Setup

To setup requirements for building docs you can run:

poetry install --with test

Serving documentation locally

To run the documentation server locally you can run:

make serve-docs

Execute notebooks

If you would like to automatically execute all of the notebooks, to mimic the "Run notebooks" GHA, you can run:

python docs/_scripts/prepare_notebooks_for_ci.py
./docs/_scripts/execute_notebooks.sh

Note: if you want to run the notebooks without %pip install cells, you can run:

python docs/_scripts/prepare_notebooks_for_ci.py --comment-install-cells
./docs/_scripts/execute_notebooks.sh

prepare_notebooks_for_ci.py script will add VCR cassette context manager for each cell in the notebook, so that:

when the notebook is run for the first time, cells with network requests will be recorded to a VCR cassette file
when the notebook is run subsequently, the cells with network requests will be replayed from the cassettes

Note: this is currently limited only to the notebooks in docs/docs/how-tos

Adding new notebooks

If you are adding a notebook with API requests, it's recommended to record network requests so that they can be subsequently replayed. If this is not done, the notebook runner will make API requests every time the notebook is run, which can be costly and slow.

To record network requests, please make sure to first run prepare_notebooks_for_ci.py script.

Then, run

jupyter execute <path_to_notebook>

Once the notebook is executed, you should see the new VCR cassettes recorded in docs/cassettes directory and discard the updated notebook.

Updating existing notebooks

If you are updating an existing notebook, please make sure to remove any existing cassettes for the notebook in docs/cassettes directory (each cassette is prefixed with the notebook name), and then run the steps from the "Adding new notebooks" section above.

To delete cassettes for a notebook, you can run:

rm docs/cassettes/<notebook_name>*

How to Deploy to LangGraph Cloud

LangGraph Cloud is available within LangSmith. To deploy a LangGraph Cloud API, navigate to the LangSmith UI.

Prerequisites

LangGraph Cloud applications are deployed from GitHub repositories. Configure and upload a LangGraph Cloud application to a GitHub repository in order to deploy it to LangGraph Cloud.
Verify that the LangGraph API runs locally. If the API does not run successfully (i.e. langgraph dev), deploying to LangGraph Cloud will fail as well.

Create New Deployment

Starting from the LangSmith UI...

In the left-hand navigation panel, select LangGraph Cloud. The LangGraph Cloud view contains a list of existing LangGraph Cloud deployments.
In the top-right corner, select + New Deployment to create a new deployment.
In the Create New Deployment panel, fill out the required fields.
1. Deployment details
2. Select the desired Deployment Type.
  1. Development deployments are meant for non-production use cases and are provisioned with minimal resources.
  2. Production deployments can serve up to 500 requests/second and are provisioned with highly available storage with automatic backups.
3. Determine if the deployment should be Shareable through LangGraph Studio.
  1. If unchecked, the deployment will only be accessible with a valid LangSmith API key for the workspace.
  2. If checked, the deployment will be accessible through LangGraph Studio to any LangSmith user. A direct URL to LangGraph Studio for the deployment will be provided to share with other LangSmith users.
4. Specify Environment Variables and secrets. See the Environment Variables reference to configure additional variables for the deployment.
  1. Sensitive values such as API keys (e.g. OPENAI_API_KEY) should be specified as secrets.
  2. Additional non-secret environment variables can be specified as well.
5. A new LangSmith Tracing Project is automatically created with the same name as the deployment.
In the top-right corner, select Submit. After a few seconds, the Deployment view appears and the new deployment will be queued for provisioning.

Create New Revision

When creating a new deployment, a new revision is created by default. Subsequent revisions can be created to deploy new code changes.

Starting from the LangSmith UI...

In the left-hand navigation panel, select LangGraph Cloud. The LangGraph Cloud view contains a list of existing LangGraph Cloud deployments.
Select an existing deployment to create a new revision for.
In the Deployment view, in the top-right corner, select + New Revision.
In the New Revision modal, fill out the required fields.
1. Specify the full path to the LangGraph API config file including the file name. For example, if the file langgraph.json is in the root of the repository, simply specify langgraph.json.
2. Determine if the deployment should be Shareable through LangGraph Studio.
  1. If unchecked, the deployment will only be accessible with a valid LangSmith API key for the workspace.
  2. If checked, the deployment will be accessible through LangGraph Studio to any LangSmith user. A direct URL to LangGraph Studio for the deployment will be provided to share with other LangSmith users.
3. Specify Environment Variables and secrets. Existing secrets and environment variables are prepopulated. See the Environment Variables reference to configure additional variables for the revision.
  1. Add new secrets or environment variables.
  2. Remove existing secrets or environment variables.
  3. Update the value of existing secrets or environment variables.
Select Submit. After a few seconds, the New Revision modal will close and the new revision will be queued for deployment.

View Build and Deployment Logs

Build and deployment logs are available for each revision.

Starting from the LangGraph Cloud view...

Select the desired revision from the Revisions table. A panel slides open from the right-hand side and the Build tab is selected by default, which displays build logs for the revision.
In the panel, select the Deploy tab to view deployment logs for the revision.
Within the Deploy tab, adjust the date/time range picker as needed. By default, the date/time range picker is set to the Last 15 minutes.

Interrupt Revision

Interrupting a revision will stop deployment of the revision.

!!! warning "Undefined Behavior" Interrupted revisions have undefined behavior. This is only useful if you need to deploy a new revision and you already have a revision "stuck" in progress. In the future, this feature may be removed.

Starting from the LangGraph Cloud view...

Select the menu icon (three dots) on the right-hand side of the row for the desired revision from the Revisions table.
Select Interrupt from the menu.
A modal will appear. Review the confirmation message. Select Interrupt revision.

Delete Deployment

Starting from the LangSmith UI...

In the left-hand navigation panel, select LangGraph Cloud. The LangGraph Cloud view contains a list of existing LangGraph Cloud deployments.
Select the menu icon (three dots) on the right-hand side of the row for the desired deployment and select Delete.
A Confirmation modal will appear. Select Delete.

Deployment Settings

Starting from the LangGraph Cloud view...

In the top-right corner, select the gear icon (Deployment Settings).
Update the Git Branch to the desired branch.
Check/uncheck checkbox to Automatically update deployment on push to branch.
1. Branch creation/deletion and tag creation/deletion events will not trigger an update. Only pushes to an existing branch will trigger an update.
2. Pushes in quick succession to a branch will not trigger subsequent updates. In the future, this functionality may be changed/improved.

How to customize Dockerfile

Users can add an array of additional lines to add to the Dockerfile following the import from the parent LangGraph image. In order to do this, you simply need to modify your langgraph.json file by passing in the commands you want run to the dockerfile_lines key. For example, if we wanted to use Pillow in our graph you would need to add the following dependencies:

{
    "dependencies": ["."],
    "graphs": {
        "openai_agent": "./openai_agent.py:agent",
    },
    "env": "./.env",
    "dockerfile_lines": [
        "RUN apt-get update && apt-get install -y libjpeg-dev zlib1g-dev libpng-dev",
        "RUN pip install Pillow"
    ]
}

This would install the system packages required to use Pillow if we were working with jpeq or png image formats.

Rebuild Graph at Runtime

You might need to rebuild your graph with a different configuration for a new run. For example, you might need to use a different graph state or graph structure depending on the config. This guide shows how you can do this.

!!! note "Note" In most cases, customizing behavior based on the config should be handled by a single graph where each node can read a config and change its behavior based on it

Prerequisites

Make sure to check out this how-to guide on setting up your app for deployment first.

Define graphs

Let's say you have an app with a simple graph that calls an LLM and returns the response to the user. The app file directory looks like the following:

my-app/
|-- requirements.txt
|-- .env
|-- openai_agent.py     # code for your graph

where the graph is defined in openai_agent.py.

No rebuild

In the standard LangGraph API configuration, the server uses the compiled graph instance that's defined at the top level of openai_agent.py, which looks like the following:

from langchain_openai import ChatOpenAI
from langgraph.graph import END, START, MessageGraph

model = ChatOpenAI(temperature=0)

graph_workflow = MessageGraph()

graph_workflow.add_node("agent", model)
graph_workflow.add_edge("agent", END)
graph_workflow.add_edge(START, "agent")

agent = graph_workflow.compile()

To make the server aware of your graph, you need to specify a path to the variable that contains the CompiledStateGraph instance in your LangGraph API configuration (langgraph.json), e.g.:

{
    "dependencies": ["."],
    "graphs": {
        "openai_agent": "./openai_agent.py:agent",
    },
    "env": "./.env"
}

Rebuild

To make your graph rebuild on each new run with custom configuration, you need to rewrite openai_agent.py to instead provide a function that takes a config and returns a graph (or compiled graph) instance. Let's say we want to return our existing graph for user ID '1', and a tool-calling agent for other users. We can modify openai_agent.py as follows:

from typing import Annotated
from typing_extensions import TypedDict
from langchain_openai import ChatOpenAI
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
from langchain_core.tools import tool
from langchain_core.messages import BaseMessage
from langchain_core.runnables import RunnableConfig


class State(TypedDict):
    messages: Annotated[list[BaseMessage], add_messages]


model = ChatOpenAI(temperature=0)

def make_default_graph():
    """Make a simple LLM agent"""
    graph_workflow = StateGraph(State)
    def call_model(state):
        return {"messages": [model.invoke(state["messages"])]}

    graph_workflow.add_node("agent", call_model)
    graph_workflow.add_edge("agent", END)
    graph_workflow.add_edge(START, "agent")

    agent = graph_workflow.compile()
    return agent


def make_alternative_graph():
    """Make a tool-calling agent"""

    @tool
    def add(a: float, b: float):
        """Adds two numbers."""
        return a + b

    tool_node = ToolNode([add])
    model_with_tools = model.bind_tools([add])
    def call_model(state):
        return {"messages": [model_with_tools.invoke(state["messages"])]}

    def should_continue(state: State):
        if state["messages"][-1].tool_calls:
            return "tools"
        else:
            return END

    graph_workflow = StateGraph(State)

    graph_workflow.add_node("agent", call_model)
    graph_workflow.add_node("tools", tool_node)
    graph_workflow.add_edge("tools", "agent")
    graph_workflow.add_edge(START, "agent")
    graph_workflow.add_conditional_edges("agent", should_continue)

    agent = graph_workflow.compile()
    return agent


## this is the graph making function that will decide which graph to
## build based on the provided config
def make_graph(config: RunnableConfig):
    user_id = config.get("configurable", {}).get("user_id")
    # route to different graph state / structure based on the user ID
    if user_id == "1":
        return make_default_graph()
    else:
        return make_alternative_graph()

Finally, you need to specify the path to your graph-making function (make_graph) in langgraph.json:

{
    "dependencies": ["."],
    "graphs": {
        "openai_agent": "./openai_agent.py:make_graph",
    },
    "env": "./.env"
}

See more info on LangGraph API configuration file here

How to add semantic search to your LangGraph deployment

This guide explains how to add semantic search to your LangGraph deployment's cross-thread store, so that your agent can search for memories and other documents by semantic similarity.

Prerequisites

A LangGraph deployment (see how to deploy)
API keys for your embedding provider (in this case, OpenAI)
langchain >= 0.3.8 (if you specify using the string format below)

Steps

Update your langgraph.json configuration file to include the store configuration:

{
    ...
    "store": {
        "index": {
            "embed": "openai:text-embeddings-3-small",
            "dims": 1536,
            "fields": ["$"]
        }
    }
}

This configuration:

Uses OpenAI's text-embeddings-3-small model for generating embeddings
Sets the embedding dimension to 1536 (matching the model's output)
Indexes all fields in your stored data (["$"] means index everything, or specify specific fields like ["text", "metadata.title"])

To use the string embedding format above, make sure your dependencies include langchain >= 0.3.8:

## In pyproject.toml
[project]
dependencies = [
    "langchain>=0.3.8"
]

Or if using requirements.txt:

langchain>=0.3.8

Usage

Once configured, you can use semantic search in your LangGraph nodes. The store requires a namespace tuple to organize memories:

def search_memory(state: State, *, store: BaseStore):
    # Search the store using semantic similarity
    # The namespace tuple helps organize different types of memories
    # e.g., ("user_facts", "preferences") or ("conversation", "summaries")
    results = store.search(
        namespace=("memory", "facts"),  # Organize memories by type
        query="your search query",
        limit=3  # number of results to return
    )
    return results

Custom Embeddings

If you want to use custom embeddings, you can pass a path to a custom embedding function:

{
    ...
    "store": {
        "index": {
            "embed": "path/to/embedding_function.py:embed",
            "dims": 1536,
            "fields": ["$"]
        }
    }
}

The deployment will look for the function in the specified path. The function must be async and accept a list of strings:

## path/to/embedding_function.py
from openai import AsyncOpenAI

client = AsyncOpenAI()

async def aembed_texts(texts: list[str]) -> list[list[float]]:
    """Custom embedding function that must:
    1. Be async
    2. Accept a list of strings
    3. Return a list of float arrays (embeddings)
    """
    response = await client.embeddings.create(
        model="text-embedding-3-small",
        input=texts
    )
    return [e.embedding for e in response.data]

Querying via the API

You can also query the store using the LangGraph SDK. Since the SDK uses async operations:

from langgraph_sdk import get_client

async def search_store():
    client = get_client()
    results = await client.store.search_items(
        ("memory", "facts"),
        query="your search query",
        limit=3  # number of results to return
    )
    return results

## Use in an async context
results = await search_store()

How to Set Up a LangGraph Application for Deployment

A LangGraph application must be configured with a LangGraph API configuration file in order to be deployed to LangGraph Cloud (or to be self-hosted). This how-to guide discusses the basic steps to setup a LangGraph application for deployment using requirements.txt to specify project dependencies.

This walkthrough is based on this repository, which you can play around with to learn more about how to setup your LangGraph application for deployment.

!!! tip "Setup with pyproject.toml" If you prefer using poetry for dependency management, check out this how-to guide on using pyproject.toml for LangGraph Cloud.

!!! tip "Setup with a Monorepo" If you are interested in deploying a graph located inside a monorepo, take a look at this repository for an example of how to do so.

The final repo structure will look something like this:

my-app/
Ôö£ÔöÇÔöÇ my_agent # all project code lies within here
Ôöé   Ôö£ÔöÇÔöÇ utils # utilities for your graph
Ôöé   Ôöé   Ôö£ÔöÇÔöÇ __init__.py
Ôöé   Ôöé   Ôö£ÔöÇÔöÇ tools.py # tools for your graph
Ôöé   Ôöé   Ôö£ÔöÇÔöÇ nodes.py # node functions for you graph
Ôöé   Ôöé   ÔööÔöÇÔöÇ state.py # state definition of your graph
Ôöé┬á┬á Ôö£ÔöÇÔöÇ requirements.txt # package dependencies
Ôöé┬á┬á Ôö£ÔöÇÔöÇ __init__.py
Ôöé┬á┬á ÔööÔöÇÔöÇ agent.py # code for constructing your graph
Ôö£ÔöÇÔöÇ .env # environment variables
ÔööÔöÇÔöÇ langgraph.json # configuration file for LangGraph

After each step, an example file directory is provided to demonstrate how code can be organized.

Specify Dependencies

Dependencies can optionally be specified in one of the following files: pyproject.toml, setup.py, or requirements.txt. If none of these files is created, then dependencies can be specified later in the LangGraph API configuration file.

The dependencies below will be included in the image, you can also use them in your code, as long as with a compatible version range:

langgraph>=0.2.56,<0.3.0
langgraph-checkpoint>=2.0.5,<3.0
langchain-core>=0.2.38,<0.4.0
langsmith>=0.1.63
orjson>=3.9.7
httpx>=0.25.0
tenacity>=8.0.0
uvicorn>=0.26.0
sse-starlette>=2.1.0
uvloop>=0.18.0
httptools>=0.5.0
jsonschema-rs>=0.16.3
croniter>=1.0.1
structlog>=23.1.0
redis>=5.0.0,<6.0.0

Example requirements.txt file:

langgraph
langchain_anthropic
tavily-python
langchain_community
langchain_openai

Example file directory:

my-app/
Ôö£ÔöÇÔöÇ my_agent # all project code lies within here
Ôöé┬á┬á ÔööÔöÇÔöÇ requirements.txt # package dependencies

Specify Environment Variables

Environment variables can optionally be specified in a file (e.g. .env). See the Environment Variables reference to configure additional variables for a deployment.

Example .env file:

MY_ENV_VAR_1=foo
MY_ENV_VAR_2=bar
OPENAI_API_KEY=key

Example file directory:

my-app/
Ôö£ÔöÇÔöÇ my_agent # all project code lies within here
Ôöé┬á┬á ÔööÔöÇÔöÇ requirements.txt # package dependencies
ÔööÔöÇÔöÇ .env # environment variables

Define Graphs

Implement your graphs! Graphs can be defined in a single file or multiple files. Make note of the variable names of each [CompiledGraph][langgraph.graph.graph.CompiledGraph] to be included in the LangGraph application. The variable names will be used later when creating the LangGraph API configuration file.

Example agent.py file, which shows how to import from other modules you define (code for the modules is not shown here, please see this repo to see their implementation):

## my_agent/agent.py
from typing import Literal
from typing_extensions import TypedDict

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

## Define the config
class GraphConfig(TypedDict):
    model_name: Literal["anthropic", "openai"]

workflow = StateGraph(AgentState, config_schema=GraphConfig)
workflow.add_node("agent", call_model)
workflow.add_node("action", tool_node)
workflow.add_edge(START, "agent")
workflow.add_conditional_edges(
    "agent",
    should_continue,
    {
        "continue": "action",
        "end": END,
    },
)
workflow.add_edge("action", "agent")

graph = workflow.compile()

!!! warning "Assign CompiledGraph to Variable" The build process for LangGraph Cloud requires that the CompiledGraph object be assigned to a variable at the top-level of a Python module (alternatively, you can provide a function that creates a graph).

Example file directory:

my-app/
Ôö£ÔöÇÔöÇ my_agent # all project code lies within here
Ôöé   Ôö£ÔöÇÔöÇ utils # utilities for your graph
Ôöé   Ôöé   Ôö£ÔöÇÔöÇ __init__.py
Ôöé   Ôöé   Ôö£ÔöÇÔöÇ tools.py # tools for your graph
Ôöé   Ôöé   Ôö£ÔöÇÔöÇ nodes.py # node functions for you graph
Ôöé   Ôöé   ÔööÔöÇÔöÇ state.py # state definition of your graph
Ôöé┬á┬á Ôö£ÔöÇÔöÇ requirements.txt # package dependencies
Ôöé┬á┬á Ôö£ÔöÇÔöÇ __init__.py
Ôöé┬á┬á ÔööÔöÇÔöÇ agent.py # code for constructing your graph
ÔööÔöÇÔöÇ .env # environment variables

Create LangGraph API Config

Create a LangGraph API configuration file called langgraph.json. See the LangGraph CLI reference for detailed explanations of each key in the JSON object of the configuration file.

Example langgraph.json file:

{
  "dependencies": ["./my_agent"],
  "graphs": {
    "agent": "./my_agent/agent.py:graph"
  },
  "env": ".env"
}

Note that the variable name of the CompiledGraph appears at the end of the value of each subkey in the top-level graphs key (i.e. :<variable_name>).

!!! warning "Configuration Location" The LangGraph API configuration file must be placed in a directory that is at the same level or higher than the Python files that contain compiled graphs and associated dependencies.

Example file directory:

my-app/
Ôö£ÔöÇÔöÇ my_agent # all project code lies within here
Ôöé   Ôö£ÔöÇÔöÇ utils # utilities for your graph
Ôöé   Ôöé   Ôö£ÔöÇÔöÇ __init__.py
Ôöé   Ôöé   Ôö£ÔöÇÔöÇ tools.py # tools for your graph
Ôöé   Ôöé   Ôö£ÔöÇÔöÇ nodes.py # node functions for you graph
Ôöé   Ôöé   ÔööÔöÇÔöÇ state.py # state definition of your graph
Ôöé┬á┬á Ôö£ÔöÇÔöÇ requirements.txt # package dependencies
Ôöé┬á┬á Ôö£ÔöÇÔöÇ __init__.py
Ôöé┬á┬á ÔööÔöÇÔöÇ agent.py # code for constructing your graph
Ôö£ÔöÇÔöÇ .env # environment variables
ÔööÔöÇÔöÇ langgraph.json # configuration file for LangGraph

Next

After you setup your project and place it in a github repo, it's time to deploy your app.

How to Set Up a LangGraph.js Application for Deployment

A LangGraph.js application must be configured with a LangGraph API configuration file in order to be deployed to LangGraph Cloud (or to be self-hosted). This how-to guide discusses the basic steps to setup a LangGraph.js application for deployment using package.json to specify project dependencies.

This walkthrough is based on this repository, which you can play around with to learn more about how to setup your LangGraph application for deployment.

The final repo structure will look something like this:

my-app/
Ôö£ÔöÇÔöÇ src # all project code lies within here
Ôöé   Ôö£ÔöÇÔöÇ utils # optional utilities for your graph
Ôöé   Ôöé   Ôö£ÔöÇÔöÇ tools.ts # tools for your graph
Ôöé   Ôöé   Ôö£ÔöÇÔöÇ nodes.ts # node functions for you graph
Ôöé   Ôöé   ÔööÔöÇÔöÇ state.ts # state definition of your graph
Ôöé┬á┬á ÔööÔöÇÔöÇ agent.ts # code for constructing your graph
Ôö£ÔöÇÔöÇ package.json # package dependencies
Ôö£ÔöÇÔöÇ .env # environment variables
ÔööÔöÇÔöÇ langgraph.json # configuration file for LangGraph

After each step, an example file directory is provided to demonstrate how code can be organized.

Specify Dependencies

Dependencies can be specified in a package.json. If none of these files is created, then dependencies can be specified later in the LangGraph API configuration file.

Example package.json file:

{
  "name": "langgraphjs-studio-starter",
  "packageManager": "yarn@1.22.22",
  "dependencies": {
    "@langchain/community": "^0.2.31",
    "@langchain/core": "^0.2.31",
    "@langchain/langgraph": "^0.2.0",
    "@langchain/openai": "^0.2.8"
  }
}

Example file directory:

my-app/
ÔööÔöÇÔöÇ package.json # package dependencies

Specify Environment Variables

Environment variables can optionally be specified in a file (e.g. .env). See the Environment Variables reference to configure additional variables for a deployment.

Example .env file:

MY_ENV_VAR_1=foo
MY_ENV_VAR_2=bar
OPENAI_API_KEY=key
TAVILY_API_KEY=key_2

Example file directory:

my-app/
Ôö£ÔöÇÔöÇ package.json
ÔööÔöÇÔöÇ .env # environment variables

Define Graphs

Implement your graphs! Graphs can be defined in a single file or multiple files. Make note of the variable names of each compiled graph to be included in the LangGraph application. The variable names will be used later when creating the LangGraph API configuration file.

Here is an example agent.ts:

import type { AIMessage } from "@langchain/core/messages";
import { TavilySearchResults } from "@langchain/community/tools/tavily_search";
import { ChatOpenAI } from "@langchain/openai";

import { MessagesAnnotation, StateGraph } from "@langchain/langgraph";
import { ToolNode } from "@langchain/langgraph/prebuilt";

const tools = [
  new TavilySearchResults({ maxResults: 3, }),
];

// Define the function that calls the model
async function callModel(
  state: typeof MessagesAnnotation.State,
) {
  /**
   * Call the LLM powering our agent.
   * Feel free to customize the prompt, model, and other logic!
   */
  const model = new ChatOpenAI({
    model: "gpt-4o",
  }).bindTools(tools);

  const response = await model.invoke([
    {
      role: "system",
      content: `You are a helpful assistant. The current date is ${new Date().getTime()}.`
    },
    ...state.messages
  ]);

  // MessagesAnnotation supports returning a single message or array of messages
  return { messages: response };
}

// Define the function that determines whether to continue or not
function routeModelOutput(state: typeof MessagesAnnotation.State) {
  const messages = state.messages;
  const lastMessage: AIMessage = messages[messages.length - 1];
  // If the LLM is invoking tools, route there.
  if ((lastMessage?.tool_calls?.length ?? 0) > 0) {
    return "tools";
  }
  // Otherwise end the graph.
  return "__end__";
}

// Define a new graph.
// See https://langchain-ai.github.io/langgraphjs/how-tos/define-state/#getting-started for
// more on defining custom graph states.
const workflow = new StateGraph(MessagesAnnotation)
  // Define the two nodes we will cycle between
  .addNode("callModel", callModel)
  .addNode("tools", new ToolNode(tools))
  // Set the entrypoint as `callModel`
  // This means that this node is the first one called
  .addEdge("__start__", "callModel")
  .addConditionalEdges(
    // First, we define the edges' source node. We use `callModel`.
    // This means these are the edges taken after the `callModel` node is called.
    "callModel",
    // Next, we pass in the function that will determine the sink node(s), which
    // will be called after the source node is called.
    routeModelOutput,
    // List of the possible destinations the conditional edge can route to.
    // Required for conditional edges to properly render the graph in Studio
    [
      "tools",
      "__end__"
    ],
  )
  // This means that after `tools` is called, `callModel` node is called next.
  .addEdge("tools", "callModel");

// Finally, we compile it!
// This compiles it into a graph you can invoke and deploy.
export const graph = workflow.compile();

!!! info "Assign CompiledGraph to Variable" The build process for LangGraph Cloud requires that the CompiledGraph object be assigned to a variable at the top-level of a JavaScript module (alternatively, you can provide a function that creates a graph).

Example file directory:

my-app/
Ôö£ÔöÇÔöÇ src # all project code lies within here
Ôöé   Ôö£ÔöÇÔöÇ utils # optional utilities for your graph
Ôöé   Ôöé   Ôö£ÔöÇÔöÇ tools.ts # tools for your graph
Ôöé   Ôöé   Ôö£ÔöÇÔöÇ nodes.ts # node functions for you graph
Ôöé   Ôöé   ÔööÔöÇÔöÇ state.ts # state definition of your graph
Ôöé┬á┬á ÔööÔöÇÔöÇ agent.ts # code for constructing your graph
Ôö£ÔöÇÔöÇ package.json # package dependencies
Ôö£ÔöÇÔöÇ .env # environment variables
ÔööÔöÇÔöÇ langgraph.json # configuration file for LangGraph

Create LangGraph API Config

Create a LangGraph API configuration file called langgraph.json. See the LangGraph CLI reference for detailed explanations of each key in the JSON object of the configuration file.

Example langgraph.json file:

{
  "node_version": "20",
  "dockerfile_lines": [],
  "dependencies": ["."],
  "graphs": {
    "agent": "./src/agent.ts:graph"
  },
  "env": ".env"
}

Note that the variable name of the CompiledGraph appears at the end of the value of each subkey in the top-level graphs key (i.e. :<variable_name>).

!!! info "Configuration Location" The LangGraph API configuration file must be placed in a directory that is at the same level or higher than the TypeScript files that contain compiled graphs and associated dependencies.

Next

After you setup your project and place it in a github repo, it's time to deploy your app.

How to Set Up a LangGraph Application for Deployment

A LangGraph application must be configured with a LangGraph API configuration file in order to be deployed to LangGraph Cloud (or to be self-hosted). This how-to guide discusses the basic steps to setup a LangGraph application for deployment using pyproject.toml to define your package's dependencies.

This walkthrough is based on this repository, which you can play around with to learn more about how to setup your LangGraph application for deployment.

!!! tip "Setup with requirements.txt" If you prefer using requirements.txt for dependency management, check out this how-to guide.

!!! tip "Setup with a Monorepo" If you are interested in deploying a graph located inside a monorepo, take a look at this repository for an example of how to do so.

The final repo structure will look something like this:

my-app/
Ôö£ÔöÇÔöÇ my_agent # all project code lies within here
Ôöé   Ôö£ÔöÇÔöÇ utils # utilities for your graph
Ôöé   Ôöé   Ôö£ÔöÇÔöÇ __init__.py
Ôöé   Ôöé   Ôö£ÔöÇÔöÇ tools.py # tools for your graph
Ôöé   Ôöé   Ôö£ÔöÇÔöÇ nodes.py # node functions for you graph
Ôöé   Ôöé   ÔööÔöÇÔöÇ state.py # state definition of your graph
Ôöé┬á┬á Ôö£ÔöÇÔöÇ __init__.py
Ôöé┬á┬á ÔööÔöÇÔöÇ agent.py # code for constructing your graph
Ôö£ÔöÇÔöÇ .env # environment variables
Ôö£ÔöÇÔöÇ langgraph.json  # configuration file for LangGraph
ÔööÔöÇÔöÇ pyproject.toml # dependencies for your project

After each step, an example file directory is provided to demonstrate how code can be organized.

Specify Dependencies

Dependencies can optionally be specified in one of the following files: pyproject.toml, setup.py, or requirements.txt. If none of these files is created, then dependencies can be specified later in the LangGraph API configuration file.

The dependencies below will be included in the image, you can also use them in your code, as long as with a compatible version range:

langgraph>=0.2.56,<0.3.0
langgraph-checkpoint>=2.0.5,<3.0
langchain-core>=0.2.38,<0.4.0
langsmith>=0.1.63
orjson>=3.9.7
httpx>=0.25.0
tenacity>=8.0.0
uvicorn>=0.26.0
sse-starlette>=2.1.0
uvloop>=0.18.0
httptools>=0.5.0
jsonschema-rs>=0.16.3
croniter>=1.0.1
structlog>=23.1.0
redis>=5.0.0,<6.0.0

Example pyproject.toml file:

[tool.poetry]
name = "my-agent"
version = "0.0.1"
description = "An excellent agent build for LangGraph cloud."
authors = ["Polly the parrot <1223+polly@users.noreply.github.com>"]
license = "MIT"
readme = "README.md"

[tool.poetry.dependencies]
python = ">=3.9.0,<3.13"
langgraph = "^0.2.0"
langchain-fireworks = "^0.1.3"


[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"

Example file directory:

my-app/
ÔööÔöÇÔöÇ pyproject.toml   # Python packages required for your graph

Specify Environment Variables

Environment variables can optionally be specified in a file (e.g. .env). See the Environment Variables reference to configure additional variables for a deployment.

Example .env file:

MY_ENV_VAR_1=foo
MY_ENV_VAR_2=bar
FIREWORKS_API_KEY=key

Example file directory:

my-app/
Ôö£ÔöÇÔöÇ .env             # file with environment variables
ÔööÔöÇÔöÇ pyproject.toml

Define Graphs

Implement your graphs! Graphs can be defined in a single file or multiple files. Make note of the variable names of each [CompiledGraph][langgraph.graph.graph.CompiledGraph] to be included in the LangGraph application. The variable names will be used later when creating the LangGraph API configuration file.

Example agent.py file, which shows how to import from other modules you define (code for the modules is not shown here, please see this repo to see their implementation):

## my_agent/agent.py
from typing import Literal
from typing_extensions import TypedDict

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

## Define the config
class GraphConfig(TypedDict):
    model_name: Literal["anthropic", "openai"]

workflow = StateGraph(AgentState, config_schema=GraphConfig)
workflow.add_node("agent", call_model)
workflow.add_node("action", tool_node)
workflow.add_edge(START, "agent")
workflow.add_conditional_edges(
    "agent",
    should_continue,
    {
        "continue": "action",
        "end": END,
    },
)
workflow.add_edge("action", "agent")

graph = workflow.compile()

!!! warning "Assign CompiledGraph to Variable" The build process for LangGraph Cloud requires that the CompiledGraph object be assigned to a variable at the top-level of a Python module.

Example file directory:

my-app/
Ôö£ÔöÇÔöÇ my_agent # all project code lies within here
Ôöé   Ôö£ÔöÇÔöÇ utils # utilities for your graph
Ôöé   Ôöé   Ôö£ÔöÇÔöÇ __init__.py
Ôöé   Ôöé   Ôö£ÔöÇÔöÇ tools.py # tools for your graph
Ôöé   Ôöé   Ôö£ÔöÇÔöÇ nodes.py # node functions for you graph
Ôöé   Ôöé   ÔööÔöÇÔöÇ state.py # state definition of your graph
Ôöé┬á┬á Ôö£ÔöÇÔöÇ __init__.py
Ôöé┬á┬á ÔööÔöÇÔöÇ agent.py # code for constructing your graph
Ôö£ÔöÇÔöÇ .env
ÔööÔöÇÔöÇ pyproject.toml

Create LangGraph API Config

Create a LangGraph API configuration file called langgraph.json. See the LangGraph CLI reference for detailed explanations of each key in the JSON object of the configuration file.

Example langgraph.json file:

{
  "dependencies": ["."],
  "graphs": {
    "agent": "./my_agent/agent.py:graph"
  },
  "env": ".env"
}

Note that the variable name of the CompiledGraph appears at the end of the value of each subkey in the top-level graphs key (i.e. :<variable_name>).

!!! warning "Configuration Location" The LangGraph API configuration file must be placed in a directory that is at the same level or higher than the Python files that contain compiled graphs and associated dependencies.

Example file directory:

my-app/
Ôö£ÔöÇÔöÇ my_agent # all project code lies within here
Ôöé   Ôö£ÔöÇÔöÇ utils # utilities for your graph
Ôöé   Ôöé   Ôö£ÔöÇÔöÇ __init__.py
Ôöé   Ôöé   Ôö£ÔöÇÔöÇ tools.py # tools for your graph
Ôöé   Ôöé   Ôö£ÔöÇÔöÇ nodes.py # node functions for you graph
Ôöé   Ôöé   ÔööÔöÇÔöÇ state.py # state definition of your graph
Ôöé┬á┬á Ôö£ÔöÇÔöÇ __init__.py
Ôöé┬á┬á ÔööÔöÇÔöÇ agent.py # code for constructing your graph
Ôö£ÔöÇÔöÇ .env # environment variables
Ôö£ÔöÇÔöÇ langgraph.json  # configuration file for LangGraph
ÔööÔöÇÔöÇ pyproject.toml # dependencies for your project

Next

After you setup your project and place it in a github repo, it's time to deploy your app.

How to test a LangGraph app locally

This guide assumes you have a LangGraph app correctly set up with a proper configuration file and a corresponding compiled graph, and that you have a proper LangChain API key.

Testing locally ensures that there are no errors or conflicts with Python dependencies and confirms that the configuration file is specified correctly.

Setup

Install the LangGraph CLI package:

pip install -U "langgraph-cli[inmem]"

Ensure you have an API key, which you can create from the LangSmith UI (Settings > API Keys). This is required to authenticate that you have LangGraph Cloud access. After you have saved the key to a safe place, place the following line in your .env file:

LANGSMITH_API_KEY = *********

Start the API server

Once you have installed the CLI, you can run the following command to start the API server for local testing:

langgraph dev

This will start up the LangGraph API server locally. If this runs successfully, you should see something like:

Ready!

API: http://localhost:2024

Docs: http://localhost:2024/docs

LangGraph Studio Web UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024

!!! note "In-Memory Mode"

The `langgraph dev` command starts LangGraph Server in an in-memory mode. This mode is suitable for development and testing purposes. For production use, you should deploy LangGraph Server with access to a persistent storage backend.

If you want to test your application with a persistent storage backend, you can use the `langgraph up` command instead of `langgraph dev`. You will
need to have `docker` installed on your machine to use this command.

Interact with the server

We can now interact with the API server using the LangGraph SDK. First, we need to start our client, select our assistant (in this case a graph we called "agent", make sure to select the proper assistant you wish to test).

You can either initialize by passing authentication or by setting an environment variable.

Initialize with authentication

=== "Python"

```python
from langgraph_sdk import get_client

# only pass the url argument to get_client() if you changed the default port when calling langgraph dev
client = get_client(url=<DEPLOYMENT_URL>,api_key=<LANGSMITH_API_KEY>)
# Using the graph deployed with the name "agent"
assistant_id = "agent"
thread = await client.threads.create()
```

=== "Javascript"

```js
import { Client } from "@langchain/langgraph-sdk";

// only set the apiUrl if you changed the default port when calling langgraph dev
const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <LANGSMITH_API_KEY> });
// Using the graph deployed with the name "agent"
const assistantId = "agent";
const thread = await client.threads.create();
```

=== "CURL"

```bash
curl --request POST \
  --url <DEPLOYMENT_URL>/threads \
  --header 'Content-Type: application/json'
  --header 'x-api-key: <LANGSMITH_API_KEY>'
```

Initialize with environment variables

If you have a LANGSMITH_API_KEY set in your environment, you do not need to explicitly pass authentication to the client

=== "Python"

```python
from langgraph_sdk import get_client

# only pass the url argument to get_client() if you changed the default port when calling langgraph dev
client = get_client()
# Using the graph deployed with the name "agent"
assistant_id = "agent"
thread = await client.threads.create()
```

=== "Javascript"

```js
import { Client } from "@langchain/langgraph-sdk";

// only set the apiUrl if you changed the default port when calling langgraph dev
const client = new Client();
// Using the graph deployed with the name "agent"
const assistantId = "agent";
const thread = await client.threads.create();
```

=== "CURL"

```bash
curl --request POST \
  --url <DEPLOYMENT_URL>/threads \
  --header 'Content-Type: application/json'
```

Now we can invoke our graph to ensure it is working. Make sure to change the input to match the proper schema for your graph.

=== "Python"

```python
input = {"messages": [{"role": "user", "content": "what's the weather in sf"}]}
async for chunk in client.runs.stream(
    thread["thread_id"],
    assistant_id,
    input=input,
    stream_mode="updates",
):
    print(f"Receiving new event of type: {chunk.event}...")
    print(chunk.data)
    print("\n\n")
```

=== "Javascript"

```js
const input = { "messages": [{ "role": "user", "content": "what's the weather in sf"}] }

const streamResponse = client.runs.stream(
  thread["thread_id"],
  assistantId,
  {
    input: input,
    streamMode: "updates",
  }
);
for await (const chunk of streamResponse) {
  console.log(`Receiving new event of type: ${chunk.event}...`);
  console.log(chunk.data);
  console.log("\n\n");
}
```

=== "CURL"

```bash
curl --request POST \
 --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
 --header 'Content-Type: application/json' \
 --data "{
   \"assistant_id\": \"agent\",
   \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in sf\"}]},
   \"stream_mode\": [
     \"events\"
   ]
 }" | \
 sed 's/\r$//' | \
 awk '
 /^event:/ {
     if (data_content != "") {
         print data_content "\n"
     }
     sub(/^event: /, "Receiving event of type: ", $0)
     printf "%s...\n", $0
     data_content = ""
 }
 /^data:/ {
     sub(/^data: /, "", $0)
     data_content = $0
 }
 END {
     if (data_content != "") {
         print data_content "\n"
     }
 }
 ' 
```

If your graph works correctly, you should see your graph output displayed in the console. Of course, there are many more ways you might need to test your graph, for a full list of commands you can send with the SDK, see the Python and JS/TS references.

How to version assistants

In this how-to guide we will walk through how you can create and manage different assistant versions. If you haven't already, you can read this conceptual guide to gain a better understanding of what assistant versioning is. This how-to assumes you have a graph that is configurable, which means you have defined a config schema and passed it to your graph as follows:

=== "Python"

```python
class Config(BaseModel):
    model_name: Literal["anthropic", "openai"] = "anthropic"
    system_prompt: str

agent = StateGraph(State, config_schema=Config)
```

=== "Javascript"

```js
const ConfigAnnotation = Annotation.Root({
    modelName: Annotation<z.enum(["openai", "anthropic"])>({
        default: () => "anthropic",
    }),
    systemPrompt: Annotation<String>
});

// the rest of your code

const agent = new StateGraph(StateAnnotation, ConfigAnnotation);
```

Setup

First let's set up our client and thread. If you are using the Studio, just open the application to the graph called "agent". If using cURL, you don't need to do anything except copy down your deployment URL and the name of the graph you want to use.

=== "Python"

```python
from langgraph_sdk import get_client

client = get_client(url=<DEPLOYMENT_URL>)
# Using the graph deployed with the name "agent"
graph_name = "agent"
```

=== "Javascript"

```js
import { Client } from "@langchain/langgraph-sdk";

const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
// Using the graph deployed with the name "agent"
const graphName = "agent";
```

Create an assistant

For this example, we will create an assistant by modifying the model name that is used in our graph. We can create a new assistant called "openai_assistant" for this:

=== "Python"

```python
openai_assistant = await client.assistants.create(graph_name, config={"configurable": {"model_name": "openai"}}, name="openai_assistant")
```

=== "Javascript"

```js
const openaiAssistant = await client.assistants.create({graphId: graphName, config: { configurable: {"modelName": "openai"}}, name: "openaiAssistant"});
```

=== "CURL"

```bash
curl --request POST \
--url <DEPOLYMENT_URL>/assistants \
--header 'Content-Type: application/json' \
--data '{
"graph_id": "agent",
"config": {"model_name": "openai"},
"name": "openai_assistant"
}'
```

Using the studio

To create an assistant using the studio do the following steps:

Click on the "Create New Assistant" button:
Use the create assistant pane to enter info for the assistant you wish to create, and then click create:
See that your assistant was created and is displayed in the Studio
Click on the edit button next to the selected assistant to manage your created assistant:

Create a new version for your assistant

Let's now say we wanted to add a system prompt to our assistant. We can do this by using the update endpoint as follows. Please note that you must pass in the ENTIRE config (and metadata if you are using it). The update endpoint creates new versions completely from scratch and does not rely on previously entered config. In this case, we need to continue telling the assistant to use "openai" as the model.

=== "Python"

```python
openai_assistant_v2 = await client.assistants.update(openai_assistant['assistant_id'], config={"configurable": {"model_name": "openai", "system_prompt": "You are a helpful assistant!"}})
```

=== "Javascript"

```js
const openaiAssistantV2 = await client.assistants.update(openaiAssistant['assistant_id'], {config: { configurable: {"modelName": "openai", "systemPrompt": "You are a helpful assistant!"}}});
```

=== "CURL"

```bash
curl --request PATCH \
--url <DEPOLYMENT_URL>/assistants/<ASSISTANT_ID> \
--header 'Content-Type: application/json' \
--data '{
"config": {"model_name": "openai", "system_prompt": "You are a helpful assistant!"}
}'
```

Using the studio

First, click on the edit button next to the openai_assistant. Then, add a system prompt and click "Save New Version":
Then you can see it is selected in the assistant dropdown:
And you can see all the version history in the edit pane for the assistant:

Point your assistant to a different version

After having created multiple versions, we can change the version our assistant points to both by using the SDK and also the Studio. In this case we will be resetting the openai_assistant we just created two versions for to point back to the first version. When you create a new version (by using the update endpoint) the assistant automatically points to the newly created version, so following the code above our openai_assistant is pointing to the second version. Here we will change it to point to the first version:

=== "Python"

```python
await client.assistants.set_latest(openai_assistant['assistant_id'], 1)
```

=== "Javascript"

```js
await client.assistants.setLatest(openaiAssistant['assistant_id'], 1);
```

=== "CURL"

```bash
curl --request POST \
--url <DEPLOYMENT_URL>/assistants/<ASSISTANT_ID>/latest \
--header 'Content-Type: application/json' \
--data '{
"version": 1
}'
```

Using the studio

To change the version, all you have to do is click into the edit pane for an assistant, select the version you want to change to, and then click the "Set As Current Version" button

Using your assistant versions

Whether you are a business user iterating without writing code, or a developer using the SDK - assistant versioning allows you to quickly test different agents in a controlled environment, making it easy to iterate fast. You can use any of the assistant versions just how you would a normal assistant, and can read more about how to stream output from these assistants by reading these guides or this one if you are using the Studio.

!!! warning "Deleting Assistants" Deleting as assistant will delete ALL of it's versions, since they all point to the same assistant ID. There is currently no way to just delete a single version, but by pointing your assistant to the correct version you can skip any versions that you don't wish to use.

How to kick off background runs

This guide covers how to kick off background runs for your agent. This can be useful for long running jobs.

