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. It is designed to integrate these features into existing code that may use standard language primitives for branching and control flow, such as if statements, for loops, and function calls. Unlike many data orchestration frameworks that require restructuring code into an explicit pipeline or DAG, the Functional API allows you to incorporate these capabilities without enforcing a rigid execution model. The Functional API uses two key building blocks:
  • @entrypoint – Marks a function as the starting point of a workflow, encapsulating logic and managing execution flow, including handling long-running tasks and interrupts.
  • @task – Represents a discrete unit of work, such as an API call or data processing step, that can be executed asynchronously within an entrypoint. Tasks return a future-like object that can be awaited or resolved synchronously.
This provides a minimal abstraction for building workflows with state management and streaming.
For information on how to use the functional API, see Use Functional API.

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Functional API vs. Graph API

For users who prefer a more declarative approach, LangGraph’s Graph API allows you to define workflows using a Graph paradigm. Both APIs share the same underlying runtime, so you can use them together in the same application. Here are some key differences:
  • Control flow: The Functional API does not require thinking about graph structure. You can use standard Python constructs to define workflows. This will usually trim the amount of code you need to write.
  • Short-term memory: The GraphAPI requires declaring a State and may require defining reducers to manage updates to the graph state. @entrypoint and @tasks do not require explicit state management as their state is scoped to the function and is not shared across functions.
  • Checkpointing: Both APIs generate and use checkpoints. In the Graph API a new checkpoint is generated after every superstep. In the Functional API, when tasks are executed, their results are saved to an existing checkpoint associated with the given entrypoint instead of creating a new checkpoint.
  • Visualization: The Graph API makes it easy to visualize the workflow as a graph which can be useful for debugging, understanding the workflow, and sharing with others. The Functional API does not support visualization as the graph is dynamically generated during runtime.

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Example

Below we demonstrate a simple application that writes an essay and interrupts to request human review. 
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.func import entrypoint, task
from langgraph.types import interrupt

@task
def write_essay(topic: str) -> str:
    """Write an essay about the given topic."""
    time.sleep(1) # A placeholder for a long-running task.
    return f"An essay about topic: {topic}"

@entrypoint(checkpointer=InMemorySaver())
def workflow(topic: str) -> dict:
    """A simple workflow that writes an essay and asks for a review."""
    essay = write_essay("cat").result()
    is_approved = interrupt({
        # Any json-serializable payload provided to interrupt as argument.
        # It will be surfaced on the client side as an Interrupt when streaming data
        # from the workflow.
        "essay": essay, # The essay we want reviewed.
        # We can add any additional information that we need.
        # For example, introduce a key called "action" with some instructions.
        "action": "Please approve/reject the essay",
    })

    return {
        "essay": essay, # The essay that was generated
        "is_approved": is_approved, # Response from HIL
    }

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Entrypoint

The @entrypoint decorator can be used to create a workflow from a function. It encapsulates workflow logic and manages execution flow, including handling long-running tasks and interrupts.

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Definition

An entrypoint is defined by decorating a function with the @entrypoint decorator. The function must accept a single positional argument, which serves as the workflow input. If you need to pass multiple pieces of data, use a dictionary as the input type for the first argument. Decorating a function with an entrypoint produces a Pregel instance which helps to manage the execution of the workflow (e.g., handles streaming, resumption, and checkpointing). You will usually want to pass a checkpointer to the @entrypoint decorator to enable persistence and use features like human-in-the-loop. 
from langgraph.func import entrypoint

@entrypoint(checkpointer=checkpointer)
def my_workflow(some_input: dict) -> int:
    # some logic that may involve long-running tasks like API calls,
    # and may be interrupted for human-in-the-loop.
    ...
    return result
Serialization The inputs and outputs of entrypoints must be JSON-serializable to support checkpointing. Please see the serialization section for more details.

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Injectable parameters

When declaring an entrypoint, you can request access to additional parameters that will be injected automatically at run time. These parameters include:
ParameterDescription
previousAccess the state associated with the previous checkpoint for the given thread. See short-term-memory.
storeAn instance of [BaseStore][langgraph.store.base.BaseStore]. Useful for long-term memory.
writerUse to access the StreamWriter when working with Async Python < 3.11. See streaming with functional API for details.
configFor accessing run time configuration. See RunnableConfig for information.
Declare the parameters with the appropriate name and type annotation.

