[docs] LangGraph / LangGraph Platform docs updates (#4479)

Main changes made: - Add top level horizontal tabs - Reorganize the sidenav - Build out README/index page - Consolidate how-tos under each section - Remove duplicate content --------- Signed-off-by: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com> Co-authored-by: Tat Dat Duong <david@duong.cz> Co-authored-by: Sydney Runkle <sydneymarierunkle@gmail.com> Co-authored-by: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com> Co-authored-by: Vadym Barda <vadym@langchain.dev> Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com> Co-authored-by: ccurme <chester.curme@gmail.com> Co-authored-by: Andrew Nguonly <andrewnguonly@users.noreply.github.com> Co-authored-by: David Asamu <david.asamu@langchain.dev> Co-authored-by: infra <mukil@langchain.dev> Co-authored-by: Arjun Natarajan <arjun@langchain.dev>
2026-09-06 17:57:49 +02:00 · 2025-05-09 16:09:43 -04:00
parent 909a4591a8
commit 3055c4b9cc
239 changed files with 14663 additions and 27446 deletions
@@ -0,0 +1,15 @@
+## Cron jobs
+
+There are many situations in which it is useful to run an assistant on a schedule. 
+
+For example, say that you're building an assistant that runs daily and sends an email summary
+of the day's news. You could use a cron job to run the assistant every day at 8:00 PM.
+
+LangGraph Cloud supports cron jobs, which run on a user-defined schedule. The user specifies a schedule, an assistant, and some input. After that, on the specified schedule, the server will:
+
+- Create a new thread with the specified assistant
+- Send the specified input to that thread
+
+Note that this sends the same input to the thread every time. See the [how-to guide](../../cloud/how-tos/cron_jobs.md) for creating cron jobs.
+
+The LangGraph Cloud API provides several endpoints for creating and managing cron jobs. See the [API reference](../../cloud/reference/api/api_ref.html#tag/runscreate/POST/threads/{thread_id}/runs/crons) for more details.
@@ -0,0 +1,5 @@
+# Runs
+
+A run is an invocation of an [assistant](../../concepts/assistants.md). Each run may have its own input, configuration, and metadata, which may affect execution and output of the underlying graph. A run can optionally be executed on a [thread](./threads.md).
+
+The LangGraph Cloud API provides several endpoints for creating and managing runs. See the [API reference](../../cloud/reference/api/api_ref.html#tag/thread-runs/) for more details.
@@ -0,0 +1,138 @@
+# Streaming
+
+Streaming is critical for making LLM applications feel responsive to end users.  
+When creating a streaming run, the **streaming mode** determines what kinds of data are streamed back to the API client.
+
+## Supported streaming modes
+
+LangGraph Platform supports the following streaming modes:
+
+| Mode                 | Description                                                                                                                                                    | LangGraph Library Method                                                |
+|----------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------|
+| **`values`**         | Stream the full graph state after each [super-step](https://langchain-ai.github.io/langgraph/concepts/low_level/#graphs). [Guide](../how-tos/streaming.md#stream-graph-state) | `.stream()` / `.astream()` with `stream_mode="values"`                  |
+| **`updates`**        | Stream only the updates to the graph state after each node. [Guide](../how-tos/streaming.md#stream-graph-state)                                                              | `.stream()` / `.astream()` with `stream_mode="updates"`                 |
+| **`messages-tuple`** | Stream LLM tokens for any messages generated inside the graph (useful for chat apps). [Guide](../how-tos/streaming.md#messages)                                   | `.stream()` / `.astream()` with `stream_mode="messages"`                |
+| **`debug`**          | Stream debug information throughout graph execution. [Guide](../how-tos/streaming.md#debug)                                                                       | `.stream()` / `.astream()` with `stream_mode="debug"`                   |
+| **`custom`**         | Stream custom data. [Guide](../../how-tos/streaming.md#stream-custom-data)                                                                                                                                             | `.stream()` / `.astream()` with `stream_mode="custom"`                  |
+| **`events`**         | Stream all events (including the state of the graph); mainly useful when migrating large LCEL apps. [Guide](../how-tos/streaming.md#stream-events)                       | `.astream_events()`                                                     |
+
+✅ You can also **combine multiple modes** at the same time.  See the [how-to guide](../how-tos/streaming.md#stream-multiple-modes) for configuration details.
+
+## Stateless runs
+
+If you don't want to **persist the outputs** of a streaming run in the [checkpointer](../../concepts/persistence.md) DB, you can create a stateless run without creating a thread:
+
+=== "Python"
+
+    ```python
+    from langgraph_sdk import get_client
+    client = get_client(url=<DEPLOYMENT_URL>, api_key=<API_KEY>)
+
+    async for chunk in client.runs.stream(
+        # highlight-next-line
+        None,  # (1)!
+        assistant_id,
+        input=inputs,
+        stream_mode="updates"
+    ):
+        print(chunk.data)
+    ```
+
+    1. We are passing `None` instead of a `thread_id` UUID.
+
+=== "JavaScript"
+
+    ```js
+    import { Client } from "@langchain/langgraph-sdk";
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <API_KEY> });
+
+    // create a streaming run
+    // highlight-next-line
+    const streamResponse = client.runs.stream(
+      // highlight-next-line
+      null,  // (1)!
+      assistantID,
+      {
+        input,
+        streamMode: "updates"
+      }
+    );
+    for await (const chunk of streamResponse) {
+      console.log(chunk.data);
+    }
+    ```
+
+    1. We are passing `None` instead of a `thread_id` UUID.
+
+=== "cURL"
+
+    ```bash
+    curl --request POST \
+    --url <DEPLOYMENT_URL>/runs/stream \
+    --header 'Content-Type: application/json' \
+    --header 'x-api-key: <API_KEY>'
+    --data "{
+      \"assistant_id\": \"agent\",
+      \"input\": <inputs>,
+      \"stream_mode\": \"updates\"
+    }"
+    ```
+
+## Join and stream
+
+LangGraph Platform allows you to join an active [background run](../how-tos/background_run.md) and stream outputs from it. To do so, you can use [LangGraph SDK's](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/) `client.runs.join_stream` method:
+
+=== "Python"
+
+    ```python
+    from langgraph_sdk import get_client
+    client = get_client(url=<DEPLOYMENT_URL>, api_key=<API_KEY>)
+
+    # highlight-next-line
+    async for chunk in client.runs.join_stream(
+        thread_id,
+        # highlight-next-line
+        run_id,  # (1)!
+    ):
+        print(chunk)
+    ```
+
+    1. This is the `run_id` of an existing run you want to join.
+
+
+=== "JavaScript"
+
+    ```js
+    import { Client } from "@langchain/langgraph-sdk";
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <API_KEY> });
+
+    // highlight-next-line
+    const streamResponse = client.runs.joinStream(
+      threadID,
+      // highlight-next-line
+      runId  // (1)!
+    );
+    for await (const chunk of streamResponse) {
+      console.log(chunk);
+    }
+    ```
+
+    1. This is the `run_id` of an existing run you want to join.
+
+=== "cURL"
+
+    ```bash
+    curl --request GET \
+    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/<RUN_ID>/stream \
+    --header 'Content-Type: application/json' \
+    --header 'x-api-key: <API_KEY>'
+    ```
+
+!!! warning "Outputs not buffered"
+
+    When you use `.join_stream`, output is not buffered, so any output produced before joining will not be received.
+
+## API Reference
+
+For API usage and implementation, refer to the [API reference](../reference/api/api_ref.html#tag/thread-runs/POST/threads/{thread_id}/runs/stream). 
+
@@ -0,0 +1,11 @@
+# Threads
+
+A thread contains the accumulated state of a sequence of [runs](./runs.md). If a run is executed on a thread, then the [state](../../concepts/low_level.md#state) of the underlying graph of the assistant will be persisted to the thread.
+
+A thread's current and historical state can be retrieved. To persist state, a thread must be created prior to executing a run.
+
+The state of a thread at a particular point in time is called a [checkpoint](../../concepts/persistence.md#checkpoints). Checkpoints can be used to restore the state of a thread at a later time.
+
+For more on threads and checkpoints, see this section of the [LangGraph conceptual guide](../../concepts/persistence.md).
+
+The LangGraph Cloud API provides several endpoints for creating and managing threads and thread state. See the [API reference](../../cloud/reference/api/api_ref.html#tag/threads) for more details.
@@ -0,0 +1,7 @@
+# Webhooks
+
+Webhooks enable event-driven communication from your LangGraph Cloud application to external services. For example, you may want to issue an update to a separate service once an API call to LangGraph Cloud has finished running.
+
+Many LangGraph Cloud endpoints accept a `webhook` parameter. If this parameter is specified by a an endpoint that can accept POST requests, LangGraph Cloud will send a request at the completion of a run.
+
+See the corresponding [how-to guide](../../cloud/how-tos/webhooks.md) for more detail.
@@ -1,11 +1,11 @@
-# How to Deploy to Cloud SaaS (Beta)
+# How to Deploy to Cloud SaaS