Setup

First let's set up our client and thread:

=== "Python"

```python
from langgraph_sdk import get_client

client = get_client(url=<DEPLOYMENT_URL>)
# Using the graph deployed with the name "agent"
assistant_id = "agent"
# create thread
thread = await client.threads.create()
print(thread)
```

=== "Javascript"

```js
import { Client } from "@langchain/langgraph-sdk";

const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
// Using the graph deployed with the name "agent"
const assistantID = "agent";
// create thread
const thread = await client.threads.create();
console.log(thread);
```

=== "CURL"

```bash
curl --request POST \
  --url <DEPLOYMENT_URL>/threads \
  --header 'Content-Type: application/json' \
  --data '{}'
```

Output:

{
    'thread_id': '5cb1e8a1-34b3-4a61-a34e-71a9799bd00d',
    'created_at': '2024-08-30T20:35:52.062934+00:00',
    'updated_at': '2024-08-30T20:35:52.062934+00:00',
    'metadata': {},
    'status': 'idle',
    'config': {},
    'values': None
}

Check runs on thread

If we list the current runs on this thread, we will see that it's empty:

=== "Python"

```python
runs = await client.runs.list(thread["thread_id"])
print(runs)
```

=== "Javascript"

```js
let runs = await client.runs.list(thread['thread_id']);
console.log(runs);
```

=== "CURL"

```bash
curl --request GET \
    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs
```

Output:

[]

Start runs on thread

Now let's kick off a run:

=== "Python"

```python
input = {"messages": [{"role": "user", "content": "what's the weather in sf"}]}
run = await client.runs.create(thread["thread_id"], assistant_id, input=input)
```

=== "Javascript"

```js
let input = {"messages": [{"role": "user", "content": "what's the weather in sf"}]};
let run = await client.runs.create(thread["thread_id"], assistantID, { input });
```

=== "CURL"

```bash
curl --request POST \
    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs \
    --header 'Content-Type: application/json' \
    --data '{
        "assistant_id": <ASSISTANT_ID>
    }'
```

The first time we poll it, we can see status=pending:

=== "Python"

```python
print(await client.runs.get(thread["thread_id"], run["run_id"]))
```

=== "Javascript"

```js
console.log(await client.runs.get(thread["thread_id"], run["run_id"]));
```

=== "CURL"

```bash
curl --request GET \
    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/<RUN_ID>
```

Output:

    {
        "run_id": "1ef6a5f8-bd86-6763-bbd6-bff042db7b1b",
        "thread_id": "7885f0cf-94ad-4040-91d7-73f7ba007c8a",
        "assistant_id": "fe096781-5601-53d2-b2f6-0d3403f7e9ca",
        "created_at": "2024-09-04T01:46:47.244887+00:00",
        "updated_at": "2024-09-04T01:46:47.244887+00:00",
        "metadata": {},
        "status": "pending",
        "kwargs": {
            "input": {
                "messages": [
                    {
                        "role": "user",
                        "content": "what's the weather in sf"
                    }
                ]
            },
            "config": {
                "metadata": {
                    "created_by": "system"
                },
                "configurable": {
                    "run_id": "1ef6a5f8-bd86-6763-bbd6-bff042db7b1b",
                    "user_id": "",
                    "graph_id": "agent",
                    "thread_id": "7885f0cf-94ad-4040-91d7-73f7ba007c8a",
                    "assistant_id": "fe096781-5601-53d2-b2f6-0d3403f7e9ca",
                    "checkpoint_id": null
                }
            },
            "webhook": null,
            "temporary": false,
            "stream_mode": [
                "values"
            ],
            "feedback_keys": null,
            "interrupt_after": null,
            "interrupt_before": null
        },
        "multitask_strategy": "reject"
    }

Now we can join the run, wait for it to finish and check that status again:

=== "Python"

```python
await client.runs.join(thread["thread_id"], run["run_id"])
print(await client.runs.get(thread["thread_id"], run["run_id"]))
```

=== "Javascript"

```js
await client.runs.join(thread["thread_id"], run["run_id"]);
console.log(await client.runs.get(thread["thread_id"], run["run_id"]));
```

=== "CURL"

```bash
curl --request GET \
    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/<RUN_ID>/join &&
curl --request GET \
    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/<RUN_ID>
```

Output:

{
    "run_id": "1ef6a5f8-bd86-6763-bbd6-bff042db7b1b",
    "thread_id": "7885f0cf-94ad-4040-91d7-73f7ba007c8a",
    "assistant_id": "fe096781-5601-53d2-b2f6-0d3403f7e9ca",
    "created_at": "2024-09-04T01:46:47.244887+00:00",
    "updated_at": "2024-09-04T01:46:47.244887+00:00",
    "metadata": {},
    "status": "success",
    "kwargs": {
        "input": {
            "messages": [
                {
                    "role": "user",
                    "content": "what's the weather in sf"
                }
            ]
        },
        "config": {
            "metadata": {
                "created_by": "system"
            },
            "configurable": {
                "run_id": "1ef6a5f8-bd86-6763-bbd6-bff042db7b1b",
                "user_id": "",
                "graph_id": "agent",
                "thread_id": "7885f0cf-94ad-4040-91d7-73f7ba007c8a",
                "assistant_id": "fe096781-5601-53d2-b2f6-0d3403f7e9ca",
                "checkpoint_id": null
            }
        },
        "webhook": null,
        "temporary": false,
        "stream_mode": [
            "values"
        ],
        "feedback_keys": null,
        "interrupt_after": null,
        "interrupt_before": null
    },
    "multitask_strategy": "reject"
}

Perfect! The run succeeded as we would expect. We can double check that the run worked as expected by printing out the final state:

=== "Python"

```python
final_result = await client.threads.get_state(thread["thread_id"])
print(final_result)
```

=== "Javascript"

```js
let finalResult = await client.threads.getState(thread["thread_id"]);
console.log(finalResult);
```

=== "CURL"

```bash
curl --request GET \
    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state
```

Output:

{
    "values": {
        "messages": [
            {
                "content": "what's the weather in sf",
                "additional_kwargs": {},
                "response_metadata": {},
                "type": "human",
                "name": null,
                "id": "beba31bf-320d-4125-9c37-cadf526ac47a",
                "example": false
            },
            {
                "content": [
                    {
                        "id": "toolu_01AaNPSPzqia21v7aAKwbKYm",
                        "input": {},
                        "name": "tavily_search_results_json",
                        "type": "tool_use",
                        "index": 0,
                        "partial_json": "{\"query\": \"weather in san francisco\"}"
                    }
                ],
                "additional_kwargs": {},
                "response_metadata": {
                    "stop_reason": "tool_use",
                    "stop_sequence": null
                },
                "type": "ai",
                "name": null,
                "id": "run-f220faf8-1d27-4f73-ad91-6bb3f47e8639",
                "example": false,
                "tool_calls": [
                    {
                        "name": "tavily_search_results_json",
                        "args": {
                            "query": "weather in san francisco"
                        },
                        "id": "toolu_01AaNPSPzqia21v7aAKwbKYm",
                        "type": "tool_call"
                    }
                ],
                "invalid_tool_calls": [],
                "usage_metadata": {
                    "input_tokens": 273,
                    "output_tokens": 61,
                    "total_tokens": 334
                }
            },
            {
                "content": "[{\"url\": \"https://www.weatherapi.com/\", \"content\": \"{'location': {'name': 'San Francisco', 'region': 'California', 'country': 'United States of America', 'lat': 37.78, 'lon': -122.42, 'tz_id': 'America/Los_Angeles', 'localtime_epoch': 1725052131, 'localtime': '2024-08-30 14:08'}, 'current': {'last_updated_epoch': 1725051600, 'last_updated': '2024-08-30 14:00', 'temp_c': 21.1, 'temp_f': 70.0, 'is_day': 1, 'condition': {'text': 'Partly cloudy', 'icon': '//cdn.weatherapi.com/weather/64x64/day/116.png', 'code': 1003}, 'wind_mph': 11.9, 'wind_kph': 19.1, 'wind_degree': 290, 'wind_dir': 'WNW', 'pressure_mb': 1018.0, 'pressure_in': 30.07, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 59, 'cloud': 25, 'feelslike_c': 21.1, 'feelslike_f': 70.0, 'windchill_c': 18.6, 'windchill_f': 65.5, 'heatindex_c': 18.6, 'heatindex_f': 65.5, 'dewpoint_c': 12.2, 'dewpoint_f': 54.0, 'vis_km': 16.0, 'vis_miles': 9.0, 'uv': 5.0, 'gust_mph': 15.0, 'gust_kph': 24.2}}\"}]",
                "additional_kwargs": {},
                "response_metadata": {},
                "type": "tool",
                "name": "tavily_search_results_json",
                "id": "686b2487-f332-4e58-9508-89b3a814cd81",
                "tool_call_id": "toolu_01AaNPSPzqia21v7aAKwbKYm",
                "artifact": {
                    "query": "weather in san francisco",
                    "follow_up_questions": null,
                    "answer": null,
                    "images": [],
                    "results": [
                        {
                            "title": "Weather in San Francisco",
                            "url": "https://www.weatherapi.com/",
                            "content": "{'location': {'name': 'San Francisco', 'region': 'California', 'country': 'United States of America', 'lat': 37.78, 'lon': -122.42, 'tz_id': 'America/Los_Angeles', 'localtime_epoch': 1725052131, 'localtime': '2024-08-30 14:08'}, 'current': {'last_updated_epoch': 1725051600, 'last_updated': '2024-08-30 14:00', 'temp_c': 21.1, 'temp_f': 70.0, 'is_day': 1, 'condition': {'text': 'Partly cloudy', 'icon': '//cdn.weatherapi.com/weather/64x64/day/116.png', 'code': 1003}, 'wind_mph': 11.9, 'wind_kph': 19.1, 'wind_degree': 290, 'wind_dir': 'WNW', 'pressure_mb': 1018.0, 'pressure_in': 30.07, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 59, 'cloud': 25, 'feelslike_c': 21.1, 'feelslike_f': 70.0, 'windchill_c': 18.6, 'windchill_f': 65.5, 'heatindex_c': 18.6, 'heatindex_f': 65.5, 'dewpoint_c': 12.2, 'dewpoint_f': 54.0, 'vis_km': 16.0, 'vis_miles': 9.0, 'uv': 5.0, 'gust_mph': 15.0, 'gust_kph': 24.2}}",
                            "score": 0.976148,
                            "raw_content": null
                        }
                    ],
                    "response_time": 3.07
                },
                "status": "success"
            },
            {
                "content": [
                    {
                        "text": "\n\nThe search results provide the current weather conditions in San Francisco. According to the data, as of 2:00 PM on August 30, 2024, the temperature in San Francisco is 70\u00b0F (21.1\u00b0C) with partly cloudy skies. The wind is blowing from the west-northwest at around 12 mph (19 km/h). The humidity is 59% and visibility is 9 miles (16 km). Overall, it looks like a nice late summer day in San Francisco with comfortable temperatures and partly sunny conditions.",
                        "type": "text",
                        "index": 0
                    }
                ],
                "additional_kwargs": {},
                "response_metadata": {
                    "stop_reason": "end_turn",
                    "stop_sequence": null
                },
                "type": "ai",
                "name": null,
                "id": "run-8fecc61d-3d9f-4e16-8e8a-92f702be498a",
                "example": false,
                "tool_calls": [],
                "invalid_tool_calls": [],
                "usage_metadata": {
                    "input_tokens": 837,
                    "output_tokens": 124,
                    "total_tokens": 961
                }
            }
        ]
    },
    "next": [],
    "tasks": [],
    "metadata": {
        "step": 3,
        "run_id": "1ef67140-eb23-684b-8253-91d4c90bb05e",
        "source": "loop",
        "writes": {
            "agent": {
                "messages": [
                    {
                        "id": "run-8fecc61d-3d9f-4e16-8e8a-92f702be498a",
                        "name": null,
                        "type": "ai",
                        "content": [
                            {
                                "text": "\n\nThe search results provide the current weather conditions in San Francisco. According to the data, as of 2:00 PM on August 30, 2024, the temperature in San Francisco is 70\u00b0F (21.1\u00b0C) with partly cloudy skies. The wind is blowing from the west-northwest at around 12 mph (19 km/h). The humidity is 59% and visibility is 9 miles (16 km). Overall, it looks like a nice late summer day in San Francisco with comfortable temperatures and partly sunny conditions.",
                                "type": "text",
                                "index": 0
                            }
                        ],
                        "example": false,
                        "tool_calls": [],
                        "usage_metadata": {
                            "input_tokens": 837,
                            "total_tokens": 961,
                            "output_tokens": 124
                        },
                        "additional_kwargs": {},
                        "response_metadata": {
                            "stop_reason": "end_turn",
                            "stop_sequence": null
                        },
                        "invalid_tool_calls": []
                    }
                ]
            }
        },
        "user_id": "",
        "graph_id": "agent",
        "thread_id": "5cb1e8a1-34b3-4a61-a34e-71a9799bd00d",
        "created_by": "system",
        "assistant_id": "fe096781-5601-53d2-b2f6-0d3403f7e9ca"
    },
    "created_at": "2024-08-30T21:09:00.079909+00:00",
    "checkpoint_id": "1ef67141-3ca2-6fae-8003-fe96832e57d6",
    "parent_checkpoint_id": "1ef67141-2129-6b37-8002-61fc3bf69cb5"
}

We can also just print the content of the last AIMessage:

=== "Python"

```python
print(final_result['values']['messages'][-1]['content'][0]['text'])
```

=== "Javascript"

```js
console.log(finalResult['values']['messages'][finalResult['values']['messages'].length-1]['content'][0]['text']);
```

=== "CURL"

```bash
curl --request GET \
    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state | jq -r '.values.messages[-1].content.[0].text'
```

Output:

The search results provide the current weather conditions in San Francisco. According to the data, as of 2:00 PM on August 30, 2024, the temperature in San Francisco is 70┬░F (21.1┬░C) with partly cloudy skies. The wind is blowing from the west-northwest at around 12 mph (19 km/h). The humidity is 59% and visibility is 9 miles (16 km). Overall, it looks like a nice late summer day in San Francisco with comfortable temperatures and partly sunny conditions.

Check the Status of your Threads

Setup

To start, we can setup our client with whatever URL you are hosting your graph from:

SDK initialization

First, we need to setup our client so that we can communicate with our hosted graph:

=== "Python"

```python
from langgraph_sdk import get_client
client = get_client(url=<DEPLOYMENT_URL>)
# Using the graph deployed with the name "agent"
assistant_id = "agent"
thread = await client.threads.create()
```

=== "Javascript"

```js
import { Client } from "@langchain/langgraph-sdk";

const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
// Using the graph deployed with the name "agent"
const assistantId = "agent";
const thread = await client.threads.create();
```

=== "CURL"

```bash
curl --request POST \
  --url <DEPLOYMENT_URL>/threads \
  --header 'Content-Type: application/json' \
  --data '{}'
```

Find idle threads

We can use the following commands to find threads that are idle, which means that all runs executed on the thread have finished running:

=== "Python"

```python
print(await client.threads.search(status="idle",limit=1))
```

=== "Javascript"

```js
console.log(await client.threads.search({ status: "idle", limit: 1 }));
```

=== "CURL"

```bash
curl --request POST \  
--url <DEPLOYMENT_URL>/threads/search \
--header 'Content-Type: application/json' \
--data '{"status": "idle", "limit": 1}'
```

Output:

[{'thread_id': 'cacf79bb-4248-4d01-aabc-938dbd60ed2c',
'created_at': '2024-08-14T17:36:38.921660+00:00',
'updated_at': '2024-08-14T17:36:38.921660+00:00',
'metadata': {'graph_id': 'agent'},
'status': 'idle',
'config': {'configurable': {}}}]

Find interrupted threads

We can use the following commands to find threads that have been interrupted in the middle of a run, which could either mean an error occurred before the run finished or a human-in-the-loop breakpoint was reached and the run is waiting to continue:

=== "Python"

```python
print(await client.threads.search(status="interrupted",limit=1))
```

=== "Javascript"

```js
console.log(await client.threads.search({ status: "interrupted", limit: 1 }));
```

=== "CURL"

```bash
curl --request POST \  
--url <DEPLOYMENT_URL>/threads/search \
--header 'Content-Type: application/json' \
--data '{"status": "interrupted", "limit": 1}'
```

Output:

[{'thread_id': '0d282b22-bbd5-4d95-9c61-04dcc2e302a5',
'created_at': '2024-08-14T17:41:50.235455+00:00',
'updated_at': '2024-08-14T17:41:50.235455+00:00',
'metadata': {'graph_id': 'agent'},
'status': 'interrupted',
'config': {'configurable': {}}}]

Find busy threads

We can use the following commands to find threads that are busy, meaning they are currently handling the execution of a run:

=== "Python"

```python
print(await client.threads.search(status="busy",limit=1))
```

=== "Javascript"

```js
console.log(await client.threads.search({ status: "busy", limit: 1 }));
```

=== "CURL"

```bash
curl --request POST \  
--url <DEPLOYMENT_URL>/threads/search \
--header 'Content-Type: application/json' \
--data '{"status": "busy", "limit": 1}'
```

Output:

[{'thread_id': '0d282b22-bbd5-4d95-9c61-04dcc2e302a5',
'created_at': '2024-08-14T17:41:50.235455+00:00',
'updated_at': '2024-08-14T17:41:50.235455+00:00',
'metadata': {'graph_id': 'agent'},
'status': 'busy',
'config': {'configurable': {}}}]

Find specific threads

You may also want to check the status of specific threads, which you can do in a few ways:

Find by ID

You can use the get function to find the status of a specific thread, as long as you have the ID saved

=== "Python"

```python
print((await client.threads.get(<THREAD_ID>))['status'])
```

=== "Javascript"

```js
console.log((await client.threads.get(<THREAD_ID>)).status);
```

=== "CURL"

```bash
curl --request GET \ 
--url <DEPLOYMENT_URL>/threads/<THREAD_ID> \
--header 'Content-Type: application/json' | jq -r '.status'
```

Output:

'idle'

Find by metadata

The search endpoint for threads also allows you to filter on metadata, which can be helpful if you use metadata to tag threads in order to keep them organized:

=== "Python"

```python
print((await client.threads.search(metadata={"foo":"bar"},limit=1))[0]['status'])
```

=== "Javascript"

```js
console.log((await client.threads.search({ metadata: { "foo": "bar" }, limit: 1 }))[0].status);
```

=== "CURL"

```bash
curl --request POST \  
--url <DEPLOYMENT_URL>/threads/search \
--header 'Content-Type: application/json' \
--data '{"metadata": {"foo":"bar"}, "limit": 1}' | jq -r '.[0].status'
```

Output:

'idle'

How to create agents with configuration

One of the benefits of LangGraph API is that it lets you create agents with different configurations. This is useful when you want to:

Define a cognitive architecture once as a LangGraph
Let that LangGraph be configurable across some attributes (for example, system message or LLM to use)
Let users create agents with arbitrary configurations, save them, and then use them in the future

In this guide we will show how to do that for the default agent we have built in.

If you look at the agent we defined, you can see that inside the call_model node we have created the model based on some configuration. That node looks like:

=== "Python"

```python
def call_model(state, config):
    messages = state["messages"]
    model_name = config.get('configurable', {}).get("model_name", "anthropic")
    model = _get_model(model_name)
    response = model.invoke(messages)
    # We return a list, because this will get added to the existing list
    return {"messages": [response]}
```

=== "Javascript"

```js
function callModel(state: State, config: RunnableConfig) {
  const messages = state.messages;
  const modelName = config.configurable?.model_name ?? "anthropic";
  const model = _getModel(modelName);
  const response = model.invoke(messages);
  // We return a list, because this will get added to the existing list
  return { messages: [response] };
}
```

We are looking inside the config for a model_name parameter (which defaults to anthropic if none is found). That means that by default we are using Anthropic as our model provider. In this example we will see an example of how to create an example agent that is configured to use OpenAI.

First let's set up our client and thread:

=== "Python"

```python
from langgraph_sdk import get_client

client = get_client(url=<DEPLOYMENT_URL>)
# Select an assistant that is not configured
assistants = await client.assistants.search()
assistant = [a for a in assistants if not a["config"]][0]
```

=== "Javascript"

```js
import { Client } from "@langchain/langgraph-sdk";

const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
// Select an assistant that is not configured
const assistants = await client.assistants.search();
const assistant = assistants.find(a => !a.config);
```

=== "CURL"

```bash
curl --request POST \
    --url <DEPLOYMENT_URL>/assistants/search \
    --header 'Content-Type: application/json' \
    --data '{
        "limit": 10,
        "offset": 0
    }' | jq -c 'map(select(.config == null or .config == {})) | .[0]'
```

We can now call .get_schemas to get schemas associated with this graph:

=== "Python"

```python
schemas = await client.assistants.get_schemas(
    assistant_id=assistant["assistant_id"]
)
# There are multiple types of schemas
# We can get the `config_schema` to look at the configurable parameters
print(schemas["config_schema"])
```

=== "Javascript"

```js
const schemas = await client.assistants.getSchemas(
  assistant["assistant_id"]
);
// There are multiple types of schemas
// We can get the `config_schema` to look at the configurable parameters
console.log(schemas.config_schema);
```

=== "CURL"

```bash
curl --request GET \
    --url <DEPLOYMENT_URL>/assistants/<ASSISTANT_ID>/schemas | jq -r '.config_schema'
```

Output:

{
    'model_name': 
        {
            'title': 'Model Name',
            'enum': ['anthropic', 'openai'],
            'type': 'string'
        }
}

Now we can initialize an assistant with config:

=== "Python"

```python
openai_assistant = await client.assistants.create(
    # "agent" is the name of a graph we deployed
    "agent", config={"configurable": {"model_name": "openai"}}
)

print(openai_assistant)
```

=== "Javascript"

```js
let openAIAssistant = await client.assistants.create(
  // "agent" is the name of a graph we deployed
  "agent", { "configurable": { "model_name": "openai" } }
);

console.log(openAIAssistant);
```

=== "CURL"

```bash
curl --request POST \
    --url <DEPLOYMENT_URL>/assistants \
    --header 'Content-Type: application/json' \
    --data '{"graph_id":"agent","config":{"configurable":{"model_name":"open_ai"}}}'
```

Output:

{
    "assistant_id": "62e209ca-9154-432a-b9e9-2d75c7a9219b",
    "graph_id": "agent",
    "created_at": "2024-08-31T03:09:10.230718+00:00",
    "updated_at": "2024-08-31T03:09:10.230718+00:00",
    "config": {
        "configurable": {
            "model_name": "open_ai"
        }
    },
    "metadata": {}
}

We can verify the config is indeed taking effect:

=== "Python"

```python
thread = await client.threads.create()
input = {"messages": [{"role": "user", "content": "who made you?"}]}
async for event in client.runs.stream(
    thread["thread_id"],
    openai_assistant["assistant_id"],
    input=input,
    stream_mode="updates",
):
    print(f"Receiving event of type: {event.event}")
    print(event.data)
    print("\n\n")
```

=== "Javascript"

```js
const thread = await client.threads.create();
let input = { "messages": [{ "role": "user", "content": "who made you?" }] };

const streamResponse = client.runs.stream(
  thread["thread_id"],
  openAIAssistant["assistant_id"],
  {
    input,
    streamMode: "updates"
  }
);

for await (const event of streamResponse) {
  console.log(`Receiving event of type: ${event.event}`);
  console.log(event.data);
  console.log("\n\n");
}
```

=== "CURL"

```bash
thread_id=$(curl --request POST \
    --url <DEPLOYMENT_URL>/threads \
    --header 'Content-Type: application/json' \
    --data '{}' | jq -r '.thread_id') && \
curl --request POST \
    --url "<DEPLOYMENT_URL>/threads/${thread_id}/runs/stream" \
    --header 'Content-Type: application/json' \
    --data '{
        "assistant_id": <OPENAI_ASSISTANT_ID>,
        "input": {
            "messages": [
                {
                    "role": "user",
                    "content": "who made you?"
                }
            ]
        },
        "stream_mode": [
            "updates"
        ]
    }' | \
    sed 's/\r$//' | \
    awk '
    /^event:/ {
        if (data_content != "") {
            print data_content "\n"
        }
        sub(/^event: /, "Receiving event of type: ", $0)
        printf "%s...\n", $0
        data_content = ""
    }
    /^data:/ {
        sub(/^data: /, "", $0)
        data_content = $0
    }
    END {
        if (data_content != "") {
            print data_content "\n\n"
        }
    }
'
```

Output:

Receiving event of type: metadata
{'run_id': '1ef6746e-5893-67b1-978a-0f1cd4060e16'}



Receiving event of type: updates
{'agent': {'messages': [{'content': 'I was created by OpenAI, a research organization focused on developing and advancing artificial intelligence technology.', 'additional_kwargs': {}, 'response_metadata': {'finish_reason': 'stop', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_157b3831f5'}, 'type': 'ai', 'name': None, 'id': 'run-e1a6b25c-8416-41f2-9981-f9cfe043f414', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}

Copying Threads

You may wish to copy (i.e. "fork") an existing thread in order to keep the existing thread's history and create independent runs that do not affect the original thread. This guide shows how you can do that.

Setup

This code assumes you already have a thread to copy. You can read about what a thread is here and learn how to stream a run on a thread in these how-to guides.

SDK initialization

First, we need to setup our client so that we can communicate with our hosted graph:

=== "Python"

```python
from langgraph_sdk import get_client
client = get_client(url="<DEPLOYMENT_URL>")
assistant_id = "agent"
thread = await client.threads.create()
```

=== "Javascript"

```js
import { Client } from "@langchain/langgraph-sdk";

const client = new Client({ apiUrl: "<DEPLOYMENT_URL>" });
const assistantId = "agent";
const thread = await client.threads.create();
```

=== "CURL"

```bash
curl --request POST \
  --url <DEPLOYMENT_URL>/threads \
  --header 'Content-Type: application/json' \
  --data '{
    "metadata": {}
  }'
```

Copying a thread

The code below assumes that a thread you'd like to copy already exists.

Copying a thread will create a new thread with the same history as the existing thread, and then allow you to continue executing runs.

Create copy

=== "Python"

```python
copied_thread = await client.threads.copy(<THREAD_ID>)
```

=== "Javascript"

```js
let copiedThread = await client.threads.copy(<THREAD_ID>);
```

=== "CURL"

```bash
curl --request POST --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/copy \
--header 'Content-Type: application/json'
```

Verify copy

We can verify that the history from the prior thread did indeed copy over correctly:

=== "Python"

```python
def remove_thread_id(d):
  if 'metadata' in d and 'thread_id' in d['metadata']:
      del d['metadata']['thread_id']
  return d

original_thread_history = list(map(remove_thread_id,await client.threads.get_history(<THREAD_ID>)))
copied_thread_history = list(map(remove_thread_id,await client.threads.get_history(copied_thread['thread_id'])))

# Compare the two histories
assert original_thread_history == copied_thread_history
# if we made it here the assertion passed!
print("The histories are the same.")
```

=== "Javascript"

```js
function removeThreadId(d) {
  if (d.metadata && d.metadata.thread_id) {
    delete d.metadata.thread_id;
  }
  return d;
}

// Assuming `client.threads.getHistory(threadId)` is an async function that returns a list of dicts
async function compareThreadHistories(threadId, copiedThreadId) {
  const originalThreadHistory = (await client.threads.getHistory(threadId)).map(removeThreadId);
  const copiedThreadHistory = (await client.threads.getHistory(copiedThreadId)).map(removeThreadId);

  // Compare the two histories
  console.assert(JSON.stringify(originalThreadHistory) === JSON.stringify(copiedThreadHistory));
  // if we made it here the assertion passed!
  console.log("The histories are the same.");
}

// Example usage
compareThreadHistories(<THREAD_ID>, copiedThread.thread_id);
```

=== "CURL"

```bash
if diff <(
    curl --request GET --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/history | jq -S 'map(del(.metadata.thread_id))'
) <(
    curl --request GET --url <DEPLOYMENT_URL>/threads/<COPIED_THREAD_ID>/history | jq -S 'map(del(.metadata.thread_id))'
) >/dev/null; then
    echo "The histories are the same."
else
    echo "The histories are different."
fi
```

Output:

The histories are the same.

Cron Jobs

Sometimes you don't want to run your graph based on user interaction, but rather you would like to schedule your graph to run on a schedule - for example if you wish for your graph to compose and send out a weekly email of to-dos for your team. LangGraph Cloud allows you to do this without having to write your own script by using the Crons client. To schedule a graph job, you need to pass a cron expression to inform the client when you want to run the graph. Cron jobs are run in the background and do not interfere with normal invocations of the graph.

Setup

First, let's setup our SDK client, assistant, and thread:

=== "Python"

```python
from langgraph_sdk import get_client

client = get_client(url=<DEPLOYMENT_URL>)
# Using the graph deployed with the name "agent"
assistant_id = "agent"
# create thread
thread = await client.threads.create()
print(thread)
```

=== "Javascript"

```js
import { Client } from "@langchain/langgraph-sdk";

const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
// Using the graph deployed with the name "agent"
const assistantId = "agent";
// create thread
const thread = await client.threads.create();
console.log(thread);
```

=== "CURL"

```bash
curl --request POST \
    --url <DEPLOYMENT_URL>/assistants/search \
    --header 'Content-Type: application/json' \
    --data '{
        "limit": 10,
        "offset": 0
    }' | jq -c 'map(select(.config == null or .config == {})) | .[0].graph_id' && \
curl --request POST \
    --url <DEPLOYMENT_URL>/threads \
    --header 'Content-Type: application/json' \
    --data '{}'
```

Output:

{
    'thread_id': '9dde5490-2b67-47c8-aa14-4bfec88af217', 
    'created_at': '2024-08-30T23:07:38.242730+00:00', 
    'updated_at': '2024-08-30T23:07:38.242730+00:00', 
    'metadata': {}, 
    'status': 'idle', 
    'config': {}, 
    'values': None
}

Cron job on a thread

To create a cron job associated with a specific thread, you can write:

=== "Python"

```python
# This schedules a job to run at 15:27 (3:27PM) every day
cron_job = await client.crons.create_for_thread(
    thread["thread_id"],
    assistant_id,
    schedule="27 15 * * *",
    input={"messages": [{"role": "user", "content": "What time is it?"}]},
)
```

=== "Javascript"

```js
// This schedules a job to run at 15:27 (3:27PM) every day
const cronJob = await client.crons.create_for_thread(
  thread["thread_id"],
  assistantId,
  {
    schedule: "27 15 * * *",
    input: { messages: [{ role: "user", content: "What time is it?" }] }
  }
);
```

=== "CURL"

```bash
curl --request POST \
    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/crons \
    --header 'Content-Type: application/json' \
    --data '{
        "assistant_id": <ASSISTANT_ID>,
    }'
```

Note that it is very important to delete Cron jobs that are no longer useful. Otherwise you could rack up unwanted API charges to the LLM! You can delete a Cron job using the following code:

=== "Python"

```python
await client.crons.delete(cron_job["cron_id"])
```

=== "Javascript"

```js
await client.crons.delete(cronJob["cron_id"]);
```

=== "CURL"

```bash
curl --request DELETE \
    --url <DEPLOYMENT_URL>/runs/crons/<CRON_ID>
```

Cron job stateless

You can also create stateless cron jobs by using the following code:

=== "Python"

```python
# This schedules a job to run at 15:27 (3:27PM) every day
cron_job_stateless = await client.crons.create(
    assistant_id,
    schedule="27 15 * * *",
    input={"messages": [{"role": "user", "content": "What time is it?"}]},
)
```

=== "Javascript"

```js
// This schedules a job to run at 15:27 (3:27PM) every day
const cronJobStateless = await client.crons.create(
  assistantId,
  {
    schedule: "27 15 * * *",
    input: { messages: [{ role: "user", content: "What time is it?" }] }
  }
);
```

=== "CURL"

```bash
curl --request POST \
    --url <DEPLOYMENT_URL>/runs/crons \
    --header 'Content-Type: application/json' \
    --data '{
        "assistant_id": <ASSISTANT_ID>,
    }'
```

Again, remember to delete your job once you are done with it!

=== "Python"

```python
await client.crons.delete(cron_job_stateless["cron_id"])
```

=== "Javascript"

```js
await client.crons.delete(cronJobStateless["cron_id"]);
```

=== "CURL"

```bash
curl --request DELETE \
    --url <DEPLOYMENT_URL>/runs/crons/<CRON_ID>
```

Adding nodes as dataset examples in Studio

In LangGraph Studio you can create dataset examples from the thread history in the right-hand pane. This can be especially useful when you want to evaluate intermediate steps of the agent.

Click on the Add to Dataset button to enter the dataset mode.
Select nodes which you want to add to dataset.
Select the target dataset to create the example in.

You can edit the example payload before sending it to the dataset, which is useful if you need to make changes to conform the example to the dataset schema.

Finally, you can customise the target dataset by clicking on the Settings button.

See Evaluating intermediate steps for more details on how to evaluate intermediate steps.

Enqueue

This guide assumes knowledge of what double-texting is, which you can learn about in the double-texting conceptual guide.

The guide covers the enqueue option for double texting, which adds the interruptions to a queue and executes them in the order they are received by the client. Below is a quick example of using the enqueue option.

Setup

First, we will define a quick helper function for printing out JS and CURL model outputs (you can skip this if using Python):

=== "Javascript"

```js
function prettyPrint(m) {
  const padded = " " + m['type'] + " ";
  const sepLen = Math.floor((80 - padded.length) / 2);
  const sep = "=".repeat(sepLen);
  const secondSep = sep + (padded.length % 2 ? "=" : "");
  
  console.log(`${sep}${padded}${secondSep}`);
  console.log("\n\n");
  console.log(m.content);
}
```

=== "CURL"

```bash
# PLACE THIS IN A FILE CALLED pretty_print.sh
pretty_print() {
  local type="$1"
  local content="$2"
  local padded=" $type "
  local total_width=80
  local sep_len=$(( (total_width - ${#padded}) / 2 ))
  local sep=$(printf '=%.0s' $(eval "echo {1.."${sep_len}"}"))
  local second_sep=$sep
  if (( (total_width - ${#padded}) % 2 )); then
    second_sep="${second_sep}="
  fi

  echo "${sep}${padded}${second_sep}"
  echo
  echo "$content"
}
```

Then, let's import our required packages and instantiate our client, assistant, and thread.

=== "Python"

```python
import asyncio

import httpx
from langchain_core.messages import convert_to_messages
from langgraph_sdk import get_client

client = get_client(url=<DEPLOYMENT_URL>)
# Using the graph deployed with the name "agent"
assistant_id = "agent"
thread = await client.threads.create()
```

=== "Javascript"

```js
import { Client } from "@langchain/langgraph-sdk";


const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
// Using the graph deployed with the name "agent"
const assistantId = "agent";
const thread = await client.threads.create();
```

=== "CURL"

```bash
curl --request POST \
  --url <DEPLOYMENT_URL>/threads \
  --header 'Content-Type: application/json' \
  --data '{}'
```

Create runs

Now let's start two runs, with the second interrupting the first one with a multitask strategy of "enqueue":

=== "Python"

```python
first_run = await client.runs.create(
    thread["thread_id"],
    assistant_id,
    input={"messages": [{"role": "user", "content": "what's the weather in sf?"}]},
)
second_run = await client.runs.create(
    thread["thread_id"],
    assistant_id,
    input={"messages": [{"role": "user", "content": "what's the weather in nyc?"}]},
    multitask_strategy="enqueue",
)
```

=== "Javascript"

```js
const firstRun = await client.runs.create(
  thread["thread_id"],
  assistantId,
  input={"messages": [{"role": "user", "content": "what's the weather in sf?"}]},
)

const secondRun = await client.runs.create(
  thread["thread_id"],
  assistantId,
  input={"messages": [{"role": "user", "content": "what's the weather in nyc?"}]},
  multitask_strategy="enqueue",
)
```

=== "CURL"

```bash
curl --request POST \
--url <DEPLOY<ENT_URL>>/threads/<THREAD_ID>/runs \
--header 'Content-Type: application/json' \
--data "{
  \"assistant_id\": \"agent\",
  \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what\'s the weather in sf?\"}]},
}" && curl --request POST \
--url <DEPLOY<ENT_URL>>/threads/<THREAD_ID>/runs \
--header 'Content-Type: application/json' \
--data "{
  \"assistant_id\": \"agent\",
  \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what\'s the weather in nyc?\"}]},
  \"multitask_strategy\": \"enqueue\"
}"
```

View run results

Verify that the thread has data from both runs:

=== "Python"

```python
# wait until the second run completes
await client.runs.join(thread["thread_id"], second_run["run_id"])

state = await client.threads.get_state(thread["thread_id"])

for m in convert_to_messages(state["values"]["messages"]):
    m.pretty_print()
```

=== "Javascript"

```js
await client.runs.join(thread["thread_id"], secondRun["run_id"]);

const state = await client.threads.getState(thread["thread_id"]);

for (const m of state["values"]["messages"]) {
  prettyPrint(m);
}
```

=== "CURL"

```bash
source pretty_print.sh && curl --request GET \
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/<RUN_ID>/join && \
curl --request GET --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state | \
jq -c '.values.messages[]' | while read -r element; do
    type=$(echo "$element" | jq -r '.type')
    content=$(echo "$element" | jq -r '.content | if type == "array" then tostring else . end')
    pretty_print "$type" "$content"
done
```

Output:

================================ Human Message =================================

what's the weather in sf?
================================== Ai Message ==================================

[{'id': 'toolu_01Dez1sJre4oA2Y7NsKJV6VT', 'input': {'query': 'weather in san francisco'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
Tool Calls:
  tavily_search_results_json (toolu_01Dez1sJre4oA2Y7NsKJV6VT)
 Call ID: toolu_01Dez1sJre4oA2Y7NsKJV6VT
  Args:
    query: weather in san francisco
================================= Tool Message =================================
Name: tavily_search_results_json

[{"url": "https://www.accuweather.com/en/us/san-francisco/94103/weather-forecast/347629", "content": "Get the current and future weather conditions for San Francisco, CA, including temperature, precipitation, wind, air quality and more. See the hourly and 10-day outlook, radar maps, alerts and allergy information."}]
================================== Ai Message ==================================

According to AccuWeather, the current weather conditions in San Francisco are:

Temperature: 57┬░F (14┬░C)
Conditions: Mostly Sunny
Wind: WSW 10 mph
Humidity: 72%

The forecast for the next few days shows partly sunny skies with highs in the upper 50s to mid 60s F (14-18┬░C) and lows in the upper 40s to low 50s F (9-11┬░C). Typical mild, dry weather for San Francisco this time of year.

Some key details from the AccuWeather forecast:

Today: Mostly sunny, high of 62┬░F (17┬░C)
Tonight: Partly cloudy, low of 49┬░F (9┬░C) 
Tomorrow: Partly sunny, high of 59┬░F (15┬░C)
Saturday: Mostly sunny, high of 64┬░F (18┬░C)
Sunday: Partly sunny, high of 61┬░F (16┬░C)

So in summary, expect seasonable spring weather in San Francisco over the next several days, with a mix of sun and clouds and temperatures ranging from the upper 40s at night to the low 60s during the days. Typical dry conditions with no rain in the forecast.
================================ Human Message =================================

what's the weather in nyc?
================================== Ai Message ==================================

[{'text': 'Here are the current weather conditions and forecast for New York City:', 'type': 'text'}, {'id': 'toolu_01FFft5Sx9oS6AdVJuRWWcGp', 'input': {'query': 'weather in new york city'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
Tool Calls:
  tavily_search_results_json (toolu_01FFft5Sx9oS6AdVJuRWWcGp)
 Call ID: toolu_01FFft5Sx9oS6AdVJuRWWcGp
  Args:
    query: weather in new york city
================================= Tool Message =================================
Name: tavily_search_results_json

[{"url": "https://www.weatherapi.com/", "content": "{'location': {'name': 'New York', 'region': 'New York', 'country': 'United States of America', 'lat': 40.71, 'lon': -74.01, 'tz_id': 'America/New_York', 'localtime_epoch': 1718734479, 'localtime': '2024-06-18 14:14'}, 'current': {'last_updated_epoch': 1718733600, 'last_updated': '2024-06-18 14:00', 'temp_c': 29.4, 'temp_f': 84.9, 'is_day': 1, 'condition': {'text': 'Sunny', 'icon': '//cdn.weatherapi.com/weather/64x64/day/113.png', 'code': 1000}, 'wind_mph': 2.2, 'wind_kph': 3.6, 'wind_degree': 158, 'wind_dir': 'SSE', 'pressure_mb': 1025.0, 'pressure_in': 30.26, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 63, 'cloud': 0, 'feelslike_c': 31.3, 'feelslike_f': 88.3, 'windchill_c': 28.3, 'windchill_f': 82.9, 'heatindex_c': 29.6, 'heatindex_f': 85.3, 'dewpoint_c': 18.4, 'dewpoint_f': 65.2, 'vis_km': 16.0, 'vis_miles': 9.0, 'uv': 7.0, 'gust_mph': 16.5, 'gust_kph': 26.5}}"}]
================================== Ai Message ==================================

According to the weather data from WeatherAPI:

Current Conditions in New York City (as of 2:00 PM local time):
- Temperature: 85┬░F (29┬░C)
- Conditions: Sunny
- Wind: 2 mph (4 km/h) from the SSE
- Humidity: 63%
- Heat Index: 85┬░F (30┬░C)

The forecast shows sunny and warm conditions persisting over the next few days:

Today: Sunny, high of 85┬░F (29┬░C)
Tonight: Clear, low of 68┬░F (20┬░C)
Tomorrow: Sunny, high of 88┬░F (31┬░C) 
Thursday: Mostly sunny, high of 90┬░F (32┬░C)
Friday: Partly cloudy, high of 87┬░F (31┬░C)

So New York City is experiencing beautiful sunny weather with seasonably warm temperatures in the mid-to-upper 80s Fahrenheit (around 30┬░C). Humidity is moderate in the 60% range. Overall, ideal late spring/early summer conditions for being outdoors in the city over the next several days.

How to Add Breakpoints

When creating LangGraph agents, it is often nice to add a human-in-the-loop component. This can be helpful when giving them access to tools. Often in these situations you may want to manually approve an action before taking.

This can be in several ways, but the primary supported way is to add an "interrupt" before a node is executed. This interrupts execution at that node. You can then resume from that spot to continue.

Setup

Code for your graph

In this how-to we use a simple ReAct style hosted graph (you can see the full code for defining it here). The important thing is that there are two nodes (one named agent that calls the LLM, and one named action that calls the tool), and a routing function from agent that determines whether to call action next or just end the graph run (the action node always calls the agent node after execution).

SDK Initialization

=== "Python"

```python
from langgraph_sdk import get_client
client = get_client(url=<DEPLOYMENT_URL>)
# Using the graph deployed with the name "agent"
assistant_id = "agent"
thread = await client.threads.create()
```

=== "Javascript"

```js
import { Client } from "@langchain/langgraph-sdk";

const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
// Using the graph deployed with the name "agent"
const assistantId = "agent";
const thread = await client.threads.create();
```

=== "CURL"

```bash
curl --request POST \
  --url <DEPLOYMENT_URL>/threads \
  --header 'Content-Type: application/json' \
  --data '{}'
```

Adding a breakpoint

We now want to add a breakpoint in our graph run, which we will do before a tool is called. We can do this by adding interrupt_before=["action"], which tells us to interrupt before calling the action node. We can do this either when compiling the graph or when kicking off a run. Here we will do it when kicking of a run, if you would like to to do it at compile time you need to edit the python file where your graph is defined and add the interrupt_before parameter when you call .compile.

First let's access our hosted LangGraph instance through the SDK:

And, now let's compile it with a breakpoint before the tool node:

=== "Python"

```python
input = {"messages": [{"role": "user", "content": "what's the weather in sf"}]}
async for chunk in client.runs.stream(
    thread["thread_id"],
    assistant_id,
    input=input,
    stream_mode="updates",
    interrupt_before=["action"],
):
    print(f"Receiving new event of type: {chunk.event}...")
    print(chunk.data)
    print("\n\n")
```

=== "Javascript"

```js
const input = { messages: [{ role: "human", content: "what's the weather in sf" }] };

const streamResponse = client.runs.stream(
  thread["thread_id"],
  assistantId,
  {
    input: input,
    streamMode: "updates",
    interruptBefore: ["action"]
  }
);

for await (const chunk of streamResponse) {
  console.log(`Receiving new event of type: ${chunk.event}...`);
  console.log(chunk.data);
  console.log("\n\n");
}
```

=== "CURL"

```bash
curl --request POST \
 --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
 --header 'Content-Type: application/json' \
 --data "{
   \"assistant_id\": \"agent\",
   \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in sf\"}]},
   \"interrupt_before\": [\"action\"],
   \"stream_mode\": [
     \"messages\"
   ]
 }" | \
 sed 's/\r$//' | \
 awk '
 /^event:/ {
     if (data_content != "") {
         print data_content "\n"
     }
     sub(/^event: /, "Receiving event of type: ", $0)
     printf "%s...\n", $0
     data_content = ""
 }
 /^data:/ {
     sub(/^data: /, "", $0)
     data_content = $0
 }
 END {
     if (data_content != "") {
         print data_content "\n"
     }
 }
 '
```

Output:

Receiving new event of type: metadata...
{'run_id': '3b77ef83-687a-4840-8858-0371f91a92c3'}



Receiving new event of type: data...
{'agent': {'messages': [{'content': [{'id': 'toolu_01HwZqM1ptX6E15A5LAmyZTB', 'input': {'query': 'weather in san francisco'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-e5d17791-4d37-4ad2-815f-a0c4cba62585', 'example': False, 'tool_calls': [{'name': 'tavily_search_results_json', 'args': {'query': 'weather in san francisco'}, 'id': 'toolu_01HwZqM1ptX6E15A5LAmyZTB'}], 'invalid_tool_calls': []}]}}



Receiving new event of type: end...
None

How to Edit State of a Deployed Graph

When creating LangGraph agents, it is often nice to add a human-in-the-loop component. This can be helpful when giving them access to tools. Often in these situations you may want to edit the graph state before continuing (for example, to edit what tool is being called, or how it is being called).

This can be in several ways, but the primary supported way is to add an "interrupt" before a node is executed. This interrupts execution at that node. You can then use update_state to update the state, and then resume from that spot to continue.

Setup

We are not going to show the full code for the graph we are hosting, but you can see it here if you want to. Once this graph is hosted, we are ready to invoke it and wait for user input.

SDK initialization

First, we need to setup our client so that we can communicate with our hosted graph:

=== "Python"

```python
from langgraph_sdk import get_client
client = get_client(url=<DEPLOYMENT_URL>)
# Using the graph deployed with the name "agent"
assistant_id = "agent"
thread = await client.threads.create()
```

=== "Javascript"

```js
import { Client } from "@langchain/langgraph-sdk";

const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
// Using the graph deployed with the name "agent"
const assistantId = "agent";
const thread = await client.threads.create();
```

=== "CURL"

```bash
curl --request POST \
  --url <DEPLOYMENT_URL>/threads \
  --header 'Content-Type: application/json' \
  --data '{}'
```

Editing state

Initial invocation

Now let's invoke our graph, making sure to interrupt before the action node.