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Executing

Using the @entrypoint yields a Pregel object that can be executed using the invoke, ainvoke, stream, and astream methods. 
config = {
    "configurable": {
        "thread_id": "some_thread_id"
    }
}
my_workflow.invoke(some_input, config)  # Wait for the result synchronously

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Resuming

Resuming an execution after an interrupt can be done by passing a resume value to the Command primitive. 
from langgraph.types import Command

config = {
    "configurable": {
        "thread_id": "some_thread_id"
    }
}

my_workflow.invoke(Command(resume=some_resume_value), config)
Resuming after an error To resume after an error, run the entrypoint with a None and the same thread id (config). This assumes that the underlying error has been resolved and execution can proceed successfully. 

config = {
    "configurable": {
        "thread_id": "some_thread_id"
    }
}

my_workflow.invoke(None, config)

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Short-term memory

When an entrypoint is defined with a checkpointer, it stores information between successive invocations on the same thread id in checkpoints. This allows accessing the state from the previous invocation using the previous parameter. By default, the previous parameter is the return value of the previous invocation. 
@entrypoint(checkpointer=checkpointer)
def my_workflow(number: int, *, previous: Any = None) -> int:
    previous = previous or 0
    return number + previous

config = {
    "configurable": {
        "thread_id": "some_thread_id"
    }
}

my_workflow.invoke(1, config)  # 1 (previous was None)
my_workflow.invoke(2, config)  # 3 (previous was 1 from the previous invocation)

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entrypoint.final

entrypoint.final is a special primitive that can be returned from an entrypoint and allows decoupling the value that is saved in the checkpoint from the return value of the entrypoint. The first value is the return value of the entrypoint, and the second value is the value that will be saved in the checkpoint. The type annotation is entrypoint.final[return_type, save_type]. 
@entrypoint(checkpointer=checkpointer)
def my_workflow(number: int, *, previous: Any = None) -> entrypoint.final[int, int]:
    previous = previous or 0
    # This will return the previous value to the caller, saving
    # 2 * number to the checkpoint, which will be used in the next invocation
    # for the `previous` parameter.
    return entrypoint.final(value=previous, save=2 * number)

config = {
    "configurable": {
        "thread_id": "1"
    }
}

my_workflow.invoke(3, config)  # 0 (previous was None)
my_workflow.invoke(1, config)  # 6 (previous was 3 * 2 from the previous invocation)

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Task

A task represents a discrete unit of work, such as an API call or data processing step. It has two key characteristics:
  • Asynchronous Execution: Tasks are designed to be executed asynchronously, allowing multiple operations to run concurrently without blocking.
  • Checkpointing: Task results are saved to a checkpoint, enabling resumption of the workflow from the last saved state. (See persistence for more details).

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Definition

Tasks are defined using the @task decorator, which wraps a regular Python function. 
from langgraph.func import task

@task()
def slow_computation(input_value):
    # Simulate a long-running operation
    ...
    return result
Serialization The outputs of tasks must be JSON-serializable to support checkpointing.

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Execution

Tasks can only be called from within an entrypoint, another task, or a state graph node. Tasks cannot be called directly from the main application code. When you call a task, it returns immediately with a future object. A future is a placeholder for a result that will be available later. To obtain the result of a task, you can either wait for it synchronously (using result()) or await it asynchronously (using await). 
@entrypoint(checkpointer=checkpointer)
def my_workflow(some_input: int) -> int:
    future = slow_computation(some_input)
    return future.result()  # Wait for the result synchronously

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When to use a task

Tasks are useful in the following scenarios:
  • Checkpointing: When you need to save the result of a long-running operation to a checkpoint, so you don’t need to recompute it when resuming the workflow.
  • Human-in-the-loop: If you’re building a workflow that requires human intervention, you MUST use tasks to encapsulate any randomness (e.g., API calls) to ensure that the workflow can be resumed correctly. See the determinism section for more details.
  • Parallel Execution: For I/O-bound tasks, tasks enable parallel execution, allowing multiple operations to run concurrently without blocking (e.g., calling multiple APIs).
  • Observability: Wrapping operations in tasks provides a way to track the progress of the workflow and monitor the execution of individual operations using LangSmith.
  • Retryable Work: When work needs to be retried to handle failures or inconsistencies, tasks provide a way to encapsulate and manage the retry logic.