 Before deploying, review the [conceptual guide for the Cloud SaaS](../../concepts/langgraph_cloud.md) deployment option.

 ## Prerequisites

 1. 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.
-1. [Verify that the LangGraph API runs locally](test_locally.md). If the API does not run successfully (i.e. `langgraph dev`), deploying to LangGraph Cloud will fail as well.
+1. [Verify that the LangGraph API runs locally](../../tutorials/langgraph-platform/local-server.md). If the API does not run successfully (i.e. `langgraph dev`), deploying to LangGraph Cloud will fail as well.

 ## Create New Deployment

@@ -1,4 +1,4 @@
-# How to Deploy Self-Hosted Control Plane (Beta)
+# How to Deploy Self-Hosted Control Plane

 Before deploying, review the [conceptual guide for the Self-Hosted Control Plane](../../concepts/langgraph_self_hosted_control_plane.md) deployment option.

@@ -6,7 +6,7 @@ Before deploying, review the [conceptual guide for the Self-Hosted Control Plane

 1. You are using Kubernetes.
 1. You have self-hosted LangSmith deployed.
-1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to [test your application locally](./test_locally.md).
+1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to [test your application locally](../../tutorials/langgraph-platform/local-server.md).
 1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to build a Docker image (i.e. `langgraph build`) and push it to a registry your Kubernetes cluster has access to.
 1. `KEDA` is installed on your cluster.

@@ -46,4 +46,4 @@ Before deploying, review the [conceptual guide for the Self-Hosted Control Plane
 1. In your `values.yaml` file, configure the `hostBackendImage` and `operatorImage` options (if you need to mirror images)

 1. You can also configure base templates for your agents by overriding the base templates [here](https://github.com/langchain-ai/helm/blob/main/charts/langsmith/values.yaml#L898).
-1. You create a deployment from the [Control Plane UI](../../concepts/langgraph_control_plane.md#control-plane-ui).
+1. You create a deployment from the [control plane UI](../../concepts/langgraph_control_plane.md#control-plane-ui).
@@ -1,10 +1,10 @@
-# How to Deploy Self-Hosted Data Plane (Beta)
+# How to Deploy Self-Hosted Data Plane

 Before deploying, review the [conceptual guide for the Self-Hosted Data Plane](../../concepts/langgraph_self_hosted_data_plane.md) deployment option.

 ## Prerequisites

-1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to [test your application locally](./test_locally.md).
+1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to [test your application locally](../../tutorials/langgraph-platform/local-server.md).
 1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to build a Docker image (i.e. `langgraph build`) and push it to a registry your Kubernetes cluster or Amazon ECS cluster has access to.

 ## Kubernetes
@@ -46,7 +46,7 @@ Before deploying, review the [conceptual guide for the Self-Hosted Data Plane](.
        langgraph-dataplane-listener-7fccd788-wn2dx   0/1     Running             0          9s
        langgraph-dataplane-redis-0                   0/1     ContainerCreating   0          9s

-1. You create a deployment from the [Control Plane UI](../../concepts/langgraph_control_plane.md#control-plane-ui).
+1. You create a deployment from the [control plane UI](../../concepts/langgraph_control_plane.md#control-plane-ui).

 ## Amazon ECS

@@ -1,6 +1,6 @@
-# How to Set Up a LangGraph Application for Deployment
+# How to Set Up a LangGraph Application with requirements.txt

-A LangGraph application must be configured with a [LangGraph API configuration file](../reference/cli.md#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.
+A LangGraph application must be configured with a [LangGraph configuration file](../reference/cli.md#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](https://github.com/langchain-ai/langgraph-example), which you can play around with to learn more about how to setup your LangGraph application for deployment.

@@ -8,9 +8,9 @@ This walkthrough is based on [this repository](https://github.com/langchain-ai/l
    If you prefer using poetry for dependency management, check out [this how-to guide](./setup_pyproject.md) 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](https://github.com/langchain-ai/langgraph-example-monorepo) repository for an example of how to do so.
+    If you are interested in deploying a graph located inside a monorepo, take a look at [this repository](https://github.com/langchain-ai/langgraph-example-monorepo) for an example of how to do so.

-The final repo structure will look something like this:
+The final repository structure will look something like this:

 ```bash
 my-app/
@@ -31,17 +31,17 @@ After each step, an example file directory is provided to demonstrate how code c

 ## 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](#create-langgraph-api-config).
+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 configuration file](#create-langgraph-api-config).

 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,<0.4.0
-langgraph-sdk>=0.1.53
-langgraph-checkpoint>=2.0.15,<3.0
-langchain-core>=0.2.38,<0.4.0
+langgraph>=0.3.27
+langgraph-sdk>=0.1.66
+langgraph-checkpoint>=2.0.23
+langchain-core>=0.2.38
 langsmith>=0.1.63
-orjson>=3.9.7
+orjson>=3.9.7,<3.10.17
 httpx>=0.25.0
 tenacity>=8.0.0
 uvicorn>=0.26.0
@@ -49,7 +49,8 @@ sse-starlette>=2.1.0,<2.2.0
 uvloop>=0.18.0
 httptools>=0.5.0
 jsonschema-rs>=0.20.0
-structlog>=23.1.0
+structlog>=24.1.0
+cloudpickle>=3.0.0
 ```

 Example `requirements.txt` file:
@@ -94,9 +95,9 @@ my-app/

 ## 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](../reference/cli.md#configuration-file).
+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 configuration file](../reference/cli.md#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](https://github.com/langchain-ai/langgraph-example) to see their implementation):
+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 repository](https://github.com/langchain-ai/langgraph-example) to see their implementation):

 ```python
 # my_agent/agent.py
@@ -147,9 +148,9 @@ my-app/
 └── .env # environment variables
 ```

-## Create LangGraph API Config
+## Create LangGraph Configuration File

-Create a [LangGraph API configuration file](../reference/cli.md#configuration-file) called `langgraph.json`. See the [LangGraph CLI reference](../reference/cli.md#configuration-file) for detailed explanations of each key in the JSON object of the configuration file.
+Create a [LangGraph configuration file](../reference/cli.md#configuration-file) called `langgraph.json`. See the [LangGraph configuration file reference](../reference/cli.md#configuration-file) for detailed explanations of each key in the JSON object of the configuration file.

