langgraph/docs/docs/how-tos/use-functional-api.md

Use the functional API

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.

!!! tip

For conceptual information on the functional API, see [Functional API](../concepts/functional_api.md).

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.

:::python

@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})

:::

:::js

const checkpointer = new MemorySaver();

const myWorkflow = entrypoint(
  { checkpointer, name: "myWorkflow" },
  async (inputs: { value: number; anotherValue: number }) => {
    const value = inputs.value;
    const anotherValue = inputs.anotherValue;
    // ...
  }
);

await myWorkflow.invoke({ value: 1, anotherValue: 2 });

:::

??? example "Extended example: simple workflow"

:::python
```python
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)
```
:::

:::js
```typescript
import { v4 as uuidv4 } from "uuid";
import { entrypoint, task, MemorySaver } from "@langchain/langgraph";

// Task that checks if a number is even
const isEven = task("isEven", async (number: number) => {
  return number % 2 === 0;
});

// Task that formats a message
const formatMessage = task("formatMessage", async (isEven: boolean) => {
  return isEven ? "The number is even." : "The number is odd.";
});

// Create a checkpointer for persistence
const checkpointer = new MemorySaver();

const workflow = entrypoint(
  { checkpointer, name: "workflow" },
  async (inputs: { number: number }) => {
    // Simple workflow to classify a number
    const even = await isEven(inputs.number);
    return await formatMessage(even);
  }
);

// Run the workflow with a unique thread ID
const config = { configurable: { thread_id: uuidv4() } };
const result = await workflow.invoke({ number: 7 }, config);
console.log(result);
```
:::

??? example "Extended example: Compose an essay with an LLM"

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.

:::python
```python
import uuid
from langchain.chat_models import init_chat_model
from langgraph.func import entrypoint, task
from langgraph.checkpoint.memory import InMemorySaver

llm = init_chat_model('openai: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 llm.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)
```
:::

:::js
```typescript
import { v4 as uuidv4 } from "uuid";
import { ChatOpenAI } from "@langchain/openai";
import { entrypoint, task, MemorySaver } from "@langchain/langgraph";

const llm = new ChatOpenAI({ model: "gpt-3.5-turbo" });

// Task: generate essay using an LLM
const composeEssay = task("composeEssay", async (topic: string) => {
  // Generate an essay about the given topic
  const response = await llm.invoke([
    { role: "system", content: "You are a helpful assistant that writes essays." },
    { role: "user", content: `Write an essay about ${topic}.` }
  ]);
  return response.content as string;
});

// Create a checkpointer for persistence
const checkpointer = new MemorySaver();

const workflow = entrypoint(
  { checkpointer, name: "workflow" },
  async (topic: string) => {
    // Simple workflow that generates an essay with an LLM
    return await composeEssay(topic);
  }
);

// Execute the workflow
const config = { configurable: { thread_id: uuidv4() } };
const result = await workflow.invoke("the history of flight", config);
console.log(result);
```
:::

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).

:::python

@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]

:::

:::js

const addOne = task("addOne", async (number: number) => {
  return number + 1;
});

const graph = entrypoint(
  { checkpointer, name: "graph" },
  async (numbers: number[]) => {
    return await Promise.all(numbers.map(addOne));
  }
);

:::

??? example "Extended example: parallel LLM calls"

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.

:::python
```python
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
llm = init_chat_model("openai:gpt-3.5-turbo")

# Task that generates a paragraph about a given topic
@task
def generate_paragraph(topic: str) -> str:
    response = llm.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)
```
:::