=== "Python"

```python
input = { 'messages':[{ "role":"user", "content":"search for weather in SF" }] }

async for chunk in client.runs.stream(
    thread["thread_id"],
    assistant_id,
    input=input,
    stream_mode="updates",
    interrupt_before=["action"],
):
    if chunk.data and chunk.event != "metadata": 
        print(chunk.data)
```

=== "Javascript"

```js
const input = { messages: [{ role: "human", content: "search for weather in SF" }] };

const streamResponse = client.runs.stream(
  thread["thread_id"],
  assistantId,
  {
    input: input,
    streamMode: "updates",
    interruptBefore: ["action"],
  }
);

for await (const chunk of streamResponse) {
  if (chunk.data && chunk.event !== "metadata") {
    console.log(chunk.data);
  }
}
```

=== "CURL"

```bash
curl --request POST \
 --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
 --header 'Content-Type: application/json' \
 --data "{
   \"assistant_id\": \"agent\",
   \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"search for weather in SF\"}]},
   \"interrupt_before\": [\"action\"],
   \"stream_mode\": [
     \"updates\"
   ]
 }" | \
 sed 's/\r$//' | \
 awk '
 /^event:/ {
     if (data_content != "" && event_type != "metadata") {
         print data_content "\n"
     }
     sub(/^event: /, "", $0)
     event_type = $0
     data_content = ""
 }
 /^data:/ {
     sub(/^data: /, "", $0)
     data_content = $0
 }
 END {
     if (data_content != "" && event_type != "metadata") {
         print data_content "\n"
     }
 }
 '
```

Output:

{'agent': {'messages': [{'content': [{'text': "Certainly! I'll search for the current weather in San Francisco for you using the search function. Here's how I'll do that:", 'type': 'text'}, {'id': 'toolu_01KEJMBFozSiZoS4mAcPZeqQ', 'input': {'query': 'current weather in San Francisco'}, 'name': 'search', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-6dbb0167-f8f6-4e2a-ab68-229b2d1fbb64', 'example': False, 'tool_calls': [{'name': 'search', 'args': {'query': 'current weather in San Francisco'}, 'id': 'toolu_01KEJMBFozSiZoS4mAcPZeqQ'}], 'invalid_tool_calls': [], 'usage_metadata': None}]}}

Edit the state

Now, let's assume we actually meant to search for the weather in Sidi Frej (another city with the initials SF). We can edit the state to properly reflect that:

=== "Python"

```python
# First, lets get the current state
current_state = await client.threads.get_state(thread['thread_id'])

# Let's now get the last message in the state
# This is the one with the tool calls that we want to update
last_message = current_state['values']['messages'][-1]

# Let's now update the args for that tool call
last_message['tool_calls'][0]['args'] = {'query': 'current weather in Sidi Frej'}

# Let's now call `update_state` to pass in this message in the `messages` key
# This will get treated as any other update to the state
# It will get passed to the reducer function for the `messages` key
# That reducer function will use the ID of the message to update it
# It's important that it has the right ID! Otherwise it would get appended
# as a new message
await client.threads.update_state(thread['thread_id'], {"messages": last_message})
```

=== "Javascript"

```js
// First, let's get the current state
const currentState = await client.threads.getState(thread["thread_id"]);

// Let's now get the last message in the state
// This is the one with the tool calls that we want to update
let lastMessage = currentState.values.messages.slice(-1)[0];

// Let's now update the args for that tool call
lastMessage.tool_calls[0].args = { query: "current weather in Sidi Frej" };

// Let's now call `update_state` to pass in this message in the `messages` key
// This will get treated as any other update to the state
// It will get passed to the reducer function for the `messages` key
// That reducer function will use the ID of the message to update it
// It's important that it has the right ID! Otherwise it would get appended
// as a new message
await client.threads.updateState(thread["thread_id"], { values: { messages: lastMessage } });
```

=== "CURL"

```bash
curl --request GET --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state | \                                                                                      
jq '.values.messages[-1] | (.tool_calls[0].args = {"query": "current weather in Sidi Frej"})' | \
curl --request POST \
  --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state \
  --header 'Content-Type: application/json' \
  --data @-
```

Output:

{'configurable': {'thread_id': '9c8f1a43-9dd8-4017-9271-2c53e57cf66a',
  'checkpoint_ns': '',
  'checkpoint_id': '1ef58e7e-3641-649f-8002-8b4305a64858'}}

Resume invocation

Now we can resume our graph run but with the updated state:

=== "Python"

```python
async for chunk in client.runs.stream(
    thread["thread_id"],
    assistant_id,
    input=None,
    stream_mode="updates",
):
    if chunk.data and chunk.event != "metadata": 
        print(chunk.data)
```

=== "Javascript"

```js
const streamResponse = client.runs.stream(
  thread["thread_id"],
  assistantId,
  {
    input: null,
    streamMode: "updates",
  }
);

for await (const chunk of streamResponse) {
  if (chunk.data && chunk.event !== "metadata") {
    console.log(chunk.data);
  }
}
```

=== "CURL"

```bash
curl --request POST \                                                                             
 --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
 --header 'Content-Type: application/json' \
 --data "{
   \"assistant_id\": \"agent\",
   \"stream_mode\": [
     \"updates\"
   ]
 }"| \ 
 sed 's/\r$//' | \
 awk '
 /^event:/ {
     if (data_content != "" && event_type != "metadata") {
         print data_content "\n"
     }
     sub(/^event: /, "", $0)
     event_type = $0
     data_content = ""
 }
 /^data:/ {
     sub(/^data: /, "", $0)
     data_content = $0
 }
 END {
     if (data_content != "" && event_type != "metadata") {
         print data_content "\n"
     }
 }
 '
```

Output:

{'action': {'messages': [{'content': '["I looked up: current weather in Sidi Frej. Result: It\'s sunny in San Francisco, but you better look out if you\'re a Gemini ƒÿê."]', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'search', 'id': '1161b8d1-bee4-4188-9be8-698aecb69f10', 'tool_call_id': 'toolu_01KEJMBFozSiZoS4mAcPZeqQ'}]}}
{'agent': {'messages': [{'content': [{'text': 'I apologize for the confusion in my search query. It seems the search function interpreted "SF" as "Sidi Frej" instead of "San Francisco" as we intended. Let me search again with the full city name to get the correct information:', 'type': 'text'}, {'id': 'toolu_0111rrwgfAcmurHZn55qjqTR', 'input': {'query': 'current weather in San Francisco'}, 'name': 'search', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-b8c25779-cfb4-46fc-a421-48553551242f', 'example': False, 'tool_calls': [{'name': 'search', 'args': {'query': 'current weather in San Francisco'}, 'id': 'toolu_0111rrwgfAcmurHZn55qjqTR'}], 'invalid_tool_calls': [], 'usage_metadata': None}]}}
{'action': {'messages': [{'content': '["I looked up: current weather in San Francisco. Result: It\'s sunny in San Francisco, but you better look out if you\'re a Gemini ƒÿê."]', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'search', 'id': '6bc632ae-5ee6-4d01-9532-79c524a2d443', 'tool_call_id': 'toolu_0111rrwgfAcmurHZn55qjqTR'}]}}
{'agent': {'messages': [{'content': "Now, based on the search results, I can provide you with information about the current weather in San Francisco:\n\nThe weather in San Francisco is currently sunny. \n\nIt's worth noting that the search result included an unusual comment about Gemini, which doesn't seem directly related to the weather. This might be due to the search engine including some astrological information or a joke in its results. However, for the purpose of weather information, we can focus on the fact that it's sunny in San Francisco right now.\n\nIs there anything else you'd like to know about the weather in San Francisco or any other location?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-227a042b-dd97-476e-af32-76a3703af5d8', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}

As you can see it now looks up the current weather in Sidi Frej (although our dummy search node still returns results for SF because we don't actually do a search in this example, we just return the same "It's sunny in San Francisco ..." result every time).

Review Tool Calls

Human-in-the-loop (HIL) interactions are crucial for agentic systems. A common pattern is to add some human in the loop step after certain tool calls. These tool calls often lead to either a function call or saving of some information. Examples include:

A tool call to execute SQL, which will then be run by the tool
A tool call to generate a summary, which will then be saved to the State of the graph

Note that using tool calls is common whether actually calling tools or not.

There are typically a few different interactions you may want to do here:

Approve the tool call and continue
Modify the tool call manually and then continue
Give natural language feedback, and then pass that back to the agent instead of continuing

We can implement this in LangGraph using a breakpoint: breakpoints allow us to interrupt graph execution before a specific step. At this breakpoint, we can manually update the graph state taking one of the three options above

Setup

We are not going to show the full code for the graph we are hosting, but you can see it here if you want to. Once this graph is hosted, we are ready to invoke it and wait for user input.

SDK initialization

First, we need to setup our client so that we can communicate with our hosted graph:

=== "Python"

```python
from langgraph_sdk import get_client
client = get_client(url=<DEPLOYMENT_URL>)
# Using the graph deployed with the name "agent"
assistant_id = "agent"
thread = await client.threads.create()
```

=== "Javascript"

```js
import { Client } from "@langchain/langgraph-sdk";

const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
// Using the graph deployed with the name "agent"
const assistantId = "agent";
const thread = await client.threads.create();
```

=== "CURL"

```bash
curl --request POST \
  --url <DEPLOYMENT_URL>/threads \
  --header 'Content-Type: application/json' \
  --data '{}'
```

Example with no review

Let's look at an example when no review is required (because no tools are called)

=== "Python"

```python
input = { 'messages':[{ "role":"user", "content":"hi!" }] }

async for chunk in client.runs.stream(
    thread["thread_id"],
    assistant_id,
    input=input,
    stream_mode="updates",
    interrupt_before=["action"],
):
    if chunk.data and chunk.event != "metadata": 
        print(chunk.data)
```

=== "Javascript"

```js
const input = { "messages": [{ "role": "user", "content": "hi!" }] };

const streamResponse = client.runs.stream(
  thread["thread_id"],
  assistantId,
  {
    input: input,
    streamMode: "updates",
    interruptBefore: ["action"],
  }
);

for await (const chunk of streamResponse) {
  if (chunk.data && chunk.event !== "metadata") {
    console.log(chunk.data);
  }
}
```

=== "CURL"

```bash
curl --request POST \
 --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
 --header 'Content-Type: application/json' \
 --data "{
   \"assistant_id\": \"agent\",
   \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"hi!\"}]},
   \"stream_mode\": [
     \"updates\"
   ],
   \"interrupt_before\": [\"action\"]
 }" | \
 sed 's/\r$//' | \
 awk '
 /^event:/ {
     if (data_content != "" && event_type != "metadata") {
         print data_content "\n"
     }
     sub(/^event: /, "", $0)
     event_type = $0
     data_content = ""
 }
 /^data:/ {
     sub(/^data: /, "", $0)
     data_content = $0
 }
 END {
     if (data_content != "" && event_type != "metadata") {
         print data_content "\n"
     }
 }
 '
```

Output:

{'messages': [{'content': 'hi!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '39c51f14-2d5c-4690-883a-d940854b1845', 'example': False}]}
{'messages': [{'content': 'hi!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '39c51f14-2d5c-4690-883a-d940854b1845', 'example': False}, {'content': [{'text': "Hello! Welcome. How can I assist you today? Is there anything specific you'd like to know or any information you're looking for?", 'type': 'text', 'index': 0}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'end_turn', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-d65e07fb-43ff-4d98-ab6b-6316191b9c8b', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 355, 'output_tokens': 31, 'total_tokens': 386}}]}

If we check the state, we can see that it is finished

=== "Python"

```python
state = await client.threads.get_state(thread["thread_id"])

print(state['next'])
```

=== "Javascript"

```js
const state = await client.threads.getState(thread["thread_id"]);

console.log(state.next);
```

=== "CURL"

```bash
curl --request GET \
    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state | jq -c '.next'
```

Output:

[]

Example of approving tool

Let's now look at what it looks like to approve a tool call. Note that we don't need to pass an interrupt to our streaming calls because the graph (defined here) was already compiled with an interrupt before the human_review_node.

=== "Python"

```python
input = {"messages": [{"role": "user", "content": "what's the weather in sf?"}]}

async for chunk in client.runs.stream(
    thread["thread_id"],
    assistant_id,
    input=input,
):
    if chunk.data and chunk.event != "metadata": 
        print(chunk.data)
```

=== "Javascript"

```js
const input = { "messages": [{ "role": "user", "content": "what's the weather in sf?" }] };

const streamResponse = client.runs.stream(
  thread["thread_id"],
  assistantId,
  {
    input: input,
  }
);

for await (const chunk of streamResponse) {
  if (chunk.data && chunk.event !== "metadata") {
    console.log(chunk.data);
  }
}
```

=== "CURL"

```bash
curl --request POST \
 --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
 --header 'Content-Type: application/json' \
 --data "{
   \"assistant_id\": \"agent\",
   \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in sf?\"}]}
 }" | \
 sed 's/\r$//' | \
 awk '
 /^event:/ {
     if (data_content != "" && event_type != "metadata") {
         print data_content "\n"
     }
     sub(/^event: /, "", $0)
     event_type = $0
     data_content = ""
 }
 /^data:/ {
     sub(/^data: /, "", $0)
     data_content = $0
 }
 END {
     if (data_content != "" && event_type != "metadata") {
         print data_content "\n"
     }
 }
 '
```

Output:

{'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '54e19d6e-89fa-44fb-b92c-12e7dd4ddf08', 'example': False}]}
{'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '54e19d6e-89fa-44fb-b92c-12e7dd4ddf08', 'example': False}, {'content': [{'text': "Certainly! I can help you check the weather in San Francisco. To get this information, I'll use the weather search function. Let me do that for you right away.", 'type': 'text', 'index': 0}, {'id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-45a6b6c3-ac69-42a4-8957-d982203d6392', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 90, 'total_tokens': 450}}]}

If we now check, we can see that it is waiting on human review:

=== "Python"

```python
state = await client.threads.get_state(thread["thread_id"])

print(state['next'])
```

=== "Javascript"

```js
const state = await client.threads.getState(thread["thread_id"]);

console.log(state.next);
```

=== "CURL"

```bash
curl --request GET \
    --url <DELPOYMENT_URL>/threads/<THREAD_ID>/state | jq -c '.next'
```

Output:

['human_review_node']

To approve the tool call, we can just continue the thread with no edits. To do this, we just create a new run with no inputs.

=== "Python"

```python
async for chunk in client.runs.stream(
    thread["thread_id"],
    assistant_id,
    input=None,
    stream_mode="values",
):
    if chunk.data and chunk.event != "metadata": 
        print(chunk.data)
```

=== "Javascript"

```js
const streamResponse = client.runs.stream(
  thread["thread_id"],
  assistantId,
  {
    input: null,
    streamMode: "values",
  }
);

for await (const chunk of streamResponse) {
  if (chunk.data && chunk.event !== "metadata") {
    console.log(chunk.data);
  }
}
```

=== "CURL"

```bash
curl --request POST \
 --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
 --header 'Content-Type: application/json' \
 --data "{
   \"assistant_id\": \"agent\"
 }" | \
 sed 's/\r$//' | \
 awk '
 /^event:/ {
     if (data_content != "" && event_type != "metadata") {
         print data_content "\n"
     }
     sub(/^event: /, "", $0)
     event_type = $0
     data_content = ""
 }
 /^data:/ {
     sub(/^data: /, "", $0)
     data_content = $0
 }
 END {
     if (data_content != "" && event_type != "metadata") {
         print data_content "\n"
     }
 }
 '
```

Output:

{'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '54e19d6e-89fa-44fb-b92c-12e7dd4ddf08', 'example': False}, {'content': [{'text': "Certainly! I can help you check the weather in San Francisco. To get this information, I'll use the weather search function. Let me do that for you right away.", 'type': 'text', 'index': 0}, {'id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-45a6b6c3-ac69-42a4-8957-d982203d6392', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 90, 'total_tokens': 450}}, {'content': 'Sunny!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '826cd0f2-9cc6-46f0-b7df-daa6a05d13d2', 'tool_call_id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'artifact': None, 'status': 'success'}]}
{'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '54e19d6e-89fa-44fb-b92c-12e7dd4ddf08', 'example': False}, {'content': [{'text': "Certainly! I can help you check the weather in San Francisco. To get this information, I'll use the weather search function. Let me do that for you right away.", 'type': 'text', 'index': 0}, {'id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-45a6b6c3-ac69-42a4-8957-d982203d6392', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 90, 'total_tokens': 450}}, {'content': 'Sunny!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '826cd0f2-9cc6-46f0-b7df-daa6a05d13d2', 'tool_call_id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'artifact': None, 'status': 'success'}, {'content': [{'text': "\n\nGreat news! The weather in San Francisco is sunny today. It's a beautiful day in the city by the bay. Is there anything else you'd like to know about the weather or any other information I can help you with?", 'type': 'text', 'index': 0}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'end_turn', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-5d5fd0f1-a939-447e-801a-9aaa812322d3', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 464, 'output_tokens': 50, 'total_tokens': 514}}]}

Edit Tool Call

Let's now say we want to edit the tool call. E.g. change some of the parameters (or even the tool called!) but then execute that tool.

=== "Python"

```python
input = {"messages": [{"role": "user", "content": "what's the weather in sf?"}]}

async for chunk in client.runs.stream(
    thread["thread_id"],
    assistant_id,
    input=input,
    stream_mode="values",
):
    if chunk.data and chunk.event != "metadata": 
        print(chunk.data)
```

=== "Javascript"

```js
const input = { "messages": [{ "role": "user", "content": "what's the weather in sf?" }] };

const streamResponse = client.runs.stream(
  thread["thread_id"],
  assistantId,
  {
    input: input,
    streamMode: "values",
  }
);

for await (const chunk of streamResponse) {
  if (chunk.data && chunk.event !== "metadata") {
    console.log(chunk.data);
  }
}
```

=== "CURL"

```bash
curl --request POST \
 --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
 --header 'Content-Type: application/json' \
 --data "{
   \"assistant_id\": \"agent\",
   \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in sf?\"}]}
 }" | \
 sed 's/\r$//' | \
 awk '
 /^event:/ {
     if (data_content != "" && event_type != "metadata") {
         print data_content "\n"
     }
     sub(/^event: /, "", $0)
     event_type = $0
     data_content = ""
 }
 /^data:/ {
     sub(/^data: /, "", $0)
     data_content = $0
 }
 END {
     if (data_content != "" && event_type != "metadata") {
         print data_content "\n"
     }
 }
 '
```

Output:

{'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'cec11391-84da-464b-bd2a-bd4f0d93b9ee', 'example': False}]}
{'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'cec11391-84da-464b-bd2a-bd4f0d93b9ee', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01SunSpDurNfcnXppWLPrtjC', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-6326da9f-6061-4e12-8586-482e32ab4cab', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01SunSpDurNfcnXppWLPrtjC', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}}]}

To do this, we first need to update the state. We can do this by passing a message in with the same id of the message we want to overwrite. This will have the effect of replacing that old message. Note that this is only possible because of the reducer we are using that replaces messages with the same ID - read more about that here.

=== "Python"

```python
# To get the ID of the message we want to replace, we need to fetch the current state and find it there.
state = await client.threads.get_state(thread['thread_id'])
print("Current State:")
print(state['values'])
print("\nCurrent Tool Call ID:")
current_content = state['values']['messages'][-1]['content']
current_id = state['values']['messages'][-1]['id']
tool_call_id = state['values']['messages'][-1]['tool_calls'][0]['id']
print(tool_call_id)

# We now need to construct a replacement tool call.
# We will change the argument to be `San Francisco, USA`
# Note that we could change any number of arguments or tool names - it just has to be a valid one
new_message = {
    "role": "assistant", 
    "content": current_content,
    "tool_calls": [
        {
            "id": tool_call_id,
            "name": "weather_search",
            "args": {"city": "San Francisco, USA"}
        }
    ],
    # This is important - this needs to be the same as the message you replacing!
    # Otherwise, it will show up as a separate message
    "id": current_id
}
await client.threads.update_state(
    # This is the config which represents this thread
    thread['thread_id'], 
    # This is the updated value we want to push
    {"messages": [new_message]}, 
    # We push this update acting as our human_review_node
    as_node="human_review_node"
)

print("\nResuming Execution")
# Let's now continue executing from here
async for chunk in client.runs.stream(
    thread["thread_id"],
    assistant_id,
    input=None,
):
    if chunk.data and chunk.event != "metadata": 
        print(chunk.data)
```

=== "Javascript"

```js
const state = await client.threads.getState(thread.thread_id);
console.log("Current State:");
console.log(state.values);

console.log("\nCurrent Tool Call ID:");
const lastMessage = state.values.messages[state.values.messages.length - 1];
const currentContent = lastMessage.content;
const currentId = lastMessage.id;
const toolCallId = lastMessage.tool_calls[0].id;
console.log(toolCallId);

// Construct a replacement tool call
const newMessage = {
  role: "assistant",
  content: currentContent,
  tool_calls: [
    {
      id: toolCallId,
      name: "weather_search",
      args: { city: "San Francisco, USA" }
    }
  ],
  // Ensure the ID is the same as the message you're replacing
  id: currentId
};

await client.threads.updateState(
  thread.thread_id,  // Thread ID
  {
    values: { "messages": [newMessage] },  // Updated message
    asNode: "human_review_node"
  }  // Acting as human_review_node
);

console.log("\nResuming Execution");
// Continue executing from here
const streamResponseResumed = client.runs.stream(
  thread["thread_id"],
  assistantId,
  {
    input: null,
  }
);

for await (const chunk of streamResponseResumed) {
  if (chunk.data && chunk.event !== "metadata") {
    console.log(chunk.data);
  }
}
```

=== "CURL"

```bash
curl --request POST \
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state \
--header 'Content-Type: application/json' \
--data "{
    \"values\": { \"messages\": [$(curl --request GET \
        --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state |
        jq -c '{
        role: "assistant",
        content: .values.messages[-1].content,
        tool_calls: [
            {
            id: .values.messages[-1].tool_calls[0].id,
            name: "weather_search",
            args: { city: "San Francisco, USA" }
            }
        ],
        id: .values.messages[-1].id
        }')
    ]},
    \"as_node\": \"human_review_node\"
}" && echo "Resuming Execution" && curl --request POST \
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
--header 'Content-Type: application/json' \
--data '{
"assistant_id": "agent"
}' | \
sed 's/\r$//' | \
awk '
/^event:/ {
    if (data_content != "" && event_type != "metadata") {
        print data_content "\n"
    }
    sub(/^event: /, "", $0)
    event_type = $0
    data_content = ""
}
/^data:/ {
    sub(/^data: /, "", $0)
    data_content = $0
}
END {
    if (data_content != "" && event_type != "metadata") {
        print data_content "\n"
    }
}
'
```

Output:

Current State:
{'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '8713d1fa-9b26-4eab-b768-dafdaac70590', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-ede13f26-daf5-4d8f-817a-7611075bbcf1', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}}]}

Current Tool Call ID:
toolu_01VzagzsUGZsNMwW1wHkcw7h

Resuming Execution
{'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '8713d1fa-9b26-4eab-b768-dafdaac70590', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-ede13f26-daf5-4d8f-817a-7611075bbcf1', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'Sunny!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '7fc7d463-66bf-4555-9929-6af483de169b', 'tool_call_id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'artifact': None, 'status': 'success'}]}
{'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '8713d1fa-9b26-4eab-b768-dafdaac70590', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-ede13f26-daf5-4d8f-817a-7611075bbcf1', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'Sunny!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '7fc7d463-66bf-4555-9929-6af483de169b', 'tool_call_id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'artifact': None, 'status': 'success'}, {'content': [{'text': "\n\nBased on the search result, the weather in San Francisco is sunny! It's a beautiful day in the city by the bay. Is there anything else you'd like to know about the weather or any other information I can help you with?", 'type': 'text', 'index': 0}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'end_turn', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-d90ce97a-39f9-4330-985e-67c5f351a0c5', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 455, 'output_tokens': 52, 'total_tokens': 507}}]}

Give feedback to a tool call

Sometimes, you may not want to execute a tool call, but you also may not want to ask the user to manually modify the tool call. In that case it may be better to get natural language feedback from the user. You can then insert these feedback as a mock RESULT of the tool call.

There are multiple ways to do this:

You could add a new message to the state (representing the "result" of a tool call) You could add TWO new messages to the state - one representing an "error" from the tool call, other HumanMessage representing the feedback Both are similar in that they involve adding messages to the state. The main difference lies in the logic AFTER the human_node and how it handles different types of messages.

For this example we will just add a single tool call representing the feedback. Let's see this in action!

=== "Python"

```python
input = {"messages": [{"role": "user", "content": "what's the weather in sf?"}]}

async for chunk in client.runs.stream(
    thread["thread_id"],
    assistant_id,
    input=input,
):
    if chunk.data and chunk.event != "metadata": 
        print(chunk.data)
```

=== "Javascript"

```js
const input = { "messages": [{ "role": "user", "content": "what's the weather in sf?" }] };

const streamResponse = client.runs.stream(
  thread["thread_id"],
  assistantId,
  {
    input: input,
  }
);

for await (const chunk of streamResponse) {
  if (chunk.data && chunk.event !== "metadata") {
    console.log(chunk.data);
  }
}
```

=== "CURL"

```bash
curl --request POST \
 --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
 --header 'Content-Type: application/json' \
 --data "{
   \"assistant_id\": \"agent\",
   \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in sf?\"}]}
 }" | \
 sed 's/\r$//' | \
 awk '
 /^event:/ {
     if (data_content != "" && event_type != "metadata") {
         print data_content "\n"
     }
     sub(/^event: /, "", $0)
     event_type = $0
     data_content = ""
 }
 /^data:/ {
     sub(/^data: /, "", $0)
     data_content = $0
 }
 END {
     if (data_content != "" && event_type != "metadata") {
         print data_content "\n"
     }
 }
 '
```

Output:

{'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'c80f13d0-674d-4233-b6a0-3940509d3cf3', 'example': False}]}
{'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'c80f13d0-674d-4233-b6a0-3940509d3cf3', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_016XyTdFA8NuPWeLyZPSzoM3', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-4911ac27-3d7c-4edf-a3ca-c2908e3922eb', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_016XyTdFA8NuPWeLyZPSzoM3', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}}]}

To do this, we first need to update the state. We can do this by passing a message in with the same tool call id of the tool call we want to respond to. Note that this is a different* ID from above

=== "Python"

```python
# To get the ID of the message we want to replace, we need to fetch the current state and find it there.
state = await client.threads.get_state(thread['thread_id'])
print("Current State:")
print(state['values'])
print("\nCurrent Tool Call ID:")
tool_call_id = state['values']['messages'][-1]['tool_calls'][0]['id']
print(tool_call_id)

# We now need to construct a replacement tool call.
# We will change the argument to be `San Francisco, USA`
# Note that we could change any number of arguments or tool names - it just has to be a valid one
new_message = {
    "role": "tool", 
    # This is our natural language feedback
    "content": "User requested changes: pass in the country as well",
    "name": "weather_search",
    "tool_call_id": tool_call_id
}
await client.threads.update_state(
    # This is the config which represents this thread
    thread['thread_id'], 
    # This is the updated value we want to push
    {"messages": [new_message]}, 
    # We push this update acting as our human_review_node
    as_node="human_review_node"
)

print("\nResuming execution")
# Let's now continue executing from here
async for chunk in client.runs.stream(
    thread["thread_id"],
    assistant_id,
    input=None,
    stream_mode="values",
):
    if chunk.data and chunk.event != "metadata": 
        print(chunk.data)
```

=== "Javascript"

```js
const state = await client.threads.getState(thread.thread_id);
console.log("Current State:");
console.log(state.values);

console.log("\nCurrent Tool Call ID:");
const lastMessage = state.values.messages[state.values.messages.length - 1];
const toolCallId = lastMessage.tool_calls[0].id;
console.log(toolCallId);

// Construct a replacement tool call
const newMessage = {
  role: "tool",
  content: "User requested changes: pass in the country as well",
  name: "weather_search",
  tool_call_id: toolCallId,
};

await client.threads.updateState(
  thread.thread_id,  // Thread ID
  {
    values: { "messages": [newMessage] },  // Updated message
    asNode: "human_review_node"
  }  // Acting as human_review_node
);

console.log("\nResuming Execution");
// Continue executing from here
const streamResponseEdited = client.runs.stream(
  thread["thread_id"],
  assistantId,
  {
    input: null,
    streamMode: "values",
    interruptBefore: ["action"],
  }
);

for await (const chunk of streamResponseEdited) {
  if (chunk.data && chunk.event !== "metadata") {
    console.log(chunk.data);
  }
}
```

=== "CURL"

```bash
curl --request POST \
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state \
--header 'Content-Type: application/json' \
--data "{
    \"values\": { \"messages\": [$(curl --request GET \
        --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state |
        jq -c '{
        role: "tool",
        content: "User requested changes: pass in the country as well",
        name: "get_weather",
        tool_call_id: .values.messages[-1].id.tool_calls[0].id
        }')
    ]},
    \"as_node\": \"human_review_node\"
}" && echo "Resuming Execution" && curl --request POST \
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
--header 'Content-Type: application/json' \
--data '{
"assistant_id": "agent"
}' | \
sed 's/\r$//' | \
awk '
/^event:/ {
    if (data_content != "" && event_type != "metadata") {
        print data_content "\n"
    }
    sub(/^event: /, "", $0)
    event_type = $0
    data_content = ""
}
/^data:/ {
    sub(/^data: /, "", $0)
    data_content = $0
}
END {
    if (data_content != "" && event_type != "metadata") {
        print data_content "\n"
    }
}
'
```

Output:

Current State:
{'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '3b2bbc38-d11b-49eb-80c0-c24a40dab5a8', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-c5a50900-abf5-4885-9cdb-da2bf0d892ac', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}}]}

Current Tool Call ID:
toolu_01NNw18j57GEGPZvsa9f1wvX

Resuming execution
{'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '3b2bbc38-d11b-49eb-80c0-c24a40dab5a8', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-c5a50900-abf5-4885-9cdb-da2bf0d892ac', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}}, {'content': 'User requested changes: pass in the country as well', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '787288be-213c-4fd3-8503-4a009bdb1b00', 'tool_call_id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'artifact': None, 'status': 'success'}, {'content': [{'text': '\n\nI apologize for the oversight. It seems the function requires additional information. Let me try again with a more specific request.', 'type': 'text', 'index': 0}, {'id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco, USA"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-5c355a56-cfe3-4046-b49f-f5b09fc397ef', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 461, 'output_tokens': 83, 'total_tokens': 544}}]}

We can see that we now get to another breakpoint - because it went back to the model and got an entirely new prediction of what to call. Let's now approve this one and continue

=== "Python"

```python
async for chunk in client.runs.stream(
    thread["thread_id"],
    assistant_id,
    input=None,
):
    if chunk.data and chunk.event != "metadata": 
        print(chunk.data)
```

=== "Javascript"

```js
const streamResponseResumed = client.runs.stream(
  thread["thread_id"],
  assistantId,
  {
    input: null,
  }
);

for await (const chunk of streamResponseResumed) {
  if (chunk.data && chunk.event !== "metadata") {
    console.log(chunk.data);
  }
}
```

=== "CURL"

```bash
curl --request POST \
 --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
 --header 'Content-Type: application/json' \
 --data "{
   \"assistant_id\": \"agent\"
 }" | \
 sed 's/\r$//' | \
 awk '
 /^event:/ {
     if (data_content != "" && event_type != "metadata") {
         print data_content "\n"
     }
     sub(/^event: /, "", $0)
     event_type = $0
     data_content = ""
 }
 /^data:/ {
     sub(/^data: /, "", $0)
     data_content = $0
 }
 END {
     if (data_content != "" && event_type != "metadata") {
         print data_content "\n"
     }
 }
 '
```

Output:

{'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '3b2bbc38-d11b-49eb-80c0-c24a40dab5a8', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-c5a50900-abf5-4885-9cdb-da2bf0d892ac', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}}, {'content': 'User requested changes: pass in the country as well', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '787288be-213c-4fd3-8503-4a009bdb1b00', 'tool_call_id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'artifact': None, 'status': 'success'}, {'content': [{'text': '\n\nI apologize for the oversight. It seems the function requires additional information. Let me try again with a more specific request.', 'type': 'text', 'index': 0}, {'id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco, USA"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-5c355a56-cfe3-4046-b49f-f5b09fc397ef', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 461, 'output_tokens': 83, 'total_tokens': 544}}, {'content': 'Sunny!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '3b857482-bca2-4a73-a9ab-1f35a3e43e5f', 'tool_call_id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'artifact': None, 'status': 'success'}]}
{'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '3b2bbc38-d11b-49eb-80c0-c24a40dab5a8', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-c5a50900-abf5-4885-9cdb-da2bf0d892ac', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}}, {'content': 'User requested changes: pass in the country as well', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '787288be-213c-4fd3-8503-4a009bdb1b00', 'tool_call_id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'artifact': None, 'status': 'success'}, {'content': [{'text': '\n\nI apologize for the oversight. It seems the function requires additional information. Let me try again with a more specific request.', 'type': 'text', 'index': 0}, {'id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco, USA"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-5c355a56-cfe3-4046-b49f-f5b09fc397ef', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 461, 'output_tokens': 83, 'total_tokens': 544}}, {'content': 'Sunny!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '3b857482-bca2-4a73-a9ab-1f35a3e43e5f', 'tool_call_id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'artifact': None, 'status': 'success'}, {'content': [{'text': "\n\nGreat news! The weather in San Francisco is sunny today. Is there anything else you'd like to know about the weather or any other information I can help you with?", 'type': 'text', 'index': 0}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'end_turn', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-6a857bb1-f65b-4b86-93d6-c025e003c777', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 557, 'output_tokens': 38, 'total_tokens': 595}}]}

How to Replay and Branch from Prior States

With LangGraph Cloud you have the ability to return to any of your prior states and either re-run the graph to reproduce issues noticed during testing, or branch out in a different way from what was originally done in the prior states. In this guide we will show a quick example of how to rerun past states and how to branch off from previous states as well.

Setup

We are not going to show the full code for the graph we are hosting, but you can see it here if you want to. Once this graph is hosted, we are ready to invoke it and wait for user input.

SDK initialization

First, we need to setup our client so that we can communicate with our hosted graph:

=== "Python"

```python
from langgraph_sdk import get_client
client = get_client(url=<DEPLOYMENT_URL>)
# Using the graph deployed with the name "agent"
assistant_id = "agent"
thread = await client.threads.create()
```

=== "Javascript"

```js
import { Client } from "@langchain/langgraph-sdk";

const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
// Using the graph deployed with the name "agent"
const assistantId = "agent";
const thread = await client.threads.create();
```

=== "CURL"

```bash
curl --request POST \
  --url <DEPLOYMENT_URL>/threads \
  --header 'Content-Type: application/json' \
  --data '{}'
```

Replay a state

Initial invocation

Before replaying a state - we need to create states to replay from! In order to do this, let's invoke our graph with a simple message:

=== "Python"

```python
input = {"messages": [{"role": "user", "content": "Please search the weather in SF"}]}

async for chunk in client.runs.stream(
    thread["thread_id"],
    assistant_id,
    input=input,
    stream_mode="updates",
):
    if chunk.data and chunk.event != "metadata": 
        print(chunk.data)
```

=== "Javascript"

```js
const input = { "messages": [{ "role": "user", "content": "Please search the weather in SF" }] }

const streamResponse = client.runs.stream(
  thread["thread_id"],
  assistantId,
  {
    input: input,
    streamMode: "updates",
  }
);
for await (const chunk of streamResponse) {
  if (chunk.data && chunk.event !== "metadata") {
    console.log(chunk.data);
  }
}
```

=== "CURL"

```bash
curl --request POST \
 --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
 --header 'Content-Type: application/json' \
 --data "{
   \"assistant_id\": \"agent\",
   \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"Please search the weather in SF\"}]},
   \"stream_mode\": [
     \"updates\"
   ]
 }" | \
 sed 's/\r$//' | \
 awk '
 /^event:/ {
     if (data_content != "" && event_type != "metadata") {
         print data_content "\n"
     }
     sub(/^event: /, "", $0)
     event_type = $0
     data_content = ""
 }
 /^data:/ {
     sub(/^data: /, "", $0)
     data_content = $0
 }
 END {
     if (data_content != "" && event_type != "metadata") {
         print data_content "\n"
     }
 }
 '
```

Output:

{'agent': {'messages': [{'content': [{'text': "Certainly! I'll use the search function to look up the current weather in San Francisco for you. Let me do that now.", 'type': 'text'}, {'id': 'toolu_011vroKUtWU7SBdrngpgpFMn', 'input': {'query': 'current weather in San Francisco'}, 'name': 'search', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-ee639877-d97d-40f8-96dc-d0d1ae22d203', 'example': False, 'tool_calls': [{'name': 'search', 'args': {'query': 'current weather in San Francisco'}, 'id': 'toolu_011vroKUtWU7SBdrngpgpFMn'}], 'invalid_tool_calls': [], 'usage_metadata': None}]}}
{'action': {'messages': [{'content': '["I looked up: current weather in San Francisco. Result: It\'s sunny in San Francisco, but you better look out if you\'re a Gemini ƒÿê."]', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'search', 'id': '7bad0e72-5ebe-4b08-9b8a-b99b0fe22fb7', 'tool_call_id': 'toolu_011vroKUtWU7SBdrngpgpFMn'}]}}
{'agent': {'messages': [{'content': "Based on the search results, I can provide you with information about the current weather in San Francisco:\n\nThe weather in San Francisco is currently sunny. This is great news for outdoor activities and enjoying the city's beautiful sights.\n\nIt's worth noting that the search result included an unusual comment about Geminis, which isn't typically part of a weather report. This might be due to the search engine including some astrological information or a joke in its results. However, for the purpose of answering your question about the weather, we can focus on the fact that it's sunny in San Francisco.\n\nIf you need any more specific information about the weather in San Francisco, such as temperature, wind speed, or forecast for the coming days, please let me know, and I'd be happy to search for that information for you.", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-dbac539a-33c8-4f0c-9e20-91f318371e7c', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}

Now let's get our list of states, and invoke from the third state (right before the tool get called):

=== "Python"

```python
states = await client.threads.get_history(thread['thread_id'])

# We can confirm that this state is correct by checking the 'next' attribute and seeing that it is the tool call node
state_to_replay = states[2]
print(state_to_replay['next'])
```

=== "Javascript"

```js
const states = await client.threads.getHistory(thread['thread_id']);

// We can confirm that this state is correct by checking the 'next' attribute and seeing that it is the tool call node
const stateToReplay = states[2];
console.log(stateToReplay['next']);
```

=== "CURL"

```bash
curl --request GET --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/history | jq -r '.[2].next'
```

Output:

['action']

To rerun from a state, we need first issue an empty update to the thread state. Then we need to pass in the resulting checkpoint_id as follows:

=== "Python"

```python
state_to_replay = states[2]
updated_config = await client.threads.update_state(
    thread["thread_id"],
    {"messages": []},
    checkpoint_id=state_to_replay["checkpoint_id"]
)
async for chunk in client.runs.stream(
    thread["thread_id"],
    assistant_id, # graph_id
    input=None,
    stream_mode="updates",
    checkpoint_id=updated_config["checkpoint_id"]
):
    if chunk.data and chunk.event != "metadata": 
        print(chunk.data)
```

=== "Javascript"

```js
const stateToReplay = states[2];
const config = await client.threads.updateState(thread["thread_id"], { values: {"messages": [] }, checkpointId: stateToReplay["checkpoint_id"] });
const streamResponse = client.runs.stream(
  thread["thread_id"],
  assistantId,
  {
    input: null,
    streamMode: "updates",
    checkpointId: config["checkpoint_id"]
  }
);
for await (const chunk of streamResponse) {
  if (chunk.data && chunk.event !== "metadata") {
    console.log(chunk.data);
  }
}
```

=== "CURL"

```bash
curl --request GET --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/history | jq -c '
    .[2] as $state_to_replay |
    {
        values: { messages: .[2].values.messages[-1] },
        checkpoint_id: $state_to_replay.checkpoint_id
    }' | \
curl --request POST \
    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state \
    --header 'Content-Type: application/json' \
    --data @- | jq .checkpoint_id | \
curl --request POST \
 --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
 --header 'Content-Type: application/json' \
 --data "{
   \"assistant_id\": \"agent\",
   \"checkpoint_id\": \"$1\",
   \"stream_mode\": [
     \"updates\"
   ]
 }" | \
 sed 's/\r$//' | \
 awk '
 /^event:/ {
     if (data_content != "" && event_type != "metadata") {
         print data_content "\n"
     }
     sub(/^event: /, "", $0)
     event_type = $0
     data_content = ""
 }
 /^data:/ {
     sub(/^data: /, "", $0)
     data_content = $0
 }
 END {
     if (data_content != "" && event_type != "metadata") {
         print data_content "\n"
     }
 }
 '
```

Output:

{'action': {'messages': [{'content': '["I looked up: current weather in San Francisco. Result: It\'s sunny in San Francisco, but you better look out if you\'re a Gemini ƒÿê."]', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'search', 'id': 'eba650e5-400e-4938-8508-f878dcbcc532', 'tool_call_id': 'toolu_011vroKUtWU7SBdrngpgpFMn'}]}}
{'agent': {'messages': [{'content': "Based on the search results, I can provide you with information about the current weather in San Francisco:\n\nThe weather in San Francisco is currently sunny. This is great news if you're planning any outdoor activities or simply want to enjoy a pleasant day in the city.\n\nIt's worth noting that the search result included an unusual comment about Geminis, which doesn't seem directly related to the weather. This appears to be a playful or humorous addition to the weather report, possibly from the source where this information was obtained.\n\nIs there anything else you'd like to know about the weather in San Francisco or any other information you need?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-bc6dca3f-a1e2-4f59-a69b-fe0515a348bb', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}

As we can see, the graph restarted from the tool node with the same input as our original graph run.

Branch off from previous state

Using LangGraph's checkpointing, you can do more than just replay past states. You can branch off previous locations to let the agent explore alternate trajectories or to let a user "version control" changes in a workflow.

Let's show how to do this to edit the state at a particular point in time. Let's update the state to change the input to the tool

=== "Python"

```python
# Let's now get the last message in the state
# This is the one with the tool calls that we want to update
last_message = state_to_replay['values']['messages'][-1]

# Let's now update the args for that tool call
last_message['tool_calls'][0]['args'] = {'query': 'current weather in SF'}

config = await client.threads.update_state(thread['thread_id'],{"messages":[last_message]},checkpoint_id=state_to_replay['checkpoint_id'])
```

=== "Javascript"

```js
// Let's now get the last message in the state
// This is the one with the tool calls that we want to update
let lastMessage = stateToReplay['values']['messages'][-1];

// Let's now update the args for that tool call
lastMessage['tool_calls'][0]['args'] = { 'query': 'current weather in SF' };

const config = await client.threads.updateState(thread['thread_id'], { values: { "messages": [lastMessage] }, checkpointId: stateToReplay['checkpoint_id'] });
```

=== "CURL"

```bash
curl -s --request GET --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/history | \
jq -c '
    .[2] as $state_to_replay |
    .[2].values.messages[-1].tool_calls[0].args.query = "current weather in SF" |
    {
        values: { messages: .[2].values.messages[-1] },
        checkpoint_id: $state_to_replay.checkpoint_id
    }' | \
curl --request POST \
    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state \
    --header 'Content-Type: application/json' \
    --data @-
```

Now we can rerun our graph with this new config, starting from the new_state, which is a branch of our state_to_replay:

=== "Python"

```python
async for chunk in client.runs.stream(
    thread["thread_id"],
    assistant_id,
    input=None,
    stream_mode="updates",
    checkpoint_id=config['checkpoint_id']
):
    if chunk.data and chunk.event != "metadata": 
        print(chunk.data)
```

=== "Javascript"

```js
const streamResponse = client.runs.stream(
  thread["thread_id"],
  assistantId,
  {
    input: null,
    streamMode: "updates",
    checkpointId: config['checkpoint_id'],
  }
);
for await (const chunk of streamResponse) {
  if (chunk.data && chunk.event !== "metadata") {
    console.log(chunk.data);
  }
}
```

=== "CURL"

```bash
curl -s --request GET --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state | \
jq -c '.checkpoint_id' | \
curl --request POST \
 --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
 --header 'Content-Type: application/json' \
 --data "{
   \"assistant_id\": \"agent\",
   \"checkpoint_id\": \"$1\",
   \"stream_mode\": [
     \"updates\"
   ]
 }" | \
 sed 's/\r$//' | \
 awk '
 /^event:/ {
     if (data_content != "" && event_type != "metadata") {
         print data_content "\n"
     }
     sub(/^event: /, "", $0)
     event_type = $0
     data_content = ""
 }
 /^data:/ {
     sub(/^data: /, "", $0)
     data_content = $0
 }
 END {
     if (data_content != "" && event_type != "metadata") {
         print data_content "\n"
     }
 }
 '
```

Output:

{'action': {'messages': [{'content': '["I looked up: current weather in SF. Result: It\'s sunny in San Francisco, but you better look out if you\'re a Gemini ƒÿê."]', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'search', 'id': '2baf9941-4fda-4081-9f87-d76795d289f1', 'tool_call_id': 'toolu_011vroKUtWU7SBdrngpgpFMn'}]}}
{'agent': {'messages': [{'content': "Based on the search results, I can provide you with information about the current weather in San Francisco (SF):\n\nThe weather in San Francisco is currently sunny. This means it's a clear day with plenty of sunshine. \n\nIt's worth noting that the specific temperature wasn't provided in the search result, but sunny weather in San Francisco typically means comfortable temperatures. San Francisco is known for its mild climate, so even on sunny days, it's often not too hot.\n\nThe search result also included a playful reference to astrological signs, mentioning Gemini. However, this is likely just a joke or part of the search engine's presentation and not related to the actual weather conditions.\n\nIs there any specific information about the weather in San Francisco you'd like to know more about? I'd be happy to perform another search if you need details on temperature, wind conditions, or the forecast for the coming days.", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-a83de52d-ed18-4402-9384-75c462485743', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}

As we can see, the search query changed from San Francisco to SF, just as we had hoped!

How to Wait for User Input

One of the main human-in-the-loop interaction patterns is waiting for human input. A key use case involves asking the user clarifying questions. One way to accomplish this is simply go to the END node and exit the graph. Then, any user response comes back in as fresh invocation of the graph. This is basically just creating a chatbot architecture.

The issue with this is it is tough to resume back in a particular point in the graph. Often times the agent is halfway through some process, and just needs a bit of a user input. Although it is possible to design your graph in such a way where you have a conditional_entry_point to route user messages back to the right place, that is not super scalable (as it essentially involves having a routing function that can end up almost anywhere).

A separate way to do this is to have a node explicitly for getting user input. This is easy to implement in a notebook setting - you just put an input() call in the node. But that isn't exactly production ready.

Luckily, LangGraph makes it possible to do similar things in a production way. The basic idea is:

Set up a node that represents human input. This can have specific incoming/outgoing edges (as you desire). There shouldn't actually be any logic inside this node.
Add a breakpoint before the node. This will stop the graph before this node executes (which is good, because there's no real logic in it anyways)
Use .update_state to update the state of the graph. Pass in whatever human response you get. The key here is to use the as_node parameter to apply this update as if you were that node. This will have the effect of making it so that when you resume execution next it resumes as if that node just acted, and not from the beginning.

Setup

We are not going to show the full code for the graph we are hosting, but you can see it here if you want to. Once this graph is hosted, we are ready to invoke it and wait for user input.