 Example `langgraph.json` file:

@@ -165,8 +166,8 @@ Example `langgraph.json` file:

 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.
+!!! warning "Configuration File Location"
+    The LangGraph 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:

@@ -187,4 +188,4 @@ my-app/

 ## Next

-After you setup your project and place it in a github repo, it's time to [deploy your app](./cloud.md).
+After you setup your project and place it in a GitHub repository, it's time to [deploy your app](./cloud.md).
@@ -1,10 +1,10 @@
-# How to Set Up a LangGraph.js Application for Deployment
+# How to Set Up a LangGraph.js Application

-A [LangGraph.js](https://langchain-ai.github.io/langgraphjs/) application must be configured with a [LangGraph API configuration file](../reference/cli.md#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.
+A [LangGraph.js](https://langchain-ai.github.io/langgraphjs/) application must be configured with a [LangGraph configuration file](../reference/cli.md#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](https://github.com/langchain-ai/langgraphjs-studio-starter), 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:
+The final repository structure will look something like this:

 ```bash
 my-app/
@@ -23,7 +23,7 @@ After each step, an example file directory is provided to demonstrate how code c

 ## 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](#create-langgraph-api-config).
+Dependencies can be specified in a `package.json`. If none of these files is created, then dependencies can be specified later in the [LangGraph configuration file](#create-langgraph-api-config).

 Example `package.json` file:

@@ -78,7 +78,7 @@ my-app/

 ## 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](../reference/cli.md#configuration-file).
+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 configuration file](../reference/cli.md#configuration-file).

 Here is an example `agent.ts`:

@@ -176,7 +176,7 @@ my-app/

 ## Create LangGraph API Config

-Create a [LangGraph API configuration file](../reference/cli.md#configuration-file) called `langgraph.json`. See the [LangGraph CLI reference](../reference/cli.md#configuration-file) for detailed explanations of each key in the JSON object of the configuration file.
+Create a [LangGraph configuration file](../reference/cli.md#configuration-file) called `langgraph.json`. See the [LangGraph configuration file reference](../reference/cli.md#configuration-file) for detailed explanations of each key in the JSON object of the configuration file.

 Example `langgraph.json` file:

@@ -196,8 +196,8 @@ Note that the variable name of the `CompiledGraph` appears at the end of the val

 !!! 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.
+    The LangGraph 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](./cloud.md).
+After you setup your project and place it in a GitHub repository, it's time to [deploy your app](./cloud.md).
@@ -1,6 +1,6 @@
-# How to Set Up a LangGraph Application for Deployment
+# How to Set Up a LangGraph Application with pyproject.toml

-A LangGraph application must be configured with a [LangGraph API configuration file](../reference/cli.md#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.
+A LangGraph application must be configured with a [LangGraph configuration file](../reference/cli.md#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](https://github.com/langchain-ai/langgraph-example-pyproject), which you can play around with to learn more about how to setup your LangGraph application for deployment.

@@ -10,7 +10,7 @@ This walkthrough is based on [this repository](https://github.com/langchain-ai/l
 !!! tip "Setup with a Monorepo"
    If you are interested in deploying a graph located inside a monorepo, take a look at [this](https://github.com/langchain-ai/langgraph-example-monorepo) repository for an example of how to do so.

-The final repo structure will look something like this:
+The final repository structure will look something like this:

 ```bash
 my-app/
@@ -31,17 +31,17 @@ After each step, an example file directory is provided to demonstrate how code c

 ## 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](#create-langgraph-api-config).
+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 configuration file](#create-langgraph-api-config).

 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,<0.4.0
-langgraph-sdk>=0.1.53
-langgraph-checkpoint>=2.0.15,<3.0
-langchain-core>=0.2.38,<0.4.0
+langgraph>=0.3.27
+langgraph-sdk>=0.1.66
+langgraph-checkpoint>=2.0.23
+langchain-core>=0.2.38
 langsmith>=0.1.63
-orjson>=3.9.7
+orjson>=3.9.7,<3.10.17
 httpx>=0.25.0
 tenacity>=8.0.0
 uvicorn>=0.26.0
@@ -49,7 +49,8 @@ sse-starlette>=2.1.0,<2.2.0
 uvloop>=0.18.0
 httptools>=0.5.0
 jsonschema-rs>=0.20.0
-structlog>=23.1.0
+structlog>=24.1.0
+cloudpickle>=3.0.0
 ```

 Example `pyproject.toml` file:
@@ -68,7 +69,6 @@ python = ">=3.9"
 langgraph = "^0.2.0"
 langchain-fireworks = "^0.1.3"

-
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"
@@ -97,15 +97,15 @@ Example file directory:

 ```bash
 my-app/
-├── .env             # file with environment variables
+├── .env # file with environment variables
 └── 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](../reference/cli.md#configuration-file).
+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 configuration file](../reference/cli.md#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](https://github.com/langchain-ai/langgraph-example-pyproject) to see their implementation):
+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 repository](https://github.com/langchain-ai/langgraph-example-pyproject) to see their implementation):

 ```python
 # my_agent/agent.py
@@ -156,9 +156,9 @@ my-app/
 └── pyproject.toml
 ```

-## Create LangGraph API Config
+## Create LangGraph Configuration File

-Create a [LangGraph API configuration file](../reference/cli.md#configuration-file) called `langgraph.json`. See the [LangGraph CLI reference](../reference/cli.md#configuration-file) for detailed explanations of each key in the JSON object of the configuration file.
+Create a [LangGraph configuration file](../reference/cli.md#configuration-file) called `langgraph.json`. See the [LangGraph configuration file reference](../reference/cli.md#configuration-file) for detailed explanations of each key in the JSON object of the configuration file.

 Example `langgraph.json` file:

@@ -174,8 +174,8 @@ Example `langgraph.json` file:

 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.
+!!! warning "Configuration File Location"
+    The LangGraph 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:

@@ -196,4 +196,4 @@ my-app/

 ## Next

-After you setup your project and place it in a github repo, it's time to [deploy your app](./cloud.md).
+After you setup your project and place it in a GitHub repository, it's time to [deploy your app](./cloud.md).
@@ -4,7 +4,7 @@ Before deploying, review the [conceptual guide for the Standalone Container](../

 ## Prerequisites

-1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to [test your application locally](./test_locally.md).
+1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to [test your application locally](../../tutorials/langgraph-platform/local-server.md).
 1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to build a Docker image (i.e. `langgraph build`).
 1. The following environment variables are needed for a standalone container deployment.
    1. `REDIS_URI`: Connection details to a Redis instance. Redis will be used as a pub-sub broker to enable streaming real time output from background runs. The value of `REDIS_URI` must be a valid [Redis connection URI](https://redis-py.readthedocs.io/en/stable/connections.html#redis.Redis.from_url).
@@ -21,7 +21,7 @@ Before deploying, review the [conceptual guide for the Standalone Container](../

            `<database_name_1>` and `database_name_2` are different databases within the same instance, but `<hostname_1>` is shared. **The same database cannot be used for separate deployments**.

-    1. `LANGSMITH_API_KEY`: (if using [Lite](../../concepts/langgraph_data_plane.md#lite-vs-enterprise)) LangSmith API key. This will be used to authenticate ONCE at server start up.
+    1. `LANGSMITH_API_KEY`: (if using [Lite](../../concepts/langgraph_server.md#server-versions)) LangSmith API key. This will be used to authenticate ONCE at server start up.
    1. `LANGGRAPH_CLOUD_LICENSE_KEY`: (if using [Enterprise](../../concepts/langgraph_data_plane.md#lite-vs-enterprise)) LangGraph Platform license key. This will be used to authenticate ONCE at server start up.
    1. `LANGSMITH_ENDPOINT`: To send traces to a [self-hosted LangSmith](https://docs.smith.langchain.com/self_hosting) instance, set `LANGSMITH_ENDPOINT` to the hostname of the self-hosted LangSmith instance.