:::js
```typescript
import { v4 as uuidv4 } from "uuid";
import { ChatOpenAI } from "@langchain/openai";
import { entrypoint, task, MemorySaver } from "@langchain/langgraph";

// Initialize the LLM model
const llm = new ChatOpenAI({ model: "gpt-3.5-turbo" });

// Task that generates a paragraph about a given topic
const generateParagraph = task("generateParagraph", async (topic: string) => {
  const response = await llm.invoke([
    { role: "system", content: "You are a helpful assistant that writes educational paragraphs." },
    { role: "user", content: `Write a paragraph about ${topic}.` }
  ]);
  return response.content as string;
});

// Create a checkpointer for persistence
const checkpointer = new MemorySaver();

const workflow = entrypoint(
  { checkpointer, name: "workflow" },
  async (topics: string[]) => {
    // Generates multiple paragraphs in parallel and combines them
    const paragraphs = await Promise.all(topics.map(generateParagraph));
    return paragraphs.join("\n\n");
  }
);

// Run the workflow
const config = { configurable: { thread_id: uuidv4() } };
const result = await workflow.invoke(["quantum computing", "climate change", "history of aviation"], config);
console.log(result);
```
:::

This example uses LangGraph's concurrency model to improve execution time, especially when tasks involve I/O like LLM completions.

Calling graphs

The Functional API and the Graph API can be used together in the same application as they share the same underlying runtime.

:::python

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
    }

:::

:::js

import { entrypoint } from "@langchain/langgraph";
import { StateGraph } from "@langchain/langgraph";

const builder = new StateGraph(/* ... */);
// ...
const someGraph = builder.compile();

const someWorkflow = entrypoint(
  { name: "someWorkflow" },
  async (someInput: Record<string, any>) => {
    // Call a graph defined using the graph API
    const result1 = await someGraph.invoke(/* ... */);
    // Call another graph defined using the graph API
    const result2 = await anotherGraph.invoke(/* ... */);
    return {
      result1,
      result2,
    };
  }
);

:::

??? example "Extended example: calling a simple graph from the functional API"

:::python
```python
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}
```
:::

:::js
```typescript
import { v4 as uuidv4 } from "uuid";
import { entrypoint, MemorySaver } from "@langchain/langgraph";
import { StateGraph } from "@langchain/langgraph";
import { z } from "zod";

// Define the shared state type
const State = z.object({
  foo: z.number(),
});

// Build the graph using the Graph API
const builder = new StateGraph(State)
  .addNode("double", (state) => {
    return { foo: state.foo * 2 };
  })
  .addEdge("__start__", "double");
const graph = builder.compile();

// Define the functional API workflow
const checkpointer = new MemorySaver();

const workflow = entrypoint(
  { checkpointer, name: "workflow" },
  async (x: number) => {
    const result = await graph.invoke({ foo: x });
    return { bar: result.foo };
  }
);

// Execute the workflow
const config = { configurable: { thread_id: uuidv4() } };
console.log(await workflow.invoke(5, config)); // Output: { bar: 10 }
```
:::

Call other entrypoints

You can call other entrypoints from within an entrypoint or a task.

:::python

@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

:::

:::js

// Will automatically use the checkpointer from the parent entrypoint
const someOtherWorkflow = entrypoint(
  { name: "someOtherWorkflow" },
  async (inputs: { value: number }) => {
    return inputs.value;
  }
);

const myWorkflow = entrypoint(
  { checkpointer, name: "myWorkflow" },
  async (inputs: { value: number }) => {
    const value = await someOtherWorkflow.invoke({ value: 1 });
    return value;
  }
);

:::

??? example "Extended example: calling another entrypoint"

:::python
```python
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}
```
:::

:::js
```typescript
import { v4 as uuidv4 } from "uuid";
import { entrypoint, MemorySaver } from "@langchain/langgraph";

// Initialize a checkpointer
const checkpointer = new MemorySaver();

// A reusable sub-workflow that multiplies a number
const multiply = entrypoint(
  { name: "multiply" },
  async (inputs: { a: number; b: number }) => {
    return inputs.a * inputs.b;
  }
);

// Main workflow that invokes the sub-workflow
const main = entrypoint(
  { checkpointer, name: "main" },
  async (inputs: { x: number; y: number }) => {
    const result = await multiply.invoke({ a: inputs.x, b: inputs.y });
    return { product: result };
  }
);

// Execute the main workflow
const config = { configurable: { thread_id: uuidv4() } };
console.log(await main.invoke({ x: 6, y: 7 }, config)); // Output: { product: 42 }
```
:::

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.