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Serialization

There are two key aspects to serialization in LangGraph:
  1. entrypoint inputs and outputs must be JSON-serializable.
  2. task outputs must be JSON-serializable.
These requirements are necessary for enabling checkpointing and workflow resumption. Use python primitives like dictionaries, lists, strings, numbers, and booleans to ensure that your inputs and outputs are serializable. Serialization ensures that workflow state, such as task results and intermediate values, can be reliably saved and restored. This is critical for enabling human-in-the-loop interactions, fault tolerance, and parallel execution. Providing non-serializable inputs or outputs will result in a runtime error when a workflow is configured with a checkpointer.

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Determinism

To utilize features like human-in-the-loop, any randomness should be encapsulated inside of tasks. This guarantees that when execution is halted (e.g., for human in the loop) and then resumed, it will follow the same sequence of steps, even if task results are non-deterministic. LangGraph achieves this behavior by persisting task and subgraph results as they execute. A well-designed workflow ensures that resuming execution follows the same sequence of steps, allowing previously computed results to be retrieved correctly without having to re-execute them. This is particularly useful for long-running tasks or tasks with non-deterministic results, as it avoids repeating previously done work and allows resuming from essentially the same. While different runs of a workflow can produce different results, resuming a specific run should always follow the same sequence of recorded steps. This allows LangGraph to efficiently look up task and subgraph results that were executed prior to the graph being interrupted and avoid recomputing them.

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Idempotency

Idempotency ensures that running the same operation multiple times produces the same result. This helps prevent duplicate API calls and redundant processing if a step is rerun due to a failure. Always place API calls inside tasks functions for checkpointing, and design them to be idempotent in case of re-execution. Re-execution can occur if a task starts, but does not complete successfully. Then, if the workflow is resumed, the task will run again. Use idempotency keys or verify existing results to avoid duplication.

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Common Pitfalls

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Handling side effects

Encapsulate side effects (e.g., writing to a file, sending an email) in tasks to ensure they are not executed multiple times when resuming a workflow.
In this example, a side effect (writing to a file) is directly included in the workflow, so it will be executed a second time when resuming the workflow.
@entrypoint(checkpointer=checkpointer)
def my_workflow(inputs: dict) -> int:
    # This code will be executed a second time when resuming the workflow.
    # Which is likely not what you want.
    # highlight-next-line
    with open("output.txt", "w") as f:
        # highlight-next-line
        f.write("Side effect executed")
    value = interrupt("question")
    return value

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Non-deterministic control flow

Operations that might give different results each time (like getting current time or random numbers) should be encapsulated in tasks to ensure that on resume, the same result is returned.
  • In a task: Get random number (5) → interrupt → resume → (returns 5 again) → …
  • Not in a task: Get random number (5) → interrupt → resume → get new random number (7) → …
This is especially important when using human-in-the-loop workflows with multiple interrupts calls. LangGraph keeps a list of resume values for each task/entrypoint. When an interrupt is encountered, it’s matched with the corresponding resume value. This matching is strictly index-based, so the order of the resume values should match the order of the interrupts. If order of execution is not maintained when resuming, one interrupt call may be matched with the wrong resume value, leading to incorrect results. Please read the section on determinism for more details.
In this example, the workflow uses the current time to determine which task to execute. This is non-deterministic because the result of the workflow depends on the time at which it is executed.
from langgraph.func import entrypoint

@entrypoint(checkpointer=checkpointer)
def my_workflow(inputs: dict) -> int:
    t0 = inputs["t0"]
    # highlight-next-line
    t1 = time.time()

    delta_t = t1 - t0

    if delta_t > 1:
        result = slow_task(1).result()
        value = interrupt("question")
    else:
        result = slow_task(2).result()
        value = interrupt("question")

    return {
        "result": result,
        "value": value
    }