@@ -1,196 +0,0 @@
-# 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:
-
-```bash
-pip install -U "langgraph-cli[inmem]"
-```
-
-Ensure you have an API key, which you can create from the [LangSmith UI](https://smith.langchain.com) (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:
-
-```python
-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:
-
-```shell
-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](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](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/) and [JS/TS](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/) references.
@@ -1,6 +1,11 @@
-# How to version assistants
+# 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](../../concepts/assistants.md#versioning-assistants) 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:
+!!! info "Prerequisites"
+
+    - [Assistants Overview](../../concepts/assistants.md)
+    - [How to create an Assistant](./configuration_cloud.md)
+
+In this guide we will show you how to create, manage and use multiple versions of an assistant. If you have not already, please first see [this](./configuration_cloud.md) guide on creating an assistant. For this example, assume you have a graph with the following configuration schema:

 === "Python"

@@ -9,7 +14,7 @@ In this how-to guide we will walk through how you can create and manage differen
        model_name: Literal["anthropic", "openai"] = "anthropic"
        system_prompt: str

-    agent = StateGraph(State, config_schema=Config)
+    builder = StateGraph(State, config_schema=Config)
    ```

 === "Javascript"
@@ -24,96 +29,63 @@ In this how-to guide we will walk through how you can create and manage differen

    // the rest of your code

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

-## Setup
+And that you have the following assistant already created:

-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:
-
-1. Click on the "Create New Assistant" button:
-
-    ![click create](./img/click_create_assistant.png)
-
-1. Use the create assistant pane to enter info for the assistant you wish to create, and then click create:
-
-    ![create](./img/create_assistant.png)
-
-1. See that your assistant was created and is displayed in the Studio
-
-    ![view create](./img/create_assistant_view.png)
-
-1. Click on the edit button next to the selected assistant to manage your created assistant:
-
-    ![create edit](./img/edit_created_assistant.png)
+    {
+        "assistant_id": "62e209ca-9154-432a-b9e9-2d75c7a9219b",
+        "graph_id": "agent",
+        "name": "Open AI Assistant"
+        "config": {
+            "configurable": {
+                "model_name": "openai",
+                "system_prompt": "You are a helpful assistant."
+            }
+        },
+        "metadata": {}
+        "created_at": "2024-08-31T03:09:10.230718+00:00",
+        "updated_at": "2024-08-31T03:09:10.230718+00:00",
+    }

 ## 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.
+### LangGraph SDK

+To edit the assistant, use the `update` method. This will create a new version of the assistant with the provided edits. See the [Python](../reference/sdk/python_sdk_ref.md#langgraph_sdk.client.AssistantsClient.update) and [JS](../reference/sdk/js_ts_sdk_ref.md#update) SDK reference docs for more information.
+
+!!! note "Note"
+    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 versions.
+
+For example, to update your assistant's system prompt:
 === "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!"}})
+    openai_assistant_v2 = await client.assistants.update(
+        openai_assistant["assistant_id"],
+        config={
+            "configurable": {
+                "model_name": "openai",
+                "system_prompt": "You are an unhelpful assistant!",
+            }
+        },
+    )
    ```

 === "Javascript"

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

 === "CURL"
@@ -123,27 +95,29 @@ Let's now say we wanted to add a system prompt to our assistant. We can do this
    --url <DEPOLYMENT_URL>/assistants/<ASSISTANT_ID> \
    --header 'Content-Type: application/json' \
    --data '{
-    "config": {"model_name": "openai", "system_prompt": "You are a helpful assistant!"}
+    "config": {"model_name": "openai", "system_prompt": "You are an unhelpful assistant!"}
    }'
    ```

-### Using the studio
+This will create a new version of the assistant with the updated parameters and set this as the active version of your assistant. If you now run your graph and pass in this assistant id, it will use this latest version.

-1. First, click on the edit button next to the `openai_assistant`. Then, add a system prompt and click "Save New Version":
+### LangGraph Platform UI

-    ![create new version](./img/create_new_version.png)
+You can also edit assistants from the LangGraph Platform UI.

-1. Then you can see it is selected in the assistant dropdown:
+Inside your deployment, select the "Assistants" tab. This will load a table of all of the assistants in your deployment, across all graphs.

-    ![see version dropdown](./img/see_new_version.png)
+To edit an existing assistant, select the "Edit" button for the specified assistant. This will open a form where you can edit the assistant's name, description, and configuration.

-1. And you can see all the version history in the edit pane for the assistant:
+Additionally, if using LangGraph Studio, you can edit the assistants and create new versions via the "Manage Assistants" button.

-    ![see versions](./img/see_version_history.png)
+## Use a previous assistant version

-## Point your assistant to a different version
+### LangGraph SDK

-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:
+You can also change the active version of your assistant. To do so, use the `setLatest` method.
+
+In the example above, to rollback to the first version of the assistant:

 === "Python"

@@ -168,16 +142,11 @@ After having created multiple versions, we can change the version our assistant
    }'
    ```

+If you now run your graph and pass in this assistant id, it will use the first version of the assistant.

-### Using the studio
+### LangGraph Platform UI

-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
-
-![set version](./img/select_different_version.png)
-
-## 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](https://langchain-ai.github.io/langgraph/cloud/how-tos/#streaming) or [this one](https://langchain-ai.github.io/langgraph/cloud/how-tos/invoke_studio/) if you are using the Studio.
+If using LangGraph Studio, to set the active version of your asssistant, click the "Manage Assistants" button and locate the assistant you would like to use. Select the assistant and the version, and then click the "Active" toggle. This will update the assistant to make the selected version active.

 !!! 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.
+    Deleting as assistant will delete ALL of it's versions. There is currently no way to 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.
@@ -1,6 +1,6 @@
 # Configurable Headers

-LangGraph allows runtime configuration to modify agent behavior and permissions dynamically. When using the [LangGraph Platform](../quick_start.md), you can pass this configuration in the request body (`config`) or specific request headers. This enables adjustments based on user identity or other request data (see the [configuration how-to](../../how-tos/configuration.ipynb) for more details on how to access within your graph).
+LangGraph allows runtime configuration to modify agent behavior and permissions dynamically. When using the [LangGraph Platform](../quick_start.md), you can pass this configuration in the request body (`config`) or specific request headers. This enables adjustments based on user identity or other request data.

 For privacy, control which headers are passed to the runtime configuration via the `http.configurable_headers` section in your `langgraph.json` file.