:::python

from langgraph.func import entrypoint
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.config import get_stream_writer # (1)!

checkpointer = InMemorySaver()

@entrypoint(checkpointer=checkpointer)
def main(inputs: dict) -> int:
    writer = get_stream_writer() # (2)!
    writer("Started processing") # (3)!
    result = inputs["x"] * 2
    writer(f"Result is {result}") # (4)!
    return result

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

# highlight-next-line
for mode, chunk in main.stream( # (5)!
    {"x": 5},
    stream_mode=["custom", "updates"], # (6)!
    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})

!!! important "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](../how-tos/streaming.md#async) for more details.

```python
from langgraph.types import StreamWriter

@entrypoint(checkpointer=checkpointer)
# highlight-next-line
async def main(inputs: dict, writer: StreamWriter) -> int:
    ...
```

:::

:::js

import {
  entrypoint,
  MemorySaver,
  LangGraphRunnableConfig,
} from "@langchain/langgraph";

const checkpointer = new MemorySaver();

const main = entrypoint(
  { checkpointer, name: "main" },
  async (
    inputs: { x: number },
    config: LangGraphRunnableConfig
  ): Promise<number> => {
    config.writer?.("Started processing"); // (1)!
    const result = inputs.x * 2;
    config.writer?.(`Result is ${result}`); // (2)!
    return result;
  }
);

const config = { configurable: { thread_id: "abc" } };

// (3)!
for await (const [mode, chunk] of await main.stream(
  { x: 5 },
  { streamMode: ["custom", "updates"], ...config } // (4)!
)) {
  console.log(`${mode}: ${JSON.stringify(chunk)}`);
}

1. Emit custom data before computation begins.
2. Emit another custom message after computing the result.
3. Use .stream() to process streamed output.
4. Specify which streaming modes to use.

updates: {"addOne": 2}
updates: {"addTwo": 3}
custom: "hello"
custom: "world"
updates: {"main": 5}

:::

Retry policy

:::python

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'

:::

:::js

import {
  MemorySaver,
  entrypoint,
  task,
  RetryPolicy,
} from "@langchain/langgraph";

// This variable is just used for demonstration purposes to simulate a network failure.
// It's not something you will have in your actual code.
let attempts = 0;

// Let's configure the RetryPolicy to retry on ValueError.
// The default RetryPolicy is optimized for retrying specific network errors.
const retryPolicy: RetryPolicy = { retryOn: (error) => error instanceof Error };

const getInfo = task(
  {
    name: "getInfo",
    retry: retryPolicy,
  },
  () => {
    attempts += 1;

    if (attempts < 2) {
      throw new Error("Failure");
    }
    return "OK";
  }
);

const checkpointer = new MemorySaver();

const main = entrypoint(
  { checkpointer, name: "main" },
  async (inputs: Record<string, any>) => {
    return await getInfo();
  }
);

const config = {
  configurable: {
    thread_id: "1",
  },
};

await main.invoke({ any_input: "foobar" }, config);

'OK'

:::

Caching Tasks

:::python

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))  # (1)!
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. :::

:::js

import {
  InMemoryCache,
  entrypoint,
  task,
  CachePolicy,
} from "@langchain/langgraph";

const slowAdd = task(
  {
    name: "slowAdd",
    cache: { ttl: 120 }, // (1)!
  },
  async (x: number) => {
    await new Promise((resolve) => setTimeout(resolve, 1000));
    return x * 2;
  }
);

const main = entrypoint(
  { cache: new InMemoryCache(), name: "main" },
  async (inputs: { x: number }) => {
    const result1 = await slowAdd(inputs.x);
    const result2 = await slowAdd(inputs.x);
    return { result1, result2 };
  }
);

for await (const chunk of await main.stream(
  { x: 5 },
  { streamMode: "updates" }
)) {
  console.log(chunk);
}

//> { slowAdd: 10 }
//> { slowAdd: 10, '__metadata__': { cached: true } }
//> { main: { result1: 10, result2: 10 } }

1. ttl is specified in seconds. The cache will be invalidated after this time. :::

Resuming after an error

:::python

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.'