SDK initialization

First, we need to setup our client so that we can communicate with our hosted graph:

=== "Python"

```python
from langgraph_sdk import get_client
client = get_client(url=<DEPLOYMENT_URL>)
# Using the graph deployed with the name "agent"
assistant_id = "agent"
thread = await client.threads.create()
```

=== "Javascript"

```js
import { Client } from "@langchain/langgraph-sdk";

const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
// Using the graph deployed with the name "agent"
const assistantId = "agent";
const thread = await client.threads.create();
```

=== "CURL"

```bash
curl --request POST \
  --url <DEPLOYMENT_URL>/threads \
  --header 'Content-Type: application/json' \
  --data '{}'
```

Waiting for user input

Initial invocation

Now, let's invoke our graph by interrupting before ask_human node:

=== "Python"

```python
input = {
    "messages": [
        {
            "role": "user",
            "content": "Use the search tool to ask the user where they are, then look up the weather there",
        }
    ]
}

async for chunk in client.runs.stream(
    thread["thread_id"],
    assistant_id,
    input=input,
    stream_mode="updates",
    interrupt_before=["ask_human"],
):
    if chunk.data and chunk.event != "metadata": 
        print(chunk.data)
```

=== "Javascript"

```js
const input = {
  messages: [
    {
      role: "human",
      content: "Use the search tool to ask the user where they are, then look up the weather there"
    }
  ]
};

const streamResponse = client.runs.stream(
  thread["thread_id"],
  assistantId,
  {
    input: input,
    streamMode: "updates",
    interruptBefore: ["ask_human"]
  }
);

for await (const chunk of streamResponse) {
  if (chunk.data && chunk.event !== "metadata") {
    console.log(chunk.data);
  }
}
```

=== "CURL"

```bash
curl --request POST \
 --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
 --header 'Content-Type: application/json' \
 --data "{
   \"assistant_id\": \"agent\",
   \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"Use the search tool to ask the user where they are, then look up the weather there\"}]},
   \"interrupt_before\": [\"ask_human\"],
   \"stream_mode\": [
     \"updates\"
   ]
 }" | \
 sed 's/\r$//' | \
 awk '
 /^event:/ {
     if (data_content != "" && event_type != "metadata") {
         print data_content "\n"
     }
     sub(/^event: /, "", $0)
     event_type = $0
     data_content = ""
 }
 /^data:/ {
     sub(/^data: /, "", $0)
     data_content = $0
 }
 END {
     if (data_content != "" && event_type != "metadata") {
         print data_content "\n"
     }
 }
 '
```

Output:

{'agent': {'messages': [{'content': [{'text': "Certainly! I'll use the AskHuman function to ask the user about their location, and then I'll use the search function to look up the weather for that location. Let's start by asking the user where they are.", 'type': 'text'}, {'id': 'toolu_01RFahzYPvnPWTb2USk2RdKR', 'input': {'question': 'Where are you currently located?'}, 'name': 'AskHuman', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-a8422215-71d3-4093-afb4-9db141c94ddb', 'example': False, 'tool_calls': [{'name': 'AskHuman', 'args': {'question': 'Where are you currently located?'}, 'id': 'toolu_01RFahzYPvnPWTb2USk2RdKR'}], 'invalid_tool_calls': [], 'usage_metadata': None}]}}

Adding user input to state

We now want to update this thread with a response from the user. We then can kick off another run.

Because we are treating this as a tool call, we will need to update the state as if it is a response from a tool call. In order to do this, we will need to check the state to get the ID of the tool call.

=== "Python"

```python
state = await client.threads.get_state(thread['thread_id'])
tool_call_id = state['values']['messages'][-1]['tool_calls'][0]['id']

# We now create the tool call with the id and the response we want
tool_message = [{"tool_call_id": tool_call_id, "type": "tool", "content": "san francisco"}]

await client.threads.update_state(thread['thread_id'], {"messages": tool_message}, as_node="ask_human")
```

=== "Javascript"

```js
const state = await client.threads.getState(thread["thread_id"]);
const toolCallId = state.values.messages[state.values.messages.length - 1].tool_calls[0].id;

// We now create the tool call with the id and the response we want
const toolMessage = [
  {
    tool_call_id: toolCallId,
    type: "tool",
    content: "san francisco"
  }
];

await client.threads.updateState(
  thread["thread_id"],
  { values: { messages: toolMessage } },
  { asNode: "ask_human" }
);
```

=== "CURL"

```bash
curl --request GET \
 --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state \
 | jq -r '.values.messages[-1].tool_calls[0].id' \
 | sh -c '
     TOOL_CALL_ID="$1"
     
     # Construct the JSON payload
     JSON_PAYLOAD=$(printf "{\"messages\": [{\"tool_call_id\": \"%s\", \"type\": \"tool\", \"content\": \"san francisco\"}], \"as_node\": \"ask_human\"}" "$TOOL_CALL_ID")
     
     # Send the updated state
     curl --request POST \
          --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state \
          --header "Content-Type: application/json" \
          --data "${JSON_PAYLOAD}"
 ' _ 
```

Output:

{'configurable': {'thread_id': 'a9f322ae-4ed1-41ec-942b-38cb3d342c3a',
'checkpoint_ns': '',
'checkpoint_id': '1ef58e97-a623-63dd-8002-39a9a9b20be3'}}

Invoking after receiving human input

We can now tell the agent to continue. We can just pass in None as the input to the graph, since no additional input is needed:

=== "Python"

```python
async for chunk in client.runs.stream(
    thread["thread_id"],
    assistant_id,
    input=None,
    stream_mode="updates",
):
    if chunk.data and chunk.event != "metadata": 
        print(chunk.data)
```

=== "Javascript"

```js
const streamResponse = client.runs.stream(
  thread["thread_id"],
  assistantId,
  {
    input: null,
    streamMode: "updates"
  }
);

for await (const chunk of streamResponse) {
  if (chunk.data && chunk.event !== "metadata") {
    console.log(chunk.data);
  }
}
```

=== "CURL"

```bash
curl --request POST \                                                                             
 --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
 --header 'Content-Type: application/json' \
 --data "{
   \"assistant_id\": \"agent\",
   \"stream_mode\": [
     \"updates\"
   ]
 }"| \ 
 sed 's/\r$//' | \
 awk '
 /^event:/ {
     if (data_content != "" && event_type != "metadata") {
         print data_content "\n"
     }
     sub(/^event: /, "", $0)
     event_type = $0
     data_content = ""
 }
 /^data:/ {
     sub(/^data: /, "", $0)
     data_content = $0
 }
 END {
     if (data_content != "" && event_type != "metadata") {
         print data_content "\n"
     }
 }
 '
```

Output:

{'agent': {'messages': [{'content': [{'text': "Thank you for letting me know that you're in San Francisco. Now, I'll use the search function to look up the weather in San Francisco.", 'type': 'text'}, {'id': 'toolu_01K57ofmgG2wyJ8tYJjbq5k7', 'input': {'query': 'current weather in San Francisco'}, 'name': 'search', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-241baed7-db5e-44ce-ac3c-56431705c22b', 'example': False, 'tool_calls': [{'name': 'search', 'args': {'query': 'current weather in San Francisco'}, 'id': 'toolu_01K57ofmgG2wyJ8tYJjbq5k7'}], 'invalid_tool_calls': [], 'usage_metadata': None}]}}
{'action': {'messages': [{'content': '["I looked up: current weather in San Francisco. Result: It\'s sunny in San Francisco, but you better look out if you\'re a Gemini ƒÿê."]', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'search', 'id': '8b699b95-8546-4557-8e66-14ea71a15ed8', 'tool_call_id': 'toolu_01K57ofmgG2wyJ8tYJjbq5k7'}]}}
{'agent': {'messages': [{'content': "Based on the search results, I can provide you with information about the current weather in San Francisco:\n\nThe weather in San Francisco is currently sunny. It's a beautiful day in the city! \n\nHowever, I should note that the search result included an unusual comment about Gemini zodiac signs. This appears to be either a joke or potentially irrelevant information added by the search engine. For accurate and detailed weather information, you might want to check a reliable weather service or app for San Francisco.\n\nIs there anything else you'd like to know about the weather or San Francisco?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-b4d7309f-f849-46aa-b6ef-475bcabd2be9', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}

Interrupt

This guide assumes knowledge of what double-texting is, which you can learn about in the double-texting conceptual guide.

The guide covers the interrupt option for double texting, which interrupts the prior run of the graph and starts a new one with the double-text. This option does not delete the first run, but rather keeps it in the database but sets its status to interrupted. Below is a quick example of using the interrupt option.

Setup

First, we will define a quick helper function for printing out JS and CURL model outputs (you can skip this if using Python):

=== "Javascript"

```js
function prettyPrint(m) {
  const padded = " " + m['type'] + " ";
  const sepLen = Math.floor((80 - padded.length) / 2);
  const sep = "=".repeat(sepLen);
  const secondSep = sep + (padded.length % 2 ? "=" : "");
  
  console.log(`${sep}${padded}${secondSep}`);
  console.log("\n\n");
  console.log(m.content);
}
```

=== "CURL"

```bash
# PLACE THIS IN A FILE CALLED pretty_print.sh
pretty_print() {
  local type="$1"
  local content="$2"
  local padded=" $type "
  local total_width=80
  local sep_len=$(( (total_width - ${#padded}) / 2 ))
  local sep=$(printf '=%.0s' $(eval "echo {1.."${sep_len}"}"))
  local second_sep=$sep
  if (( (total_width - ${#padded}) % 2 )); then
    second_sep="${second_sep}="
  fi

  echo "${sep}${padded}${second_sep}"
  echo
  echo "$content"
}
```

Now, let's import our required packages and instantiate our client, assistant, and thread.

=== "Python"

```python
import asyncio

from langchain_core.messages import convert_to_messages
from langgraph_sdk import get_client

client = get_client(url=<DEPLOYMENT_URL>)
# Using the graph deployed with the name "agent"
assistant_id = "agent"
thread = await client.threads.create()
```

=== "Javascript"

```js
import { Client } from "@langchain/langgraph-sdk";

const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
// Using the graph deployed with the name "agent"
const assistantId = "agent";
const thread = await client.threads.create();
```

=== "CURL"

```bash
curl --request POST \
  --url <DEPLOYMENT_URL>/threads \
  --header 'Content-Type: application/json' \
  --data '{}'
```

Create runs

Now we can start our two runs and join the second one until it has completed:

=== "Python"

```python
# the first run will be interrupted
interrupted_run = await client.runs.create(
    thread["thread_id"],
    assistant_id,
    input={"messages": [{"role": "user", "content": "what's the weather in sf?"}]},
)
# sleep a bit to get partial outputs from the first run
await asyncio.sleep(2)
run = await client.runs.create(
    thread["thread_id"],
    assistant_id,
    input={"messages": [{"role": "user", "content": "what's the weather in nyc?"}]},
    multitask_strategy="interrupt",
)
# wait until the second run completes
await client.runs.join(thread["thread_id"], run["run_id"])
```

=== "Javascript"

```js
// the first run will be interrupted
let interruptedRun = await client.runs.create(
  thread["thread_id"],
  assistantId,
  { input: { messages: [{ role: "human", content: "what's the weather in sf?" }] } }
);
// sleep a bit to get partial outputs from the first run
await new Promise(resolve => setTimeout(resolve, 2000)); 

let run = await client.runs.create(
  thread["thread_id"],
  assistantId,
  { 
    input: { messages: [{ role: "human", content: "what's the weather in nyc?" }] },
    multitaskStrategy: "interrupt" 
  }
);

// wait until the second run completes
await client.runs.join(thread["thread_id"], run["run_id"]);
```

=== "CURL"

```bash
curl --request POST \
--url <DEPLOY<ENT_URL>>/threads/<THREAD_ID>/runs \
--header 'Content-Type: application/json' \
--data "{
  \"assistant_id\": \"agent\",
  \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what\'s the weather in sf?\"}]},
}" && sleep 2 && curl --request POST \
--url <DEPLOY<ENT_URL>>/threads/<THREAD_ID>/runs \
--header 'Content-Type: application/json' \
--data "{
  \"assistant_id\": \"agent\",
  \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what\'s the weather in nyc?\"}]},
  \"multitask_strategy\": \"interrupt\"
}" && curl --request GET \
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/<RUN_ID>/join
```

View run results

We can see that the thread has partial data from the first run + data from the second run

=== "Python"

```python
state = await client.threads.get_state(thread["thread_id"])

for m in convert_to_messages(state["values"]["messages"]):
    m.pretty_print()
```

=== "Javascript"

```js
const state = await client.threads.getState(thread["thread_id"]);

for (const m of state['values']['messages']) {
  prettyPrint(m);
}
```

=== "CURL"

```bash
source pretty_print.sh && curl --request GET \
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state | \
jq -c '.values.messages[]' | while read -r element; do
    type=$(echo "$element" | jq -r '.type')
    content=$(echo "$element" | jq -r '.content | if type == "array" then tostring else . end')
    pretty_print "$type" "$content"
done
```

Output:

================================ Human Message =================================

what's the weather in sf?
================================== Ai Message ==================================

[{'id': 'toolu_01MjNtVJwEcpujRGrf3x6Pih', 'input': {'query': 'weather in san francisco'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
Tool Calls:
  tavily_search_results_json (toolu_01MjNtVJwEcpujRGrf3x6Pih)
 Call ID: toolu_01MjNtVJwEcpujRGrf3x6Pih
  Args:
    query: weather in san francisco
================================= Tool Message =================================
Name: tavily_search_results_json

[{"url": "https://www.wunderground.com/hourly/us/ca/san-francisco/KCASANFR2002/date/2024-6-18", "content": "High 64F. Winds W at 10 to 20 mph. A few clouds from time to time. Low 49F. Winds W at 10 to 20 mph. Temp. San Francisco Weather Forecasts. Weather Underground provides local & long-range weather ..."}]
================================ Human Message =================================

what's the weather in nyc?
================================== Ai Message ==================================

[{'id': 'toolu_01KtE1m1ifPLQAx4fQLyZL9Q', 'input': {'query': 'weather in new york city'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
Tool Calls:
  tavily_search_results_json (toolu_01KtE1m1ifPLQAx4fQLyZL9Q)
 Call ID: toolu_01KtE1m1ifPLQAx4fQLyZL9Q
  Args:
    query: weather in new york city
================================= Tool Message =================================
Name: tavily_search_results_json

[{"url": "https://www.accuweather.com/en/us/new-york/10021/june-weather/349727", "content": "Get the monthly weather forecast for New York, NY, including daily high/low, historical averages, to help you plan ahead."}]
================================== Ai Message ==================================

The search results provide weather forecasts and information for New York City. Based on the top result from AccuWeather, here are some key details about the weather in NYC:

- This is a monthly weather forecast for New York City for the month of June.
- It includes daily high and low temperatures to help plan ahead.
- Historical averages for June in NYC are also provided as a reference point.
- More detailed daily or hourly forecasts with precipitation chances, humidity, wind, etc. can be found by visiting the AccuWeather page.

So in summary, the search provides a convenient overview of the expected weather conditions in New York City over the next month to give you an idea of what to prepare for if traveling or making plans there. Let me know if you need any other details!

Verify that the original, interrupted run was interrupted

=== "Python"

```python
print((await client.runs.get(thread["thread_id"], interrupted_run["run_id"]))["status"])
```

=== "Javascript"

```js
console.log((await client.runs.get(thread['thread_id'], interruptedRun["run_id"]))["status"])
```

Output:

'interrupted'

Invoke Assistant

The LangGraph Studio lets you test different configurations and inputs to your graph. It also provides a nice visualization of your graph during execution so it is easy to see which nodes are being run and what the outputs of each individual node are.

The LangGraph Studio UI displays a visualization of the selected assistant.
1. In the top-left dropdown menu of the left-hand pane, select an assistant.
2. In the bottom of the left-hand pane, edit the Input and Configure the assistant.
3. Select Submit to invoke the selected assistant.
View output of the invocation in the right-hand pane.

The following video shows these exact steps being carried out:

Reject

This guide assumes knowledge of what double-texting is, which you can learn about in the double-texting conceptual guide.

The guide covers the reject option for double texting, which rejects the new run of the graph by throwing an error and continues with the original run until completion. Below is a quick example of using the reject option.

Setup

First, we will define a quick helper function for printing out JS and CURL model outputs (you can skip this if using Python):

=== "Javascript"

```js
function prettyPrint(m) {
  const padded = " " + m['type'] + " ";
  const sepLen = Math.floor((80 - padded.length) / 2);
  const sep = "=".repeat(sepLen);
  const secondSep = sep + (padded.length % 2 ? "=" : "");
  
  console.log(`${sep}${padded}${secondSep}`);
  console.log("\n\n");
  console.log(m.content);
}
```

=== "CURL"

```bash
# PLACE THIS IN A FILE CALLED pretty_print.sh
pretty_print() {
  local type="$1"
  local content="$2"
  local padded=" $type "
  local total_width=80
  local sep_len=$(( (total_width - ${#padded}) / 2 ))
  local sep=$(printf '=%.0s' $(eval "echo {1.."${sep_len}"}"))
  local second_sep=$sep
  if (( (total_width - ${#padded}) % 2 )); then
    second_sep="${second_sep}="
  fi

  echo "${sep}${padded}${second_sep}"
  echo
  echo "$content"
}
```

Now, let's import our required packages and instantiate our client, assistant, and thread.

=== "Python"

```python
import httpx
from langchain_core.messages import convert_to_messages
from langgraph_sdk import get_client

client = get_client(url=<DEPLOYMENT_URL>)
# Using the graph deployed with the name "agent"
assistant_id = "agent"
thread = await client.threads.create()
```

=== "Javascript"

```js
import { Client } from "@langchain/langgraph-sdk";

const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
// Using the graph deployed with the name "agent"
const assistantId = "agent";
const thread = await client.threads.create();
```

=== "CURL"

```bash
curl --request POST \
  --url <DEPLOYMENT_URL>/threads \
  --header 'Content-Type: application/json' \
  --data '{}'
```

Create runs

Now we can run a thread and try to run a second one with the "reject" option, which should fail since we have already started a run:

=== "Python"

```python
run = await client.runs.create(
    thread["thread_id"],
    assistant_id,
    input={"messages": [{"role": "user", "content": "what's the weather in sf?"}]},
)
try:
    await client.runs.create(
        thread["thread_id"],
        assistant_id,
        input={
            "messages": [{"role": "user", "content": "what's the weather in nyc?"}]
        },
        multitask_strategy="reject",
    )
except httpx.HTTPStatusError as e:
    print("Failed to start concurrent run", e)
```

=== "Javascript"

```js
const run = await client.runs.create(
  thread["thread_id"],
  assistantId,
  input={"messages": [{"role": "user", "content": "what's the weather in sf?"}]},
);

try {
  await client.runs.create(
    thread["thread_id"],
    assistantId,
    { 
      input: {"messages": [{"role": "user", "content": "what's the weather in nyc?"}]},
      multitask_strategy:"reject"
    },
  );
} catch (e) {
  console.error("Failed to start concurrent run", e);
}
```

=== "CURL"

```bash
curl --request POST \
--url <DEPLOY<ENT_URL>>/threads/<THREAD_ID>/runs \
--header 'Content-Type: application/json' \
--data "{
  \"assistant_id\": \"agent\",
  \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what\'s the weather in sf?\"}]},
}" && curl --request POST \
--url <DEPLOY<ENT_URL>>/threads/<THREAD_ID>/runs \
--header 'Content-Type: application/json' \
--data "{
  \"assistant_id\": \"agent\",
  \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what\'s the weather in nyc?\"}]},
  \"multitask_strategy\": \"reject\"
}" || { echo "Failed to start concurrent run"; echo "Error: $?" >&2; }
```

Output:

Failed to start concurrent run Client error '409 Conflict' for url 'http://localhost:8123/threads/f9e7088b-8028-4e5c-88d2-9cc9a2870e50/runs'
For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/409

View run results

We can verify that the original thread finished executing:

=== "Python"

```python
# wait until the original run completes
await client.runs.join(thread["thread_id"], run["run_id"])

state = await client.threads.get_state(thread["thread_id"])

for m in convert_to_messages(state["values"]["messages"]):
    m.pretty_print()
```

=== "Javascript"

```js
await client.runs.join(thread["thread_id"], run["run_id"]);

const state = await client.threads.getState(thread["thread_id"]);

for (const m of state["values"]["messages"]) {
  prettyPrint(m);
}
```

=== "CURL"

```bash
source pretty_print.sh && curl --request GET \
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/<RUN_ID>/join && \
curl --request GET --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state | \
jq -c '.values.messages[]' | while read -r element; do
    type=$(echo "$element" | jq -r '.type')
    content=$(echo "$element" | jq -r '.content | if type == "array" then tostring else . end')
    pretty_print "$type" "$content"
done
```

Output:

================================ Human Message =================================

what's the weather in sf?
================================== Ai Message ==================================

[{'id': 'toolu_01CyewEifV2Kmi7EFKHbMDr1', 'input': {'query': 'weather in san francisco'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
Tool Calls:
  tavily_search_results_json (toolu_01CyewEifV2Kmi7EFKHbMDr1)
 Call ID: toolu_01CyewEifV2Kmi7EFKHbMDr1
  Args:
    query: weather in san francisco
================================= Tool Message =================================
Name: tavily_search_results_json

[{"url": "https://www.accuweather.com/en/us/san-francisco/94103/june-weather/347629", "content": "Get the monthly weather forecast for San Francisco, CA, including daily high/low, historical averages, to help you plan ahead."}]
================================== Ai Message ==================================

According to the search results from Tavily, the current weather in San Francisco is:

The average high temperature in San Francisco in June is around 65┬░F (18┬░C), with average lows around 54┬░F (12┬░C). June tends to be one of the cooler and foggier months in San Francisco due to the marine layer of fog that often blankets the city during the summer months.

Some key points about the typical June weather in San Francisco:

- Mild temperatures with highs in the 60s F and lows in the 50s F
- Foggy mornings that often burn off to sunny afternoons
- Little to no rainfall, as June falls in the dry season
- Breezy conditions, with winds off the Pacific Ocean
- Layers are recommended for changing weather conditions

So in summary, you can expect mild, foggy mornings giving way to sunny but cool afternoons in San Francisco this time of year. The marine layer keeps temperatures moderate compared to other parts of California in June.

Rollback

This guide assumes knowledge of what double-texting is, which you can learn about in the double-texting conceptual guide.

The guide covers the rollback option for double texting, which interrupts the prior run of the graph and starts a new one with the double-text. This option is very similar to the interrupt option, but in this case the first run is completely deleted from the database and cannot be restarted. Below is a quick example of using the rollback option.

Setup

First, we will define a quick helper function for printing out JS and CURL model outputs (you can skip this if using Python):

=== "Javascript"

```js
function prettyPrint(m) {
  const padded = " " + m['type'] + " ";
  const sepLen = Math.floor((80 - padded.length) / 2);
  const sep = "=".repeat(sepLen);
  const secondSep = sep + (padded.length % 2 ? "=" : "");
  
  console.log(`${sep}${padded}${secondSep}`);
  console.log("\n\n");
  console.log(m.content);
}
```

=== "CURL"

```bash
# PLACE THIS IN A FILE CALLED pretty_print.sh
pretty_print() {
  local type="$1"
  local content="$2"
  local padded=" $type "
  local total_width=80
  local sep_len=$(( (total_width - ${#padded}) / 2 ))
  local sep=$(printf '=%.0s' $(eval "echo {1.."${sep_len}"}"))
  local second_sep=$sep
  if (( (total_width - ${#padded}) % 2 )); then
    second_sep="${second_sep}="
  fi

  echo "${sep}${padded}${second_sep}"
  echo
  echo "$content"
}
```

Now, let's import our required packages and instantiate our client, assistant, and thread.

=== "Python"

```python
import asyncio

import httpx
from langchain_core.messages import convert_to_messages
from langgraph_sdk import get_client

client = get_client(url=<DEPLOYMENT_URL>)
# Using the graph deployed with the name "agent"
assistant_id = "agent"
thread = await client.threads.create()
```

=== "Javascript"

```js
import { Client } from "@langchain/langgraph-sdk";

const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
// Using the graph deployed with the name "agent"
const assistantId = "agent";
const thread = await client.threads.create();
```

=== "CURL"

```bash
curl --request POST \
  --url <DEPLOYMENT_URL>/threads \
  --header 'Content-Type: application/json' \
  --data '{}'
```

Create runs

Now let's run a thread with the multitask parameter set to "rollback":

=== "Python"

```python
# the first run will be rolled back
rolled_back_run = await client.runs.create(
    thread["thread_id"],
    assistant_id,
    input={"messages": [{"role": "user", "content": "what's the weather in sf?"}]},
)
run = await client.runs.create(
    thread["thread_id"],
    assistant_id,
    input={"messages": [{"role": "user", "content": "what's the weather in nyc?"}]},
    multitask_strategy="rollback",
)
# wait until the second run completes
await client.runs.join(thread["thread_id"], run["run_id"])
```

=== "Javascript"

```js
// the first run will be interrupted
let rolledBackRun = await client.runs.create(
  thread["thread_id"],
  assistantId,
  { input: { messages: [{ role: "human", content: "what's the weather in sf?" }] } }
);

let run = await client.runs.create(
  thread["thread_id"],
  assistant_id,
  { 
    input: { messages: [{ role: "human", content: "what's the weather in nyc?" }] },
    multitaskStrategy: "rollback" 
  }
);

// wait until the second run completes
await client.runs.join(thread["thread_id"], run["run_id"]);
```

=== "CURL"

```bash
curl --request POST \
--url <DEPLOY<ENT_URL>>/threads/<THREAD_ID>/runs \
--header 'Content-Type: application/json' \
--data "{
  \"assistant_id\": \"agent\",
  \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what\'s the weather in sf?\"}]},
}" && curl --request POST \
--url <DEPLOY<ENT_URL>>/threads/<THREAD_ID>/runs \
--header 'Content-Type: application/json' \
--data "{
  \"assistant_id\": \"agent\",
  \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what\'s the weather in nyc?\"}]},
  \"multitask_strategy\": \"rollback\"
}" && curl --request GET \
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/<RUN_ID>/join
```

View run results

We can see that the thread has data only from the second run

=== "Python"

```python
state = await client.threads.get_state(thread["thread_id"])

for m in convert_to_messages(state["values"]["messages"]):
    m.pretty_print()
```

=== "Javascript"

```js
const state = await client.threads.getState(thread["thread_id"]);

for (const m of state['values']['messages']) {
  prettyPrint(m);
}
```

=== "CURL"

```bash
source pretty_print.sh && curl --request GET \
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state | \
jq -c '.values.messages[]' | while read -r element; do
    type=$(echo "$element" | jq -r '.type')
    content=$(echo "$element" | jq -r '.content | if type == "array" then tostring else . end')
    pretty_print "$type" "$content"
done
```

Output:

================================ Human Message =================================

what's the weather in nyc?
================================== Ai Message ==================================

[{'id': 'toolu_01JzPqefao1gxwajHQ3Yh3JD', 'input': {'query': 'weather in nyc'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
Tool Calls:
  tavily_search_results_json (toolu_01JzPqefao1gxwajHQ3Yh3JD)
 Call ID: toolu_01JzPqefao1gxwajHQ3Yh3JD
  Args:
    query: weather in nyc
================================= Tool Message =================================
Name: tavily_search_results_json

[{"url": "https://www.weatherapi.com/", "content": "{'location': {'name': 'New York', 'region': 'New York', 'country': 'United States of America', 'lat': 40.71, 'lon': -74.01, 'tz_id': 'America/New_York', 'localtime_epoch': 1718734479, 'localtime': '2024-06-18 14:14'}, 'current': {'last_updated_epoch': 1718733600, 'last_updated': '2024-06-18 14:00', 'temp_c': 29.4, 'temp_f': 84.9, 'is_day': 1, 'condition': {'text': 'Sunny', 'icon': '//cdn.weatherapi.com/weather/64x64/day/113.png', 'code': 1000}, 'wind_mph': 2.2, 'wind_kph': 3.6, 'wind_degree': 158, 'wind_dir': 'SSE', 'pressure_mb': 1025.0, 'pressure_in': 30.26, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 63, 'cloud': 0, 'feelslike_c': 31.3, 'feelslike_f': 88.3, 'windchill_c': 28.3, 'windchill_f': 82.9, 'heatindex_c': 29.6, 'heatindex_f': 85.3, 'dewpoint_c': 18.4, 'dewpoint_f': 65.2, 'vis_km': 16.0, 'vis_miles': 9.0, 'uv': 7.0, 'gust_mph': 16.5, 'gust_kph': 26.5}}"}]
================================== Ai Message ==================================

The weather API results show that the current weather in New York City is sunny with a temperature of around 85┬░F (29┬░C). The wind is light at around 2-3 mph from the south-southeast. Overall it looks like a nice sunny summer day in NYC.

Verify that the original, rolled back run was deleted

=== "Python"

```python
try:
    await client.runs.get(thread["thread_id"], rolled_back_run["run_id"])
except httpx.HTTPStatusError as _:
    print("Original run was correctly deleted")
```

=== "Javascript"

```js
try {
  await client.runs.get(thread["thread_id"], rolledBackRun["run_id"]);
} catch (e) {
  console.log("Original run was correctly deleted");
}
```

Output:

Original run was correctly deleted

How to run multiple agents on the same thread

In LangGraph Cloud, a thread is not explicitly associated with a particular agent. This means that you can run multiple agents on the same thread, which allows a different agent to continue from an initial agent's progress.

In this example, we will create two agents and then call them both on the same thread. You'll see that the second agent will respond using information from the checkpoint generated in the thread by the first agent as context.

Setup

=== "Python"

```python
from langgraph_sdk import get_client

client = get_client(url=<DEPLOYMENT_URL>)

openai_assistant = await client.assistants.create(
    graph_id="agent", config={"configurable": {"model_name": "openai"}}
)

# There should always be a default assistant with no configuration
assistants = await client.assistants.search()
default_assistant = [a for a in assistants if not a["config"]][0]
```

=== "Javascript"

```js
import { Client } from "@langchain/langgraph-sdk";

const client = new Client({ apiUrl: <DEPLOYMENT_URL> });

const openAIAssistant = await client.assistants.create(
  { graphId: "agent", config: {"configurable": {"model_name": "openai"}}}
);

const assistants = await client.assistants.search();
const defaultAssistant = assistants.find(a => !a.config);
```

=== "CURL"

```bash
curl --request POST \
    --url <DEPLOYMENT_URL>/assistants \
    --header 'Content-Type: application/json' \
    --data '{
        "graph_id": "agent",
        "config": { "configurable": { "model_name": "openai" } }
    }' && \
curl --request POST \
    --url <DEPLOYMENT_URL>/assistants/search \
    --header 'Content-Type: application/json' \
    --data '{
        "limit": 10,
        "offset": 0
    }' | jq -c 'map(select(.config == null or .config == {})) | .[0]'
```

We can see that these agents are different:

=== "Python"

```python
print(openai_assistant)
```

=== "Javascript"

```js
console.log(openAIAssistant);
```

=== "CURL"

```bash
curl --request GET \
    --url <DEPLOYMENT_URL>/assistants/<OPENAI_ASSISTANT_ID>
```

Output:

{
    "assistant_id": "db87f39d-b2b1-4da8-ac65-cf81beb3c766",
    "graph_id": "agent",
    "created_at": "2024-08-30T21:18:51.850581+00:00",
    "updated_at": "2024-08-30T21:18:51.850581+00:00",
    "config": {
        "configurable": {
            "model_name": "openai"
        }
    },
    "metadata": {}
}

=== "Python"

```python
print(default_assistant)
```

=== "Javascript"

```js
console.log(defaultAssistant);
```

=== "CURL"

```bash
curl --request GET \
    --url <DEPLOYMENT_URL>/assistants/<DEFAULT_ASSISTANT_ID>
```

Output:

{
    "assistant_id": "fe096781-5601-53d2-b2f6-0d3403f7e9ca",
    "graph_id": "agent",
    "created_at": "2024-08-08T22:45:24.562906+00:00",
    "updated_at": "2024-08-08T22:45:24.562906+00:00",
    "config": {},
    "metadata": {
        "created_by": "system"
    }
}

Run assistants on thread

Run OpenAI assistant

We can now run the OpenAI assistant on the thread first.

=== "Python"

```python
thread = await client.threads.create()
input = {"messages": [{"role": "user", "content": "who made you?"}]}
async for event in client.runs.stream(
    thread["thread_id"],
    openai_assistant["assistant_id"],
    input=input,
    stream_mode="updates",
):
    print(f"Receiving event of type: {event.event}")
    print(event.data)
    print("\n\n")
```

=== "Javascript"

```js
const thread = await client.threads.create();
let input =  {"messages": [{"role": "user", "content": "who made you?"}]}

const streamResponse = client.runs.stream(
  thread["thread_id"],
  openAIAssistant["assistant_id"],
  {
    input,
    streamMode: "updates"
  }
);
for await (const event of streamResponse) {
  console.log(`Receiving event of type: ${event.event}`);
  console.log(event.data);
  console.log("\n\n");
}
```

=== "CURL"

```bash
thread_id=$(curl --request POST \
    --url <DEPLOYMENT_URL>/threads \
    --header 'Content-Type: application/json' \
    --data '{}' | jq -r '.thread_id') && \
curl --request POST \
    --url "<DEPLOYMENT_URL>/threads/${thread_id}/runs/stream" \
    --header 'Content-Type: application/json' \
    --data '{
        "assistant_id": <OPENAI_ASSISTANT_ID>,
        "input": {
            "messages": [
                {
                    "role": "user",
                    "content": "who made you?"
                }
            ]
        },
        "stream_mode": [
            "updates"
        ]
    }' | \
    sed 's/\r$//' | \
    awk '
    /^event:/ {
        if (data_content != "") {
            print data_content "\n"
        }
        sub(/^event: /, "Receiving event of type: ", $0)
        printf "%s...\n", $0
        data_content = ""
    }
    /^data:/ {
        sub(/^data: /, "", $0)
        data_content = $0
    }
    END {
        if (data_content != "") {
            print data_content "\n\n"
        }
    }
'
```

Output:

Receiving event of type: metadata
{'run_id': '1ef671c5-fb83-6e70-b698-44dba2d9213e'}


Receiving event of type: updates
{'agent': {'messages': [{'content': 'I was created by OpenAI, a research organization focused on developing and advancing artificial intelligence technology.', 'additional_kwargs': {}, 'response_metadata': {'finish_reason': 'stop', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_157b3831f5'}, 'type': 'ai', 'name': None, 'id': 'run-f5735b86-b80d-4c71-8dc3-4782b5a9c7c8', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}

Run default assistant

Now, we can run it on the default assistant and see that this second assistant is aware of the initial question, and can answer the question, "and you?":

=== "Python"

```python
input = {"messages": [{"role": "user", "content": "and you?"}]}
async for event in client.runs.stream(
    thread["thread_id"],
    default_assistant["assistant_id"],
    input=input,
    stream_mode="updates",
):
    print(f"Receiving event of type: {event.event}")
    print(event.data)
    print("\n\n")
```

=== "Javascript"

```js
let input =  {"messages": [{"role": "user", "content": "and you?"}]}

const streamResponse = client.runs.stream(
  thread["thread_id"],
  defaultAssistant["assistant_id"],
  {
    input,
    streamMode: "updates"
  }
);
for await (const event of streamResponse) {
  console.log(`Receiving event of type: ${event.event}`);
  console.log(event.data);
  console.log("\n\n");
}
```

=== "CURL"

```bash
curl --request POST \
    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
    --header 'Content-Type: application/json' \
    --data '{
        "assistant_id": <DEFAULT_ASSISTANT_ID>,
        "input": {
            "messages": [
                {
                    "role": "user",
                    "content": "and you?"
                }
            ]
        },
        "stream_mode": [
            "updates"
        ]
    }' | \
    sed 's/\r$//' | \
    awk '
    /^event:/ {
        if (data_content != "") {
            print data_content "\n"
        }
        sub(/^event: /, "Receiving event of type: ", $0)
        printf "%s...\n", $0
        data_content = ""
    }
    /^data:/ {
        sub(/^data: /, "", $0)
        data_content = $0
    }
    END {
        if (data_content != "") {
            print data_content "\n\n"
        }
    }
'
```

Output:

Receiving event of type: metadata
{'run_id': '1ef6722d-80b3-6fbb-9324-253796b1cd13'}


Receiving event of type: updates
{'agent': {'messages': [{'content': [{'text': 'I am an artificial intelligence created by Anthropic, not by OpenAI. I should not have stated that OpenAI created me, as that is incorrect. Anthropic is the company that developed and trained me using advanced language models and AI technology. I will be more careful about providing accurate information regarding my origins in the future.', 'type': 'text', 'index': 0}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'end_turn', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-ebaacf62-9dd9-4165-9535-db432e4793ec', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 302, 'output_tokens': 72, 'total_tokens': 374}}]}}

Stateless Runs

Most of the time, you provide a thread_id to your client when you run your graph in order to keep track of prior runs through the persistent state implemented in LangGraph Cloud. However, if you don't need to persist the runs you don't need to use the built in persistent state and can create stateless runs.

Setup

First, let's setup our client:

=== "Python"

```python
from langgraph_sdk import get_client

client = get_client(url=<DEPLOYMENT_URL>)
# Using the graph deployed with the name "agent"
assistant_id = "agent"
# create thread
thread = await client.threads.create()
```

=== "Javascript"

```js
import { Client } from "@langchain/langgraph-sdk";

const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
// Using the graph deployed with the name "agent"
const assistantId = "agent";
// create thread
const thread = await client.threads.create();
```

=== "CURL"

```bash
curl --request POST \
    --url <DEPLOYMENT_URL>/assistants/search \
    --header 'Content-Type: application/json' \
    --data '{
        "limit": 10,
        "offset": 0
    }' | jq -c 'map(select(.config == null or .config == {})) | .[0].graph_id' && \
curl --request POST \
    --url <DEPLOYMENT_URL>/threads \
    --header 'Content-Type: application/json' \
    --data '{}'
```

Stateless streaming

We can stream the results of a stateless run in an almost identical fashion to how we stream from a run with the state attribute, but instead of passing a value to the thread_id parameter, we pass None:

=== "Python"

```python
input = {
    "messages": [
        {"role": "user", "content": "Hello! My name is Bagatur and I am 26 years old."}
    ]
}

async for chunk in client.runs.stream(
    # Don't pass in a thread_id and the stream will be stateless
    None,
    assistant_id,
    input=input,
    stream_mode="updates",
):
    if chunk.data and "run_id" not in chunk.data:
        print(chunk.data)
```

=== "Javascript"

```js
let input = {
  messages: [
    { role: "user", content: "Hello! My name is Bagatur and I am 26 years old." }
  ]
};

const streamResponse = client.runs.stream(
  // Don't pass in a thread_id and the stream will be stateless
  null,
  assistantId,
  {
    input,
    streamMode: "updates"
  }
);
for await (const chunk of streamResponse) {
  if (chunk.data && !("run_id" in chunk.data)) {
    console.log(chunk.data);
  }
}
```

=== "CURL"

```bash
curl --request POST \
    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
    --header 'Content-Type: application/json' \
    --data "{
        \"assistant_id\": \"agent\",
        \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"Hello! My name is Bagatur and I am 26 years old.\"}]},
        \"stream_mode\": [
            \"updates\"
        ]
    }" | jq -c 'select(.data and (.data | has("run_id") | not)) | .data'
```

Output:

{'agent': {'messages': [{'content': "Hello Bagatur! It's nice to meet you. Thank you for introducing yourself and sharing your age. Is there anything specific you'd like to know or discuss? I'm here to help with any questions or topics you're interested in.", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-489ec573-1645-4ce2-a3b8-91b391d50a71', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}

Waiting for stateless results

In addition to streaming, you can also wait for a stateless result by using the .wait function like follows:

=== "Python"

```python
stateless_run_result = await client.runs.wait(
    None,
    assistant_id,
    input=input,
)
print(stateless_run_result)
```

=== "Javascript"

```js
let statelessRunResult = await client.runs.wait(
  null,
  assistantId,
  { input: input }
);
console.log(statelessRunResult);
```

=== "CURL"

```bash
curl --request POST \
    --url <DEPLOYMENT_URL>/runs/runs/wait \
    --header 'Content-Type: application/json' \
    --data '{
        "assistant_id": <ASSISTANT_IDD>,
    }'
```

Output:

{
    'messages': [
        {
            'content': 'Hello! My name is Bagatur and I am 26 years old.',
            'additional_kwargs': {},
            'response_metadata': {},
            'type': 'human',
            'name': None,
            'id': '5e088543-62c2-43de-9d95-6086ad7f8b48',
            'example': False}
        ,
        {
            'content': "Hello Bagatur! It's nice to meet you. Thank you for introducing yourself and sharing your age. Is there anything specific you'd like to know or discuss? I'm here to help with any questions or topics you'd like to explore.",
            'additional_kwargs': {},
            'response_metadata': {},
            'type': 'ai',
            'name': None,
            'id': 'run-d6361e8d-4d4c-45bd-ba47-39520257f773',
            'example': False,
            'tool_calls': [],
            'invalid_tool_calls': [],
            'usage_metadata': None
        }
    ]
}

How to stream debug events

!!! info "Prerequisites" * Streaming

This guide covers how to stream debug events from your graph (stream_mode="debug"). Streaming debug events produces responses containing type and timestamp keys. Debug events correspond to different steps in the graph's execution, and there are three different types of steps that will get streamed back to you:

checkpoint: These events will get streamed anytime the graph saves its state, which occurs after every super-step. Read more about checkpoints here
task: These events will get streamed before each super-step, and will contain information about a single task. Each super-step works by executing a list of tasks, where each task is scoped to a specific node and input. Below we will discuss the format of these tasks in more detail.
task_result: After each task event, you will see a corresponding task_result event which as the name suggests contains information on the results of the task executed in the super-step. Scroll more to learn about the exact structure of these events.