@@ -65,8 +65,6 @@ async def generate_agent(config):
 }
 ```

-For more examples on how to use runtime configuration, check out the [configuration how-to](../../how-tos/configuration.ipynb).
-
 ### Opt-out of configurable headers

 If you'd like to opt-out of configurable headers, you can simply set a wildcard pattern in the `exclude` list:
@@ -1,19 +1,23 @@
-# How to create agents with configuration
+# How to create Assistants

-One of the benefits of LangGraph API is that it lets you create agents with different configurations.
-This is useful when you want to:
+!!! info "Prerequisites"

- 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
+    - [Assistants Overview](../../concepts/assistants.md)
+    - [Configuration](../../concepts/low_level.md#configuration)

-In this guide we will show how to do that for the default agent we have built in.
+In this guide we will show how to create and configure an assistant.

-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:
+First, as a brief refresher on the concept of configurations, consider the following simple `call_model` node and configuration schema. Observe that this node tries to read and use the `model_name` as defined by the `config` object's `configurable`.

 === "Python"

    ```python
+
+    class ConfigSchema(TypedDict):
+        model_name: str
+
+    builder = StateGraph(AgentState, config_schema=ConfigSchema)
+
    def call_model(state, config):
        messages = state["messages"]
        model_name = config.get('configurable', {}).get("model_name", "anthropic")
@@ -26,6 +30,15 @@ If you look at the agent we defined, you can see that inside the `call_model` no
 === "Javascript"

    ```js
+    import { Annotation } from "@langchain/langgraph";
+
+    const ConfigSchema = Annotation.Root({
+        model_name: Annotation<string>,
+        system_prompt:
+    });
+
+    const builder = new StateGraph(AgentState, ConfigSchema)
+
    function callModel(state: State, config: RunnableConfig) {
      const messages = state.messages;
      const modelName = config.configurable?.model_name ?? "anthropic";
@@ -36,9 +49,15 @@ If you look at the agent we defined, you can see that inside the `call_model` no
    }
    ```

-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.
+For more information on configurations, [see here](../../concepts/low_level.md#configuration).

-First let's set up our client and thread:
+## Creating an Assistant
+
+### LangGraph SDK
+
+To create an assistant, use the [LangGraph SDK](../../concepts/sdk.md) `create` method. See the [Python](../reference/sdk/python_sdk_ref.md#langgraph_sdk.client.AssistantsClient.create) and [JS](../reference/sdk/js_ts_sdk_ref.md#create) SDK reference docs for more information.
+
+This example uses the same configuration schema as above, and creates an assistant with `model_name` set to `openai`.

 === "Python"

@@ -46,84 +65,9 @@ First let's set up our client and thread:
    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"}}
+        "agent", config={"configurable": {"model_name": "openai"}}, name="Open AI Assistant"
    )

    print(openai_assistant)
@@ -132,10 +76,14 @@ Now we can initialize an assistant with config:
 === "Javascript"

    ```js
-    let openAIAssistant = await client.assistants.create(
-      // "agent" is the name of a graph we deployed
-      "agent", { "configurable": { "model_name": "openai" } }
-    );
+    import { Client } from "@langchain/langgraph-sdk";
+
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
+    let openAIAssistant = await client.assistants.create({
+        graphId: 'agent',
+        name: "Open AI Assistant",
+        config: { "configurable": { "model_name": "openai" } },
+    });

    console.log(openAIAssistant);
    ```
@@ -146,7 +94,7 @@ Now we can initialize an assistant with config:
    curl --request POST \
        --url <DEPLOYMENT_URL>/assistants \
        --header 'Content-Type: application/json' \
-        --data '{"graph_id":"agent","config":{"configurable":{"model_name":"open_ai"}}}'
+        --data '{"graph_id":"agent", "config":{"configurable":{"model_name":"openai"}}, "name": "Open AI Assistant"}'
    ```

 Output:
@@ -154,17 +102,32 @@ 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",
+        "name": "Open AI Assistant"
        "config": {
            "configurable": {
-                "model_name": "open_ai"
+                "model_name": "openai"
            }
        },
        "metadata": {}
+        "created_at": "2024-08-31T03:09:10.230718+00:00",
+        "updated_at": "2024-08-31T03:09:10.230718+00:00",
    }

-We can verify the config is indeed taking effect:
+### LangGraph Platform UI
+
+You can also create assistants from the LangGraph Platform UI.
+
+Inside your deployment, select the "Assistants" tab. This will load a table of all of the assistants in your deployment, across all graphs.
+
+To create a new assistant, select the "+ New assistant" button. This will open a form where you can specify the graph this assistant is for, as well as provide a name, description, and the desired configuration for the assistant based on the configuration schema for that graph.
+
+To confirm, click "Create assistant". This will take you to [LangGraph Studio](../../concepts/langgraph_studio.md) where you can test the assistant. If you go back to the "Assistants" tab in the deployment, you will see the newly created assistant in the table.
+
+## Using an Assistant
+
+### LangGraph SDK
+
+We have now created an assistant called "Open AI Assistant" that has `model_name` defined as `openai`. We can now use this assistant with this configuration:

 === "Python"

@@ -173,6 +136,7 @@ We can verify the config is indeed taking effect:
    input = {"messages": [{"role": "user", "content": "who made you?"}]}
    async for event in client.runs.stream(
        thread["thread_id"],
+        # this is where we specify the assistant id to use
        openai_assistant["assistant_id"],
        input=input,
        stream_mode="updates",
@@ -190,6 +154,7 @@ We can verify the config is indeed taking effect:

    const streamResponse = client.runs.stream(
      thread["thread_id"],
+      // this is where we specify the assistant id to use
      openAIAssistant["assistant_id"],
      {
        input,
@@ -260,5 +225,6 @@ Output:
    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}]}}

+### LangGraph Platform UI

-
+Inside your deployment, select the "Assistants" tab. For the assistant you would like to use, click the "Studio" button. This will open LangGraph Studio with the selected assistant. When you submit an input (either in Graph or Chat mode), the selected assistant and its configuration will be used.
@@ -4,7 +4,9 @@ You may wish to copy (i.e. "fork") an existing thread in order to keep the exist

 ## Setup

-This code assumes you already have a thread to copy. You can read about what a thread is [here](../../concepts/langgraph_server.md#threads) and learn how to stream a run on a thread in [these how-to guides](../../how-tos/index.md#streaming_1).
+This code assumes you already have a thread to copy. 
+
+For more information, see these guides on [Threads](../../cloud/concepts/threads.md) and [Streaming](../../concepts/streaming.md).

 ### SDK initialization

@@ -8,9 +8,9 @@
    * [LangGraph Glossary](../../concepts/low_level.md)
    

-Human-in-the-loop (HIL) interactions are crucial for [agentic systems](../../concepts/agentic_concepts.md#human-in-the-loop). [Breakpoints](../../concepts/low_level.md#breakpoints) are a common HIL interaction pattern, allowing the graph to stop at specific steps and seek human approval before proceeding (e.g., for sensitive actions).
+Human-in-the-loop (HIL) interactions are crucial for [agentic systems](../../concepts/agentic_concepts.md#human-in-the-loop). [Breakpoints](../../concepts/breakpoints.md) are a common HIL interaction pattern, allowing the graph to stop at specific steps and seek human approval before proceeding (e.g., for sensitive actions).

-Breakpoints are built on top of LangGraph [checkpoints](../../concepts/low_level.md#persistence), which save the graph's state after each node execution. Checkpoints are saved in [threads](../../concepts/low_level.md#threads) that preserve graph state and can be accessed after a graph has finished execution. This allows for graph execution to pause at specific points, await human approval, and then resume execution from the last checkpoint.
+Breakpoints are built on top of LangGraph [checkpoints](../../concepts/persistence.md#checkpoints), which save the graph's state after each node execution. Checkpoints are saved in [threads](../../concepts/persistence.md#threads) that preserve graph state and can be accessed after a graph has finished execution. This allows for graph execution to pause at specific points, await human approval, and then resume execution from the last checkpoint.

 ## Setup

@@ -6,7 +6,7 @@ This can be in several ways, but the primary supported way is to add an "interru

 ## Setup

-We are not going to show the full code for the graph we are hosting, but you can see it [here](../../how-tos/human_in_the_loop/edit-graph-state.ipynb#agent) if you want to. Once this graph is hosted, we are ready to invoke it and wait for user input.