:::

:::js

import { entrypoint, task, MemorySaver } from "@langchain/langgraph";

// This variable is just used for demonstration purposes to simulate a network failure.
// It's not something you will have in your actual code.
let attempts = 0;

const getInfo = task("getInfo", async () => {
  /**
   * Simulates a task that fails once before succeeding.
   * Throws an exception on the first attempt, then returns "OK" on subsequent tries.
   */
  attempts += 1;

  if (attempts < 2) {
    throw new Error("Failure"); // Simulate a failure on the first attempt
  }
  return "OK";
});

// Initialize an in-memory checkpointer for persistence
const checkpointer = new MemorySaver();

const slowTask = task("slowTask", async () => {
  /**
   * Simulates a slow-running task by introducing a 1-second delay.
   */
  await new Promise((resolve) => setTimeout(resolve, 1000));
  return "Ran slow task.";
});

const main = entrypoint(
  { checkpointer, name: "main" },
  async (inputs: Record<string, any>) => {
    /**
     * Main workflow function that runs the slowTask and getInfo tasks sequentially.
     *
     * Parameters:
     * - inputs: Record<string, any> containing workflow input values.
     *
     * The workflow first executes `slowTask` and then attempts to execute `getInfo`,
     * which will fail on the first invocation.
     */
    const slowTaskResult = await slowTask(); // Blocking call to slowTask
    await getInfo(); // Exception will be raised here on the first attempt
    return slowTaskResult;
  }
);

// Workflow execution configuration with a unique thread identifier
const config = {
  configurable: {
    thread_id: "1", // Unique identifier to track workflow execution
  },
};

// This invocation will take ~1 second due to the slowTask execution
try {
  // First invocation will raise an exception due to the `getInfo` task failing
  await main.invoke({ any_input: "foobar" }, config);
} catch (err) {
  // Handle the failure gracefully
}

When we resume execution, we won't need to re-run the slowTask as its result is already saved in the checkpoint.

await main.invoke(null, config);

'Ran slow task.'

:::

Human-in-the-loop

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

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".

:::python

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"

:::

:::js

import { entrypoint, task, interrupt, Command } from "@langchain/langgraph";

const step1 = task("step1", async (inputQuery: string) => {
  // Append bar
  return `${inputQuery} bar`;
});

const humanFeedback = task("humanFeedback", async (inputQuery: string) => {
  // Append user input
  const feedback = interrupt(`Please provide feedback: ${inputQuery}`);
  return `${inputQuery} ${feedback}`;
});

const step3 = task("step3", async (inputQuery: string) => {
  // Append qux
  return `${inputQuery} qux`;
});

:::

We can now compose these tasks in an entrypoint:

:::python

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

:::

:::js

import { MemorySaver } from "@langchain/langgraph";

const checkpointer = new MemorySaver();

const graph = entrypoint(
  { checkpointer, name: "graph" },
  async (inputQuery: string) => {
    const result1 = await step1(inputQuery);
    const result2 = await humanFeedback(result1);
    const result3 = await step3(result2);

    return result3;
  }
);

:::

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:

:::python

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

for event in graph.stream("foo", config):
    print(event)
    print("\n")

:::

:::js

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

for await (const event of await graph.stream("foo", config)) {
  console.log(event);
  console.log("\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.

:::python

# Continue execution
for event in graph.stream(Command(resume="baz"), config):
    print(event)
    print("\n")

:::

:::js

// Continue execution
for await (const event of await graph.stream(
  new Command({ resume: "baz" }),
  config
)) {
  console.log(event);
  console.log("\n");
}

:::

After resuming, the run proceeds through the remaining step and terminates as expected.