Setup

First let's set up our client and thread:

=== "Python"

```python
from langgraph_sdk import get_client

client = get_client(url=<DEPLOYMENT_URL>)
# Using the graph deployed with the name "agent"
assistant_id = "agent"
# create thread
thread = await client.threads.create()
print(thread)
```

=== "Javascript"

```js
import { Client } from "@langchain/langgraph-sdk";

const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
// Using the graph deployed with the name "agent"
const assistantID = "agent";
// create thread
const thread = await client.threads.create();
console.log(thread);
```

=== "CURL"

```bash
curl --request POST \
  --url <DEPLOYMENT_URL>/threads \
  --header 'Content-Type: application/json' \
  --data '{}'
```

Output:

{
    'thread_id': 'd0cbe9ad-f11c-443a-9f6f-dca0ae5a0dd3',
    'created_at': '2024-06-21T22:10:27.696862+00:00',
    'updated_at': '2024-06-21T22:10:27.696862+00:00',
    'metadata': {},
    'status': 'idle',
    'config': {},
    'values': None
}

Stream graph in debug mode

=== "Python"

```python
# create input
input = {
    "messages": [
        {
            "role": "user",
            "content": "What's the weather in SF?",
        }
    ]
}

# stream debug
async for chunk in client.runs.stream(
    thread_id=thread["thread_id"],
    assistant_id=assistant_id,
    input=input,
    stream_mode="debug",
):
    print(f"Receiving new event of type: {chunk.event}...")
    print(chunk.data)
    print("\n\n")
```

=== "Javascript"

```js
// create input
const input = {
  messages: [
    {
      role: "human",
      content: "What's the weather in SF?",
    }
  ]
};

// stream debug
const streamResponse = client.runs.stream(
  thread["thread_id"],
  assistantID,
  {
    input,
    streamMode: "debug"
  }
);

for await (const chunk of streamResponse) {
  console.log(`Receiving new event of type: ${chunk.event}...`);
  console.log(chunk.data);
  console.log("\n\n");
}
```

=== "CURL"

```bash
curl --request POST \
 --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
 --header 'Content-Type: application/json' \
 --data "{
   \"assistant_id\": \"agent\",
   \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"What's the weather in SF?\"}]},
   \"stream_mode\": [
     \"debug\"
   ]
 }" | \
 sed 's/\r$//' | \
 awk '
 /^event:/ {
     if (data_content != "") {
         print data_content "\n"
     }
     sub(/^event: /, "Receiving event of type: ", $0)
     printf "%s...\n", $0
     data_content = ""
 }
 /^data:/ {
     sub(/^data: /, "", $0)
     data_content = $0
 }
 END {
     if (data_content != "") {
         print data_content "\n"
     }
 }
 ' 
```

Output:

Receiving new event of type: metadata...
{'run_id': '1ef65938-d7c7-68db-b786-011aa1cb3cd2'}



Receiving new event of type: debug...
{'type': 'checkpoint', 'timestamp': '2024-08-28T23:16:28.134680+00:00', 'step': -1, 'payload': {'config': {'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef65938-d7c7-68db-b786-011aa1cb3cd2', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'be4fd54d-ff22-4e9e-8876-d5cccc0e8048', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'callbacks': [None], 'recursion_limit': 25, 'configurable': {'run_id': '1ef65938-d7c7-68db-b786-011aa1cb3cd2', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'be4fd54d-ff22-4e9e-8876-d5cccc0e8048', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'checkpoint_id': '1ef65938-d8f3-6b25-bfff-30a8ed6460bd', 'checkpoint_ns': ''}, 'run_id': '1ef65938-d7c7-68db-b786-011aa1cb3cd2'}, 'values': {'messages': [], 'search_results': []}, 'metadata': {'source': 'input', 'writes': {'messages': [{'role': 'human', 'content': "What's the weather in SF?"}]}, 'step': -1}, 'next': ['__start__'], 'tasks': [{'id': 'b40d2c90-dc1e-52db-82d6-08751b769c55', 'name': '__start__', 'interrupts': []}]}}



Receiving new event of type: debug...
{'type': 'checkpoint', 'timestamp': '2024-08-28T23:16:28.139821+00:00', 'step': 0, 'payload': {'config': {'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef65938-d7c7-68db-b786-011aa1cb3cd2', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'be4fd54d-ff22-4e9e-8876-d5cccc0e8048', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'callbacks': [None], 'recursion_limit': 25, 'configurable': {'run_id': '1ef65938-d7c7-68db-b786-011aa1cb3cd2', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'be4fd54d-ff22-4e9e-8876-d5cccc0e8048', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'checkpoint_id': '1ef65938-d900-63f1-8000-70fe53e0da5c', 'checkpoint_ns': ''}, 'run_id': '1ef65938-d7c7-68db-b786-011aa1cb3cd2'}, 'values': {'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '4123a12c-46cb-4815-bdcc-32537af0cb5b', 'example': False}], 'search_results': []}, 'metadata': {'source': 'loop', 'writes': None, 'step': 0}, 'next': ['call_model'], 'tasks': [{'id': '685d89f6-542b-5e11-8cff-2963e7f4ea63', 'name': 'call_model', 'interrupts': []}]}}



Receiving new event of type: debug...
{'type': 'task', 'timestamp': '2024-08-28T23:16:28.139928+00:00', 'step': 1, 'payload': {'id': '600a6ff3-7ff1-570a-b626-f887e9a70f1c', 'name': 'call_model', 'input': {'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '4123a12c-46cb-4815-bdcc-32537af0cb5b', 'example': False}], 'search_results': [], 'final_answer': None}, 'triggers': ['start:call_model']}}



Receiving new event of type: debug...
{'type': 'task_result', 'timestamp': '2024-08-28T23:16:28.584833+00:00', 'step': 1, 'payload': {'id': '600a6ff3-7ff1-570a-b626-f887e9a70f1c', 'name': 'call_model', 'error': None, 'result': [['messages', {'content': 'Current weather in San Francisco', 'additional_kwargs': {}, 'response_metadata': {'finish_reason': 'stop', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_a2ff031fb5'}, 'type': 'ai', 'name': None, 'id': 'run-0407bff9-3692-4ab5-9e57-2e9f396a3ee4', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]], 'interrupts': []}}



Receiving new event of type: debug...
{'type': 'checkpoint', 'timestamp': '2024-08-28T23:16:28.584991+00:00', 'step': 1, 'payload': {'config': {'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef65938-d7c7-68db-b786-011aa1cb3cd2', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'be4fd54d-ff22-4e9e-8876-d5cccc0e8048', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'callbacks': [None], 'recursion_limit': 25, 'configurable': {'run_id': '1ef65938-d7c7-68db-b786-011aa1cb3cd2', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'be4fd54d-ff22-4e9e-8876-d5cccc0e8048', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'checkpoint_id': '1ef65938-dd3f-616f-8001-ce1c6f31e130', 'checkpoint_ns': ''}, 'run_id': '1ef65938-d7c7-68db-b786-011aa1cb3cd2'}, 'values': {'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '4123a12c-46cb-4815-bdcc-32537af0cb5b', 'example': False}, {'content': 'Current weather in San Francisco', 'additional_kwargs': {}, 'response_metadata': {'finish_reason': 'stop', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_a2ff031fb5'}, 'type': 'ai', 'name': None, 'id': 'run-0407bff9-3692-4ab5-9e57-2e9f396a3ee4', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}], 'search_results': []}, 'metadata': {'source': 'loop', 'writes': {'call_model': {'messages': {'content': 'Current weather in San Francisco', 'additional_kwargs': {}, 'response_metadata': {'finish_reason': 'stop', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_a2ff031fb5'}, 'type': 'ai', 'name': None, 'id': 'run-0407bff9-3692-4ab5-9e57-2e9f396a3ee4', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}}}, 'step': 1}, 'next': ['exa_search', 'tavily_search'], 'tasks': [{'id': '43865935-be38-5f6e-8d38-d44ef369c278', 'name': 'exa_search', 'interrupts': []}, {'id': 'dc220677-2720-56c7-a524-caaff60fce2c', 'name': 'tavily_search', 'interrupts': []}]}}



Receiving new event of type: debug...
{'type': 'task', 'timestamp': '2024-08-28T23:16:28.585219+00:00', 'step': 2, 'payload': {'id': '870b5854-2f84-533d-8e7d-87158ee948fc', 'name': 'exa_search', 'input': {'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '4123a12c-46cb-4815-bdcc-32537af0cb5b', 'example': False}, {'content': 'Current weather in San Francisco', 'additional_kwargs': {}, 'response_metadata': {'finish_reason': 'stop', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_a2ff031fb5'}, 'type': 'ai', 'name': None, 'id': 'run-0407bff9-3692-4ab5-9e57-2e9f396a3ee4', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}], 'search_results': [], 'final_answer': None}, 'triggers': ['call_model']}}



Receiving new event of type: debug...
{'type': 'task', 'timestamp': '2024-08-28T23:16:28.585219+00:00', 'step': 2, 'payload': {'id': '7589abfc-04df-58c6-8835-be172f84a7ff', 'name': 'tavily_search', 'input': {'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '4123a12c-46cb-4815-bdcc-32537af0cb5b', 'example': False}, {'content': 'Current weather in San Francisco', 'additional_kwargs': {}, 'response_metadata': {'finish_reason': 'stop', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_a2ff031fb5'}, 'type': 'ai', 'name': None, 'id': 'run-0407bff9-3692-4ab5-9e57-2e9f396a3ee4', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}], 'search_results': [], 'final_answer': None}, 'triggers': ['call_model']}}



Receiving new event of type: debug...
{'type': 'task_result', 'timestamp': '2024-08-28T23:16:32.422243+00:00', 'step': 2, 'payload': {'id': '7589abfc-04df-58c6-8835-be172f84a7ff', 'name': 'tavily_search', 'error': None, 'result': [['search_results', ["{'location': {'name': 'San Francisco', 'region': 'California', 'country': 'United States of America', 'lat': 37.78, 'lon': -122.42, 'tz_id': 'America/Los_Angeles', 'localtime_epoch': 1724886988, 'localtime': '2024-08-28 16:16'}, 'current': {'last_updated_epoch': 1724886900, 'last_updated': '2024-08-28 16:15', 'temp_c': 22.2, 'temp_f': 72.0, 'is_day': 1, 'condition': {'text': 'Partly cloudy', 'icon': '//cdn.weatherapi.com/weather/64x64/day/116.png', 'code': 1003}, 'wind_mph': 16.1, 'wind_kph': 25.9, 'wind_degree': 300, 'wind_dir': 'WNW', 'pressure_mb': 1013.0, 'pressure_in': 29.91, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 61, 'cloud': 25, 'feelslike_c': 24.6, 'feelslike_f': 76.4, 'windchill_c': 19.6, 'windchill_f': 67.2, 'heatindex_c': 19.7, 'heatindex_f': 67.4, 'dewpoint_c': 13.0, 'dewpoint_f': 55.5, 'vis_km': 16.0, 'vis_miles': 9.0, 'uv': 5.0, 'gust_mph': 18.7, 'gust_kph': 30.0}}"]]], 'interrupts': []}}



Receiving new event of type: debug...
{'type': 'task_result', 'timestamp': '2024-08-28T23:16:34.750124+00:00', 'step': 2, 'payload': {'id': '870b5854-2f84-533d-8e7d-87158ee948fc', 'name': 'exa_search', 'error': None, 'result': [['search_results', ['The time period when the sun is no more than 6 degrees below the horizon at either sunrise or sunset. The horizon should be clearly defined and the brightest stars should be visible under good atmospheric conditions (i.e. no moonlight, or other lights). One still should be able to carry on ordinary outdoor activities. The time period when the sun is between 6 and 12 degrees below the horizon at either sunrise or sunset. The horizon is well defined and the outline of objects might be visible without artificial light. Ordinary outdoor activities are not possible at this time without extra illumination. The time period when the sun is between 12 and 18 degrees below the horizon at either sunrise or sunset. The sun does not contribute to the illumination of the sky before this time in the morning, or after this time in the evening. In the beginning of morning astronomical twilight and at the end of astronomical twilight in the evening, sky illumination is very faint, and might be undetectable. The time of Civil Sunset minus the time of Civil Sunrise. The time of Actual Sunset minus the time of Actual Sunrise. The change in length of daylight between today and tomorrow is also listed when available.']]], 'interrupts': []}}



Receiving new event of type: debug...
{'type': 'checkpoint', 'timestamp': '2024-08-28T23:16:34.750266+00:00', 'step': 2, 'payload': {'config': {'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef65938-d7c7-68db-b786-011aa1cb3cd2', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'be4fd54d-ff22-4e9e-8876-d5cccc0e8048', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'callbacks': [None], 'recursion_limit': 25, 'configurable': {'run_id': '1ef65938-d7c7-68db-b786-011aa1cb3cd2', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'be4fd54d-ff22-4e9e-8876-d5cccc0e8048', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'checkpoint_id': '1ef65939-180b-6087-8002-f969296f8e3d', 'checkpoint_ns': ''}, 'run_id': '1ef65938-d7c7-68db-b786-011aa1cb3cd2'}, 'values': {'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '4123a12c-46cb-4815-bdcc-32537af0cb5b', 'example': False}, {'content': 'Current weather in San Francisco', 'additional_kwargs': {}, 'response_metadata': {'finish_reason': 'stop', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_a2ff031fb5'}, 'type': 'ai', 'name': None, 'id': 'run-0407bff9-3692-4ab5-9e57-2e9f396a3ee4', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}], 'search_results': ['The time period when the sun is no more than 6 degrees below the horizon at either sunrise or sunset. The horizon should be clearly defined and the brightest stars should be visible under good atmospheric conditions (i.e. no moonlight, or other lights). One still should be able to carry on ordinary outdoor activities. The time period when the sun is between 6 and 12 degrees below the horizon at either sunrise or sunset. The horizon is well defined and the outline of objects might be visible without artificial light. Ordinary outdoor activities are not possible at this time without extra illumination. The time period when the sun is between 12 and 18 degrees below the horizon at either sunrise or sunset. The sun does not contribute to the illumination of the sky before this time in the morning, or after this time in the evening. In the beginning of morning astronomical twilight and at the end of astronomical twilight in the evening, sky illumination is very faint, and might be undetectable. The time of Civil Sunset minus the time of Civil Sunrise. The time of Actual Sunset minus the time of Actual Sunrise. The change in length of daylight between today and tomorrow is also listed when available.', "{'location': {'name': 'San Francisco', 'region': 'California', 'country': 'United States of America', 'lat': 37.78, 'lon': -122.42, 'tz_id': 'America/Los_Angeles', 'localtime_epoch': 1724886988, 'localtime': '2024-08-28 16:16'}, 'current': {'last_updated_epoch': 1724886900, 'last_updated': '2024-08-28 16:15', 'temp_c': 22.2, 'temp_f': 72.0, 'is_day': 1, 'condition': {'text': 'Partly cloudy', 'icon': '//cdn.weatherapi.com/weather/64x64/day/116.png', 'code': 1003}, 'wind_mph': 16.1, 'wind_kph': 25.9, 'wind_degree': 300, 'wind_dir': 'WNW', 'pressure_mb': 1013.0, 'pressure_in': 29.91, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 61, 'cloud': 25, 'feelslike_c': 24.6, 'feelslike_f': 76.4, 'windchill_c': 19.6, 'windchill_f': 67.2, 'heatindex_c': 19.7, 'heatindex_f': 67.4, 'dewpoint_c': 13.0, 'dewpoint_f': 55.5, 'vis_km': 16.0, 'vis_miles': 9.0, 'uv': 5.0, 'gust_mph': 18.7, 'gust_kph': 30.0}}"]}, 'metadata': {'source': 'loop', 'writes': {'exa_search': {'search_results': ['The time period when the sun is no more than 6 degrees below the horizon at either sunrise or sunset. The horizon should be clearly defined and the brightest stars should be visible under good atmospheric conditions (i.e. no moonlight, or other lights). One still should be able to carry on ordinary outdoor activities. The time period when the sun is between 6 and 12 degrees below the horizon at either sunrise or sunset. The horizon is well defined and the outline of objects might be visible without artificial light. Ordinary outdoor activities are not possible at this time without extra illumination. The time period when the sun is between 12 and 18 degrees below the horizon at either sunrise or sunset. The sun does not contribute to the illumination of the sky before this time in the morning, or after this time in the evening. In the beginning of morning astronomical twilight and at the end of astronomical twilight in the evening, sky illumination is very faint, and might be undetectable. The time of Civil Sunset minus the time of Civil Sunrise. The time of Actual Sunset minus the time of Actual Sunrise. The change in length of daylight between today and tomorrow is also listed when available.']}, 'tavily_search': {'search_results': ["{'location': {'name': 'San Francisco', 'region': 'California', 'country': 'United States of America', 'lat': 37.78, 'lon': -122.42, 'tz_id': 'America/Los_Angeles', 'localtime_epoch': 1724886988, 'localtime': '2024-08-28 16:16'}, 'current': {'last_updated_epoch': 1724886900, 'last_updated': '2024-08-28 16:15', 'temp_c': 22.2, 'temp_f': 72.0, 'is_day': 1, 'condition': {'text': 'Partly cloudy', 'icon': '//cdn.weatherapi.com/weather/64x64/day/116.png', 'code': 1003}, 'wind_mph': 16.1, 'wind_kph': 25.9, 'wind_degree': 300, 'wind_dir': 'WNW', 'pressure_mb': 1013.0, 'pressure_in': 29.91, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 61, 'cloud': 25, 'feelslike_c': 24.6, 'feelslike_f': 76.4, 'windchill_c': 19.6, 'windchill_f': 67.2, 'heatindex_c': 19.7, 'heatindex_f': 67.4, 'dewpoint_c': 13.0, 'dewpoint_f': 55.5, 'vis_km': 16.0, 'vis_miles': 9.0, 'uv': 5.0, 'gust_mph': 18.7, 'gust_kph': 30.0}}"]}}, 'step': 2}, 'next': ['summarize_search_results'], 'tasks': [{'id': '7263c738-516d-5708-b318-2c8ef54d4a33', 'name': 'summarize_search_results', 'interrupts': []}]}}



Receiving new event of type: debug...
{'type': 'task', 'timestamp': '2024-08-28T23:16:34.750394+00:00', 'step': 3, 'payload': {'id': '5beaa05d-57d4-5acd-95c1-c7093990910f', 'name': 'summarize_search_results', 'input': {'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '4123a12c-46cb-4815-bdcc-32537af0cb5b', 'example': False}, {'content': 'Current weather in San Francisco', 'additional_kwargs': {}, 'response_metadata': {'finish_reason': 'stop', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_a2ff031fb5'}, 'type': 'ai', 'name': None, 'id': 'run-0407bff9-3692-4ab5-9e57-2e9f396a3ee4', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}], 'search_results': ['The time period when the sun is no more than 6 degrees below the horizon at either sunrise or sunset. The horizon should be clearly defined and the brightest stars should be visible under good atmospheric conditions (i.e. no moonlight, or other lights). One still should be able to carry on ordinary outdoor activities. The time period when the sun is between 6 and 12 degrees below the horizon at either sunrise or sunset. The horizon is well defined and the outline of objects might be visible without artificial light. Ordinary outdoor activities are not possible at this time without extra illumination. The time period when the sun is between 12 and 18 degrees below the horizon at either sunrise or sunset. The sun does not contribute to the illumination of the sky before this time in the morning, or after this time in the evening. In the beginning of morning astronomical twilight and at the end of astronomical twilight in the evening, sky illumination is very faint, and might be undetectable. The time of Civil Sunset minus the time of Civil Sunrise. The time of Actual Sunset minus the time of Actual Sunrise. The change in length of daylight between today and tomorrow is also listed when available.', "{'location': {'name': 'San Francisco', 'region': 'California', 'country': 'United States of America', 'lat': 37.78, 'lon': -122.42, 'tz_id': 'America/Los_Angeles', 'localtime_epoch': 1724886988, 'localtime': '2024-08-28 16:16'}, 'current': {'last_updated_epoch': 1724886900, 'last_updated': '2024-08-28 16:15', 'temp_c': 22.2, 'temp_f': 72.0, 'is_day': 1, 'condition': {'text': 'Partly cloudy', 'icon': '//cdn.weatherapi.com/weather/64x64/day/116.png', 'code': 1003}, 'wind_mph': 16.1, 'wind_kph': 25.9, 'wind_degree': 300, 'wind_dir': 'WNW', 'pressure_mb': 1013.0, 'pressure_in': 29.91, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 61, 'cloud': 25, 'feelslike_c': 24.6, 'feelslike_f': 76.4, 'windchill_c': 19.6, 'windchill_f': 67.2, 'heatindex_c': 19.7, 'heatindex_f': 67.4, 'dewpoint_c': 13.0, 'dewpoint_f': 55.5, 'vis_km': 16.0, 'vis_miles': 9.0, 'uv': 5.0, 'gust_mph': 18.7, 'gust_kph': 30.0}}"], 'final_answer': None}, 'triggers': ['exa_search', 'tavily_search']}}



Receiving new event of type: debug...
{'type': 'task_result', 'timestamp': '2024-08-28T23:16:35.851058+00:00', 'step': 3, 'payload': {'id': '5beaa05d-57d4-5acd-95c1-c7093990910f', 'name': 'summarize_search_results', 'error': None, 'result': [['final_answer', {'content': "The provided data details various twilight periods based on the sun's position relative to the horizon, alongside current weather information for San Francisco, California, as of August 28, 2024. The weather is partly cloudy with a temperature of 22.2┬░C (72.0┬░F), moderate wind from the WNW at 16.1 mph, and the UV index is 5.", 'additional_kwargs': {}, 'response_metadata': {'finish_reason': 'stop', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_157b3831f5'}, 'type': 'ai', 'name': None, 'id': 'run-928c997b-9d85-4664-bd20-97ade4cc655e', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]], 'interrupts': []}}



Receiving new event of type: debug...
{'type': 'checkpoint', 'timestamp': '2024-08-28T23:16:35.851194+00:00', 'step': 3, 'payload': {'config': {'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef65938-d7c7-68db-b786-011aa1cb3cd2', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'be4fd54d-ff22-4e9e-8876-d5cccc0e8048', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'callbacks': [None], 'recursion_limit': 25, 'configurable': {'run_id': '1ef65938-d7c7-68db-b786-011aa1cb3cd2', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'be4fd54d-ff22-4e9e-8876-d5cccc0e8048', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'checkpoint_id': '1ef65939-228a-6d93-8003-8b06d7483024', 'checkpoint_ns': ''}, 'run_id': '1ef65938-d7c7-68db-b786-011aa1cb3cd2'}, 'values': {'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '4123a12c-46cb-4815-bdcc-32537af0cb5b', 'example': False}, {'content': 'Current weather in San Francisco', 'additional_kwargs': {}, 'response_metadata': {'finish_reason': 'stop', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_a2ff031fb5'}, 'type': 'ai', 'name': None, 'id': 'run-0407bff9-3692-4ab5-9e57-2e9f396a3ee4', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}], 'search_results': ['The time period when the sun is no more than 6 degrees below the horizon at either sunrise or sunset. The horizon should be clearly defined and the brightest stars should be visible under good atmospheric conditions (i.e. no moonlight, or other lights). One still should be able to carry on ordinary outdoor activities. The time period when the sun is between 6 and 12 degrees below the horizon at either sunrise or sunset. The horizon is well defined and the outline of objects might be visible without artificial light. Ordinary outdoor activities are not possible at this time without extra illumination. The time period when the sun is between 12 and 18 degrees below the horizon at either sunrise or sunset. The sun does not contribute to the illumination of the sky before this time in the morning, or after this time in the evening. In the beginning of morning astronomical twilight and at the end of astronomical twilight in the evening, sky illumination is very faint, and might be undetectable. The time of Civil Sunset minus the time of Civil Sunrise. The time of Actual Sunset minus the time of Actual Sunrise. The change in length of daylight between today and tomorrow is also listed when available.', "{'location': {'name': 'San Francisco', 'region': 'California', 'country': 'United States of America', 'lat': 37.78, 'lon': -122.42, 'tz_id': 'America/Los_Angeles', 'localtime_epoch': 1724886988, 'localtime': '2024-08-28 16:16'}, 'current': {'last_updated_epoch': 1724886900, 'last_updated': '2024-08-28 16:15', 'temp_c': 22.2, 'temp_f': 72.0, 'is_day': 1, 'condition': {'text': 'Partly cloudy', 'icon': '//cdn.weatherapi.com/weather/64x64/day/116.png', 'code': 1003}, 'wind_mph': 16.1, 'wind_kph': 25.9, 'wind_degree': 300, 'wind_dir': 'WNW', 'pressure_mb': 1013.0, 'pressure_in': 29.91, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 61, 'cloud': 25, 'feelslike_c': 24.6, 'feelslike_f': 76.4, 'windchill_c': 19.6, 'windchill_f': 67.2, 'heatindex_c': 19.7, 'heatindex_f': 67.4, 'dewpoint_c': 13.0, 'dewpoint_f': 55.5, 'vis_km': 16.0, 'vis_miles': 9.0, 'uv': 5.0, 'gust_mph': 18.7, 'gust_kph': 30.0}}"], 'final_answer': {'content': "The provided data details various twilight periods based on the sun's position relative to the horizon, alongside current weather information for San Francisco, California, as of August 28, 2024. The weather is partly cloudy with a temperature of 22.2┬░C (72.0┬░F), moderate wind from the WNW at 16.1 mph, and the UV index is 5.", 'additional_kwargs': {}, 'response_metadata': {'finish_reason': 'stop', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_157b3831f5'}, 'type': 'ai', 'name': None, 'id': 'run-928c997b-9d85-4664-bd20-97ade4cc655e', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}}, 'metadata': {'source': 'loop', 'writes': {'summarize_search_results': {'final_answer': {'content': "The provided data details various twilight periods based on the sun's position relative to the horizon, alongside current weather information for San Francisco, California, as of August 28, 2024. The weather is partly cloudy with a temperature of 22.2┬░C (72.0┬░F), moderate wind from the WNW at 16.1 mph, and the UV index is 5.", 'additional_kwargs': {}, 'response_metadata': {'finish_reason': 'stop', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_157b3831f5'}, 'type': 'ai', 'name': None, 'id': 'run-928c997b-9d85-4664-bd20-97ade4cc655e', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}}}, 'step': 3}, 'next': [], 'tasks': []}}

We see that our debug events start with two checkpoint events at step 0 and 1, which represent checkpointing before the graph is created and after it has been created. We then see a single task and corresponding task_result which corresponds to our first node, call_model, being triggered. After it has finished, the entire super-step is over so the graph saves another checkpoint and we see the corresponding checkpoint event.

The next super-step executed two search nodes in parallel - specifically one node will execute an Exa search, while the other will use Tavily. Executing these nodes in parallel in the same super-step creates 2 task events and two corresponding task_result events. After we receive both of those task_result events, we see another checkpoint event as we would expect.

Lastly, we see a final task and task_result pair corresponding to the summarize_search_results node, which is the last node in our graph. As soon as this super-step is done we see one final checkpoint event corresponding to the final checkpoint of this run.

How to stream events

!!! info "Prerequisites" * Streaming

This guide covers how to stream events from your graph (stream_mode="events"). Depending on the use case and user experience of your LangGraph application, your application may process event types differently.

Setup

=== "Python"

```python
from langgraph_sdk import get_client

client = get_client(url=<DEPLOYMENT_URL>)
# Using the graph deployed with the name "agent"
assistant_id = "agent"
# create thread
thread = await client.threads.create()
print(thread)
```

=== "Javascript"

```js
import { Client } from "@langchain/langgraph-sdk";

const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
// Using the graph deployed with the name "agent"
const assistantID = "agent";
// create thread
const thread = await client.threads.create();
console.log(thread);
```

=== "CURL"

```bash
curl --request POST \
  --url <DEPLOYMENT_URL>/threads \
  --header 'Content-Type: application/json' \
  --data '{}'
```

Output:

{
    'thread_id': '3f4c64e0-f792-4a5e-aa07-a4404e06e0bd',
    'created_at': '2024-06-24T22:16:29.301522+00:00',
    'updated_at': '2024-06-24T22:16:29.301522+00:00',
    'metadata': {},
    'status': 'idle',
    'config': {},
    'values': None
}

Stream graph in events mode

Streaming events produces responses containing an event key (in addition to other keys such as data). See the LangChain Runnable.astream_events() reference for all event types.

=== "Python"

```python
# create input
input = {
    "messages": [
        {
            "role": "user",
            "content": "What's the weather in SF?",
        }
    ]
}

# stream events
async for chunk in client.runs.stream(
    thread_id=thread["thread_id"],
    assistant_id=assistant_id,
    input=input,
    stream_mode="events",
):
    print(f"Receiving new event of type: {chunk.event}...")
    print(chunk.data)
    print("\n\n")
```

=== "Javascript"

```js
// create input
const input = {
  "messages": [
    {
      "role": "user",
      "content": "What's the weather in SF?",
    }
  ]
}

// stream events
const streamResponse = client.runs.stream(
  thread["thread_id"],
  assistantID,
  {
    input,
    streamMode: "events"
  }
);
for await (const chunk of streamResponse) {
  console.log(`Receiving new event of type: ${chunk.event}...`);
  console.log(chunk.data);
  console.log("\n\n");
}
```

=== "CURL"

```bash
curl --request POST \
 --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
 --header 'Content-Type: application/json' \
 --data "{
   \"assistant_id\": \"agent\",
   \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"What's the weather in sf\"}]},
   \"stream_mode\": [
     \"events\"
   ]
 }" | \
 sed 's/\r$//' | \
 awk '
 /^event:/ {
     if (data_content != "") {
         print data_content "\n"
     }
     sub(/^event: /, "Receiving event of type: ", $0)
     printf "%s...\n", $0
     data_content = ""
 }
 /^data:/ {
     sub(/^data: /, "", $0)
     data_content = $0
 }
 END {
     if (data_content != "") {
         print data_content "\n"
     }
 }
 ' 
```

Output:

Receiving new event of type: metadata...
{'run_id': '1ef301a5-b867-67de-9e9e-a32e53c5b1f8'}



Receiving new event of type: events...
{'event': 'on_chain_start', 'data': {'input': {'messages': [{'role': 'human', 'content': "What's the weather in SF?"}]}}, 'name': 'LangGraph', 'tags': [], 'run_id': '1ef301a5-b867-67de-9e9e-a32e53c5b1f8', 'metadata': {'graph_id': 'agent', 'created_by': 'system', 'run_id': '1ef301a5-b867-67de-9e9e-a32e53c5b1f8', 'user_id': '', 'thread_id': '7196a3aa-763c-4a8d-bfda-12fbfe1cd727', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'parent_ids': []}



Receiving new event of type: events...
{'event': 'on_chain_start', 'data': {}, 'name': 'agent', 'tags': ['graph:step:6'], 'run_id': '7bb08493-d507-4e28-b9e6-4a5eda9d04f0', 'metadata': {'graph_id': 'agent', 'created_by': 'system', 'run_id': '1ef301a5-b867-67de-9e9e-a32e53c5b1f8', 'user_id': '', 'thread_id': '7196a3aa-763c-4a8d-bfda-12fbfe1cd727', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 6, 'langgraph_node': 'agent', 'langgraph_triggers': ['start:agent'], 'langgraph_task_idx': 0}, 'parent_ids': ['1ef301a5-b867-67de-9e9e-a32e53c5b1f8']}



Receiving new event of type: events...
{'event': 'on_chat_model_start', 'data': {'input': {'messages': [[{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '51f2874d-f8c7-4040-8b3b-8f15429a56ae', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-5f556aa0-26ea-42e2-b9e4-7ece3a00974e', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '1faf5dd0-ae97-4235-963f-5075083a027a', 'tool_call_id': 'tool_call_id'}, {'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-ae383611-6a42-475a-912a-09d5972e9e94', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'c67e08e6-e7af-4c4a-aa5e-50c8340ae341', 'example': False}]]}}, 'name': 'FakeListChatModel', 'tags': ['seq:step:1'], 'run_id': 'cb1b98c1-c9e2-4a30-9d7a-38fa1f6224bd', 'metadata': {'graph_id': 'agent', 'created_by': 'system', 'run_id': '1ef301a5-b867-67de-9e9e-a32e53c5b1f8', 'user_id': '', 'thread_id': '7196a3aa-763c-4a8d-bfda-12fbfe1cd727', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 6, 'langgraph_node': 'agent', 'langgraph_triggers': ['start:agent'], 'langgraph_task_idx': 0, 'ls_model_type': 'chat'}, 'parent_ids': ['1ef301a5-b867-67de-9e9e-a32e53c5b1f8', '7bb08493-d507-4e28-b9e6-4a5eda9d04f0']}



Receiving new event of type: events...
{'event': 'on_chat_model_stream', 'data': {'chunk': {'content': 'b', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-cb1b98c1-c9e2-4a30-9d7a-38fa1f6224bd', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}}, 'run_id': 'cb1b98c1-c9e2-4a30-9d7a-38fa1f6224bd', 'name': 'FakeListChatModel', 'tags': ['seq:step:1'], 'metadata': {'graph_id': 'agent', 'created_by': 'system', 'run_id': '1ef301a5-b867-67de-9e9e-a32e53c5b1f8', 'user_id': '', 'thread_id': '7196a3aa-763c-4a8d-bfda-12fbfe1cd727', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 6, 'langgraph_node': 'agent', 'langgraph_triggers': ['start:agent'], 'langgraph_task_idx': 0, 'ls_model_type': 'chat'}, 'parent_ids': ['1ef301a5-b867-67de-9e9e-a32e53c5b1f8', '7bb08493-d507-4e28-b9e6-4a5eda9d04f0']}



Receiving new event of type: events...
{'event': 'on_chat_model_stream', 'data': {'chunk': {'content': 'e', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-cb1b98c1-c9e2-4a30-9d7a-38fa1f6224bd', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}}, 'run_id': 'cb1b98c1-c9e2-4a30-9d7a-38fa1f6224bd', 'name': 'FakeListChatModel', 'tags': ['seq:step:1'], 'metadata': {'graph_id': 'agent', 'created_by': 'system', 'run_id': '1ef301a5-b867-67de-9e9e-a32e53c5b1f8', 'user_id': '', 'thread_id': '7196a3aa-763c-4a8d-bfda-12fbfe1cd727', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 6, 'langgraph_node': 'agent', 'langgraph_triggers': ['start:agent'], 'langgraph_task_idx': 0, 'ls_model_type': 'chat'}, 'parent_ids': ['1ef301a5-b867-67de-9e9e-a32e53c5b1f8', '7bb08493-d507-4e28-b9e6-4a5eda9d04f0']}



Receiving new event of type: events...
{'event': 'on_chat_model_stream', 'data': {'chunk': {'content': 'g', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-cb1b98c1-c9e2-4a30-9d7a-38fa1f6224bd', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}}, 'run_id': 'cb1b98c1-c9e2-4a30-9d7a-38fa1f6224bd', 'name': 'FakeListChatModel', 'tags': ['seq:step:1'], 'metadata': {'graph_id': 'agent', 'created_by': 'system', 'run_id': '1ef301a5-b867-67de-9e9e-a32e53c5b1f8', 'user_id': '', 'thread_id': '7196a3aa-763c-4a8d-bfda-12fbfe1cd727', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 6, 'langgraph_node': 'agent', 'langgraph_triggers': ['start:agent'], 'langgraph_task_idx': 0, 'ls_model_type': 'chat'}, 'parent_ids': ['1ef301a5-b867-67de-9e9e-a32e53c5b1f8', '7bb08493-d507-4e28-b9e6-4a5eda9d04f0']}



Receiving new event of type: events...
{'event': 'on_chat_model_stream', 'data': {'chunk': {'content': 'i', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-cb1b98c1-c9e2-4a30-9d7a-38fa1f6224bd', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}}, 'run_id': 'cb1b98c1-c9e2-4a30-9d7a-38fa1f6224bd', 'name': 'FakeListChatModel', 'tags': ['seq:step:1'], 'metadata': {'graph_id': 'agent', 'created_by': 'system', 'run_id': '1ef301a5-b867-67de-9e9e-a32e53c5b1f8', 'user_id': '', 'thread_id': '7196a3aa-763c-4a8d-bfda-12fbfe1cd727', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 6, 'langgraph_node': 'agent', 'langgraph_triggers': ['start:agent'], 'langgraph_task_idx': 0, 'ls_model_type': 'chat'}, 'parent_ids': ['1ef301a5-b867-67de-9e9e-a32e53c5b1f8', '7bb08493-d507-4e28-b9e6-4a5eda9d04f0']}



Receiving new event of type: events...
{'event': 'on_chat_model_stream', 'data': {'chunk': {'content': 'n', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-cb1b98c1-c9e2-4a30-9d7a-38fa1f6224bd', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}}, 'run_id': 'cb1b98c1-c9e2-4a30-9d7a-38fa1f6224bd', 'name': 'FakeListChatModel', 'tags': ['seq:step:1'], 'metadata': {'graph_id': 'agent', 'created_by': 'system', 'run_id': '1ef301a5-b867-67de-9e9e-a32e53c5b1f8', 'user_id': '', 'thread_id': '7196a3aa-763c-4a8d-bfda-12fbfe1cd727', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 6, 'langgraph_node': 'agent', 'langgraph_triggers': ['start:agent'], 'langgraph_task_idx': 0, 'ls_model_type': 'chat'}, 'parent_ids': ['1ef301a5-b867-67de-9e9e-a32e53c5b1f8', '7bb08493-d507-4e28-b9e6-4a5eda9d04f0']}



Receiving new event of type: events...
{'event': 'on_chat_model_end', 'data': {'output': {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-cb1b98c1-c9e2-4a30-9d7a-38fa1f6224bd', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, 'input': {'messages': [[{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '51f2874d-f8c7-4040-8b3b-8f15429a56ae', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-5f556aa0-26ea-42e2-b9e4-7ece3a00974e', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '1faf5dd0-ae97-4235-963f-5075083a027a', 'tool_call_id': 'tool_call_id'}, {'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-ae383611-6a42-475a-912a-09d5972e9e94', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'c67e08e6-e7af-4c4a-aa5e-50c8340ae341', 'example': False}]]}}, 'run_id': 'cb1b98c1-c9e2-4a30-9d7a-38fa1f6224bd', 'name': 'FakeListChatModel', 'tags': ['seq:step:1'], 'metadata': {'graph_id': 'agent', 'created_by': 'system', 'run_id': '1ef301a5-b867-67de-9e9e-a32e53c5b1f8', 'user_id': '', 'thread_id': '7196a3aa-763c-4a8d-bfda-12fbfe1cd727', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 6, 'langgraph_node': 'agent', 'langgraph_triggers': ['start:agent'], 'langgraph_task_idx': 0, 'ls_model_type': 'chat'}, 'parent_ids': ['1ef301a5-b867-67de-9e9e-a32e53c5b1f8', '7bb08493-d507-4e28-b9e6-4a5eda9d04f0']}



Receiving new event of type: events...
{'event': 'on_chain_start', 'data': {'input': {'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '51f2874d-f8c7-4040-8b3b-8f15429a56ae', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-5f556aa0-26ea-42e2-b9e4-7ece3a00974e', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '1faf5dd0-ae97-4235-963f-5075083a027a', 'tool_call_id': 'tool_call_id'}, {'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-ae383611-6a42-475a-912a-09d5972e9e94', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'c67e08e6-e7af-4c4a-aa5e-50c8340ae341', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-cb1b98c1-c9e2-4a30-9d7a-38fa1f6224bd', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}], 'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}}}, 'name': 'should_continue', 'tags': ['seq:step:3'], 'run_id': 'c7fe4d2d-3fb8-4e53-946d-03de13527853', 'metadata': {'graph_id': 'agent', 'created_by': 'system', 'run_id': '1ef301a5-b867-67de-9e9e-a32e53c5b1f8', 'user_id': '', 'thread_id': '7196a3aa-763c-4a8d-bfda-12fbfe1cd727', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 6, 'langgraph_node': 'agent', 'langgraph_triggers': ['start:agent'], 'langgraph_task_idx': 0}, 'parent_ids': ['1ef301a5-b867-67de-9e9e-a32e53c5b1f8', '7bb08493-d507-4e28-b9e6-4a5eda9d04f0']}



Receiving new event of type: events...
{'event': 'on_chain_end', 'data': {'output': 'tool', 'input': {'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '51f2874d-f8c7-4040-8b3b-8f15429a56ae', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-5f556aa0-26ea-42e2-b9e4-7ece3a00974e', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '1faf5dd0-ae97-4235-963f-5075083a027a', 'tool_call_id': 'tool_call_id'}, {'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-ae383611-6a42-475a-912a-09d5972e9e94', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'c67e08e6-e7af-4c4a-aa5e-50c8340ae341', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-cb1b98c1-c9e2-4a30-9d7a-38fa1f6224bd', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}], 'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}}}, 'run_id': 'c7fe4d2d-3fb8-4e53-946d-03de13527853', 'name': 'should_continue', 'tags': ['seq:step:3'], 'metadata': {'graph_id': 'agent', 'created_by': 'system', 'run_id': '1ef301a5-b867-67de-9e9e-a32e53c5b1f8', 'user_id': '', 'thread_id': '7196a3aa-763c-4a8d-bfda-12fbfe1cd727', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 6, 'langgraph_node': 'agent', 'langgraph_triggers': ['start:agent'], 'langgraph_task_idx': 0}, 'parent_ids': ['1ef301a5-b867-67de-9e9e-a32e53c5b1f8', '7bb08493-d507-4e28-b9e6-4a5eda9d04f0']}



Receiving new event of type: events...
{'event': 'on_chain_stream', 'run_id': '7bb08493-d507-4e28-b9e6-4a5eda9d04f0', 'name': 'agent', 'tags': ['graph:step:6'], 'metadata': {'graph_id': 'agent', 'created_by': 'system', 'run_id': '1ef301a5-b867-67de-9e9e-a32e53c5b1f8', 'user_id': '', 'thread_id': '7196a3aa-763c-4a8d-bfda-12fbfe1cd727', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 6, 'langgraph_node': 'agent', 'langgraph_triggers': ['start:agent'], 'langgraph_task_idx': 0}, 'data': {'chunk': {'messages': [{'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-cb1b98c1-c9e2-4a30-9d7a-38fa1f6224bd', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}], 'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}}}, 'parent_ids': ['1ef301a5-b867-67de-9e9e-a32e53c5b1f8']}



Receiving new event of type: events...
{'event': 'on_chain_end', 'data': {'output': {'messages': [{'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-cb1b98c1-c9e2-4a30-9d7a-38fa1f6224bd', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}], 'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}}, 'input': {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '51f2874d-f8c7-4040-8b3b-8f15429a56ae', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-5f556aa0-26ea-42e2-b9e4-7ece3a00974e', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '1faf5dd0-ae97-4235-963f-5075083a027a', 'tool_call_id': 'tool_call_id'}, {'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-ae383611-6a42-475a-912a-09d5972e9e94', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'c67e08e6-e7af-4c4a-aa5e-50c8340ae341', 'example': False}], 'sleep': None}}, 'run_id': '7bb08493-d507-4e28-b9e6-4a5eda9d04f0', 'name': 'agent', 'tags': ['graph:step:6'], 'metadata': {'graph_id': 'agent', 'created_by': 'system', 'run_id': '1ef301a5-b867-67de-9e9e-a32e53c5b1f8', 'user_id': '', 'thread_id': '7196a3aa-763c-4a8d-bfda-12fbfe1cd727', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 6, 'langgraph_node': 'agent', 'langgraph_triggers': ['start:agent'], 'langgraph_task_idx': 0}, 'parent_ids': ['1ef301a5-b867-67de-9e9e-a32e53c5b1f8']}



Receiving new event of type: events...
{'event': 'on_chain_start', 'data': {}, 'name': 'tool', 'tags': ['graph:step:7'], 'run_id': 'f044fd3d-7271-488f-b8aa-e01572ff9112', 'metadata': {'graph_id': 'agent', 'created_by': 'system', 'run_id': '1ef301a5-b867-67de-9e9e-a32e53c5b1f8', 'user_id': '', 'thread_id': '7196a3aa-763c-4a8d-bfda-12fbfe1cd727', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 7, 'langgraph_node': 'tool', 'langgraph_triggers': ['branch:agent:should_continue:tool'], 'langgraph_task_idx': 0}, 'parent_ids': ['1ef301a5-b867-67de-9e9e-a32e53c5b1f8']}



Receiving new event of type: events...
{'event': 'on_chain_stream', 'run_id': 'f044fd3d-7271-488f-b8aa-e01572ff9112', 'name': 'tool', 'tags': ['graph:step:7'], 'metadata': {'graph_id': 'agent', 'created_by': 'system', 'run_id': '1ef301a5-b867-67de-9e9e-a32e53c5b1f8', 'user_id': '', 'thread_id': '7196a3aa-763c-4a8d-bfda-12fbfe1cd727', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 7, 'langgraph_node': 'tool', 'langgraph_triggers': ['branch:agent:should_continue:tool'], 'langgraph_task_idx': 0}, 'data': {'chunk': {'messages': [{'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': None, 'tool_call_id': 'tool_call_id'}]}}, 'parent_ids': ['1ef301a5-b867-67de-9e9e-a32e53c5b1f8']}



Receiving new event of type: events...
{'event': 'on_chain_end', 'data': {'output': {'messages': [{'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '1c9a16d2-5f0a-4eba-a0d2-240484a4ce7e', 'tool_call_id': 'tool_call_id'}]}, 'input': {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '51f2874d-f8c7-4040-8b3b-8f15429a56ae', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-5f556aa0-26ea-42e2-b9e4-7ece3a00974e', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '1faf5dd0-ae97-4235-963f-5075083a027a', 'tool_call_id': 'tool_call_id'}, {'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-ae383611-6a42-475a-912a-09d5972e9e94', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'c67e08e6-e7af-4c4a-aa5e-50c8340ae341', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-cb1b98c1-c9e2-4a30-9d7a-38fa1f6224bd', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}], 'sleep': None}}, 'run_id': 'f044fd3d-7271-488f-b8aa-e01572ff9112', 'name': 'tool', 'tags': ['graph:step:7'], 'metadata': {'graph_id': 'agent', 'created_by': 'system', 'run_id': '1ef301a5-b867-67de-9e9e-a32e53c5b1f8', 'user_id': '', 'thread_id': '7196a3aa-763c-4a8d-bfda-12fbfe1cd727', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 7, 'langgraph_node': 'tool', 'langgraph_triggers': ['branch:agent:should_continue:tool'], 'langgraph_task_idx': 0}, 'parent_ids': ['1ef301a5-b867-67de-9e9e-a32e53c5b1f8']}



Receiving new event of type: events...
{'event': 'on_chain_start', 'data': {}, 'name': 'agent', 'tags': ['graph:step:8'], 'run_id': '1f4f95d0-0ce1-4061-85d4-946446bbd3e5', 'metadata': {'graph_id': 'agent', 'created_by': 'system', 'run_id': '1ef301a5-b867-67de-9e9e-a32e53c5b1f8', 'user_id': '', 'thread_id': '7196a3aa-763c-4a8d-bfda-12fbfe1cd727', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 8, 'langgraph_node': 'agent', 'langgraph_triggers': ['tool'], 'langgraph_task_idx': 0}, 'parent_ids': ['1ef301a5-b867-67de-9e9e-a32e53c5b1f8']}



Receiving new event of type: events...
{'event': 'on_chat_model_start', 'data': {'input': {'messages': [[{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '51f2874d-f8c7-4040-8b3b-8f15429a56ae', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-5f556aa0-26ea-42e2-b9e4-7ece3a00974e', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '1faf5dd0-ae97-4235-963f-5075083a027a', 'tool_call_id': 'tool_call_id'}, {'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-ae383611-6a42-475a-912a-09d5972e9e94', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'c67e08e6-e7af-4c4a-aa5e-50c8340ae341', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-cb1b98c1-c9e2-4a30-9d7a-38fa1f6224bd', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '1c9a16d2-5f0a-4eba-a0d2-240484a4ce7e', 'tool_call_id': 'tool_call_id'}]]}}, 'name': 'FakeListChatModel', 'tags': ['seq:step:1'], 'run_id': '028a68fb-6435-4b46-a156-c3326f73985c', 'metadata': {'graph_id': 'agent', 'created_by': 'system', 'run_id': '1ef301a5-b867-67de-9e9e-a32e53c5b1f8', 'user_id': '', 'thread_id': '7196a3aa-763c-4a8d-bfda-12fbfe1cd727', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 8, 'langgraph_node': 'agent', 'langgraph_triggers': ['tool'], 'langgraph_task_idx': 0, 'ls_model_type': 'chat'}, 'parent_ids': ['1ef301a5-b867-67de-9e9e-a32e53c5b1f8', '1f4f95d0-0ce1-4061-85d4-946446bbd3e5']}



Receiving new event of type: events...
{'event': 'on_chat_model_stream', 'data': {'chunk': {'content': 'e', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-028a68fb-6435-4b46-a156-c3326f73985c', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}}, 'run_id': '028a68fb-6435-4b46-a156-c3326f73985c', 'name': 'FakeListChatModel', 'tags': ['seq:step:1'], 'metadata': {'graph_id': 'agent', 'created_by': 'system', 'run_id': '1ef301a5-b867-67de-9e9e-a32e53c5b1f8', 'user_id': '', 'thread_id': '7196a3aa-763c-4a8d-bfda-12fbfe1cd727', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 8, 'langgraph_node': 'agent', 'langgraph_triggers': ['tool'], 'langgraph_task_idx': 0, 'ls_model_type': 'chat'}, 'parent_ids': ['1ef301a5-b867-67de-9e9e-a32e53c5b1f8', '1f4f95d0-0ce1-4061-85d4-946446bbd3e5']}



Receiving new event of type: events...
{'event': 'on_chat_model_stream', 'data': {'chunk': {'content': 'n', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-028a68fb-6435-4b46-a156-c3326f73985c', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}}, 'run_id': '028a68fb-6435-4b46-a156-c3326f73985c', 'name': 'FakeListChatModel', 'tags': ['seq:step:1'], 'metadata': {'graph_id': 'agent', 'created_by': 'system', 'run_id': '1ef301a5-b867-67de-9e9e-a32e53c5b1f8', 'user_id': '', 'thread_id': '7196a3aa-763c-4a8d-bfda-12fbfe1cd727', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 8, 'langgraph_node': 'agent', 'langgraph_triggers': ['tool'], 'langgraph_task_idx': 0, 'ls_model_type': 'chat'}, 'parent_ids': ['1ef301a5-b867-67de-9e9e-a32e53c5b1f8', '1f4f95d0-0ce1-4061-85d4-946446bbd3e5']}