+We are not going to show the full code for the graph we are hosting, but you can see it [here](../../how-tos/human_in_the_loop/time-travel.ipynb) if you want to. Once this graph is hosted, we are ready to invoke it and wait for user input.

 ### SDK initialization

@@ -1,295 +0,0 @@
-# How to stream events
-
-!!! info "Prerequisites"
-    * [Streaming](../../concepts/streaming.md#streaming-graph-outputs-stream-and-astream)
-
-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](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.astream_events) 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
@@ -1,331 +0,0 @@
-# How to stream messages from your graph
-
-!!! info "Prerequisites"
-    * [Streaming](../../concepts/streaming.md)
-
-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",
-        ...
-      }
-    ]
-
-    ...
@@ -1,485 +0,0 @@
-# How to configure multiple streaming modes at the same time
-
-!!! info "Prerequisites"
-    * [Streaming](../../concepts/streaming.md)
-
-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
-    
-    
-
@@ -1,217 +0,0 @@
-# How to stream state updates of your graph
-
-!!! info "Prerequisites"
-    * [Streaming](../../concepts/streaming.md)
-
-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
@@ -1,335 +0,0 @@
-# How to stream full state of your graph
-
-!!! info "Prerequisites"
-    * [Streaming](../../concepts/streaming.md)
-
-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",
-          ...
-        }
-      ]
-    }
@@ -0,0 +1,879 @@
+# Stream outputs
+
+## Streaming API
+
+[LangGraph SDK](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/) allows you to stream outputs from the LangGraph API server.
+
+Basic usage example:
+
+=== "Python"
+
+    ```python
+    from langgraph_sdk import get_client
+    client = get_client(url=<DEPLOYMENT_URL>, api_key=<API_KEY>)
+
+    # Using the graph deployed with the name "agent"
+    assistant_id = "agent"
+
+    # create a thread
+    thread = await client.threads.create()
+    thread_id = thread["thread_id"]
+
+    # create a streaming run
+    # highlight-next-line
+    async for chunk in client.runs.stream(
+        thread_id,
+        assistant_id,
+        input=inputs,
+        stream_mode="updates"
+    ):
+        print(chunk.data)
+    ```
+
+=== "JavaScript"
+
+    ```js
+    import { Client } from "@langchain/langgraph-sdk";
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <API_KEY> });
+
+    // Using the graph deployed with the name "agent"
+    const assistantID = "agent";
+
+    // create a thread
+    const thread = await client.threads.create();
+    const threadID = thread["thread_id"];
+
+    // create a streaming run
+    // highlight-next-line
+    const streamResponse = client.runs.stream(
+      threadID,
+      assistantID,
+      {
+        input,
+        streamMode: "updates"
+      }
+    );
+    for await (const chunk of streamResponse) {
+      console.log(chunk.data);
+    }
+    ```
+
+=== "cURL"
+
+    Create a thread:
+
+    ```bash
+    curl --request POST \
+    --url <DEPLOYMENT_URL>/threads \
+    --header 'Content-Type: application/json' \
+    --data '{}'
+    ```
+
+    Create a streaming run:
+
+    ```bash
+    curl --request POST \
+    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+    --header 'Content-Type: application/json' \
+    --header 'x-api-key: <API_KEY>'
+    --data "{
+      \"assistant_id\": \"agent\",
+      \"input\": <inputs>,
+      \"stream_mode\": \"updates\"
+    }"
+    ```
+
+??? example "Extended example: streaming updates"
+
+    This is an example graph you can run in the LangGraph API server.
+    See [LangGraph Platform quickstart](../quick_start.md) for more details.
+
+    ```python
+    # graph.py
+    from typing import TypedDict
+    from langgraph.graph import StateGraph, START, END
+
+    class State(TypedDict):
+        topic: str
+        joke: str
+
+    def refine_topic(state: State):
+        return {"topic": state["topic"] + " and cats"}
+
+    def generate_joke(state: State):
+        return {"joke": f"This is a joke about {state['topic']}"}
+
+    graph = (
+        StateGraph(State)
+        .add_node(refine_topic)
+        .add_node(generate_joke)
+        .add_edge(START, "refine_topic")
+        .add_edge("refine_topic", "generate_joke")
+        .add_edge("generate_joke", END)
+        .compile()
+    )
+    ```
+
+    Once you have a running LangGraph API server, you can interact with it using
+    [LangGraph SDK](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/)
+
+    === "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 a thread
+        thread = await client.threads.create()
+        thread_id = thread["thread_id"]
+
+        # create a streaming run
+        # highlight-next-line
+        async for chunk in client.runs.stream(  # (1)!
+            thread_id,
+            assistant_id,
+            input={"topic": "ice cream"},
+            # highlight-next-line
+            stream_mode="updates"  # (2)!
+        ):
+            print(chunk.data)
+        ```
+
+        1. The `client.runs.stream()` method returns an iterator that yields streamed outputs.
+        2. Set `stream_mode="updates"` to stream only the updates to the graph state after each node. Other stream modes are also available. See [supported stream modes](#supported-stream-modes) for details.
+
+    === "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 a thread
+        const thread = await client.threads.create();
+        const threadID = thread["thread_id"];
+
+        // create a streaming run
+        // highlight-next-line
+        const streamResponse = client.runs.stream(  // (1)!
+          threadID,
+          assistantID,
+          {
+            input: { topic: "ice cream" },
+            // highlight-next-line
+            streamMode: "updates"  // (2)!
+          }
+        );
+        for await (const chunk of streamResponse) {
+          console.log(chunk.data);
+        }
+        ```
+
+        1. The `client.runs.stream()` method returns an iterator that yields streamed outputs.
+        2. Set `streamMode: "updates"` to stream only the updates to the graph state after each node. Other stream modes are also available. See [supported stream modes](#supported-stream-modes) for details.
+
+    === "cURL"
+
+        Create a thread:
+
+        ```bash
+        curl --request POST \
+        --url <DEPLOYMENT_URL>/threads \
+        --header 'Content-Type: application/json' \
+        --data '{}'
+        ```
+
+        Create a streaming run:
+
+        ```bash
+        curl --request POST \
+        --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+        --header 'Content-Type: application/json' \
+        --data "{
+          \"assistant_id\": \"agent\",
+          \"input\": {\"topic\": \"ice cream\"},
+          \"stream_mode\": \"updates\"
+        }"
+        ```
+
+    ```output
+    {'run_id': '1f02c2b3-3cef-68de-b720-eec2a4a8e920', 'attempt': 1}
+    {'refine_topic': {'topic': 'ice cream and cats'}}
+    {'generate_joke': {'joke': 'This is a joke about ice cream and cats'}}
+    ```
+
+
+### Supported stream modes
+
+| Mode                 | Description                                                                                                                                                    | LangGraph Library Method                                                |
+|----------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------|
+| [`values`](#stream-graph-state)         | Stream the full graph state after each [super-step](../../concepts/low_level.md#graphs). | `.stream()` / `.astream()` with [`stream_mode="values"`](../../how-tos/streaming.md#stream-graph-state)                  |
+| [`updates`](#stream-graph-state)        | Streams the updates to the state after each step of the graph. If multiple updates are made in the same step (e.g., multiple nodes are run), those updates are streamed separately.                                                              | `.stream()` / `.astream()` with [`stream_mode="updates"`](../../how-tos/streaming.md#stream-graph-state)                 |
+| [`messages-tuple`](#messages) | Streams LLM tokens and metadata for the graph node where the LLM is invoked (useful for chat apps).                                   | `.stream()` / `.astream()` with [`stream_mode="messages"`](../../how-tos/streaming.md#messages)                |
+| [`debug`](#debug)          | Streams as much information as possible throughout the execution of the graph.                                                                       | `.stream()` / `.astream()` with [`stream_mode="debug"`](../../how-tos/streaming.md#stream-graph-state)                   |
+| [`custom`](#stream-custom-data)         | Streams custom data from inside your graph                                                                                                                                             | `.stream()` / `.astream()` with [`stream_mode="custom"`](../../how-tos/streaming.md#stream-custom-data)                  |
