id": "1"}} input_message = {"role": "user", "content": "hi! I'm bob"} for chunk in workflow.stream([input_message], config, stream_mode="values"):  chunk.pretty_print()  input_message = {"role": "user", "content": "what's my name?"} for chunk in workflow.stream([input_message], config, stream_mode="values"):  chunk.pretty_print()

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Long-term memory

long-term memory allows storing information across different thread ids. This could be useful for learning information about a given user in one conversation and using it in another.

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Workflows

  • Workflows and agent guide for more examples of how to build workflows using the Functional API.

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Integrate with other libraries

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