Receiving new event of type: events...
{'event': 'on_chat_model_stream', 'data': {'chunk': {'content': 'd', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-028a68fb-6435-4b46-a156-c3326f73985c', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}}, 'run_id': '028a68fb-6435-4b46-a156-c3326f73985c', 'name': 'FakeListChatModel', 'tags': ['seq:step:1'], 'metadata': {'graph_id': 'agent', 'created_by': 'system', 'run_id': '1ef301a5-b867-67de-9e9e-a32e53c5b1f8', 'user_id': '', 'thread_id': '7196a3aa-763c-4a8d-bfda-12fbfe1cd727', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 8, 'langgraph_node': 'agent', 'langgraph_triggers': ['tool'], 'langgraph_task_idx': 0, 'ls_model_type': 'chat'}, 'parent_ids': ['1ef301a5-b867-67de-9e9e-a32e53c5b1f8', '1f4f95d0-0ce1-4061-85d4-946446bbd3e5']}



Receiving new event of type: events...
{'event': 'on_chat_model_end', 'data': {'output': {'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-028a68fb-6435-4b46-a156-c3326f73985c', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, 'input': {'messages': [[{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '51f2874d-f8c7-4040-8b3b-8f15429a56ae', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-5f556aa0-26ea-42e2-b9e4-7ece3a00974e', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '1faf5dd0-ae97-4235-963f-5075083a027a', 'tool_call_id': 'tool_call_id'}, {'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-ae383611-6a42-475a-912a-09d5972e9e94', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'c67e08e6-e7af-4c4a-aa5e-50c8340ae341', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-cb1b98c1-c9e2-4a30-9d7a-38fa1f6224bd', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '1c9a16d2-5f0a-4eba-a0d2-240484a4ce7e', 'tool_call_id': 'tool_call_id'}]]}}, 'run_id': '028a68fb-6435-4b46-a156-c3326f73985c', 'name': 'FakeListChatModel', 'tags': ['seq:step:1'], 'metadata': {'graph_id': 'agent', 'created_by': 'system', 'run_id': '1ef301a5-b867-67de-9e9e-a32e53c5b1f8', 'user_id': '', 'thread_id': '7196a3aa-763c-4a8d-bfda-12fbfe1cd727', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 8, 'langgraph_node': 'agent', 'langgraph_triggers': ['tool'], 'langgraph_task_idx': 0, 'ls_model_type': 'chat'}, 'parent_ids': ['1ef301a5-b867-67de-9e9e-a32e53c5b1f8', '1f4f95d0-0ce1-4061-85d4-946446bbd3e5']}



Receiving new event of type: events...
{'event': 'on_chain_start', 'data': {'input': {'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '51f2874d-f8c7-4040-8b3b-8f15429a56ae', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-5f556aa0-26ea-42e2-b9e4-7ece3a00974e', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '1faf5dd0-ae97-4235-963f-5075083a027a', 'tool_call_id': 'tool_call_id'}, {'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-ae383611-6a42-475a-912a-09d5972e9e94', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'c67e08e6-e7af-4c4a-aa5e-50c8340ae341', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-cb1b98c1-c9e2-4a30-9d7a-38fa1f6224bd', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '1c9a16d2-5f0a-4eba-a0d2-240484a4ce7e', 'tool_call_id': 'tool_call_id'}, {'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-028a68fb-6435-4b46-a156-c3326f73985c', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}], 'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}}}, 'name': 'should_continue', 'tags': ['seq:step:3'], 'run_id': 'f2b2dfaf-475d-422b-8bf5-02a31bcc7d1a', 'metadata': {'graph_id': 'agent', 'created_by': 'system', 'run_id': '1ef301a5-b867-67de-9e9e-a32e53c5b1f8', 'user_id': '', 'thread_id': '7196a3aa-763c-4a8d-bfda-12fbfe1cd727', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 8, 'langgraph_node': 'agent', 'langgraph_triggers': ['tool'], 'langgraph_task_idx': 0}, 'parent_ids': ['1ef301a5-b867-67de-9e9e-a32e53c5b1f8', '1f4f95d0-0ce1-4061-85d4-946446bbd3e5']}



Receiving new event of type: events...
{'event': 'on_chain_end', 'data': {'output': '__end__', 'input': {'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '51f2874d-f8c7-4040-8b3b-8f15429a56ae', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-5f556aa0-26ea-42e2-b9e4-7ece3a00974e', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '1faf5dd0-ae97-4235-963f-5075083a027a', 'tool_call_id': 'tool_call_id'}, {'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-ae383611-6a42-475a-912a-09d5972e9e94', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'c67e08e6-e7af-4c4a-aa5e-50c8340ae341', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-cb1b98c1-c9e2-4a30-9d7a-38fa1f6224bd', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '1c9a16d2-5f0a-4eba-a0d2-240484a4ce7e', 'tool_call_id': 'tool_call_id'}, {'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-028a68fb-6435-4b46-a156-c3326f73985c', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}], 'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}}}, 'run_id': 'f2b2dfaf-475d-422b-8bf5-02a31bcc7d1a', 'name': 'should_continue', 'tags': ['seq:step:3'], 'metadata': {'graph_id': 'agent', 'created_by': 'system', 'run_id': '1ef301a5-b867-67de-9e9e-a32e53c5b1f8', 'user_id': '', 'thread_id': '7196a3aa-763c-4a8d-bfda-12fbfe1cd727', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 8, 'langgraph_node': 'agent', 'langgraph_triggers': ['tool'], 'langgraph_task_idx': 0}, 'parent_ids': ['1ef301a5-b867-67de-9e9e-a32e53c5b1f8', '1f4f95d0-0ce1-4061-85d4-946446bbd3e5']}



Receiving new event of type: events...
{'event': 'on_chain_stream', 'run_id': '1f4f95d0-0ce1-4061-85d4-946446bbd3e5', 'name': 'agent', 'tags': ['graph:step:8'], 'metadata': {'graph_id': 'agent', 'created_by': 'system', 'run_id': '1ef301a5-b867-67de-9e9e-a32e53c5b1f8', 'user_id': '', 'thread_id': '7196a3aa-763c-4a8d-bfda-12fbfe1cd727', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 8, 'langgraph_node': 'agent', 'langgraph_triggers': ['tool'], 'langgraph_task_idx': 0}, 'data': {'chunk': {'messages': [{'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-028a68fb-6435-4b46-a156-c3326f73985c', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}], 'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}}}, 'parent_ids': ['1ef301a5-b867-67de-9e9e-a32e53c5b1f8']}



Receiving new event of type: events...
{'event': 'on_chain_end', 'data': {'output': {'messages': [{'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-028a68fb-6435-4b46-a156-c3326f73985c', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}], 'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}}, 'input': {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '51f2874d-f8c7-4040-8b3b-8f15429a56ae', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-5f556aa0-26ea-42e2-b9e4-7ece3a00974e', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '1faf5dd0-ae97-4235-963f-5075083a027a', 'tool_call_id': 'tool_call_id'}, {'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-ae383611-6a42-475a-912a-09d5972e9e94', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'c67e08e6-e7af-4c4a-aa5e-50c8340ae341', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-cb1b98c1-c9e2-4a30-9d7a-38fa1f6224bd', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '1c9a16d2-5f0a-4eba-a0d2-240484a4ce7e', 'tool_call_id': 'tool_call_id'}], 'sleep': None}}, 'run_id': '1f4f95d0-0ce1-4061-85d4-946446bbd3e5', 'name': 'agent', 'tags': ['graph:step:8'], 'metadata': {'graph_id': 'agent', 'created_by': 'system', 'run_id': '1ef301a5-b867-67de-9e9e-a32e53c5b1f8', 'user_id': '', 'thread_id': '7196a3aa-763c-4a8d-bfda-12fbfe1cd727', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 8, 'langgraph_node': 'agent', 'langgraph_triggers': ['tool'], 'langgraph_task_idx': 0}, 'parent_ids': ['1ef301a5-b867-67de-9e9e-a32e53c5b1f8']}



Receiving new event of type: events...
{'event': 'on_chain_end', 'data': {'output': {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '51f2874d-f8c7-4040-8b3b-8f15429a56ae', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-5f556aa0-26ea-42e2-b9e4-7ece3a00974e', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '1faf5dd0-ae97-4235-963f-5075083a027a', 'tool_call_id': 'tool_call_id'}, {'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-ae383611-6a42-475a-912a-09d5972e9e94', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'c67e08e6-e7af-4c4a-aa5e-50c8340ae341', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-cb1b98c1-c9e2-4a30-9d7a-38fa1f6224bd', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '1c9a16d2-5f0a-4eba-a0d2-240484a4ce7e', 'tool_call_id': 'tool_call_id'}, {'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-028a68fb-6435-4b46-a156-c3326f73985c', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}, 'run_id': '1ef301a5-b867-67de-9e9e-a32e53c5b1f8', 'name': 'LangGraph', 'tags': [], 'metadata': {'graph_id': 'agent', 'created_by': 'system', 'run_id': '1ef301a5-b867-67de-9e9e-a32e53c5b1f8', 'user_id': '', 'thread_id': '7196a3aa-763c-4a8d-bfda-12fbfe1cd727', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'parent_ids': []}



Receiving new event of type: end...
None

How to stream messages from your graph

!!! info "Prerequisites" * Streaming

This guide covers how to stream messages from your graph. With stream_mode="messages-tuple", messages (i.e. individual LLM tokens) from any chat model invocations inside your graph nodes will be streamed back.

Setup

First let's set up our client and thread:

=== "Python"

```python
from langgraph_sdk import get_client

client = get_client(url=<DEPLOYMENT_URL>)
# Using the graph deployed with the name "agent"
assistant_id = "agent"
# create thread
thread = await client.threads.create()
print(thread)
```

=== "Javascript"

```js
import { Client } from "@langchain/langgraph-sdk";

const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
// Using the graph deployed with the name "agent"
const assistantID = "agent";
// create thread
const thread = await client.threads.create();
console.log(thread);
```

=== "CURL"

```bash
curl --request POST \
  --url <DEPLOYMENT_URL>/threads \
  --header 'Content-Type: application/json' \
  --data '{}'
```

Output:

{
    'thread_id': 'e1431c95-e241-4d1d-a252-27eceb1e5c86',
    'created_at': '2024-06-21T15:48:59.808924+00:00',
    'updated_at': '2024-06-21T15:48:59.808924+00:00',
    'metadata': {},
    'status': 'idle',
    'config': {},
    'values': None
}

Stream graph in messages mode

Now we can stream LLM tokens for any messages generated inside a node in the form of tuples (message, metadata). Metadata contains additional information that can be useful for filtering the streamed outputs to a specific node or LLM.

=== "Python"

```python
input = {"messages": [{"role": "user", "content": "what's the weather in sf"}]}
config = {"configurable": {"model_name": "openai"}}

async for chunk in client.runs.stream(
    thread["thread_id"],
    assistant_id=assistant_id,
    input=input,
    config=config,
    stream_mode="messages-tuple",
):
    print(f"Receiving new event of type: {chunk.event}...")
    print(chunk.data)
    print("\n\n")
```

=== "Javascript"

```js
const input = {
  messages: [
    {
      role: "human",
      content: "What's the weather in sf",
    }
  ]
};
const config = { configurable: { model_name: "openai" } };

const streamResponse = client.runs.stream(
  thread["thread_id"],
  assistantID,
  {
    input,
    config,
    streamMode: "messages-tuple"
  }
);
for await (const chunk of streamResponse) {
  console.log(`Receiving new event of type: ${chunk.event}...`);
  console.log(chunk.data);
  console.log("\n\n");
}
```

=== "CURL"

```bash
curl --request POST \
 --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
 --header 'Content-Type: application/json' \
 --data "{
   \"assistant_id\": \"agent\",
   \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in la\"}]},
   \"stream_mode\": [
     \"messages-tuple\"
   ]
 }" | \
 sed 's/\r$//' | \
 awk '
 /^event:/ {
     if (data_content != "") {
         print data_content "\n"
     }
     sub(/^event: /, "Receiving event of type: ", $0)
     printf "%s...\n", $0
     data_content = ""
 }
 /^data:/ {
     sub(/^data: /, "", $0)
     data_content = $0
 }
 END {
     if (data_content != "") {
         print data_content "\n"
     }
 }
 '
```

Output:

Receiving new event of type: metadata...
{"run_id": "1ef971e0-9a84-6154-9047-247b4ce89c4d", "attempt": 1}

...

Receiving new event of type: messages...
[
  {
    "type": "AIMessageChunk",
    "tool_calls": [
      {
        "name": "tavily_search_results_json",
        "args": {
          "query": "weat"
        },
        "id": "toolu_0114XKXdNtHQEa3ozmY1uDdM",
        "type": "tool_call"
      }
    ],
    ...
  },
  {
    "graph_id": "agent",
    "langgraph_node": "agent",
    ...
  }
]



Receiving new event of type: messages...
[
  {
    "type": "AIMessageChunk",
    "tool_calls": [
      {
        "name": "tavily_search_results_json",
        "args": {
          "query": "her in san "
        },
        "id": "toolu_0114XKXdNtHQEa3ozmY1uDdM",
        "type": "tool_call"
      }
    ],
    ...
  },
  {
    "graph_id": "agent",
    "langgraph_node": "agent",
    ...
  }
]

...

Receiving new event of type: messages...
[
  {
    "type": "AIMessageChunk",
    "tool_calls": [
      {
        "name": "tavily_search_results_json",
        "args": {
          "query": "francisco"
        },
        "id": "toolu_0114XKXdNtHQEa3ozmY1uDdM",
        "type": "tool_call"
      }
    ],
    ...
  },
  {
    "graph_id": "agent",
    "langgraph_node": "agent",
    ...
  }
]

...

Receiving new event of type: messages...
[
  {
    "content": "[{\"url\": \"https://www.weatherapi.com/\", \"content\": \"{'location': {'name': 'San Francisco', 'region': 'California', 'country': 'United States of America', 'lat': 37.775, 'lon': -122.4183, 'tz_id': 'America/Los_Angeles', 'localtime_epoch': 1730475777, 'localtime': '2024-11-01 08:42'}, 'current': {'last_updated_epoch': 1730475000, 'last_updated': '2024-11-01 08:30', 'temp_c': 11.1, 'temp_f': 52.0, 'is_day': 1, 'condition': {'text': 'Partly cloudy', 'icon': '//cdn.weatherapi.com/weather/64x64/day/116.png', 'code': 1003}, 'wind_mph': 2.2, 'wind_kph': 3.6, 'wind_degree': 192, 'wind_dir': 'SSW', 'pressure_mb': 1018.0, 'pressure_in': 30.07, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 89, 'cloud': 75, 'feelslike_c': 11.5, 'feelslike_f': 52.6, 'windchill_c': 10.0, 'windchill_f': 50.1, 'heatindex_c': 10.4, 'heatindex_f': 50.7, 'dewpoint_c': 9.1, 'dewpoint_f': 48.5, 'vis_km': 16.0, 'vis_miles': 9.0, 'uv': 3.0, 'gust_mph': 6.7, 'gust_kph': 10.8}}\"}]",
    "type": "tool",
    "tool_call_id": "toolu_0114XKXdNtHQEa3ozmY1uDdM",
    ...
  },
  {
    "graph_id": "agent",
    "langgraph_node": "action",
    ...
  }
]

...

Receiving new event of type: messages...
[
  {
    "content": [
      {
        "text": "\n\nThe search",
        "type": "text",
        "index": 0
      }
    ],
    "type": "AIMessageChunk",
    ...
  },
  {
    "graph_id": "agent",
    "langgraph_node": "agent",
    ...
  }
]



Receiving new event of type: messages...
[
  {
    "content": [
      {
        "text": " results provide",
        "type": "text",
        "index": 0
      }
    ],
    "type": "AIMessageChunk",
    ...
  },
  {
    "graph_id": "agent",
    "langgraph_node": "agent",
    ...
  }
]



Receiving new event of type: messages...
[
  {
    "content": [
      {
        "text": " the current weather conditions",
        "type": "text",
        "index": 0
      }
    ],
    "type": "AIMessageChunk",
    ...
  },
  {
    "graph_id": "agent",
    "langgraph_node": "agent",
    ...
  }
]



Receiving new event of type: messages...
[
  {
    "content": [
      {
        "text": " in San Francisco.",
        "type": "text",
        "index": 0
      }
    ],
    "type": "AIMessageChunk",
    ...
  },
  {
    "graph_id": "agent",
    "langgraph_node": "agent",
    ...
  }
]

...

How to configure multiple streaming modes at the same time

!!! info "Prerequisites" * Streaming

This guide covers how to configure multiple streaming modes at the same time.

Setup

First let's set up our client and thread:

=== "Python"

```python
from langgraph_sdk import get_client

client = get_client(url=<DEPLOYMENT_URL>)
# Using the graph deployed with the name "agent"
assistant_id = "agent"
# create thread
thread = await client.threads.create()
print(thread)
```

=== "Javascript"

```js
import { Client } from "@langchain/langgraph-sdk";

const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
// Using the graph deployed with the name "agent"
const assistantID = "agent";
// create thread
const thread = await client.threads.create();
console.log(thread);
```

=== "CURL"

```bash
curl --request POST \
  --url <DEPLOYMENT_URL>/threads \
  --header 'Content-Type: application/json' \
  --data '{}'
```

Output:

{
    'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4',
    'created_at': '2024-06-24T21:30:07.980789+00:00',
    'updated_at': '2024-06-24T21:30:07.980789+00:00',
    'metadata': {},
    'status': 'idle',
    'config': {},
    'values': None
}

Stream graph with multiple modes

When configuring multiple streaming modes for a run, responses for each respective mode will be produced. In the following example, note that a list of modes (messages, events, debug) is passed to the stream_mode parameter and the response contains events, debug, messages/complete, messages/metadata, and messages/partial event types.

=== "Python"

```python
# create input
input = {
    "messages": [
        {
            "role": "user",
            "content": "What's the weather in SF?",
        }
    ]
}

# stream events with multiple streaming modes
async for chunk in client.runs.stream(
    thread_id=thread["thread_id"],
    assistant_id=assistant_id,
    input=input,
    stream_mode=["messages", "events", "debug"],
):
    print(f"Receiving new event of type: {chunk.event}...")
    print(chunk.data)
    print("\n\n")
```

=== "Javascript"

```js
// create input
const input = {
  messages: [
    {
      role: "human",
      content: "What's the weather in SF?",
    }
  ]
};

// stream events with multiple streaming modes
const streamResponse = client.runs.stream(
  thread["thread_id"],
  assistantID,
  {
    input,
    streamMode: ["messages", "events", "debug"]
  }
);
for await (const chunk of streamResponse) {
  console.log(`Receiving new event of type: ${chunk.event}...`);
  console.log(chunk.data);
  console.log("\n\n");
}
```

=== "CURL"

```bash
curl --request POST \
 --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
 --header 'Content-Type: application/json' \
 --data "{
   \"assistant_id\": \"agent\",
   \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"What's the weather in SF?\"}]},
   \"stream_mode\": [
     \"messages\",
     \"events\",
     \"debug\"
   ]
 }" | \
 sed 's/\r$//' | \
 awk '
 /^event:/ {
     if (data_content != "") {
         print data_content "\n"
     }
     sub(/^event: /, "Receiving event of type: ", $0)
     printf "%s...\n", $0
     data_content = ""
 }
 /^data:/ {
     sub(/^data: /, "", $0)
     data_content = $0
 }
 END {
     if (data_content != "") {
         print data_content "\n"
     }
 }
 '
```

Output:

Receiving new event of type: metadata...
{'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25'}



Receiving new event of type: events...
{'event': 'on_chain_start', 'data': {'input': {'messages': [{'role': 'human', 'content': "What's the weather in SF?"}]}}, 'name': 'LangGraph', 'tags': [], 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'parent_ids': []}



Receiving new event of type: debug...
{'type': 'checkpoint', 'timestamp': '2024-06-24T21:34:06.116009+00:00', 'step': -1, 'payload': {'config': {'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'callbacks': [None], 'recursion_limit': 25, 'configurable': {'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'thread_ts': '1ef32717-bc7c-6daa-bfff-6b9027c1a50e', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25'}, 'values': {'messages': []}, 'metadata': {'source': 'input', 'step': -1, 'writes': {'messages': [{'role': 'human', 'content': "What's the weather in SF?"}]}}}}



Receiving new event of type: events...
{'event': 'on_chain_stream', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'name': 'LangGraph', 'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'data': {'chunk': ['debug', {'type': 'checkpoint', 'timestamp': '2024-06-24T21:34:06.116009+00:00', 'step': -1, 'payload': {'config': {'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'callbacks': [None], 'recursion_limit': 25, 'configurable': {'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'thread_ts': '1ef32717-bc7c-6daa-bfff-6b9027c1a50e', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25'}, 'values': {'messages': []}, 'metadata': {'source': 'input', 'step': -1, 'writes': {'messages': [{'role': 'human', 'content': "What's the weather in SF?"}]}}}}]}, 'parent_ids': []}



Receiving new event of type: events...
{'event': 'on_chain_stream', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'name': 'LangGraph', 'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'data': {'chunk': ['values', {'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}]}]}, 'parent_ids': []}



Receiving new event of type: debug...
{'type': 'checkpoint', 'timestamp': '2024-06-24T21:34:06.117924+00:00', 'step': 0, 'payload': {'config': {'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'callbacks': [None], 'recursion_limit': 25, 'configurable': {'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'thread_ts': '1ef32717-bc81-68c8-8000-4e18ae7d67a5', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25'}, 'values': {'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}]}, 'metadata': {'source': 'loop', 'step': 0, 'writes': None}}}



Receiving new event of type: events...
{'event': 'on_chain_stream', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'name': 'LangGraph', 'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'data': {'chunk': ['debug', {'type': 'checkpoint', 'timestamp': '2024-06-24T21:34:06.117924+00:00', 'step': 0, 'payload': {'config': {'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'callbacks': [None], 'recursion_limit': 25, 'configurable': {'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'thread_ts': '1ef32717-bc81-68c8-8000-4e18ae7d67a5', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25'}, 'values': {'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}]}, 'metadata': {'source': 'loop', 'step': 0, 'writes': None}}}]}, 'parent_ids': []}



Receiving new event of type: debug...
{'type': 'task', 'timestamp': '2024-06-24T21:34:06.118042+00:00', 'step': 1, 'payload': {'id': '212ed9c2-a454-50c5-a202-12066bbbe7b8', 'name': 'agent', 'input': {'some_bytes': None, 'some_byte_array': None, 'dict_with_bytes': None, 'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}], 'sleep': None}, 'triggers': ['start:agent']}}



Receiving new event of type: events...
{'event': 'on_chain_stream', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'name': 'LangGraph', 'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'data': {'chunk': ['debug', {'type': 'task', 'timestamp': '2024-06-24T21:34:06.118042+00:00', 'step': 1, 'payload': {'id': '212ed9c2-a454-50c5-a202-12066bbbe7b8', 'name': 'agent', 'input': {'some_bytes': None, 'some_byte_array': None, 'dict_with_bytes': None, 'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}], 'sleep': None}, 'triggers': ['start:agent']}}]}, 'parent_ids': []}



Receiving new event of type: events...
{'event': 'on_chain_start', 'data': {}, 'name': 'agent', 'tags': ['graph:step:1'], 'run_id': '72b74d24-5792-48da-a887-102100d6e2c0', 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 1, 'langgraph_node': 'agent', 'langgraph_triggers': ['start:agent'], 'langgraph_task_idx': 0}, 'parent_ids': ['1ef32717-bc30-6cf2-8a26-33f63567bc25']}



Receiving new event of type: events...
{'event': 'on_chat_model_start', 'data': {'input': {'messages': [[{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}]]}}, 'name': 'FakeListChatModel', 'tags': ['seq:step:1'], 'run_id': '2424dd6d-5cf5-4244-8d98-357640ce6e12', 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 1, 'langgraph_node': 'agent', 'langgraph_triggers': ['start:agent'], 'langgraph_task_idx': 0, 'ls_model_type': 'chat'}, 'parent_ids': ['1ef32717-bc30-6cf2-8a26-33f63567bc25', '72b74d24-5792-48da-a887-102100d6e2c0']}



Receiving new event of type: events...
{'event': 'on_chat_model_stream', 'data': {'chunk': {'content': 'b', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}}, 'run_id': '2424dd6d-5cf5-4244-8d98-357640ce6e12', 'name': 'FakeListChatModel', 'tags': ['seq:step:1'], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 1, 'langgraph_node': 'agent', 'langgraph_triggers': ['start:agent'], 'langgraph_task_idx': 0, 'ls_model_type': 'chat'}, 'parent_ids': ['1ef32717-bc30-6cf2-8a26-33f63567bc25', '72b74d24-5792-48da-a887-102100d6e2c0']}



Receiving new event of type: messages/metadata...
{'run-2424dd6d-5cf5-4244-8d98-357640ce6e12': {'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 1, 'langgraph_node': 'agent', 'langgraph_triggers': ['start:agent'], 'langgraph_task_idx': 0, 'ls_model_type': 'chat'}}}



Receiving new event of type: messages/partial...
[{'content': 'b', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]



Receiving new event of type: events...
{'event': 'on_chat_model_stream', 'data': {'chunk': {'content': 'e', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}}, 'run_id': '2424dd6d-5cf5-4244-8d98-357640ce6e12', 'name': 'FakeListChatModel', 'tags': ['seq:step:1'], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 1, 'langgraph_node': 'agent', 'langgraph_triggers': ['start:agent'], 'langgraph_task_idx': 0, 'ls_model_type': 'chat'}, 'parent_ids': ['1ef32717-bc30-6cf2-8a26-33f63567bc25', '72b74d24-5792-48da-a887-102100d6e2c0']}



Receiving new event of type: messages/partial...
[{'content': 'be', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]



Receiving new event of type: events...
{'event': 'on_chat_model_stream', 'data': {'chunk': {'content': 'g', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}}, 'run_id': '2424dd6d-5cf5-4244-8d98-357640ce6e12', 'name': 'FakeListChatModel', 'tags': ['seq:step:1'], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 1, 'langgraph_node': 'agent', 'langgraph_triggers': ['start:agent'], 'langgraph_task_idx': 0, 'ls_model_type': 'chat'}, 'parent_ids': ['1ef32717-bc30-6cf2-8a26-33f63567bc25', '72b74d24-5792-48da-a887-102100d6e2c0']}



Receiving new event of type: messages/partial...
[{'content': 'beg', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]



Receiving new event of type: events...
{'event': 'on_chat_model_stream', 'data': {'chunk': {'content': 'i', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}}, 'run_id': '2424dd6d-5cf5-4244-8d98-357640ce6e12', 'name': 'FakeListChatModel', 'tags': ['seq:step:1'], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 1, 'langgraph_node': 'agent', 'langgraph_triggers': ['start:agent'], 'langgraph_task_idx': 0, 'ls_model_type': 'chat'}, 'parent_ids': ['1ef32717-bc30-6cf2-8a26-33f63567bc25', '72b74d24-5792-48da-a887-102100d6e2c0']}



Receiving new event of type: messages/partial...
[{'content': 'begi', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]



Receiving new event of type: events...
{'event': 'on_chat_model_stream', 'data': {'chunk': {'content': 'n', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}}, 'run_id': '2424dd6d-5cf5-4244-8d98-357640ce6e12', 'name': 'FakeListChatModel', 'tags': ['seq:step:1'], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 1, 'langgraph_node': 'agent', 'langgraph_triggers': ['start:agent'], 'langgraph_task_idx': 0, 'ls_model_type': 'chat'}, 'parent_ids': ['1ef32717-bc30-6cf2-8a26-33f63567bc25', '72b74d24-5792-48da-a887-102100d6e2c0']}



Receiving new event of type: messages/partial...
[{'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]



Receiving new event of type: events...
{'event': 'on_chat_model_end', 'data': {'output': {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, 'input': {'messages': [[{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}]]}}, 'run_id': '2424dd6d-5cf5-4244-8d98-357640ce6e12', 'name': 'FakeListChatModel', 'tags': ['seq:step:1'], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 1, 'langgraph_node': 'agent', 'langgraph_triggers': ['start:agent'], 'langgraph_task_idx': 0, 'ls_model_type': 'chat'}, 'parent_ids': ['1ef32717-bc30-6cf2-8a26-33f63567bc25', '72b74d24-5792-48da-a887-102100d6e2c0']}



Receiving new event of type: events...
{'event': 'on_chain_start', 'data': {'input': {'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}], 'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}}}, 'name': 'should_continue', 'tags': ['seq:step:3'], 'run_id': '227afb0f-f909-4d54-a042-556ca6d98a69', 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 1, 'langgraph_node': 'agent', 'langgraph_triggers': ['start:agent'], 'langgraph_task_idx': 0}, 'parent_ids': ['1ef32717-bc30-6cf2-8a26-33f63567bc25', '72b74d24-5792-48da-a887-102100d6e2c0']}



Receiving new event of type: events...
{'event': 'on_chain_end', 'data': {'output': 'tool', 'input': {'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}], 'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}}}, 'run_id': '227afb0f-f909-4d54-a042-556ca6d98a69', 'name': 'should_continue', 'tags': ['seq:step:3'], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 1, 'langgraph_node': 'agent', 'langgraph_triggers': ['start:agent'], 'langgraph_task_idx': 0}, 'parent_ids': ['1ef32717-bc30-6cf2-8a26-33f63567bc25', '72b74d24-5792-48da-a887-102100d6e2c0']}



Receiving new event of type: events...
{'event': 'on_chain_stream', 'run_id': '72b74d24-5792-48da-a887-102100d6e2c0', 'name': 'agent', 'tags': ['graph:step:1'], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 1, 'langgraph_node': 'agent', 'langgraph_triggers': ['start:agent'], 'langgraph_task_idx': 0}, 'data': {'chunk': {'messages': [{'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}], 'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}}}, 'parent_ids': ['1ef32717-bc30-6cf2-8a26-33f63567bc25']}



Receiving new event of type: events...
{'event': 'on_chain_end', 'data': {'output': {'messages': [{'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}], 'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}}, 'input': {'some_bytes': None, 'some_byte_array': None, 'dict_with_bytes': None, 'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}], 'sleep': None}}, 'run_id': '72b74d24-5792-48da-a887-102100d6e2c0', 'name': 'agent', 'tags': ['graph:step:1'], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 1, 'langgraph_node': 'agent', 'langgraph_triggers': ['start:agent'], 'langgraph_task_idx': 0}, 'parent_ids': ['1ef32717-bc30-6cf2-8a26-33f63567bc25']}



Receiving new event of type: debug...
{'type': 'task_result', 'timestamp': '2024-06-24T21:34:06.124350+00:00', 'step': 1, 'payload': {'id': '212ed9c2-a454-50c5-a202-12066bbbe7b8', 'name': 'agent', 'result': [['some_bytes', 'c29tZV9ieXRlcw=='], ['some_byte_array', 'c29tZV9ieXRlX2FycmF5'], ['dict_with_bytes', {'more_bytes': 'bW9yZV9ieXRlcw=='}], ['messages', [{'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]]]}}



Receiving new event of type: events...
{'event': 'on_chain_stream', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'name': 'LangGraph', 'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'data': {'chunk': ['debug', {'type': 'task_result', 'timestamp': '2024-06-24T21:34:06.124350+00:00', 'step': 1, 'payload': {'id': '212ed9c2-a454-50c5-a202-12066bbbe7b8', 'name': 'agent', 'result': [['some_bytes', 'c29tZV9ieXRlcw=='], ['some_byte_array', 'c29tZV9ieXRlX2FycmF5'], ['dict_with_bytes', {'more_bytes': 'bW9yZV9ieXRlcw=='}], ['messages', [{'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]]]}}]}, 'parent_ids': []}



Receiving new event of type: events...
{'event': 'on_chain_stream', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'name': 'LangGraph', 'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'data': {'chunk': ['values', {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}]}, 'parent_ids': []}



Receiving new event of type: debug...
{'type': 'checkpoint', 'timestamp': '2024-06-24T21:34:06.124510+00:00', 'step': 1, 'payload': {'config': {'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'callbacks': [None], 'recursion_limit': 25, 'configurable': {'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'thread_ts': '1ef32717-bc91-6a34-8001-26353c117c25', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25'}, 'values': {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}, 'metadata': {'source': 'loop', 'step': 1, 'writes': {'agent': {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}}}}



Receiving new event of type: events...
{'event': 'on_chain_stream', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'name': 'LangGraph', 'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'data': {'chunk': ['debug', {'type': 'checkpoint', 'timestamp': '2024-06-24T21:34:06.124510+00:00', 'step': 1, 'payload': {'config': {'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'callbacks': [None], 'recursion_limit': 25, 'configurable': {'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'thread_ts': '1ef32717-bc91-6a34-8001-26353c117c25', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25'}, 'values': {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}, 'metadata': {'source': 'loop', 'step': 1, 'writes': {'agent': {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}}}}]}, 'parent_ids': []}



Receiving new event of type: debug...
{'type': 'task', 'timestamp': '2024-06-24T21:34:06.124572+00:00', 'step': 2, 'payload': {'id': '44139125-a1be-57c2-9cb2-19eb62bbaf2f', 'name': 'tool', 'input': {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}], 'sleep': None}, 'triggers': ['branch:agent:should_continue:tool']}}



Receiving new event of type: events...
{'event': 'on_chain_stream', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'name': 'LangGraph', 'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'data': {'chunk': ['debug', {'type': 'task', 'timestamp': '2024-06-24T21:34:06.124572+00:00', 'step': 2, 'payload': {'id': '44139125-a1be-57c2-9cb2-19eb62bbaf2f', 'name': 'tool', 'input': {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}], 'sleep': None}, 'triggers': ['branch:agent:should_continue:tool']}}]}, 'parent_ids': []}



Receiving new event of type: events...
{'event': 'on_chain_start', 'data': {}, 'name': 'tool', 'tags': ['graph:step:2'], 'run_id': '91575720-886e-485e-ae2d-d6817e5346bf', 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 2, 'langgraph_node': 'tool', 'langgraph_triggers': ['branch:agent:should_continue:tool'], 'langgraph_task_idx': 0}, 'parent_ids': ['1ef32717-bc30-6cf2-8a26-33f63567bc25']}



Receiving new event of type: events...
{'event': 'on_chain_stream', 'run_id': '91575720-886e-485e-ae2d-d6817e5346bf', 'name': 'tool', 'tags': ['graph:step:2'], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 2, 'langgraph_node': 'tool', 'langgraph_triggers': ['branch:agent:should_continue:tool'], 'langgraph_task_idx': 0}, 'data': {'chunk': {'messages': [{'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': None, 'tool_call_id': 'tool_call_id'}]}}, 'parent_ids': ['1ef32717-bc30-6cf2-8a26-33f63567bc25']}



Receiving new event of type: events...
{'event': 'on_chain_end', 'data': {'output': {'messages': [{'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '639ca779-403d-4915-a066-327e1f634c8b', 'tool_call_id': 'tool_call_id'}]}, 'input': {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}], 'sleep': None}}, 'run_id': '91575720-886e-485e-ae2d-d6817e5346bf', 'name': 'tool', 'tags': ['graph:step:2'], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 2, 'langgraph_node': 'tool', 'langgraph_triggers': ['branch:agent:should_continue:tool'], 'langgraph_task_idx': 0}, 'parent_ids': ['1ef32717-bc30-6cf2-8a26-33f63567bc25']}



Receiving new event of type: debug...
{'type': 'task_result', 'timestamp': '2024-06-24T21:34:06.126828+00:00', 'step': 2, 'payload': {'id': '44139125-a1be-57c2-9cb2-19eb62bbaf2f', 'name': 'tool', 'result': [['messages', [{'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '639ca779-403d-4915-a066-327e1f634c8b', 'tool_call_id': 'tool_call_id'}]]]}}



Receiving new event of type: events...
{'event': 'on_chain_stream', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'name': 'LangGraph', 'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'data': {'chunk': ['debug', {'type': 'task_result', 'timestamp': '2024-06-24T21:34:06.126828+00:00', 'step': 2, 'payload': {'id': '44139125-a1be-57c2-9cb2-19eb62bbaf2f', 'name': 'tool', 'result': [['messages', [{'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '639ca779-403d-4915-a066-327e1f634c8b', 'tool_call_id': 'tool_call_id'}]]]}}]}, 'parent_ids': []}



Receiving new event of type: events...
{'event': 'on_chain_stream', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'name': 'LangGraph', 'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'data': {'chunk': ['values', {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '639ca779-403d-4915-a066-327e1f634c8b', 'tool_call_id': 'tool_call_id'}]}]}, 'parent_ids': []}



Receiving new event of type: messages/complete...
[{'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '639ca779-403d-4915-a066-327e1f634c8b', 'tool_call_id': 'tool_call_id'}]



Receiving new event of type: debug...
{'type': 'checkpoint', 'timestamp': '2024-06-24T21:34:06.126966+00:00', 'step': 2, 'payload': {'config': {'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'callbacks': [None], 'recursion_limit': 25, 'configurable': {'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'thread_ts': '1ef32717-bc97-6a06-8002-8e9ffc1ea75a', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25'}, 'values': {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '639ca779-403d-4915-a066-327e1f634c8b', 'tool_call_id': 'tool_call_id'}]}, 'metadata': {'source': 'loop', 'step': 2, 'writes': {'tool': {'messages': [{'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '639ca779-403d-4915-a066-327e1f634c8b', 'tool_call_id': 'tool_call_id'}]}}}}}



Receiving new event of type: events...
{'event': 'on_chain_stream', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'name': 'LangGraph', 'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'data': {'chunk': ['debug', {'type': 'checkpoint', 'timestamp': '2024-06-24T21:34:06.126966+00:00', 'step': 2, 'payload': {'config': {'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'callbacks': [None], 'recursion_limit': 25, 'configurable': {'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'thread_ts': '1ef32717-bc97-6a06-8002-8e9ffc1ea75a', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25'}, 'values': {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '639ca779-403d-4915-a066-327e1f634c8b', 'tool_call_id': 'tool_call_id'}]}, 'metadata': {'source': 'loop', 'step': 2, 'writes': {'tool': {'messages': [{'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '639ca779-403d-4915-a066-327e1f634c8b', 'tool_call_id': 'tool_call_id'}]}}}}}]}, 'parent_ids': []}



Receiving new event of type: debug...
{'type': 'task', 'timestamp': '2024-06-24T21:34:06.127034+00:00', 'step': 3, 'payload': {'id': 'f1ccf371-63b3-5268-a837-7f360a93c4ec', 'name': 'agent', 'input': {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '639ca779-403d-4915-a066-327e1f634c8b', 'tool_call_id': 'tool_call_id'}], 'sleep': None}, 'triggers': ['tool']}}



Receiving new event of type: events...
{'event': 'on_chain_stream', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'name': 'LangGraph', 'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'data': {'chunk': ['debug', {'type': 'task', 'timestamp': '2024-06-24T21:34:06.127034+00:00', 'step': 3, 'payload': {'id': 'f1ccf371-63b3-5268-a837-7f360a93c4ec', 'name': 'agent', 'input': {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '639ca779-403d-4915-a066-327e1f634c8b', 'tool_call_id': 'tool_call_id'}], 'sleep': None}, 'triggers': ['tool']}}]}, 'parent_ids': []}



Receiving new event of type: events...
{'event': 'on_chain_start', 'data': {}, 'name': 'agent', 'tags': ['graph:step:3'], 'run_id': 'b7d0900c-bfc2-43e4-b760-99bbc5bad84e', 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 3, 'langgraph_node': 'agent', 'langgraph_triggers': ['tool'], 'langgraph_task_idx': 0}, 'parent_ids': ['1ef32717-bc30-6cf2-8a26-33f63567bc25']}



Receiving new event of type: events...
{'event': 'on_chat_model_start', 'data': {'input': {'messages': [[{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '639ca779-403d-4915-a066-327e1f634c8b', 'tool_call_id': 'tool_call_id'}]]}}, 'name': 'FakeListChatModel', 'tags': ['seq:step:1'], 'run_id': '0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 3, 'langgraph_node': 'agent', 'langgraph_triggers': ['tool'], 'langgraph_task_idx': 0, 'ls_model_type': 'chat'}, 'parent_ids': ['1ef32717-bc30-6cf2-8a26-33f63567bc25', 'b7d0900c-bfc2-43e4-b760-99bbc5bad84e']}



Receiving new event of type: events...
{'event': 'on_chat_model_stream', 'data': {'chunk': {'content': 'e', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}}, 'run_id': '0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'name': 'FakeListChatModel', 'tags': ['seq:step:1'], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 3, 'langgraph_node': 'agent', 'langgraph_triggers': ['tool'], 'langgraph_task_idx': 0, 'ls_model_type': 'chat'}, 'parent_ids': ['1ef32717-bc30-6cf2-8a26-33f63567bc25', 'b7d0900c-bfc2-43e4-b760-99bbc5bad84e']}



Receiving new event of type: messages/metadata...
{'run-0f2ef0a1-0fc7-445c-9df4-55e8bb284575': {'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 3, 'langgraph_node': 'agent', 'langgraph_triggers': ['tool'], 'langgraph_task_idx': 0, 'ls_model_type': 'chat'}}}



Receiving new event of type: messages/partial...
[{'content': 'e', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]



Receiving new event of type: events...
{'event': 'on_chat_model_stream', 'data': {'chunk': {'content': 'n', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}}, 'run_id': '0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'name': 'FakeListChatModel', 'tags': ['seq:step:1'], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 3, 'langgraph_node': 'agent', 'langgraph_triggers': ['tool'], 'langgraph_task_idx': 0, 'ls_model_type': 'chat'}, 'parent_ids': ['1ef32717-bc30-6cf2-8a26-33f63567bc25', 'b7d0900c-bfc2-43e4-b760-99bbc5bad84e']}



Receiving new event of type: messages/partial...
[{'content': 'en', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]



Receiving new event of type: events...
{'event': 'on_chat_model_stream', 'data': {'chunk': {'content': 'd', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}}, 'run_id': '0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'name': 'FakeListChatModel', 'tags': ['seq:step:1'], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 3, 'langgraph_node': 'agent', 'langgraph_triggers': ['tool'], 'langgraph_task_idx': 0, 'ls_model_type': 'chat'}, 'parent_ids': ['1ef32717-bc30-6cf2-8a26-33f63567bc25', 'b7d0900c-bfc2-43e4-b760-99bbc5bad84e']}



Receiving new event of type: messages/partial...
[{'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]



Receiving new event of type: events...
{'event': 'on_chat_model_end', 'data': {'output': {'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, 'input': {'messages': [[{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '639ca779-403d-4915-a066-327e1f634c8b', 'tool_call_id': 'tool_call_id'}]]}}, 'run_id': '0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'name': 'FakeListChatModel', 'tags': ['seq:step:1'], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 3, 'langgraph_node': 'agent', 'langgraph_triggers': ['tool'], 'langgraph_task_idx': 0, 'ls_model_type': 'chat'}, 'parent_ids': ['1ef32717-bc30-6cf2-8a26-33f63567bc25', 'b7d0900c-bfc2-43e4-b760-99bbc5bad84e']}



Receiving new event of type: events...
{'event': 'on_chain_start', 'data': {'input': {'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '639ca779-403d-4915-a066-327e1f634c8b', 'tool_call_id': 'tool_call_id'}, {'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}], 'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}}}, 'name': 'should_continue', 'tags': ['seq:step:3'], 'run_id': '8af814e9-8136-4aab-acbc-dffc5bcafdfd', 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 3, 'langgraph_node': 'agent', 'langgraph_triggers': ['tool'], 'langgraph_task_idx': 0}, 'parent_ids': ['1ef32717-bc30-6cf2-8a26-33f63567bc25', 'b7d0900c-bfc2-43e4-b760-99bbc5bad84e']}



Receiving new event of type: events...
{'event': 'on_chain_end', 'data': {'output': '__end__', 'input': {'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '639ca779-403d-4915-a066-327e1f634c8b', 'tool_call_id': 'tool_call_id'}, {'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}], 'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}}}, 'run_id': '8af814e9-8136-4aab-acbc-dffc5bcafdfd', 'name': 'should_continue', 'tags': ['seq:step:3'], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 3, 'langgraph_node': 'agent', 'langgraph_triggers': ['tool'], 'langgraph_task_idx': 0}, 'parent_ids': ['1ef32717-bc30-6cf2-8a26-33f63567bc25', 'b7d0900c-bfc2-43e4-b760-99bbc5bad84e']}



Receiving new event of type: events...
{'event': 'on_chain_stream', 'run_id': 'b7d0900c-bfc2-43e4-b760-99bbc5bad84e', 'name': 'agent', 'tags': ['graph:step:3'], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 3, 'langgraph_node': 'agent', 'langgraph_triggers': ['tool'], 'langgraph_task_idx': 0}, 'data': {'chunk': {'messages': [{'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}], 'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}}}, 'parent_ids': ['1ef32717-bc30-6cf2-8a26-33f63567bc25']}



Receiving new event of type: events...
{'event': 'on_chain_end', 'data': {'output': {'messages': [{'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}], 'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}}, 'input': {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '639ca779-403d-4915-a066-327e1f634c8b', 'tool_call_id': 'tool_call_id'}], 'sleep': None}}, 'run_id': 'b7d0900c-bfc2-43e4-b760-99bbc5bad84e', 'name': 'agent', 'tags': ['graph:step:3'], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 3, 'langgraph_node': 'agent', 'langgraph_triggers': ['tool'], 'langgraph_task_idx': 0}, 'parent_ids': ['1ef32717-bc30-6cf2-8a26-33f63567bc25']}



Receiving new event of type: debug...
{'type': 'task_result', 'timestamp': '2024-06-24T21:34:06.133991+00:00', 'step': 3, 'payload': {'id': 'f1ccf371-63b3-5268-a837-7f360a93c4ec', 'name': 'agent', 'result': [['some_bytes', 'c29tZV9ieXRlcw=='], ['some_byte_array', 'c29tZV9ieXRlX2FycmF5'], ['dict_with_bytes', {'more_bytes': 'bW9yZV9ieXRlcw=='}], ['messages', [{'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]]]}}



Receiving new event of type: events...
{'event': 'on_chain_stream', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'name': 'LangGraph', 'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'data': {'chunk': ['debug', {'type': 'task_result', 'timestamp': '2024-06-24T21:34:06.133991+00:00', 'step': 3, 'payload': {'id': 'f1ccf371-63b3-5268-a837-7f360a93c4ec', 'name': 'agent', 'result': [['some_bytes', 'c29tZV9ieXRlcw=='], ['some_byte_array', 'c29tZV9ieXRlX2FycmF5'], ['dict_with_bytes', {'more_bytes': 'bW9yZV9ieXRlcw=='}], ['messages', [{'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]]]}}]}, 'parent_ids': []}



Receiving new event of type: events...
{'event': 'on_chain_stream', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'name': 'LangGraph', 'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'data': {'chunk': ['values', {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '639ca779-403d-4915-a066-327e1f634c8b', 'tool_call_id': 'tool_call_id'}, {'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}]}, 'parent_ids': []}



Receiving new event of type: debug...
{'type': 'checkpoint', 'timestamp': '2024-06-24T21:34:06.134190+00:00', 'step': 3, 'payload': {'config': {'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'callbacks': [None], 'recursion_limit': 25, 'configurable': {'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'thread_ts': '1ef32717-bca9-6418-8003-8d0d0b06845c', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25'}, 'values': {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '639ca779-403d-4915-a066-327e1f634c8b', 'tool_call_id': 'tool_call_id'}, {'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}, 'metadata': {'source': 'loop', 'step': 3, 'writes': {'agent': {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}}}}



Receiving new event of type: events...
{'event': 'on_chain_stream', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'name': 'LangGraph', 'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'data': {'chunk': ['debug', {'type': 'checkpoint', 'timestamp': '2024-06-24T21:34:06.134190+00:00', 'step': 3, 'payload': {'config': {'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'callbacks': [None], 'recursion_limit': 25, 'configurable': {'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'thread_ts': '1ef32717-bca9-6418-8003-8d0d0b06845c', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25'}, 'values': {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '639ca779-403d-4915-a066-327e1f634c8b', 'tool_call_id': 'tool_call_id'}, {'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}, 'metadata': {'source': 'loop', 'step': 3, 'writes': {'agent': {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}}}}]}, 'parent_ids': []}



Receiving new event of type: events...
{'event': 'on_chain_end', 'data': {'output': {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '639ca779-403d-4915-a066-327e1f634c8b', 'tool_call_id': 'tool_call_id'}, {'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}, 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'name': 'LangGraph', 'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'parent_ids': []}



Receiving new event of type: end...
None

How to stream state updates of your graph

!!! info "Prerequisites" * Streaming

This guide covers how to use stream_mode="updates" for your graph, which will stream the updates to the graph state that are made after each node is executed. This differs from using stream_mode="values": instead of streaming the entire value of the state at each superstep, it only streams the updates from each of the nodes that made an update to the state at that superstep.