+| [`events`](#stream-events)         | Stream all events (including the state of the graph); mainly useful when migrating large LCEL apps.                       | `.astream_events()`
+
+### Stream multiple modes
+
+You can pass a list as the `stream_mode` parameter to stream multiple modes at once.
+
+The streamed outputs will be tuples of `(mode, chunk)` where `mode` is the name of the stream mode and `chunk` is the data streamed by that mode.
+
+=== "Python"
+
+    ```python
+    async for chunk in client.runs.stream(
+        thread_id,
+        assistant_id,
+        input=inputs,
+        stream_mode=["updates", "custom"]
+    ):
+        print(chunk)
+    ```
+
+=== "JavaScript"
+
+    ```js
+    const streamResponse = client.runs.stream(
+      threadID,
+      assistantID,
+      {
+        input,
+        streamMode: ["updates", "custom"]
+      }
+    );
+    for await (const chunk of streamResponse) {
+      console.log(chunk);
+    }
+    ```
+
+=== "cURL"
+
+    ```bash
+    curl --request POST \
+     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+     --header 'Content-Type: application/json' \
+     --data "{
+       \"assistant_id\": \"agent\",
+       \"input\": <inputs>,
+       \"stream_mode\": [
+         \"updates\"
+         \"custom\"
+       ]
+     }"
+    ```
+
+## Stream graph state
+
+Use the stream modes `updates` and `values` to stream the state of the graph as it executes.
+
+* `updates` streams the **updates** to the state after each step of the graph.
+* `values` streams the **full value** of the state after each step of the graph.
+
+??? example "Example graph"
+
+    ```python
+    from typing import TypedDict
+    from langgraph.graph import StateGraph, START, END
+
+    class State(TypedDict):
+      topic: str
+      joke: str
+
+    def refine_topic(state: State):
+        return {"topic": state["topic"] + " and cats"}
+
+    def generate_joke(state: State):
+        return {"joke": f"This is a joke about {state['topic']}"}
+
+    graph = (
+      StateGraph(State)
+      .add_node(refine_topic)
+      .add_node(generate_joke)
+      .add_edge(START, "refine_topic")
+      .add_edge("refine_topic", "generate_joke")
+      .add_edge("generate_joke", END)
+      .compile()
+    )
+    ```
+
+!!! note "Stateful runs"
+
+    Examples below assume that you want to **persist the outputs** of a streaming run in the [checkpointer](../../concepts/persistence.md) DB and have created a thread. To create a 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 a thread
+        thread = await client.threads.create()
+        thread_id = thread["thread_id"]
+        ```
+
+    === "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 a thread
+        const thread = await client.threads.create();
+        const threadID = thread["thread_id"]
+        ```
+
+    === "cURL"
+
+        ```bash
+        curl --request POST \
+        --url <DEPLOYMENT_URL>/threads \
+        --header 'Content-Type: application/json' \
+        --data '{}'
+        ```
+
+    If you don't need to persist the outputs of a run, you can pass `None` instead of `thread_id` when streaming.
+
+=== "updates"
+
+    Use this to stream only the **state updates** returned by the nodes after each step. The streamed outputs include the name of the node as well as the update.
+
+    === "Python"
+
+        ```python
+        async for chunk in client.runs.stream(
+            thread_id,
+            assistant_id,
+            input={"topic": "ice cream"},
+            # highlight-next-line
+            stream_mode="updates"
+        ):
+            print(chunk.data)
+        ```
+
+    === "JavaScript"
+
+        ```js
+        const streamResponse = client.runs.stream(
+          threadID,
+          assistantID,
+          {
+            input: { topic: "ice cream" },
+            // highlight-next-line
+            streamMode: "updates"
+          }
+        );
+        for await (const chunk of streamResponse) {
+          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\": {\"topic\": \"ice cream\"},
+          \"stream_mode\": \"updates\"
+        }"
+        ```
+
+===  "values"
+
+    Use this to stream the **full state** of the graph after each step.
+
+    === "Python"
+
+        ```python
+        async for chunk in client.runs.stream(
+            thread_id,
+            assistant_id,
+            input={"topic": "ice cream"},
+            # highlight-next-line
+            stream_mode="values"
+        ):
+            print(chunk.data)
+        ```
+
+    === "JavaScript"
+
+        ```js
+        const streamResponse = client.runs.stream(
+          threadID,
+          assistantID,
+          {
+            input: { topic: "ice cream" },
+            // highlight-next-line
+            streamMode: "values"
+          }
+        );
+        for await (const chunk of streamResponse) {
+          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\": {\"topic\": \"ice cream\"},
+          \"stream_mode\": \"values\"
+        }"
+        ```
+
+
+## Subgraphs
+
+To include outputs from [subgraphs](../../concepts/subgraphs.md) in the streamed outputs, you can set `subgraphs=True` in the `.stream()` method of the parent graph. This will stream outputs from both the parent graph and any subgraphs.
+
+```python
+for chunk in client.runs.stream(
+    thread_id,
+    assistant_id,
+    input={"foo": "foo"},
+    # highlight-next-line
+    stream_subgraphs=True, # (1)!
+    stream_mode="updates",
+):
+    print(chunk)
+```
+
+1. Set `stream_subgraphs=True` to stream outputs from subgraphs.
+
+??? example "Extended example: streaming from subgraphs"
+
+    This is an example graph you can run in the LangGraph API server.
+    See [LangGraph Platform quickstart](../quick_start.md) for more details.
+
+    ```python
+    # graph.py
+    from langgraph.graph import START, StateGraph
+    from typing import TypedDict
+
+    # Define subgraph
+    class SubgraphState(TypedDict):
+        foo: str  # note that this key is shared with the parent graph state
+        bar: str
+
+    def subgraph_node_1(state: SubgraphState):
+        return {"bar": "bar"}
+
+    def subgraph_node_2(state: SubgraphState):
+        return {"foo": state["foo"] + state["bar"]}
+
+    subgraph_builder = StateGraph(SubgraphState)
+    subgraph_builder.add_node(subgraph_node_1)
+    subgraph_builder.add_node(subgraph_node_2)
+    subgraph_builder.add_edge(START, "subgraph_node_1")
+    subgraph_builder.add_edge("subgraph_node_1", "subgraph_node_2")
+    subgraph = subgraph_builder.compile()
+
+    # Define parent graph
+    class ParentState(TypedDict):
+        foo: str
+
+    def node_1(state: ParentState):
+        return {"foo": "hi! " + state["foo"]}
+
+    builder = StateGraph(ParentState)
+    builder.add_node("node_1", node_1)
+    builder.add_node("node_2", subgraph)
+    builder.add_edge(START, "node_1")
+    builder.add_edge("node_1", "node_2")
+    graph = builder.compile()
+    ```
+
+    Once you have a running LangGraph API server, you can interact with it using
+    [LangGraph SDK](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/)
+
+    === "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 a thread
+        thread = await client.threads.create()
+        thread_id = thread["thread_id"]
+    
+        async for chunk in client.runs.stream(
+            thread_id,
+            assistant_id,
+            input={"foo": "foo"},
+            # highlight-next-line
+            stream_subgraphs=True, # (1)!
+            stream_mode="updates",
+        ):
+            print(chunk)
+        ```
+        
+        1. Set `stream_subgraphs=True` to stream outputs from subgraphs.
+
+    === "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 a thread
+        const thread = await client.threads.create();
+        const threadID = thread["thread_id"];
+
+        // create a streaming run
+        const streamResponse = client.runs.stream(
+          threadID,
+          assistantID,
+          {
+            input: { foo: "foo" },
+            // highlight-next-line
+            streamSubgraphs: true,  // (1)!
+            streamMode: "updates"
+          }
+        );
+        for await (const chunk of streamResponse) {
+          console.log(chunk);
+        }
+        ```
+
+        1. Set `streamSubgraphs: true` to stream outputs from subgraphs.
+
+    === "cURL"
+
+        Create a thread:
+
+        ```bash
+        curl --request POST \
+        --url <DEPLOYMENT_URL>/threads \
+        --header 'Content-Type: application/json' \
+        --data '{}'
+        ```
+
+        Create a streaming run:
+
+        ```bash
+        curl --request POST \
+        --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+        --header 'Content-Type: application/json' \
+        --data "{
+          \"assistant_id\": \"agent\",
+          \"input\": {\"foo\": \"foo\"},
+          \"stream_subgraphs\": true,
+          \"stream_mode\": [
+            \"updates\"
+          ]
+        }"
+        ```
+