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)

:::python

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"]
        )

:::

:::js

import { ToolCall } from "@langchain/core/messages/tool";
import { ToolMessage } from "@langchain/core/messages";

function reviewToolCall(toolCall: ToolCall): ToolCall | ToolMessage {
  // Review a tool call, returning a validated version
  const humanReview = interrupt({
    question: "Is this correct?",
    tool_call: toolCall,
  });

  const reviewAction = humanReview.action;
  const reviewData = humanReview.data;

  if (reviewAction === "continue") {
    return toolCall;
  } else if (reviewAction === "update") {
    const updatedToolCall = { ...toolCall, args: reviewData };
    return updatedToolCall;
  } else if (reviewAction === "feedback") {
    return new ToolMessage({
      content: reviewData,
      name: toolCall.name,
      tool_call_id: toolCall.id,
    });
  }

  throw new Error(`Unknown review action: ${reviewAction}`);
}

:::

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.

:::python

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)

    llm_response = call_model(messages).result()
    while True:
        if not llm_response.tool_calls:
            break

        # Review tool calls
        tool_results = []
        tool_calls = []
        for i, tool_call in enumerate(llm_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:
                    llm_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,
            [llm_response, *tool_results, *remaining_tool_results],
        )

        # Call model again
        llm_response = call_model(messages).result()

    # Generate final response
    messages = add_messages(messages, llm_response)
    return entrypoint.final(value=llm_response, save=messages)

:::

:::js

import {
  MemorySaver,
  entrypoint,
  interrupt,
  Command,
  addMessages,
} from "@langchain/langgraph";
import { ToolMessage, AIMessage, BaseMessage } from "@langchain/core/messages";

const checkpointer = new MemorySaver();

const agent = entrypoint(
  { checkpointer, name: "agent" },
  async (
    messages: BaseMessage[],
    previous?: BaseMessage[]
  ): Promise<BaseMessage> => {
    if (previous !== undefined) {
      messages = addMessages(previous, messages);
    }

    let llmResponse = await callModel(messages);
    while (true) {
      if (!llmResponse.tool_calls?.length) {
        break;
      }

      // Review tool calls
      const toolResults: ToolMessage[] = [];
      const toolCalls: ToolCall[] = [];

      for (let i = 0; i < llmResponse.tool_calls.length; i++) {
        const review = reviewToolCall(llmResponse.tool_calls[i]);
        if (review instanceof ToolMessage) {
          toolResults.push(review);
        } else {
          // is a validated tool call
          toolCalls.push(review);
          if (review !== llmResponse.tool_calls[i]) {
            llmResponse.tool_calls[i] = review; // update message
          }
        }
      }

      // Execute remaining tool calls
      const remainingToolResults = await Promise.all(
        toolCalls.map((toolCall) => callTool(toolCall))
      );

      // Append to message list
      messages = addMessages(messages, [
        llmResponse,
        ...toolResults,
        ...remainingToolResults,
      ]);

      // Call model again
      llmResponse = await callModel(messages);
    }

    // Generate final response
    messages = addMessages(messages, llmResponse);
    return entrypoint.final({ value: llmResponse, save: messages });
  }
);

:::

Short-term memory

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

Manage checkpoints

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

View thread state (checkpoint)

:::python

config = {
    "configurable": {
        # highlight-next-line
        "thread_id": "1",
        # optionally provide an ID for a specific checkpoint,
        # otherwise the latest checkpoint is shown
        # highlight-next-line
        # "checkpoint_id": "1f029ca3-1f5b-6704-8004-820c16b69a5a"

    }
}
# highlight-next-line
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=()
)

:::

:::js

const config = {
  configurable: {
    // highlight-next-line
    thread_id: "1",
    // optionally provide an ID for a specific checkpoint,
    // otherwise the latest checkpoint is shown
    // highlight-next-line
    // checkpoint_id: "1f029ca3-1f5b-6704-8004-820c16b69a5a"
  },
};
// highlight-next-line
await graph.getState(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'
  },
  createdAt: '2025-05-05T16:01:24.680462+00:00',
  parentConfig: { configurable: { thread_id: '1', checkpoint_ns: '', checkpoint_id: '1f029ca3-1790-6b0a-8003-baf965b6a38f' } },
  tasks: [],
  interrupts: []
}

:::

View the history of the thread (checkpoints)

:::python

config = {
    "configurable": {
        # highlight-next-line
        "thread_id": "1"
    }
}
# highlight-next-line
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=()
    )
]