Setup

First let's set up our client and thread:

=== "Python"

```python
from langgraph_sdk import get_client

client = get_client(url=<DEPLOYMENT_URL>)
# Using the graph deployed with the name "agent"
assistant_id = "agent"
# create thread
thread = await client.threads.create()
print(thread)
```

=== "Javascript"

```js
import { Client } from "@langchain/langgraph-sdk";

const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
// Using the graph deployed with the name "agent"
const assistantID = "agent";
// create thread
const thread = await client.threads.create();
console.log(thread);
```

=== "CURL"

```bash
curl --request POST \
  --url <DEPLOYMENT_URL>/threads \
  --header 'Content-Type: application/json' \
  --data '{}'
```

Output:

{
  'thread_id': '979e3c89-a702-4882-87c2-7a59a250ce16',
  'created_at': '2024-06-21T15:22:07.453100+00:00',
  'updated_at': '2024-06-21T15:22:07.453100+00:00',
  'metadata': {},
  'status': 'idle',
  'config': {},
  'values': None 
}

Stream graph in updates mode

Now we can stream by updates, which outputs updates made to the state by each node after it has executed:

=== "Python"

```python
input = {
    "messages": [
        {
            "role": "user",
            "content": "what's the weather in la"
        }
    ]
}
async for chunk in client.runs.stream(
    thread["thread_id"],
    assistant_id,
    input=input,
    stream_mode="updates",
):
    print(f"Receiving new event of type: {chunk.event}...")
    print(chunk.data)
    print("\n\n")
```

=== "Javascript"

```js
const input = {
  messages: [
    {
      role: "human",
      content: "What's the weather in la"
    }
  ]
};

const streamResponse = client.runs.stream(
  thread["thread_id"],
  assistantID,
  {
    input,
    streamMode: "updates"
  }
);

for await (const chunk of streamResponse) {
  console.log(`Receiving new event of type: ${chunk.event}...`);
  console.log(chunk.data);
  console.log("\n\n");
}
```

=== "CURL"

```bash
curl --request POST \
 --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
 --header 'Content-Type: application/json' \
 --data "{
   \"assistant_id\": \"agent\",
   \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"What's the weather in la\"}]},
   \"stream_mode\": [
     \"updates\"
   ]
 }" | \
 sed 's/\r$//' | \
 awk '
 /^event:/ {
     if (data_content != "") {
         print data_content "\n"
     }
     sub(/^event: /, "Receiving event of type: ", $0)
     printf "%s...\n", $0
     data_content = ""
 }
 /^data:/ {
     sub(/^data: /, "", $0)
     data_content = $0
 }
 END {
     if (data_content != "") {
         print data_content "\n"
     }
 }
 '
```

Output:

Receiving new event of type: metadata...
{"run_id": "cfc96c16-ed9a-44bd-b5bb-c30e3c0725f0"}



Receiving new event of type: updates...
{
  "agent": {
    "messages": [
      {
        "type": "ai",
        "tool_calls": [
          {
            "name": "tavily_search_results_json",
            "args": {
              "query": "weather in los angeles"
            },
            "id": "toolu_0148tMmDK51iLQfG1yaNwRHM"
          }
        ],
        ...
      }
    ]
  }
}



Receiving new event of type: updates...
{
  "action": {
    "messages": [
      {
        "content": [
          {
            "url": "https://www.weatherapi.com/",
            "content": "{\"location\": {\"name\": \"Los Angeles\", \"region\": \"California\", \"country\": \"United States of America\", \"lat\": 34.05, \"lon\": -118.24, \"tz_id\": \"America/Los_Angeles\", \"localtime_epoch\": 1716062239, \"localtime\": \"2024-05-18 12:57\"}, \"current\": {\"last_updated_epoch\": 1716061500, \"last_updated\": \"2024-05-18 12:45\", \"temp_c\": 18.9, \"temp_f\": 66.0, \"is_day\": 1, \"condition\": {\"text\": \"Overcast\", \"icon\": \"//cdn.weatherapi.com/weather/64x64/day/122.png\", \"code\": 1009}, \"wind_mph\": 2.2, \"wind_kph\": 3.6, \"wind_degree\": 10, \"wind_dir\": \"N\", \"pressure_mb\": 1017.0, \"pressure_in\": 30.02, \"precip_mm\": 0.0, \"precip_in\": 0.0, \"humidity\": 65, \"cloud\": 100, \"feelslike_c\": 18.9, \"feelslike_f\": 66.0, \"vis_km\": 16.0, \"vis_miles\": 9.0, \"uv\": 6.0, \"gust_mph\": 7.5, \"gust_kph\": 12.0}}"
          }
        ],
        "type": "tool",
        "name": "tavily_search_results_json",
        "tool_call_id": "toolu_0148tMmDK51iLQfG1yaNwRHM",
        ...
      }
    ]
  }
}



Receiving new event of type: updates...
{
  "agent": {
    "messages": [
      {
        "content": "The weather in Los Angeles is currently overcast with a temperature of around 66┬░F (18.9┬░C). There are light winds from the north at around 2-3 mph. The humidity is 65% and visibility is good at 9 miles. Overall, mild spring weather conditions in LA.",
        "type": "ai",
        ...
      }
    ]
  }
}



Receiving new event of type: end...
None

How to stream full state of your graph

!!! info "Prerequisites" * Streaming

This guide covers how to use stream_mode="values", which streams the value of the state at each superstep. This differs from using stream_mode="updates": instead of streaming just the updates to the state from each node, it streams the entire graph state at that superstep.

Setup

First let's set up our client and thread:

=== "Python"

```python
from langgraph_sdk import get_client

client = get_client(url=<DEPLOYMENT_URL>)
# Using the graph deployed with the name "agent"
assistant_id = "agent"
# create thread
thread = await client.threads.create()
print(thread)
```

=== "Javascript"

```js
import { Client } from "@langchain/langgraph-sdk";

const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
// Using the graph deployed with the name "agent"
const assistantID = "agent";
// create thread
const thread = await client.threads.create();
console.log(thread);
```

=== "CURL"

```bash
curl --request POST \
  --url <DEPLOYMENT_URL>/threads \
  --header 'Content-Type: application/json' \
  --data '{}'
```

Output:

{
  'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4',
  'created_at': '2024-06-24T21:30:07.980789+00:00',
  'updated_at': '2024-06-24T21:30:07.980789+00:00',
  'metadata': {},
  'status': 'idle',
  'config': {},
  'values': None
}

Stream graph in values mode

Now we can stream by values, which streams the full state of the graph after each node has finished executing:

=== "Python"

```python
input = {"messages": [{"role": "user", "content": "what's the weather in la"}]}

# stream values
async for chunk in client.runs.stream(
    thread["thread_id"],
    assistant_id, 
    input=input,
    stream_mode="values"
):
    print(f"Receiving new event of type: {chunk.event}...")
    print(chunk.data)
    print("\n\n")
```

=== "Javascript"

```js
const input = {"messages": [{"role": "user", "content": "what's the weather in la"}]}

const streamResponse = client.runs.stream(
  thread["thread_id"],
  assistantID,
  {
    input,
    streamMode: "values"
  }
);
for await (const chunk of streamResponse) {
  console.log(`Receiving new event of type: ${chunk.event}...`);
  console.log(chunk.data);
  console.log("\n\n");
}
```

=== "CURL"

```bash
curl --request POST \
 --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
 --header 'Content-Type: application/json' \
 --data "{
   \"assistant_id\": \"agent\",
   \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in la\"}]},
   \"stream_mode\": [
     \"values\"
   ]
 }" | \
 sed 's/\r$//' | \
 awk '
 /^event:/ {
     if (data_content != "") {
         print data_content "\n"
     }
     sub(/^event: /, "Receiving event of type: ", $0)
     printf "%s...\n", $0
     data_content = ""
 }
 /^data:/ {
     sub(/^data: /, "", $0)
     data_content = $0
 }
 END {
     if (data_content != "") {
         print data_content "\n"
     }
 }
 ' 
```

Output:

Receiving new event of type: metadata...
{"run_id": "f08791ce-0a3d-44e0-836c-ff62cd2e2786"}



Receiving new event of type: values...
{
  "messages": [
    {
      "role": "human",
      "content": "what's the weather in la"
    }
  ]
}



Receiving new event of type: values...
{
  "messages": [
    {
      "content": "what's the weather in la",
      "type": "human",
      ...
    },
    {
      "content": "",
      "type": "ai",
      "tool_calls": [
        {
          "name": "tavily_search_results_json",
          "args": {
            "query": "weather in los angeles"
          },
          "id": "toolu_01E5mSaZWm5rWJnCqmt63v4g"
        }
      ],
      ...
    }
  ]
}

...

Receiving new event of type: values...
{
  "messages": [
    {
      "content": "what's the weather in la",
      "type": "human",
      ...
    },
    {
      "content": "",
      "type": "ai",
      "tool_calls": [
        {
          "name": "tavily_search_results_json",
          "args": {
            "query": "weather in los angeles"
          },
          "id": "toolu_01E5mSaZWm5rWJnCqmt63v4g"
        }
      ],
      ...
    }
    {
      "content": [
        {
          "url": "https://www.weatherapi.com/",
          "content": "{\"location\": {\"name\": \"Los Angeles\", \"region\": \"California\", \"country\": \"United States of America\", \"lat\": 34.05, \"lon\": -118.24, \"tz_id\": \"America/Los_Angeles\", \"localtime_epoch\": 1716310320, \"localtime\": \"2024-05-21 9:52\"}, \"current\": {\"last_updated_epoch\": 1716309900, \"last_updated\": \"2024-05-21 09:45\", \"temp_c\": 16.7, \"temp_f\": 62.1, \"is_day\": 1, \"condition\": {\"text\": \"Overcast\", \"icon\": \"//cdn.weatherapi.com/weather/64x64/day/122.png\", \"code\": 1009}, \"wind_mph\": 8.1, \"wind_kph\": 13.0, \"wind_degree\": 250, \"wind_dir\": \"WSW\", \"pressure_mb\": 1015.0, \"pressure_in\": 29.97, \"precip_mm\": 0.0, \"precip_in\": 0.0, \"humidity\": 65, \"cloud\": 100, \"feelslike_c\": 16.7, \"feelslike_f\": 62.1, \"vis_km\": 16.0, \"vis_miles\": 9.0, \"uv\": 5.0, \"gust_mph\": 12.5, \"gust_kph\": 20.2}}"
        }
      ],
      "type": "tool",
      "name": "tavily_search_results_json",
      "tool_call_id": "toolu_01E5mSaZWm5rWJnCqmt63v4g"
      ...
    },
    {
      "content": "Based on the weather API results, the current weather in Los Angeles is overcast with a temperature of around 62┬░F (17┬░C). There are light winds from the west-southwest around 8-13 mph. The humidity is 65% and visibility is good at 9 miles. Overall, mild spring weather conditions in LA.",
      "type": "ai",
      ...
    }
  ]
}



Receiving new event of type: end...
None

If we want to just get the final result, we can use this endpoint and just keep track of the last value we received

=== "Python"

```python
final_answer = None
async for chunk in client.runs.stream(
    thread["thread_id"],
    assistant_id,
    input=input,
    stream_mode="values"
):
    if chunk.event == "values":
        final_answer = chunk.data
```

=== "Javascript"

```js
let finalAnswer;
const streamResponse = client.runs.stream(
  thread["thread_id"],
  assistantID,
  {
    input,
    streamMode: "values"
  }
);
for await (const chunk of streamResponse) {
  finalAnswer = chunk.data;
}
```

=== "CURL"

```bash
curl --request POST \
 --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
 --header 'Content-Type: application/json' \
 --data "{
   \"assistant_id\": \"agent\",
   \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in la\"}]},
   \"stream_mode\": [
     \"values\"
   ]
 }" | \
 sed 's/\r$//' | \
 awk '
 /^data:/ { 
     sub(/^data: /, "", $0)   
     data_content = $0          
 }    
 END {                                               
     if (data_content != "") {
         print data_content
     }
 }         
 '
```

Output:

{
  "messages": [
    {
      "content": "what's the weather in la",
      "type": "human",
      ...
    },
    {
      "type": "ai",
      "tool_calls": [
        {
          "name": "tavily_search_results_json",
          "args": {
            "query": "weather in los angeles"
          },
          "id": "toolu_01E5mSaZWm5rWJnCqmt63v4g"
        }
      ],
      ...
    }
    {
      "content": [
        {
          "url": "https://www.weatherapi.com/",
          "content": "{\"location\": {\"name\": \"Los Angeles\", \"region\": \"California\", \"country\": \"United States of America\", \"lat\": 34.05, \"lon\": -118.24, \"tz_id\": \"America/Los_Angeles\", \"localtime_epoch\": 1716310320, \"localtime\": \"2024-05-21 9:52\"}, \"current\": {\"last_updated_epoch\": 1716309900, \"last_updated\": \"2024-05-21 09:45\", \"temp_c\": 16.7, \"temp_f\": 62.1, \"is_day\": 1, \"condition\": {\"text\": \"Overcast\", \"icon\": \"//cdn.weatherapi.com/weather/64x64/day/122.png\", \"code\": 1009}, \"wind_mph\": 8.1, \"wind_kph\": 13.0, \"wind_degree\": 250, \"wind_dir\": \"WSW\", \"pressure_mb\": 1015.0, \"pressure_in\": 29.97, \"precip_mm\": 0.0, \"precip_in\": 0.0, \"humidity\": 65, \"cloud\": 100, \"feelslike_c\": 16.7, \"feelslike_f\": 62.1, \"vis_km\": 16.0, \"vis_miles\": 9.0, \"uv\": 5.0, \"gust_mph\": 12.5, \"gust_kph\": 20.2}}"
        }
      ],
      "type": "tool",
      "name": "tavily_search_results_json",
      "tool_call_id": "toolu_01E5mSaZWm5rWJnCqmt63v4g"
      ...
    },
    {
      "content": "Based on the weather API results, the current weather in Los Angeles is overcast with a temperature of around 62┬░F (17┬░C). There are light winds from the west-southwest around 8-13 mph. The humidity is 65% and visibility is good at 9 miles. Overall, mild spring weather conditions in LA.",
      "type": "ai",
      ...
    }
  ]
}

Test Cloud Deployment

The LangGraph Studio UI connects directly to LangGraph Cloud deployments.

Starting from the LangSmith UI...

In the left-hand navigation panel, select LangGraph Cloud. The LangGraph Cloud view contains a list of existing LangGraph Cloud deployments.
Select an existing deployment to test with LangGraph Studio.
In the top-right corner, select Open LangGraph Studio.
Invoke an assistant or view an existing thread.

The following video shows these exact steps being carried out:

LangGraph Studio With Local Deployment

!!! warning "Browser Compatibility" Viewing the studio page of a local LangGraph deployment does not work in Safari. Use Chrome instead.

Setup

Make sure you have setup your app correctly, by creating a compiled graph, a .env file with any environment variables, and a langgraph.json config file that points to your environment file and compiled graph. See here for more detailed instructions.

After you have your app setup, head into the directory with your langgraph.json file and call langgraph dev to start the API server in watch mode which means it will restart on code changes, which is ideal for local testing. If the API server start correctly you should see logs that look something like this:

Ready!

API: http://localhost:2024

Docs: http://localhost:2024/docs

LangGraph Studio Web UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024

Read this reference to learn about all the options for starting the API server.

Access Studio

Once you have successfully started the API server, you can access the studio by going to the following URL: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024 (see warning above if using Safari).

If everything is working correctly you should see the studio show up looking something like this (with your graph diagram on the left hand side):

Use the Studio for Testing

To learn about how to use the studio for testing, read the LangGraph Studio how-tos.

Interacting with Threads in Studio

View Thread

In the top of the right-hand pane, select the New Thread dropdown menu to view existing threads.
View the state of the thread (i.e. the output) in the right-hand pane.
To create a new thread, select + New Thread.

The following video shows these exact steps being carried out:

Edit Thread State

The LangGraph Studio UI contains features for editing thread state. Explore these features in the right-hand pane. Select the Edit icon, modify the desired state, and then select Fork to invoke the assistant with the updated state.

The following video shows how to edit a thread in the studio:

Use Webhooks

You may wish to use webhooks in your client, especially when using async streams in case you want to update something in your service once the API call to LangGraph Cloud has finished running. To do so, you will need to expose an endpoint that can accept POST requests, and then pass it to your API request in the "webhook" parameter.

Currently, the SDK has not exposed this endpoint but you can access it through curl commands as follows.

The following endpoints accept webhook as a parameter:

Create Run -> POST /thread/{thread_id}/runs
Create Thread Cron -> POST /thread/{thread_id}/runs/crons
Stream Run -> POST /thread/{thread_id}/runs/stream
Wait Run -> POST /thread/{thread_id}/runs/wait
Create Cron -> POST /runs/crons
Stream Run Stateless -> POST /runs/stream
Wait Run Stateless -> POST /runs/wait

In this example, we will show calling a webhook after streaming a run.

Setup

First, let's setup our assistant and thread:

=== "Python"

```python
from langgraph_sdk import get_client

client = get_client(url=<DEPLOYMENT_URL>)
# Using the graph deployed with the name "agent"
assistant_id = "agent"
# create thread
thread = await client.threads.create()
print(thread)
```

=== "Javascript"

```js
import { Client } from "@langchain/langgraph-sdk";

const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
// Using the graph deployed with the name "agent"
const assistantID = "agent";
// create thread
const thread = await client.threads.create();
console.log(thread);
```

=== "CURL"

```bash
curl --request POST \
    --url <DEPLOYMENT_URL>/assistants/search \
    --header 'Content-Type: application/json' \
    --data '{
        "limit": 10,
        "offset": 0
    }' | jq -c 'map(select(.config == null or .config == {})) | .[0]' && \
curl --request POST \
    --url <DEPLOYMENT_URL>/threads \
    --header 'Content-Type: application/json' \
    --data '{}'
```

Output:

{
    'thread_id': '9dde5490-2b67-47c8-aa14-4bfec88af217', 
    'created_at': '2024-08-30T23:07:38.242730+00:00', 
    'updated_at': '2024-08-30T23:07:38.242730+00:00', 
    'metadata': {}, 
    'status': 'idle', 
    'config': {}, 
    'values': None
}

Use graph with a webhook

To invoke a run with a webhook, we specify the webhook parameter with the desired endpoint when creating a run. Webhook requests are triggered by the end of a run.

For example, if we can receive requests at https://my-server.app/my-webhook-endpoint, we can pass this to stream:

=== "Python"

```python
# create input
input = { "messages": [{ "role": "user", "content": "Hello!" }] }

async for chunk in client.runs.stream(
    thread_id=thread["thread_id"],
    assistant_id=assistant_id,
    input=input,
    stream_mode="events",
    webhook="https://my-server.app/my-webhook-endpoint"
):
    # Do something with the stream output
    pass
```

=== "Javascript"

```js
// create input
const input = { messages: [{ role: "human", content: "Hello!" }] };

// stream events
const streamResponse = client.runs.stream(
  thread["thread_id"],
  assistantID,
  {
    input: input,
    webhook: "https://my-server.app/my-webhook-endpoint"
  }
);
for await (const chunk of streamResponse) {
  // Do something with the stream output
}
```

=== "CURL"

```bash
curl --request POST \
    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
    --header 'Content-Type: application/json' \
    --data '{
        "assistant_id": <ASSISTANT_ID>,
        "input" : {"messages":[{"role": "user", "content": "Hello!"}]},
        "webhook": "https://my-server.app/my-webhook-endpoint"
    }'
```

The schema for the payload sent to my-webhook-endpoint is that of a run. See API Reference for more detail. Note that the run input, configuration, etc. are included in the kwargs field.

Signing webhook requests

To sign the webhook requests, we can specify a token parameter in the webhook URL, e.g.,

https://my-server.app/my-webhook-endpoint?token=...

The server should then extract the token from the request's parameters and validate it before processing the payload.

Quickstart: Deploy on LangGraph Cloud

!!! note "Prerequisites"

Before you begin, ensure you have the following:

- [GitHub account](https://github.com/)
- [LangSmith account](https://smith.langchain.com/)

Create a repository on GitHub

To deploy a LangGraph application to LangGraph Cloud, your application code must reside in a GitHub repository. Both public and private repositories are supported.

You can deploy any LangGraph Application to LangGraph Cloud.

For this guide, we'll use the pre-built Python ReAct Agent template.

??? note "Get Required API Keys for the ReAct Agent template"

This **ReAct Agent** application requires an API key from [Anthropic](https://console.anthropic.com/) and [Tavily](https://app.tavily.com/). You can get these API keys by signing up on their respective websites.

**Alternative**: If you'd prefer a scaffold application that doesn't require API keys, use the [**New LangGraph Project**](https://github.com/langchain-ai/new-langgraph-project) template instead of the **ReAct Agent** template.

Go to the ReAct Agent repository.
Fork the repository to your GitHub account by clicking the Fork button in the top right corner.

Deploy to LangGraph Cloud

??? note "1. Log in to LangSmith"

<figure markdown="1">
[![Login to LangSmith](deployment/img/01_login.png){: style="max-height:300px"}](deployment/img/01_login.png)
<figcaption>
Go to [LangSmith](https://smith.langchain.com/) and log in. If you don't have an account, you can sign up for free.
</figcaption>
</figure>

??? note "2. Click on LangGraph Platform (the left sidebar)"

<figure markdown="1">
[![Login to LangSmith](deployment/img/02_langgraph_platform.png){: style="max-height:300px"}](deployment/img/02_langgraph_platform.png)
<figcaption>
Select **LangGraph Platform** from the left sidebar.
</figcaption>
</figure>

??? note "3. Click on + New Deployment (top right corner)"

<figure markdown="1">
[![Login to LangSmith](deployment/img/03_deployments_page.png){: style="max-height:300px"}](deployment/img/03_deployments_page.png)
<figcaption>
Click on **+ New Deployment** to create a new deployment. This button is located in the top right corner.
It'll open a new modal where you can fill out the required fields.
</figcaption>
</figure>

??? note "4. Click on Import from GitHub (first time users)"

<figure markdown="1">
[![image](deployment/img/04_create_new_deployment.png)](deployment/img/04_create_new_deployment.png)
<figcaption>
Click on **Import from GitHub** and follow the instructions to connect your GitHub account. This step is needed for **first-time users** or to add private repositories that haven't been connected before.</figcaption>
    </figure>

??? note "5. Select the repository, configure ENV vars etc"

<figure markdown="1">
[![image](deployment/img/05_configure_deployment.png){: style="max-height:300px"}](deployment/img/05_configure_deployment.png)
<figcaption>
Select the <strong>repository</strong>, add env variables and secrets, and set other configuration options.
</figcaption>
</figure>

- **Repository**: Select the repository you forked earlier (or any other repository you want to deploy).
- Set the secrets and environment variables required by your application. For the **ReAct Agent** template, you need to set the following secrets:
    - **ANTHROPIC_API_KEY**: Get an API key from [Anthropic](https://console.anthropic.com/).
    - **TAVILY_API_KEY**: Get an API key on the [Tavily website](https://app.tavily.com/).

??? note "6. Click Submit to Deploy!"

<figure markdown="1">
[![image](deployment/img/05_configure_deployment.png){: style="max-height:300px"}](deployment/img/05_configure_deployment.png)
<figcaption>
    Please note that this step may ~15 minutes to complete. You can check the status of your deployment in the **Deployments** view.
    Click the <strong>Submit</strong> button at the top right corner to deploy your application.
</figcaption>
</figure>

Lagraph Studio Web UI

Once your application is deployed, you can test it in LangGraph Studio.

??? note "1. Click on an existing deployment"

<figure markdown="1">
[![image](deployment/img/07_deployments_page.png){: style="max-height:300px"}](deployment/img/07_deployments_page.png)
<figcaption>
    Click on the deployment you just created to view more details.
</figcaption>
</figure>

??? note "2. Click on LangGraph Studio"

<figure markdown="1">
[![image](deployment/img/08_deployment_view.png){: style="max-height:300px"}](deployment/img/08_deployment_view.png)
<figcaption>
    Click on the <strong>LangGraph Studio</strong> button to open LangGraph Studio.
</figcaption>
</figure>

[![image](deployment/img/09_langgraph_studio.png){: style="max-height:400px"}](deployment/img/09_langgraph_studio.png) Sample graph run in LangGraph Studio.

Test the API

!!! note

The API calls below are for the **ReAct Agent** template. If you're deploying a different application, you may need to adjust the API calls accordingly.

Before using, you need to get the URL of your LangGraph deployment. You can find this in the Deployment view. Click the URL to copy it to the clipboard.

You also need to make sure you have set up your API key properly, so you can authenticate with LangGraph Cloud.

export LANGSMITH_API_KEY=...

=== "Python SDK (Async)"

**Install the LangGraph Python SDK**

```shell
pip install langgraph-sdk
```

**Send a message to the assistant (threadless run)**

```python
from langgraph_sdk import get_client

client = get_client(url="your-deployment-url", api_key="your-langsmith-api-key")

async for chunk in client.runs.stream(
    None,  # Threadless run
    "agent", # Name of assistant. Defined in langgraph.json.
    input={
        "messages": [{
            "role": "human",
            "content": "What is LangGraph?",
        }],
    },
    stream_mode="updates",
):
    print(f"Receiving new event of type: {chunk.event}...")
    print(chunk.data)
    print("\n\n")
```

=== "Python SDK (Sync)"

**Install the LangGraph Python SDK**

```shell
pip install langgraph-sdk
```

**Send a message to the assistant (threadless run)**

```python
from langgraph_sdk import get_sync_client

client = get_sync_client(url="your-deployment-url", api_key="your-langsmith-api-key")

for chunk in client.runs.stream(
    None,  # Threadless run
    "agent", # Name of assistant. Defined in langgraph.json.
    input={
        "messages": [{
            "role": "human",
            "content": "What is LangGraph?",
        }],
    },
    stream_mode="updates",
):
    print(f"Receiving new event of type: {chunk.event}...")
    print(chunk.data)
    print("\n\n")
```

=== "Javascript SDK"

**Install the LangGraph JS SDK**

```shell
npm install @langchain/langgraph-sdk
```

**Send a message to the assistant (threadless run)**

```js
const { Client } = await import("@langchain/langgraph-sdk");

const client = new Client({ apiUrl: "your-deployment-url", apiKey: "your-langsmith-api-key" });

const streamResponse = client.runs.stream(
    null, // Threadless run
    "agent", // Assistant ID
    {
        input: {
            "messages": [
                { "role": "user", "content": "What is LangGraph?"}
            ]
        },
        streamMode: "messages",
    }
);

for await (const chunk of streamResponse) {
    console.log(`Receiving new event of type: ${chunk.event}...`);
    console.log(JSON.stringify(chunk.data));
    console.log("\n\n");
}
```

=== "Rest API"

```bash
curl -s --request POST \
    --url <DEPLOYMENT_URL> \
    --header 'Content-Type: application/json' \
    --data "{
        \"assistant_id\": \"agent\",
        \"input\": {
            \"messages\": [
                {
                    \"role\": \"human\",
                    \"content\": \"What is LangGraph?\"
                }
            ]
        },
        \"stream_mode\": \"updates\"
    }" 
```

Next Steps

Congratulations! If you've worked your way through this tutorial you are well on your way to becoming a LangGraph Cloud expert. Here are some other resources to check out to help you out on the path to expertise:

LangGraph Framework

LangGraph Tutorial: Get started with LangGraph framework.
LangGraph Concepts: Learn the foundational concepts of LangGraph.
LangGraph How-to Guides: Guides for common tasks with LangGraph.

ƒôÜ Learn More about LangGraph Platform

Expand your knowledge with these resources:

LangGraph Platform Concepts: Understand the foundational concepts of the LangGraph Platform.
LangGraph Platform How-to Guides: Discover step-by-step guides to build and deploy applications.
Launch Local LangGraph Server: This quick start guide shows how to start a LangGraph Server locally for the ReAct Agent template. The steps are similar for other templates.

API Reference

The LangGraph Cloud API reference is available with each deployment at the /docs URL path (e.g. http://localhost:8124/docs).

Click here to view the API reference.

Authentication

For deployments to LangGraph Cloud, authentication is required. Pass the X-Api-Key header with each request to the LangGraph Cloud API. The value of the header should be set to a valid LangSmith API key for the organization where the API is deployed.

Example curl command:

curl --request POST \
  --url http://localhost:8124/assistants/search \
  --header 'Content-Type: application/json' \
  --header 'X-Api-Key: LANGSMITH_API_KEY' \
  --data '{
  "metadata": {},
  "limit": 10,
  "offset": 0
}'

LangGraph CLI

The LangGraph command line interface includes commands to build and run a LangGraph Cloud API server locally in Docker. For development and testing, you can use the CLI to deploy a local API server as an alternative to the Studio desktop app.

Installation

Ensure that Docker is installed (e.g. docker --version).
Install the langgraph-cli package:

=== "pip" bash pip install langgraph-cli

=== "Homebrew (MacOS only)" bash brew install langgraph-cli
Run the command langgraph --help to confirm that the CLI is installed.

{#langgraph.json}

Configuration File {#configuration-file}

The LangGraph CLI requires a JSON configuration file with the following keys:

Key	Description
`dependencies`	Required. Array of dependencies for LangGraph Cloud API server. Dependencies can be one of the following: (1) `"."`, which will look for local Python packages, (2) `pyproject.toml`, `setup.py` or `requirements.txt` in the app directory `"./local_package"`, or (3) a package name.
`graphs`	Required. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: `./your_package/your_file.py:variable`, where `variable` is an instance of `langgraph.graph.state.CompiledStateGraph` `./your_package/your_file.py:make_graph`, where `make_graph` is a function that takes a config dictionary (`langchain_core.runnables.RunnableConfig`) and creates an instance of `langgraph.graph.state.StateGraph` / `langgraph.graph.state.CompiledStateGraph`.
`auth`	(Added in v0.0.11) Auth configuration containing the path to your authentication handler. Example: `./your_package/auth.py:auth`, where `auth` is an instance of `langgraph_sdk.Auth`. See authentication guide for details.
`env`	Path to `.env` file or a mapping from environment variable to its value.
`store`	Configuration for adding semantic search to the BaseStore. Contains the following fields: `index`: Configuration for semantic search indexing with fields: `embed`: Embedding provider (e.g., "openai:text-embedding-3-small") or path to custom embedding function `dims`: Dimension size of the embedding model. Used to initialize the vector table. `fields` (optional): List of fields to index. Defaults to `["$"]`, meaningto index entire documents. Can be specific fields like `["text", "summary", "some.value"]`
`python_version`	`3.11` or `3.12`. Defaults to `3.11`.
`pip_config_file`	Path to `pip` config file.
`dockerfile_lines`	Array of additional lines to add to Dockerfile following the import from parent image.

Note

The LangGraph CLI defaults to using the configuration file langgraph.json in the current directory.

Examples

Basic Configuration

{
  "dependencies": ["."],
  "graphs": {
    "chat": "./chat/graph.py:graph"
  }
}

Adding semantic search to the store

All deployments come with a DB-backed BaseStore. Adding an "index" configuration to your langgraph.json will enable semantic search within the BaseStore of your deployment.

The fields configuration determines which parts of your documents to embed:

If omitted or set to ["$"], the entire document will be embedded
To embed specific fields, use JSON path notation: ["metadata.title", "content.text"]
Documents missing specified fields will still be stored but won't have embeddings for those fields
You can still override which fields to embed on a specific item at put time using the index parameter

{
  "dependencies": ["."],
  "graphs": {
    "memory_agent": "./agent/graph.py:graph"
  },
  "store": {
    "index": {
      "embed": "openai:text-embedding-3-small",
      "dims": 1536,
      "fields": ["$"]
    }
  }
}

!!! note "Common model dimensions" - openai:text-embedding-3-large: 3072 - openai:text-embedding-3-small: 1536 - openai:text-embedding-ada-002: 1536 - cohere:embed-english-v3.0: 1024 - cohere:embed-english-light-v3.0: 384 - cohere:embed-multilingual-v3.0: 1024 - cohere:embed-multilingual-light-v3.0: 384

Semantic search with a custom embedding function

If you want to use semantic search with a custom embedding function, you can pass a path to a custom embedding function:

{
  "dependencies": ["."],
  "graphs": {
    "memory_agent": "./agent/graph.py:graph"
  },
  "store": {
    "index": {
      "embed": "./embeddings.py:embed_texts",
      "dims": 768,
      "fields": ["text", "summary"]
    }
  }
}

The embed field in store configuration can reference a custom function that takes a list of strings and returns a list of embeddings. Example implementation:

## embeddings.py
def embed_texts(texts: list[str]) -> list[list[float]]:
    """Custom embedding function for semantic search."""
    # Implementation using your preferred embedding model
    return [[0.1, 0.2, ...] for _ in texts]  # dims-dimensional vectors

Adding custom authentication

{
  "dependencies": ["."],
  "graphs": {
    "chat": "./chat/graph.py:graph"
  },
  "auth": {
    "path": "./auth.py:auth",
    "openapi": {
      "securitySchemes": {
        "apiKeyAuth": {
          "type": "apiKey",
          "in": "header",
          "name": "X-API-Key"
        }
      },
      "security": [
        {"apiKeyAuth": []}
      ]
    },
    "disable_studio_auth": false
  }
}

See the authentication conceptual guide for details, and the setting up custom authentication guide for a practical walk through of the process.

Commands

The base command for the LangGraph CLI is langgraph.

Usage

langgraph [OPTIONS] COMMAND [ARGS]

`dev`

Run LangGraph API server in development mode with hot reloading and debugging capabilities. This lightweight server requires no Docker installation and is suitable for development and testing. State is persisted to a local directory.

!!! note "Python only"

Currently, the CLI only supports Python >= 3.11.
JS support is coming soon.

Installation

This command requires the "inmem" extra to be installed:

pip install -U "langgraph-cli[inmem]"

Usage

langgraph dev [OPTIONS]

Options

Option	Default	Description
`-c, --config FILE`	`langgraph.json`	Path to configuration file declaring dependencies, graphs and environment variables
`--host TEXT`	`127.0.0.1`	Host to bind the server to
`--port INTEGER`	`2024`	Port to bind the server to
`--no-reload`		Disable auto-reload
`--n-jobs-per-worker INTEGER`		Number of jobs per worker. Default is 10
`--no-browser`		Disable automatic browser opening
`--debug-port INTEGER`		Port for debugger to listen on
`--help`		Display command documentation

`build`

Build LangGraph Cloud API server Docker image.

Usage

langgraph build [OPTIONS]

Options

Option	Default	Description
`--platform TEXT`		Target platform(s) to build the Docker image for. Example: `langgraph build --platform linux/amd64,linux/arm64`
`-t, --tag TEXT`		Required. Tag for the Docker image. Example: `langgraph build -t my-image`
`--pull / --no-pull`	`--pull`	Build with latest remote Docker image. Use `--no-pull` for running the LangGraph Cloud API server with locally built images.
`-c, --config FILE`	`langgraph.json`	Path to configuration file declaring dependencies, graphs and environment variables.
`--help`		Display command documentation.

`up`

Start LangGraph API server. For local testing, requires a LangSmith API key with access to LangGraph Cloud closed beta. Requires a license key for production use.

Usage

langgraph up [OPTIONS]

Options

Option	Default	Description
`--wait`		Wait for services to start before returning. Implies --detach
`--postgres-uri TEXT`	Local database	Postgres URI to use for the database.
`--watch`		Restart on file changes
`--debugger-base-url TEXT`	`http://127.0.0.1:[PORT]`	URL used by the debugger to access LangGraph API.
`--debugger-port INTEGER`		Pull the debugger image locally and serve the UI on specified port
`--verbose`		Show more output from the server logs.
`-c, --config FILE`	`langgraph.json`	Path to configuration file declaring dependencies, graphs and environment variables.
`-d, --docker-compose FILE`		Path to docker-compose.yml file with additional services to launch.
`-p, --port INTEGER`	`8123`	Port to expose. Example: `langgraph up --port 8000`
`--pull / --no-pull`	`pull`	Pull latest images. Use `--no-pull` for running the server with locally-built images. Example: `langgraph up --no-pull`
`--recreate / --no-recreate`	`no-recreate`	Recreate containers even if their configuration and image haven't changed
`--help`		Display command documentation.

`dockerfile`

Generate a Dockerfile for building a LangGraph Cloud API server Docker image.

Usage

langgraph dockerfile [OPTIONS] SAVE_PATH

Options

Option	Default	Description
`-c, --config FILE`	`langgraph.json`	Path to the configuration file declaring dependencies, graphs and environment variables.
`--help`		Show this message and exit.

Example:

langgraph dockerfile -c langgraph.json Dockerfile

This generates a Dockerfile that looks similar to:

FROM langchain/langgraph-api:3.11

ADD ./pipconf.txt /pipconfig.txt

RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt langchain_community langchain_anthropic langchain_openai wikipedia scikit-learn

ADD ./graphs /deps/__outer_graphs/src
RUN set -ex && \
    for line in '[project]' \
                'name = "graphs"' \
                'version = "0.1"' \
                '[tool.setuptools.package-data]' \
                '"*" = ["**/*"]'; do \
        echo "$line" >> /deps/__outer_graphs/pyproject.toml; \
    done

RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*

ENV LANGSERVE_GRAPHS='{"agent": "/deps/__outer_graphs/src/agent.py:graph", "storm": "/deps/__outer_graphs/src/storm.py:graph"}'

Environment Variables

The LangGraph Cloud API supports specific environment variables for configuring a deployment.

`LANGCHAIN_TRACING_SAMPLING_RATE`

Sampling rate for traces sent to LangSmith. Valid values: Any float between 0 and 1.

See LangSmith documentation for more details.

`LANGGRAPH_AUTH_TYPE`

Type of authentication for the LangGraph Cloud API deployment. Valid values: langsmith, noop.

For deployments to LangGraph Cloud, this environment variable is set automatically. For local development or deployments where authentication is handled externally (e.g. self-hosted), set this environment variable to noop.

`N_JOBS_PER_WORKER`

Number of jobs per worker for the LangGraph Cloud task queue. Defaults to 10.

Python SDK Reference

::: langgraph_sdk.client handler: python

::: langgraph_sdk.schema handler: python

::: langgraph_sdk.auth handler: python

::: langgraph_sdk.auth.types handler: python

::: langgraph_sdk.auth.exceptions handler: python

Agent architectures

Many LLM applications implement a particular control flow of steps before and / or after LLM calls. As an example, RAG performs retrieval of documents relevant to a user question, and passes those documents to an LLM in order to ground the model's response in the provided document context.

Instead of hard-coding a fixed control flow, we sometimes want LLM systems that can pick their own control flow to solve more complex problems! This is one definition of an agent: an agent is a system that uses an LLM to decide the control flow of an application. There are many ways that an LLM can control application:

An LLM can route between two potential paths
An LLM can decide which of many tools to call
An LLM can decide whether the generated answer is sufficient or more work is needed

As a result, there are many different types of agent architectures, which give an LLM varying levels of control.

Router

A router allows an LLM to select a single step from a specified set of options. This is an agent architecture that exhibits a relatively limited level of control because the LLM usually focuses on making a single decision and produces a specific output from limited set of pre-defined options. Routers typically employ a few different concepts to achieve this.

Structured Output

Structured outputs with LLMs work by providing a specific format or schema that the LLM should follow in its response. This is similar to tool calling, but more general. While tool calling typically involves selecting and using predefined functions, structured outputs can be used for any type of formatted response. Common methods to achieve structured outputs include:

Prompt engineering: Instructing the LLM to respond in a specific format via the system prompt.
Output parsers: Using post-processing to extract structured data from LLM responses.
Tool calling: Leveraging built-in tool calling capabilities of some LLMs to generate structured outputs.

Structured outputs are crucial for routing as they ensure the LLM's decision can be reliably interpreted and acted upon by the system. Learn more about structured outputs in this how-to guide.

Tool calling agent

While a router allows an LLM to make a single decision, more complex agent architectures expand the LLM's control in two key ways:

Multi-step decision making: The LLM can make a series of decisions, one after another, instead of just one.
Tool access: The LLM can choose from and use a variety of tools to accomplish tasks.

ReAct is a popular general purpose agent architecture that combines these expansions, integrating three core concepts.

Tool calling: Allowing the LLM to select and use various tools as needed.
Memory: Enabling the agent to retain and use information from previous steps.
Planning: Empowering the LLM to create and follow multi-step plans to achieve goals.

This architecture allows for more complex and flexible agent behaviors, going beyond simple routing to enable dynamic problem-solving with multiple steps. You can use it with [create_react_agent][langgraph.prebuilt.chat_agent_executor.create_react_agent].

Tool calling

Tools are useful whenever you want an agent to interact with external systems. External systems (e.g., APIs) often require a particular input schema or payload, rather than natural language. When we bind an API, for example, as a tool, we give the model awareness of the required input schema. The model will choose to call a tool based upon the natural language input from the user and it will return an output that adheres to the tool's required schema.

Many LLM providers support tool calling and tool calling interface in LangChain is simple: you can simply pass any Python function into ChatModel.bind_tools(function).

Memory

Memory is crucial for agents, enabling them to retain and utilize information across multiple steps of problem-solving. It operates on different scales:

Short-term memory: Allows the agent to access information acquired during earlier steps in a sequence.
Long-term memory: Enables the agent to recall information from previous interactions, such as past messages in a conversation.

LangGraph provides full control over memory implementation:

State: User-defined schema specifying the exact structure of memory to retain.
Checkpointers: Mechanism to store state at every step across different interactions.

This flexible approach allows you to tailor the memory system to your specific agent architecture needs. For a practical guide on adding memory to your graph, see this tutorial.

Effective memory management enhances an agent's ability to maintain context, learn from past experiences, and make more informed decisions over time.

Planning

In the ReAct architecture, an LLM is called repeatedly in a while-loop. At each step the agent decides which tools to call, and what the inputs to those tools should be. Those tools are then executed, and the outputs are fed back into the LLM as observations. The while-loop terminates when the agent decides it has enough information to solve the user request and it is not worth calling any more tools.

ReAct implementation

There are several differences between this paper and the pre-built [create_react_agent][langgraph.prebuilt.chat_agent_executor.create_react_agent] implementation:

First, we use tool-calling to have LLMs call tools, whereas the paper used prompting + parsing of raw output. This is because tool calling did not exist when the paper was written, but is generally better and more reliable.
Second, we use messages to prompt the LLM, whereas the paper used string formatting. This is because at the time of writing, LLMs didn't even expose a message-based interface, whereas now that's the only interface they expose.
Third, the paper required all inputs to the tools to be a single string. This was largely due to LLMs not being super capable at the time, and only really being able to generate a single input. Our implementation allows for using tools that require multiple inputs.
Fourth, the paper only looks at calling a single tool at the time, largely due to limitations in LLMs performance at the time. Our implementation allows for calling multiple tools at a time.
Finally, the paper asked the LLM to explicitly generate a "Thought" step before deciding which tools to call. This is the "Reasoning" part of "ReAct". Our implementation does not do this by default, largely because LLMs have gotten much better and that is not as necessary. Of course, if you wish to prompt it do so, you certainly can.

Custom agent architectures

While routers and tool-calling agents (like ReAct) are common, customizing agent architectures often leads to better performance for specific tasks. LangGraph offers several powerful features for building tailored agent systems:

Human-in-the-loop

Human involvement can significantly enhance agent reliability, especially for sensitive tasks. This can involve:

Approving specific actions
Providing feedback to update the agent's state
Offering guidance in complex decision-making processes

Human-in-the-loop patterns are crucial when full automation isn't feasible or desirable. Learn more in our human-in-the-loop guide.