:::

:::js

const config = {
  configurable: {
    // highlight-next-line
    thread_id: "1",
  },
};
// highlight-next-line
const history = [];
for await (const state of graph.getStateHistory(config)) {
  history.push(state);
}

[
  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' },
    createdAt: '2025-05-05T16:01:24.680462+00:00',
    parentConfig: { configurable: { thread_id: '1', checkpoint_ns: '', checkpoint_id: '1f029ca3-1790-6b0a-8003-baf965b6a38f' } },
    tasks: [],
    interrupts: []
  },
  // ... more state snapshots
]

:::

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.

:::python

from typing import Optional
from langgraph.func import entrypoint
from langgraph.checkpoint.memory import InMemorySaver

checkpointer = InMemorySaver()

@entrypoint(checkpointer=checkpointer)
def accumulate(n: int, *, previous: Optional[int]) -> 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

:::

:::js

import { entrypoint, MemorySaver } from "@langchain/langgraph";

const checkpointer = new MemorySaver();

const accumulate = entrypoint(
  { checkpointer, name: "accumulate" },
  async (n: number, previous?: number) => {
    const prev = previous || 0;
    const total = prev + n;
    // Return the *previous* value to the caller but save the *new* total to the checkpoint.
    return entrypoint.final({ value: prev, save: total });
  }
);

const config = { configurable: { thread_id: "my-thread" } };

console.log(await accumulate.invoke(1, config)); // 0
console.log(await accumulate.invoke(2, config)); // 1
console.log(await accumulate.invoke(3, config)); // 3

:::

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.

:::python

from langchain_core.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-3-5-sonnet-latest")

@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()

:::

:::js

import { BaseMessage } from "@langchain/core/messages";
import {
  addMessages,
  entrypoint,
  task,
  MemorySaver,
} from "@langchain/langgraph";
import { ChatAnthropic } from "@langchain/anthropic";

const model = new ChatAnthropic({ model: "claude-3-5-sonnet-latest" });

const callModel = task(
  "callModel",
  async (messages: BaseMessage[]): Promise<BaseMessage> => {
    const response = await model.invoke(messages);
    return response;
  }
);

const checkpointer = new MemorySaver();

const workflow = entrypoint(
  { checkpointer, name: "workflow" },
  async (
    inputs: BaseMessage[],
    previous?: BaseMessage[]
  ): Promise<BaseMessage> => {
    let messages = inputs;
    if (previous) {
      messages = addMessages(previous, inputs);
    }

    const response = await callModel(messages);
    return entrypoint.final({
      value: response,
      save: addMessages(messages, response),
    });
  }
);

const config = { configurable: { thread_id: "1" } };
const inputMessage = { role: "user", content: "hi! I'm bob" };

for await (const chunk of await workflow.stream([inputMessage], {
  ...config,
  streamMode: "values",
})) {
  console.log(chunk.content);
}

const inputMessage2 = { role: "user", content: "what's my name?" };
for await (const chunk of await workflow.stream([inputMessage2], {
  ...config,
  streamMode: "values",
})) {
  console.log(chunk.content);
}

:::

??? example "Extended example: build a simple chatbot"

 [How to add thread-level persistence (functional API)](./persistence-functional.ipynb): Shows how to add thread-level persistence to a functional API workflow and implements a simple chatbot.

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.

??? example "Extended example: add long-term memory"

[How to add cross-thread persistence (functional API)](./cross-thread-persistence-functional.ipynb): Shows how to add cross-thread persistence to a functional API workflow and implements a simple chatbot.

Workflows

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

Agents

• How to create an agent from scratch (Functional API): Shows how to create a simple agent from scratch using the functional API.
• How to build a multi-agent network: Shows how to build a multi-agent network using the functional API.
• How to add multi-turn conversation in a multi-agent application (functional API): allow an end-user to engage in a multi-turn conversation with one or more agents.

Integrate with other libraries

• Add LangGraph's features to other frameworks using the functional API: Add LangGraph features like persistence, memory and streaming to other agent frameworks that do not provide them out of the box.