Parallelization

Parallel processing is vital for efficient multi-agent systems and complex tasks. LangGraph supports parallelization through its Send API, enabling:

Concurrent processing of multiple states
Implementation of map-reduce-like operations
Efficient handling of independent subtasks

For practical implementation, see our map-reduce tutorial.

Subgraphs

Subgraphs are essential for managing complex agent architectures, particularly in multi-agent systems. They allow:

Isolated state management for individual agents
Hierarchical organization of agent teams
Controlled communication between agents and the main system

Subgraphs communicate with the parent graph through overlapping keys in the state schema. This enables flexible, modular agent design. For implementation details, refer to our subgraph how-to guide.

Reflection

Reflection mechanisms can significantly improve agent reliability by:

Evaluating task completion and correctness
Providing feedback for iterative improvement
Enabling self-correction and learning

While often LLM-based, reflection can also use deterministic methods. For instance, in coding tasks, compilation errors can serve as feedback. This approach is demonstrated in this video using LangGraph for self-corrective code generation.

By leveraging these features, LangGraph enables the creation of sophisticated, task-specific agent architectures that can handle complex workflows, collaborate effectively, and continuously improve their performance.

Application Structure

!!! info "Prerequisites"

- [LangGraph Server](#docsconceptslanggraph_servermd)
- [LangGraph Glossary](#docsconceptslow_levelmd)

Overview

A LangGraph application consists of one or more graphs, a LangGraph API Configuration file (langgraph.json), a file that specifies dependencies, and an optional .env file that specifies environment variables.

This guide shows a typical structure for a LangGraph application and shows how the required information to deploy a LangGraph application using the LangGraph Platform is specified.

Key Concepts

To deploy using the LangGraph Platform, the following information should be provided:

A LangGraph API Configuration file (langgraph.json) that specifies the dependencies, graphs, environment variables to use for the application.
The graphs that implement the logic of the application.
A file that specifies dependencies required to run the application.
Environment variable that are required for the application to run.

File Structure

Below are examples of directory structures for Python and JavaScript applications:

=== "Python (requirements.txt)"

```plaintext
my-app/
Ôö£ÔöÇÔöÇ my_agent # all project code lies within here
Ôöé   Ôö£ÔöÇÔöÇ utils # utilities for your graph
Ôöé   Ôöé   Ôö£ÔöÇÔöÇ __init__.py
Ôöé   Ôöé   Ôö£ÔöÇÔöÇ tools.py # tools for your graph
Ôöé   Ôöé   Ôö£ÔöÇÔöÇ nodes.py # node functions for you graph
Ôöé   Ôöé   ÔööÔöÇÔöÇ state.py # state definition of your graph
Ôöé   Ôö£ÔöÇÔöÇ requirements.txt # package dependencies
Ôöé   Ôö£ÔöÇÔöÇ __init__.py
Ôöé   ÔööÔöÇÔöÇ agent.py # code for constructing your graph
Ôö£ÔöÇÔöÇ .env # environment variables
ÔööÔöÇÔöÇ langgraph.json # configuration file for LangGraph
```

=== "Python (pyproject.toml)"

```plaintext
my-app/
Ôö£ÔöÇÔöÇ my_agent # all project code lies within here
Ôöé   Ôö£ÔöÇÔöÇ utils # utilities for your graph
Ôöé   Ôöé   Ôö£ÔöÇÔöÇ __init__.py
Ôöé   Ôöé   Ôö£ÔöÇÔöÇ tools.py # tools for your graph
Ôöé   Ôöé   Ôö£ÔöÇÔöÇ nodes.py # node functions for you graph
Ôöé   Ôöé   ÔööÔöÇÔöÇ state.py # state definition of your graph
Ôöé   Ôö£ÔöÇÔöÇ __init__.py
Ôöé   ÔööÔöÇÔöÇ agent.py # code for constructing your graph
Ôö£ÔöÇÔöÇ .env # environment variables
Ôö£ÔöÇÔöÇ langgraph.json  # configuration file for LangGraph
ÔööÔöÇÔöÇ pyproject.toml # dependencies for your project
```

=== "JS (package.json)"

```plaintext
my-app/
Ôö£ÔöÇÔöÇ src # all project code lies within here
Ôöé   Ôö£ÔöÇÔöÇ utils # optional utilities for your graph
Ôöé   Ôöé   Ôö£ÔöÇÔöÇ tools.ts # tools for your graph
Ôöé   Ôöé   Ôö£ÔöÇÔöÇ nodes.ts # node functions for you graph
Ôöé   Ôöé   ÔööÔöÇÔöÇ state.ts # state definition of your graph
Ôöé   ÔööÔöÇÔöÇ agent.ts # code for constructing your graph
Ôö£ÔöÇÔöÇ package.json # package dependencies
Ôö£ÔöÇÔöÇ .env # environment variables
ÔööÔöÇÔöÇ langgraph.json # configuration file for LangGraph
```

!!! note

The directory structure of a LangGraph application can vary depending on the programming language and the package manager used.

Configuration File

The langgraph.json file is a JSON file that specifies the dependencies, graphs, environment variables, and other settings required to deploy a LangGraph application.

The file supports specification of the following information:

Key	Description
`dependencies`	Required. Array of dependencies for LangGraph API server. Dependencies can be one of the following: (1) `"."`, which will look for local Python packages, (2) `pyproject.toml`, `setup.py` or `requirements.txt` in the app directory `"./local_package"`, or (3) a package name.
`graphs`	Required. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: `./your_package/your_file.py:variable`, where `variable` is an instance of `langgraph.graph.state.CompiledStateGraph` `./your_package/your_file.py:make_graph`, where `make_graph` is a function that takes a config dictionary (`langchain_core.runnables.RunnableConfig`) and creates an instance of `langgraph.graph.state.StateGraph` / `langgraph.graph.state.CompiledStateGraph`.
`env`	Path to `.env` file or a mapping from environment variable to its value.
`python_version`	`3.11` or `3.12`. Defaults to `3.11`.
`pip_config_file`	Path to `pip` config file.
`dockerfile_lines`	Array of additional lines to add to Dockerfile following the import from parent image.
!!! tip

The LangGraph CLI defaults to using the configuration file **langgraph.json** in the current directory.

Examples

=== "Python"

* The dependencies involve a custom local package and the `langchain_openai` package.
* A single graph will be loaded from the file `./your_package/your_file.py` with the variable `variable`.
* The environment variables are loaded from the `.env` file.

```json
{
    "dependencies": [
        "langchain_openai",
        "./your_package"
    ],
    "graphs": {
        "my_agent": "./your_package/your_file.py:agent"
    },
    "env": "./.env"
}
```

=== "JavaScript"

* The dependencies will be loaded from a dependency file in the local directory (e.g., `package.json`).
* A single graph will be loaded from the file `./your_package/your_file.js` with the function `agent`.
* The environment variable `OPENAI_API_KEY` is set inline.

```json
{
    "dependencies": [
        "."
    ],
    "graphs": {
        "my_agent": "./your_package/your_file.js:agent"
    },
    "env": {
        "OPENAI_API_KEY": "secret-key"
    }
}
```

Dependencies

A LangGraph application may depend on other Python packages or JavaScript libraries (depending on the programming language in which the application is written).

You will generally need to specify the following information for dependencies to be set up correctly:

A file in the directory that specifies the dependencies (e.g., requirements.txt, pyproject.toml, or package.json).
A dependencies key in the LangGraph configuration file that specifies the dependencies required to run the LangGraph application.
Any additional binaries or system libraries can be specified using dockerfile_lines key in the LangGraph configuration file.

Graphs

Use the graphs key in the LangGraph configuration file to specify which graphs will be available in the deployed LangGraph application.

You can specify one or more graphs in the configuration file. Each graph is identified by a name (which should be unique) and a path for either: (1) the compiled graph or (2) a function that makes a graph is defined.

Environment Variables

If you're working with a deployed LangGraph application locally, you can configure environment variables in the env key of the LangGraph configuration file.

For a production deployment, you will typically want to configure the environment variables in the deployment environment.

Related

Please see the following resources for more information:

How-to guides for Application Structure.

Assistants

!!! info "Prerequisites"

- [LangGraph Server](#docsconceptslanggraph_servermd)

When building agents, it is fairly common to make rapid changes that do not alter the graph logic. For example, simply changing prompts or the LLM selection can have significant impacts on the behavior of the agents. Assistants offer an easy way to make and save these types of changes to agent configuration. This can have at least two use-cases:

Assistants give developers a quick and easy way to modify and version agents for experimentation.
Assistants can be modified via LangGraph Studio, offering a no-code way to configure agents (e.g., for business users).

Assistants build off the concept of "configuration". While "configuration" is available in the open source LangGraph library as well, assistants are only present in LangGraph Platform. This is because Assistants are tightly coupled to your deployed graph, and so we can only make them available when we are also deploying the graphs.

Configuring Assistants

In practice, an assistant is just an instance of a graph with a specific configuration. Because of this, multiple assistants can reference the same graph but can contain different configurations, such as prompts, models, and other graph configuration options. The LangGraph Cloud API provides several endpoints for creating and managing assistants. See the API reference and this how-to for more details on how to create assistants.

Versioning Assistants

Once you've created an assistant, you can save and version it to track changes to the configuration over time. You can think about this at three levels:

The graph lays out the general agent application logic
The agent configuration options represent parameters that can be changed
Assistant versions save and track specific settings of the agent configuration options

For example, let's imagine you have a general writing agent. You have created a general graph architecture that works well for writing. However, there are different types of writing, e.g. blogs vs tweets. In order to get the best performance on each use case, you need to make some minor changes to the models and prompts used. In this setup, you could create an assistant for each use case - one for blog writing and one for tweeting. These would share the same graph structure, but they may use different models and different prompts. Read this how-to to learn how you can use assistant versioning through both the Studio and the SDK.

Resources

For more information on assistants, see the following resources:

Assistants how-to guides

Authentication & Access Control

LangGraph Platform provides a flexible authentication and authorization system that can integrate with most authentication schemes.

!!! note "Python only"

We currently only support custom authentication and authorization in Python deployments with `langgraph-api>=0.0.11`. Support for LangGraph.JS will be added soon.

Core Concepts

Authentication vs Authorization

While often used interchangeably, these terms represent distinct security concepts:

Authentication ("AuthN") verifies who you are. This runs as middleware for every request.
Authorization ("AuthZ") determines what you can do. This validates the user's privileges and roles on a per-resource basis.

In LangGraph Platform, authentication is handled by your @auth.authenticate handler, and authorization is handled by your @auth.on handlers.

Default Security Models

LangGraph Platform provides different security defaults:

LangGraph Cloud

Uses LangSmith API keys by default
Requires valid API key in x-api-key header
Can be customized with your auth handler

Self-Hosted

No default authentication
Complete flexibility to implement your security model
You control all aspects of authentication and authorization

System Architecture

A typical authentication setup involves three main components:

Authentication Provider (Identity Provider/IdP)
- A dedicated service that manages user identities and credentials
- Handles user registration, login, password resets, etc.
- Issues tokens (JWT, session tokens, etc.) after successful authentication
- Examples: Auth0, Supabase Auth, Okta, or your own auth server
LangGraph Backend (Resource Server)
- Your LangGraph application that contains business logic and protected resources
- Validates tokens with the auth provider
- Enforces access control based on user identity and permissions
- Doesn't store user credentials directly
Client Application (Frontend)
- Web app, mobile app, or API client
- Collects time-sensitive user credentials and sends to auth provider
- Receives tokens from auth provider
- Includes these tokens in requests to LangGraph backend

Here's how these components typically interact:

sequenceDiagram
    participant Client as Client App
    participant Auth as Auth Provider
    participant LG as LangGraph Backend

    Client->>Auth: 1. Login (username/password)
    Auth-->>Client: 2. Return token
    Client->>LG: 3. Request with token
    Note over LG: 4. Validate token (@auth.authenticate)
    LG-->>Auth:  5. Fetch user info
    Auth-->>LG: 6. Confirm validity
    Note over LG: 7. Apply access control (@auth.on.*)
    LG-->>Client: 8. Return resources

Your @auth.authenticate handler in LangGraph handles steps 4-6, while your @auth.on handlers implement step 7.

Authentication

Authentication in LangGraph runs as middleware on every request. Your @auth.authenticate handler receives request information and should:

Validate the credentials
Return user info containing the user's identity and user information if valid
Raise an HTTP exception or AssertionError if invalid

from langgraph_sdk import Auth

auth = Auth()

@auth.authenticate
async def authenticate(headers: dict) -> Auth.types.MinimalUserDict:
    # Validate credentials (e.g., API key, JWT token)
    api_key = headers.get("x-api-key")
    if not api_key or not is_valid_key(api_key):
        raise Auth.exceptions.HTTPException(
            status_code=401,
            detail="Invalid API key"
        )

    # Return user info - only identity and is_authenticated are required
    # Add any additional fields you need for authorization
    return {
        "identity": "user-123",        # Required: unique user identifier
        "is_authenticated": True,      # Optional: assumed True by default
        "permissions": ["read", "write"] # Optional: for permission-based auth
        # You can add more custom fields if you want to implement other auth patterns
        "role": "admin",
        "org_id": "org-456"

    }

The returned user information is available:

To your authorization handlers via ctx.user
In your application via config["configuration"]["langgraph_auth_user"]

??? tip "Supported Parameters"

The [`@auth.authenticate`](#langgraph_sdk.auth.Auth.authenticate) handler can accept any of the following parameters by name:

* request (Request): The raw ASGI request object
* body (dict): The parsed request body
* path (str): The request path, e.g., "/threads/abcd-1234-abcd-1234/runs/abcd-1234-abcd-1234/stream"
* method (str): The HTTP method, e.g., "GET"
* path_params (dict[str, str]): URL path parameters, e.g., {"thread_id": "abcd-1234-abcd-1234", "run_id": "abcd-1234-abcd-1234"}
* query_params (dict[str, str]): URL query parameters, e.g., {"stream": "true"}
* headers (dict[bytes, bytes]): Request headers
* authorization (str | None): The Authorization header value (e.g., "Bearer <token>")

In many of our tutorials, we will just show the "authorization" parameter to be concise, but you can opt to accept more information as needed
to implement your custom authentication scheme.

Authorization

After authentication, LangGraph calls your @auth.on handlers to control access to specific resources (e.g., threads, assistants, crons). These handlers can:

Add metadata to be saved during resource creation by mutating the value["metadata"] dictionary directly. See the supported actions table for the list of types the value can take for each action.
Filter resources by metadata during search/list or read operations by returning a filter dictionary.
Raise an HTTP exception if access is denied.

If you want to just implement simple user-scoped access control, you can use a single @auth.on handler for all resources and actions. If you want to have different control depending on the resource and action, you can use resource-specific handlers. See the Supported Resources section for a full list of the resources that support access control.

@auth.on
async def add_owner(
    ctx: Auth.types.AuthContext,
    value: dict  # The payload being sent to this access method
) -> dict:  # Returns a filter dict that restricts access to resources
    """Authorize all access to threads, runs, crons, and assistants.

    This handler does two things:
        - Adds a value to resource metadata (to persist with the resource so it can be filtered later)
        - Returns a filter (to restrict access to existing resources)

    Args:
        ctx: Authentication context containing user info, permissions, the path, and
        value: The request payload sent to the endpoint. For creation
              operations, this contains the resource parameters. For read
              operations, this contains the resource being accessed.

    Returns:
        A filter dictionary that LangGraph uses to restrict access to resources.
        See [Filter Operations](#filter-operations) for supported operators.
    """
    # Create filter to restrict access to just this user's resources
    filters = {"owner": ctx.user.identity}

    # Get or create the metadata dictionary in the payload
    # This is where we store persistent info about the resource
    metadata = value.setdefault("metadata", {})

    # Add owner to metadata - if this is a create or update operation,
    # this information will be saved with the resource
    # So we can filter by it later in read operations
    metadata.update(filters)

    # Return filters to restrict access
    # These filters are applied to ALL operations (create, read, update, search, etc.)
    # to ensure users can only access their own resources
    return filters

Resource-Specific Handlers {#resource-specific-handlers}

You can register handlers for specific resources and actions by chaining the resource and action names together with the @auth.on decorator. When a request is made, the most specific handler that matches that resource and action is called. Below is an example of how to register handlers for specific resources and actions. For the following setup:

Authenticated users are able to create threads, read thread, create runs on threads
Only users with the "assistants:create" permission are allowed to create new assistants
All other endpoints (e.g., e.g., delete assistant, crons, store) are disabled for all users.

!!! tip "Supported Handlers"

For a full list of supported resources and actions, see the [Supported Resources](#supported-resources) section below.

## Generic / global handler catches calls that aren't handled by more specific handlers
@auth.on
async def reject_unhandled_requests(ctx: Auth.types.AuthContext, value: Any) -> False:
    print(f"Request to {ctx.path} by {ctx.user.identity}")
    raise Auth.exceptions.HTTPException(
        status_code=403,
        detail="Forbidden"
    )

## Matches the "thread" resource and all actions - create, read, update, delete, search
## Since this is **more specific** than the generic @auth.on handler, it will take precedence
## over the generic handler for all actions on the "threads" resource
@auth.on.threads
async def on_thread_create(
    ctx: Auth.types.AuthContext,
    value: Auth.types.threads.create.value
):
    if "write" not in ctx.permissions:
        raise Auth.exceptions.HTTPException(
            status_code=403,
            detail="User lacks the required permissions."
        )
    # Setting metadata on the thread being created
    # will ensure that the resource contains an "owner" field
    # Then any time a user tries to access this thread or runs within the thread,
    # we can filter by owner
    metadata = value.setdefault("metadata", {})
    metadata["owner"] = ctx.user.identity
    return {"owner": ctx.user.identity}

## Thread creation. This will match only on thread create actions
## Since this is **more specific** than both the generic @auth.on handler and the @auth.on.threads handler,
## it will take precedence for any "create" actions on the "threads" resources
@auth.on.threads.create
async def on_thread_create(
    ctx: Auth.types.AuthContext,
    value: Auth.types.threads.create.value
):
    # Setting metadata on the thread being created
    # will ensure that the resource contains an "owner" field
    # Then any time a user tries to access this thread or runs within the thread,
    # we can filter by owner
    metadata = value.setdefault("metadata", {})
    metadata["owner"] = ctx.user.identity
    return {"owner": ctx.user.identity}

## Reading a thread. Since this is also more specific than the generic @auth.on handler, and the @auth.on.threads handler,
## it will take precedence for any "read" actions on the "threads" resource
@auth.on.threads.read
async def on_thread_read(
    ctx: Auth.types.AuthContext,
    value: Auth.types.threads.read.value
):
    # Since we are reading (and not creating) a thread,
    # we don't need to set metadata. We just need to
    # return a filter to ensure users can only see their own threads
    return {"owner": ctx.user.identity}

## Run creation, streaming, updates, etc.
## This takes precedenceover the generic @auth.on handler and the @auth.on.threads handler
@auth.on.threads.create_run
async def on_run_create(
    ctx: Auth.types.AuthContext,
    value: Auth.types.threads.create_run.value
):
    metadata = value.setdefault("metadata", {})
    metadata["owner"] = ctx.user.identity
    # Inherit thread's access control
    return {"owner": ctx.user.identity}

## Assistant creation
@auth.on.assistants.create
async def on_assistant_create(
    ctx: Auth.types.AuthContext,
    value: Auth.types.assistants.create.value
):
    if "assistants:create" not in ctx.permissions:
        raise Auth.exceptions.HTTPException(
            status_code=403,
            detail="User lacks the required permissions."
        )

Notice that we are mixing global and resource-specific handlers in the above example. Since each request is handled by the most specific handler, a request to create a thread would match the on_thread_create handler but NOT the reject_unhandled_requests handler. A request to update a thread, however would be handled by the global handler, since we don't have a more specific handler for that resource and action. Requests to create, update,

Filter Operations {#filter-operations}

Authorization handlers can return None, a boolean, or a filter dictionary.

None and True mean "authorize access to all underling resources"
False means "deny access to all underling resources (raises a 403 exception)"
A metadata filter dictionary will restrict access to resources

A filter dictionary is a dictionary with keys that match the resource metadata. It supports three operators:

The default value is a shorthand for exact match, or "$eq", below. For example, {"owner": user_id} will include only resources with metadata containing {"owner": user_id}
$eq: Exact match (e.g., {"owner": {"$eq": user_id}}) - this is equivalent to the shorthand above, {"owner": user_id}
$contains: List membership (e.g., {"allowed_users": {"$contains": user_id}}) The value here must be an element of the list. The metadata in the stored resource must be a list/container type.

A dictionary with multiple keys is treated using a logical AND filter. For example, {"owner": org_id, "allowed_users": {"$contains": user_id}} will only match resources with metadata whose "owner" is org_id and whose "allowed_users" list contains user_id. See the reference here for more information.

Common Access Patterns

Here are some typical authorization patterns:

Single-Owner Resources

This common pattern lets you scope all threads, assistants, crons, and runs to a single user. It's useful for common single-user use cases like regular chatbot-style apps.

@auth.on
async def owner_only(ctx: Auth.types.AuthContext, value: dict):
    metadata = value.setdefault("metadata", {})
    metadata["owner"] = ctx.user.identity
    return {"owner": ctx.user.identity}

Permission-based Access

This pattern lets you control access based on permissions. It's useful if you want certain roles to have broader or more restricted access to resources.

## In your auth handler:
@auth.authenticate
async def authenticate(headers: dict) -> Auth.types.MinimalUserDict:
    ...
    return {
        "identity": "user-123",
        "is_authenticated": True,
        "permissions": ["threads:write", "threads:read"]  # Define permissions in auth
    }

def _default(ctx: Auth.types.AuthContext, value: dict):
    metadata = value.setdefault("metadata", {})
    metadata["owner"] = ctx.user.identity
    return {"owner": ctx.user.identity}

@auth.on.threads.create
async def create_thread(ctx: Auth.types.AuthContext, value: dict):
    if "threads:write" not in ctx.permissions:
        raise Auth.exceptions.HTTPException(
            status_code=403,
            detail="Unauthorized"
        )
    return _default(ctx, value)


@auth.on.threads.read
async def rbac_create(ctx: Auth.types.AuthContext, value: dict):
    if "threads:read" not in ctx.permissions and "threads:write" not in ctx.permissions:
        raise Auth.exceptions.HTTPException(
            status_code=403,
            detail="Unauthorized"
        )
    return _default(ctx, value)

Supported Resources

LangGraph provides three levels of authorization handlers, from most general to most specific:

Global Handler (@auth.on): Matches all resources and actions
Resource Handler (e.g., @auth.on.threads, @auth.on.assistants, @auth.on.crons): Matches all actions for a specific resource
Action Handler (e.g., @auth.on.threads.create, @auth.on.threads.read): Matches a specific action on a specific resource

The most specific matching handler will be used. For example, @auth.on.threads.create takes precedence over @auth.on.threads for thread creation. If a more specific handler is registered, the more general handler will not be called for that resource and action.

???+ tip "Type Safety" Each handler has type hints available for its value parameter at Auth.types.on.<resource>.<action>.value. For example: ```python @auth.on.threads.create async def on_thread_create( ctx: Auth.types.AuthContext, value: Auth.types.on.threads.create.value # Specific type for thread creation ): ...

@auth.on.threads
async def on_threads(
    ctx: Auth.types.AuthContext,
    value: Auth.types.on.threads.value  # Union type of all thread actions
):
    ...

@auth.on
async def on_all(
    ctx: Auth.types.AuthContext,
    value: dict  # Union type of all possible actions
):
    ...
```
More specific handlers provide better type hints since they handle fewer action types.

Supported actions and types {#supported-actions}

Here are all the supported action handlers:

Resource	Handler	Description	Value Type
Threads	`@auth.on.threads.create`	Thread creation	`ThreadsCreate`
	`@auth.on.threads.read`	Thread retrieval	`ThreadsRead`
	`@auth.on.threads.update`	Thread updates	`ThreadsUpdate`
	`@auth.on.threads.delete`	Thread deletion	`ThreadsDelete`
	`@auth.on.threads.search`	Listing threads	`ThreadsSearch`
	`@auth.on.threads.create_run`	Creating or updating a run	`RunsCreate`
Assistants	`@auth.on.assistants.create`	Assistant creation	`AssistantsCreate`
	`@auth.on.assistants.read`	Assistant retrieval	`AssistantsRead`
	`@auth.on.assistants.update`	Assistant updates	`AssistantsUpdate`
	`@auth.on.assistants.delete`	Assistant deletion	`AssistantsDelete`
	`@auth.on.assistants.search`	Listing assistants	`AssistantsSearch`
Crons	`@auth.on.crons.create`	Cron job creation	`CronsCreate`
	`@auth.on.crons.read`	Cron job retrieval	`CronsRead`
	`@auth.on.crons.update`	Cron job updates	`CronsUpdate`
	`@auth.on.crons.delete`	Cron job deletion	`CronsDelete`
	`@auth.on.crons.search`	Listing cron jobs	`CronsSearch`

???+ note "About Runs" Runs are scoped to their parent thread for access control. This means permissions are typically inherited from the thread, reflecting the conversational nature of the data model. All run operations (reading, listing) except creation are controlled by the thread's handlers. There is a specific create_run handler for creating new runs because it had more arguments that you can view in the handler.

Next Steps

For implementation details:

Check out the introductory tutorial on setting up authentication
See the how-to guide on implementing a custom auth handlers

Breakpoints

Breakpoints pause graph execution at specific points and enable stepping through execution step by step. Breakpoints are powered by LangGraph's persistence layer, which saves the state after each graph step. Breakpoints can also be used to enable human-in-the-loop workflows, though we recommend using the interrupt function for this purpose.

Requirements

To use breakpoints, you will need to:

Specify a checkpointer to save the graph state after each step.
Set breakpoints to specify where execution should pause.
Run the graph with a thread ID to pause execution at the breakpoint.
Resume execution using invoke/ainvoke/stream/astream (see The Command primitive).

Setting breakpoints

There are two places where you can set breakpoints:

Before or after a node executes by setting breakpoints at compile time or run time. We call these static breakpoints.
Inside a node using the NodeInterrupt exception.

Static breakpoints

Static breakpoints are triggered either before or after a node executes. You can set static breakpoints by specifying interrupt_before and interrupt_after at "compile" time or run time.

=== "Compile time"

```python
graph = graph_builder.compile(
    interrupt_before=["node_a"], 
    interrupt_after=["node_b", "node_c"],
    checkpointer=..., # Specify a checkpointer
)

thread_config = {
    "configurable": {
        "thread_id": "some_thread"
    }
}

# Run the graph until the breakpoint
graph.invoke(inputs, config=thread_config)

# Optionally update the graph state based on user input
graph.update_state(update, config=thread_config)

# Resume the graph
graph.invoke(None, config=thread_config)
```

=== "Run time"

```python
graph.invoke(
    inputs, 
    config={"configurable": {"thread_id": "some_thread"}}, 
    interrupt_before=["node_a"], 
    interrupt_after=["node_b", "node_c"]
)

thread_config = {
    "configurable": {
        "thread_id": "some_thread"
    }
}

# Run the graph until the breakpoint
graph.invoke(inputs, config=thread_config)

# Optionally update the graph state based on user input
graph.update_state(update, config=thread_config)

# Resume the graph
graph.invoke(None, config=thread_config)
```

!!! note

    You cannot set static breakpoints at runtime for **sub-graphs**.
    If you have a sub-graph, you must set the breakpoints at compilation time.

Static breakpoints can be especially useful for debugging if you want to step through the graph execution one node at a time or if you want to pause the graph execution at specific nodes.

`NodeInterrupt` exception

We recommend that you use the interrupt function instead of the NodeInterrupt exception if you're trying to implement human-in-the-loop workflows. The interrupt function is easier to use and more flexible.

??? node "NodeInterrupt exception"

The developer can define some *condition* that must be met for a breakpoint to be triggered. This concept of [dynamic breakpoints](#dynamic-breakpoints) is useful when the developer wants to halt the graph under *a particular condition*. This uses a `NodeInterrupt`, which is a special type of exception that can be raised from within a node based upon some condition. As an example, we can define a dynamic breakpoint that triggers when the `input` is longer than 5 characters.

```python
def my_node(state: State) -> State:
    if len(state['input']) > 5:
        raise NodeInterrupt(f"Received input that is longer than 5 characters: {state['input']}")

    return state
```


Let's assume we run the graph with an input that triggers the dynamic breakpoint and then attempt to resume the graph execution simply by passing in `None` for the input.

```python
# Attempt to continue the graph execution with no change to state after we hit the dynamic breakpoint 
for event in graph.stream(None, thread_config, stream_mode="values"):
    print(event)
```

The graph will *interrupt* again because this node will be *re-run* with the same graph state. We need to change the graph state such that the condition that triggers the dynamic breakpoint is no longer met. So, we can simply edit the graph state to an input that meets the condition of our dynamic breakpoint (< 5 characters) and re-run the node.

```python 
# Update the state to pass the dynamic breakpoint
graph.update_state(config=thread_config, values={"input": "foo"})
for event in graph.stream(None, thread_config, stream_mode="values"):
    print(event)
```

Alternatively, what if we want to keep our current input and skip the node (`my_node`) that performs the check? To do this, we can simply perform the graph update with `as_node="my_node"` and pass in `None` for the values. This will make no update the graph state, but run the update as `my_node`, effectively skipping the node and bypassing the dynamic breakpoint.

```python
# This update will skip the node `my_node` altogether
graph.update_state(config=thread_config, values=None, as_node="my_node")
for event in graph.stream(None, thread_config, stream_mode="values"):
    print(event)
```

Additional Resources ƒôÜ

Conceptual Guide: Persistence: Read the persistence guide for more context about persistence.
Conceptual Guide: Human-in-the-loop: Read the human-in-the-loop guide for more context on integrating human feedback into LangGraph applications using breakpoints.
How to View and Update Past Graph State: Step-by-step instructions for working with graph state that demonstrate the replay and fork actions.

Bring Your Own Cloud (BYOC)

!!! note Prerequisites

- [LangGraph Platform](#docsconceptslanggraph_platformmd)
- [Deployment Options](#docsconceptsdeployment_optionsmd)

Architecture

Split control plane (hosted by us) and data plane (hosted by you, managed by us).

	Control Plane	Data Plane
What it does	Manages deployments, revisions.	Runs your LangGraph graphs, stores your data.
Where it is hosted	LangChain Cloud account	Your cloud account
Who provisions and monitors	LangChain	LangChain

LangChain has no direct access to the resources created in your cloud account, and can only interact with them via AWS APIs. Your data never leaves your cloud account / VPC at rest or in transit.

Requirements

YouÔÇÖre using AWS already.
You use langgraph-cli and/or LangGraph Studio app to test graph locally.
You use langgraph build command to build image and then push it to your AWS ECR repository (docker push).

How it works

We provide you a Terraform module which you run to set up our requirements
1. Creates an AWS role (which our control plane will later assume to provision and monitor resources)
  - https://docs.aws.amazon.com/aws-managed-policy/latest/reference/AmazonVPCReadOnlyAccess.html
    - Read VPCS to find subnets
  - https://docs.aws.amazon.com/aws-managed-policy/latest/reference/AmazonECS_FullAccess.html
    - Used to create/delete ECS resources for your LangGraph Cloud instances
  - https://docs.aws.amazon.com/aws-managed-policy/latest/reference/SecretsManagerReadWrite.html
    - Create secrets for your ECS resources
  - https://docs.aws.amazon.com/aws-managed-policy/latest/reference/CloudWatchReadOnlyAccess.html
    - Read CloudWatch metrics/logs to monitor your instances/push deployment logs
  - https://docs.aws.amazon.com/aws-managed-policy/latest/reference/AmazonRDSFullAccess.html
    - Provision RDS instances for your LangGraph Cloud instances
2. Either
  - Tags an existing vpc / subnets as langgraph-cloud-enabled
  - Creates a new vpc and subnets and tags them as langgraph-cloud-enabled
You create a LangGraph Cloud Project in smith.langchain.com providing
- the ID of the AWS role created in the step above
- the AWS ECR repo to pull the service image from
We provision the resources in your cloud account using the role above
We monitor those resources to ensure uptime and recovery from errors

Notes for customers using self-hosted LangSmith:

Creation of new LangGraph Cloud projects and revisions currently needs to be done on smith.langchain.com.
You can however set up the project to trace to your self-hosted LangSmith instance if desired

Deployment Options

!!! info "Prerequisites"

- [LangGraph Platform](#docsconceptslanggraph_platformmd)
- [LangGraph Server](#docsconceptslanggraph_servermd)
- [LangGraph Platform Plans](#docsconceptsplansmd)

Overview

There are 4 main options for deploying with the LangGraph Platform:

Self-Hosted Lite: Available for all plans.
Self-Hosted Enterprise: Available for the Enterprise plan.
Cloud SaaS: Available for Plus and Enterprise plans.
Bring Your Own Cloud: Available only for Enterprise plans and only on AWS.

Please see the LangGraph Platform Plans for more information on the different plans.

The guide below will explain the differences between the deployment options.

Self-Hosted Enterprise

!!! important

The Self-Hosted Enterprise version is only available for the **Enterprise** plan.

With a Self-Hosted Enterprise deployment, you are responsible for managing the infrastructure, including setting up and maintaining required databases and Redis instances.

YouÔÇÖll build a Docker image using the LangGraph CLI, which can then be deployed on your own infrastructure.

For more information, please see:

Self-Hosted conceptual guide
Self-Hosted Deployment how-to guide

Self-Hosted Lite

!!! important

The Self-Hosted Lite version is available for all plans.

The Self-Hosted Lite deployment option is a free (up to 1 million nodes executed), limited version of LangGraph Platform that you can run locally or in a self-hosted manner.

With a Self-Hosted Lite deployment, you are responsible for managing the infrastructure, including setting up and maintaining required databases and Redis instances.

YouÔÇÖll build a Docker image using the LangGraph CLI, which can then be deployed on your own infrastructure.

For more information, please see:

Self-Hosted conceptual guide
Self-Hosted deployment how-to guide

Cloud SaaS

!!! important

The Cloud SaaS version of LangGraph Platform is only available for **Plus** and **Enterprise** plans.

The Cloud SaaS version of LangGraph Platform is hosted as part of LangSmith.

The Cloud SaaS version of LangGraph Platform provides a simple way to deploy and manage your LangGraph applications.

This deployment option provides an integration with GitHub, allowing you to deploy code from any of your repositories on GitHub.

For more information, please see:

Cloud SaaS Conceptual Guide
How to deploy to Cloud SaaS

Bring Your Own Cloud

!!! important

The Bring Your Own Cloud version of LangGraph Platform is only available for **Enterprise** plans.

This combines the best of both worlds for Cloud and Self-Hosted. We manage the infrastructure, so you don't have to, but the infrastructure all runs within your cloud. This is currently only available on AWS.

For more information please see:

Bring Your Own Cloud Conceptual Guide

Related

For more information, please see:

LangGraph Platform plans
LangGraph Platform pricing
Deployment how-to guides

Double Texting

!!! info "Prerequisites" - LangGraph Server

Many times users might interact with your graph in unintended ways. For instance, a user may send one message and before the graph has finished running send a second message. More generally, users may invoke the graph a second time before the first run has finished. We call this "double texting".

Currently, LangGraph only addresses this as part of LangGraph Platform, not in the open source. The reason for this is that in order to handle this we need to know how the graph is deployed, and since LangGraph Platform deals with deployment the logic needs to live there. If you do not want to use LangGraph Platform, we describe the options we have implemented in detail below.

Reject

This is the simplest option, this just rejects any follow-up runs and does not allow double texting. See the how-to guide for configuring the reject double text option.

Enqueue

This is a relatively simple option which continues the first run until it completes the whole run, then sends the new input as a separate run. See the how-to guide for configuring the enqueue double text option.

Interrupt

This option interrupts the current execution but saves all the work done up until that point. It then inserts the user input and continues from there.

If you enable this option, your graph should be able to handle weird edge cases that may arise. For example, you could have called a tool but not yet gotten back a result from running that tool. You may need to remove that tool call in order to not have a dangling tool call.

See the how-to guide for configuring the interrupt double text option.

Rollback

This option interrupts the current execution AND rolls back all work done up until that point, including the original run input. It then sends the new user input in, basically as if it was the original input.

See the how-to guide for configuring the rollback double text option.

FAQ

Common questions and their answers!

Do I need to use LangChain to use LangGraph? WhatÔÇÖs the difference?

No. LangGraph is an orchestration framework for complex agentic systems and is more low-level and controllable than LangChain agents. LangChain provides a standard interface to interact with models and other components, useful for straight-forward chains and retrieval flows.

How is LangGraph different from other agent frameworks?

Other agentic frameworks can work for simple, generic tasks but fall short for complex tasks bespoke to a companyÔÇÖs needs. LangGraph provides a more expressive framework to handle companiesÔÇÖ unique tasks without restricting users to a single black-box cognitive architecture.

Does LangGraph impact the performance of my app?

LangGraph will not add any overhead to your code and is specifically designed with streaming workflows in mind.

Is LangGraph open source? Is it free?

Yes. LangGraph is an MIT-licensed open-source library and is free to use.

How are LangGraph and LangGraph Platform different?

LangGraph is a stateful, orchestration framework that brings added control to agent workflows. LangGraph Platform is a service for deploying and scaling LangGraph applications, with an opinionated API for building agent UXs, plus an integrated developer studio.

Features	LangGraph (open source)	LangGraph Platform
Description	Stateful orchestration framework for agentic applications	Scalable infrastructure for deploying LangGraph applications
SDKs	Python and JavaScript	Python and JavaScript
HTTP APIs	None	Yes - useful for retrieving & updating state or long-term memory, or creating a configurable assistant
Streaming	Basic	Dedicated mode for token-by-token messages
Checkpointer	Community contributed	Supported out-of-the-box
Persistence Layer	Self-managed	Managed Postgres with efficient storage
Deployment	Self-managed	ÔÇó Cloud SaaS ÔÇó Free self-hosted ÔÇó Enterprise (BYOC or paid self-hosted)
Scalability	Self-managed	Auto-scaling of task queues and servers
Fault-tolerance	Self-managed	Automated retries
Concurrency Control	Simple threading	Supports double-texting
Scheduling	None	Cron scheduling
Monitoring	None	Integrated with LangSmith for observability
IDE integration	LangGraph Studio for Desktop	LangGraph Studio for Desktop & Cloud

What are my deployment options for LangGraph Platform?

We currently have the following deployment options for LangGraph applications:

ÔÇìSelf-Hosted Lite: A free (up to 1M nodes executed), limited version of LangGraph Platform that you can run locally or in a self-hosted manner. This version requires a LangSmith API key and logs all usage to LangSmith. Fewer features are available than in paid plans.
Cloud SaaS: Fully managed and hosted as part of LangSmith, with automatic updates and zero maintenance.
ÔÇìBring Your Own Cloud (BYOC): Deploy LangGraph Platform within your VPC, provisioned and run as a service. Keep data in your environment while outsourcing the management of the service.
Self-Hosted Enterprise: Deploy LangGraph entirely on your own infrastructure.

Is LangGraph Platform open source?

No. LangGraph Platform is proprietary software.

There is a free, self-hosted version of LangGraph Platform with access to basic features. The Cloud SaaS deployment option is free while in beta, but will eventually be a paid service. We will always give ample notice before charging for a service and reward our early adopters with preferential pricing. The Bring Your Own Cloud (BYOC) and Self-Hosted Enterprise options are also paid services. Contact our sales team to learn more.

For more information, see our LangGraph Platform pricing page.

Does LangGraph work with LLMs that don't support tool calling?

Yes! You can use LangGraph with any LLMs. The main reason we use LLMs that support tool calling is that this is often the most convenient way to have the LLM make its decision about what to do. If your LLM does not support tool calling, you can still use it - you just need to write a bit of logic to convert the raw LLM string response to a decision about what to do.

Does LangGraph work with OSS LLMs?

Yes! LangGraph is totally ambivalent to what LLMs are used under the hood. The main reason we use closed LLMs in most of the tutorials is that they seamlessly support tool calling, while OSS LLMs often don't. But tool calling is not necessary (see this section) so you can totally use LangGraph with OSS LLMs.

Why LangGraph?

LLMs are extremely powerful, particularly when connected to other systems such as a retriever or APIs. This is why many LLM applications use a control flow of steps before and / or after LLM calls. As an example RAG performs retrieval of relevant documents to a question, and passes those documents to an LLM in order to ground the response. Often a control flow of steps before and / or after an LLM is called a "chain." Chains are a popular paradigm for programming with LLMs and offer a high degree of reliability; the same set of steps runs with each chain invocation.

However, we often want LLM systems that can pick their own control flow! This is one definition of an agent: an agent is a system that uses an LLM to decide the control flow of an application. Unlike a chain, an agent gives an LLM some degree of control over the sequence of steps in the application. Examples of using an LLM to decide the control of an application:

Using an LLM to route between two potential paths
Using an LLM to decide which of many tools to call
Using an LLM to decide whether the generated answer is sufficient or more work is need

There are many different types of agent architectures to consider, which give an LLM varying levels of control. On one extreme, a router allows an LLM to select a single step from a specified set of options and, on the other extreme, a fully autonomous long-running agent may have complete freedom to select any sequence of steps that it wants for a given problem.

Several concepts are utilized in many agent architectures:

Tool calling: this is often how LLMs make decisions
Action taking: often times, the LLMs' outputs are used as the input to an action
Memory: reliable systems need to have knowledge of things that occurred
Planning: planning steps (either explicit or implicit) are useful for ensuring that the LLM, when making decisions, makes them in the highest fidelity way.

Challenges

In practice, there is often a trade-off between control and reliability. As we give LLMs more control, the application often become less reliable. This can be due to factors such as LLM non-determinism and / or errors in selecting tools (or steps) that the agent uses (takes).

Core Principles

The motivation of LangGraph is to help bend the curve, preserving higher reliability as we give the agent more control over the application. We'll outline a few specific pillars of LangGraph that make it well suited for building reliable agents.

Controllability

LangGraph gives the developer a high degree of control by expressing the flow of the application as a set of nodes and edges. All nodes can access and modify a common state (memory). The control flow of the application can set using edges that connect nodes, either deterministically or via conditional logic.

Persistence

LangGraph gives the developer many options for persisting graph state using short-term or long-term (e.g., via a database) memory.

Human-in-the-Loop

The persistence layer enables several different human-in-the-loop interaction patterns with agents; for example, it's possible to pause an agent, review its state, edit it state, and approve a follow-up step.

Streaming

LangGraph comes with first class support for streaming, which can expose state to the user (or developer) over the course of agent execution. LangGraph supports streaming of both events (like a tool call being taken) as well as of tokens that an LLM may emit.

Debugging

Once you've built a graph, you often want to test and debug it. LangGraph Studio is a specialized IDE for visualization and debugging of LangGraph applications.

Deployment

Once you have confidence in your LangGraph application, many developers want an easy path to deployment. LangGraph Platform offers a range of options for deploying LangGraph graphs.

Human-in-the-loop

!!! tip "This guide uses the new interrupt function."

As of LangGraph 0.2.57, the recommended way to set breakpoints is using the [`interrupt` function][langgraph.types.interrupt] as it simplifies **human-in-the-loop** patterns.

If you're looking for the previous version of this conceptual guide, which relied on static breakpoints and `NodeInterrupt` exception, it is available [here](#docsconceptsv0-human-in-the-loopmd).

A human-in-the-loop (or "on-the-loop") workflow integrates human input into automated processes, allowing for decisions, validation, or corrections at key stages. This is especially useful in LLM-based applications, where the underlying model may generate occasional inaccuracies. In low-error-tolerance scenarios like compliance, decision-making, or content generation, human involvement ensures reliability by enabling review, correction, or override of model outputs.

Use cases

Key use cases for human-in-the-loop workflows in LLM-based applications include:

ƒøá´©Å Reviewing tool calls: Humans can review, edit, or approve tool calls requested by the LLM before tool execution.
Ô£à Validating LLM outputs: Humans can review, edit, or approve content generated by the LLM.
ƒÆí Providing context: Enable the LLM to explicitly request human input for clarification or additional details or to support multi-turn conversations.

`interrupt`

The [interrupt function][langgraph.types.interrupt] in LangGraph enables human-in-the-loop workflows by pausing the graph at a specific node, presenting information to a human, and resuming the graph with their input. This function is useful for tasks like approvals, edits, or collecting additional input. The [interrupt function][langgraph.types.interrupt] is used in conjunction with the Command object to resume the graph with a value provided by the human.

from langgraph.types import interrupt

def human_node(state: State):
    value = interrupt(
        # Any JSON serializable value to surface to the human.
        # For example, a question or a piece of text or a set of keys in the state
       {
          "text_to_revise": state["some_text"]
       }
    )
    # Update the state with the human's input or route the graph based on the input.
    return {
        "some_text": value
    }

graph = graph_builder.compile(
    checkpointer=checkpointer # Required for `interrupt` to work
)

## Run the graph until the interrupt
thread_config = {"configurable": {"thread_id": "some_id"}}
graph.invoke(some_input, config=thread_config)
    
## Resume the graph with the human's input
graph.invoke(Command(resume=value_from_human), config=thread_config)

{'some_text': 'Edited text'}

!!! warning Interrupts are both powerful and ergonomic. However, while they may resemble Python's input() function in terms of developer experience, it's important to note that they do not automatically resume execution from the interruption point. Instead, they rerun the entire node where the interrupt was used. For this reason, interrupts are typically best placed at the start of a node or in a dedicated node. Please read the resuming from an interrupt section for more details.

??? "Full Code"

  Here's a full example of how to use `interrupt` in a graph, if you'd like
  to see the code in action.

  ```python
  from typing import TypedDict
  import uuid

  from langgraph.checkpoint.memory import MemorySaver
  from langgraph.constants import START
  from langgraph.graph import StateGraph
  from langgraph.types import interrupt, Command

  class State(TypedDict):
     """The graph state."""
     some_text: str

  def human_node(state: Stat

DevHusnainAi/docs_langgraph_sdk

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