+    **Note** that we are receiving not just the node updates, but we also the namespaces which tell us what graph (or subgraph) we are streaming from.
+
+## Debugging {#debug}
+
+Use the `debug` streaming mode to stream as much information as possible throughout the execution of the graph. The streamed outputs include the name of the node as well as the full state.
+
+=== "Python"
+
+    ```python
+    async for chunk in client.runs.stream(
+        thread_id,
+        assistant_id,
+        input={"topic": "ice cream"},
+        # highlight-next-line
+        stream_mode="debug"
+    ):
+        print(chunk.data)
+    ```
+
+=== "JavaScript"
+
+    ```js
+    const streamResponse = client.runs.stream(
+      threadID,
+      assistantID,
+      {
+        input: { topic: "ice cream" },
+        // highlight-next-line
+        streamMode: "debug"
+      }
+    );
+    for await (const chunk of streamResponse) {
+      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\": {\"topic\": \"ice cream\"},
+      \"stream_mode\": \"debug\"
+    }"
+    ```
+
+## LLM tokens {#messages}
+
+Use the `messages-tuple` streaming mode to stream Large Language Model (LLM) outputs **token by token** from any part of your graph, including nodes, tools, subgraphs, or tasks.
+
+The streamed output from [`messages-tuple` mode](#supported-stream-modes) is a tuple `(message_chunk, metadata)` where:
+
+- `message_chunk`: the token or message segment from the LLM.
+- `metadata`: a dictionary containing details about the graph node and LLM invocation.
+
+> If your LLM is not available as a LangChain integration, you can stream its outputs using `custom` mode instead. See [use with any LLM](#use-with-any-llm) for details.
+ 
+!!! warning "Manual config required for async in Python < 3.11"
+
+    When using Python < 3.11 with async code in your graph, you must explicitly pass `RunnableConfig` to `ainvoke()` to enable proper streaming. See [Async with Python < 3.11](#async) for details or upgrade to Python 3.11+.
+
+??? example "Example graph"
+
+    ```python
+    from dataclasses import dataclass
+
+    from langchain.chat_models import init_chat_model
+    from langgraph.graph import StateGraph, START
+
+    @dataclass
+    class MyState:
+        topic: str
+        joke: str = ""
+
+    llm = init_chat_model(model="openai:gpt-4o-mini")
+
+    def call_model(state: MyState):
+        """Call the LLM to generate a joke about a topic"""
+        # highlight-next-line
+        llm_response = llm.invoke( # (1)!
+            [
+                {"role": "user", "content": f"Generate a joke about {state.topic}"}
+            ]
+        )
+        return {"joke": llm_response.content}
+
+    graph = (
+        StateGraph(MyState)
+        .add_node(call_model)
+        .add_edge(START, "call_model")
+        .compile()
+    )
+    ```
+
+    1. Note that the message events are emitted even when the LLM is run using `.invoke` rather than `.stream`.
+
+=== "Python"
+
+    ```python
+    async for chunk in client.runs.stream(
+        thread_id,
+        assistant_id,
+        input={"topic": "ice cream"},
+        # highlight-next-line
+        stream_mode="messages-tuple",
+    ):
+        if chunk.event != "messages":
+            continue
+
+        message_chunk, metadata = chunk.data  # (1)!
+        if message_chunk["content"]:
+            print(message_chunk["content"], end="|", flush=True)
+    ```
+
+    1. The "messages-tuple" stream mode returns an iterator of tuples `(message_chunk, metadata)` where `message_chunk` is the token streamed by the LLM and `metadata` is a dictionary with information about the graph node where the LLM was called and other information.
+
+=== "JavaScript"
+
+    ```js
+    const streamResponse = client.runs.stream(
+      threadID,
+      assistantID,
+      {
+        input: { topic: "ice cream" },
+        // highlight-next-line
+        streamMode: "messages-tuple"
+      }
+    );
+    for await (const chunk of streamResponse) {
+      if (chunk.event !== "messages") {
+        continue;
+      }
+      console.log(chunk.data[0]["content"]);  // (1)!
+    }
+    ```
+
+    1. The "messages-tuple" stream mode returns an iterator of tuples `(message_chunk, metadata)` where `message_chunk` is the token streamed by the LLM and `metadata` is a dictionary with information about the graph node where the LLM was called and other information.
+
+=== "cURL"
+
+    ```bash
+    curl --request POST \
+    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+    --header 'Content-Type: application/json' \
+    --data "{
+      \"assistant_id\": \"agent\",
+      \"input\": {\"topic\": \"ice cream\"},
+      \"stream_mode\": \"messages-tuple\"
+    }"
+    ```
+
+### Filter LLM tokens
+
+* To filter the streamed tokens by LLM invocation, you can [associate `tags` with LLM invocations](../../how-tos/streaming.md#filter-by-llm-invocation).
+* To stream tokens only from specific nodes, use `stream_mode="messages"` and [filter the outputs by the `langgraph_node` field](../../how-tos/streaming.md#filter-by-node) in the streamed metadata.
+
+## Stream custom data
+
+To send **custom user-defined data** from inside a LangGraph node or tool, follow these steps:
+
+1. Use `get_stream_writer()` to access the stream writer and emit custom data.
+2. Set `stream_mode="custom"` when calling `.stream()` or `.astream()` to get the custom data in the stream. You can combine multiple modes (e.g., `["updates", "custom"]`), but at least one must be `"custom"`.
+
+!!! warning "No `get_stream_writer()` in async for Python < 3.11"
+
+    In async code running on Python < 3.11, `get_stream_writer()` will not work.  
+    Instead, add a `writer` parameter to your node or tool and pass it manually.  
+    See [Async with Python < 3.11](#async) for usage examples.
+
+??? example "Example graph"
+
+      ```python
+      from typing import TypedDict
+      from langgraph.config import get_stream_writer
+      from langgraph.graph import StateGraph, START
+
+      class State(TypedDict):
+          query: str
+          answer: str
+
+      def node(state: State):
+          writer = get_stream_writer()  # (1)!
+          writer({"custom_key": "Generating custom data inside node"}) # (2)!
+          return {"answer": "some data"}
+
+      graph = (
+          StateGraph(State)
+          .add_node(node)
+          .add_edge(START, "node")
+          .compile()
+      )
+      ```
+
+      1. Get the stream writer to send custom data.
+      2. Emit a custom key-value pair (e.g., progress update).
+
+=== "Python"
+
+    ```python
+    async for chunk in client.runs.stream(
+        thread_id,
+        assistant_id,
+        input={"query": "example"},
+        # highlight-next-line
+        stream_mode="custom"
+    ):
+        print(chunk.data)
+    ```
+
+=== "JavaScript"
+
+    ```js
+    const streamResponse = client.runs.stream(
+      threadID,
+      assistantID,
+      {
+        input: { query: "example" },
+        // highlight-next-line
+        streamMode: "custom"
+      }
+    );
+    for await (const chunk of streamResponse) {
+      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\": {\"query\": \"example\"},
+      \"stream_mode\": \"custom\"
+    }"
+    ```
+
+See [this guide](../../how-tos/streaming.md#stream-custom-data) for more examples.
+
+## Stream events
+
+To stream all events, including the state of the graph:
+
+=== "Python"
+
+    ```python
+    async for chunk in client.runs.stream(
+        thread_id,
+        assistant_id,
+        input={"topic": "ice cream"},
+        # highlight-next-line
+        stream_mode="events"
+    ):
+        print(chunk.data)
+    ```
+
+=== "JavaScript"
+
+    ```js
+    const streamResponse = client.runs.stream(
+      threadID,
+      assistantID,
+      {
+        input: { topic: "ice cream" },
+        // highlight-next-line
+        streamMode: "events"
+      }
+    );
+    for await (const chunk of streamResponse) {
+      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\": {\"topic\": \"ice cream\"},
+      \"stream_mode\": \"events\"
+    }"
+    ```
@@ -0,0 +1,88 @@
+!!! info "Prerequisites"
+
+    - [LangGraph Studio Overview](../../../concepts/langgraph_studio.md)
+
+LangGraph Studio supports connecting to two types of graphs:
+
+- Graphs deployed on [LangGraph Platform](../../../cloud/quick_start.md)
+- Graphs running locally via the [LangGraph Server](../../../tutorials/langgraph-platform/local-server.md).
+
+## Deployed Application
+
+For applications that are deployed on LangGraph Platform, you can access Studio as part of that deployment. To do so, navigate to the deployment in LangGraph Platform within the LangSmith UI and click the "LangGraph Studio" button.
+
+This will load the Studio UI connected to your live deployment, allowing you to create, read, and update the [threads](../../concepts/threads.md), [assistants](../../../concepts/assistants.md), and [memory](../../../concepts//memory.md) in that deployment.
+
+## Local Development Server
+
+To test your locally running application using LangGraph Studio, ensure your application is set up following [this guide](https://langchain-ai.github.io/langgraph/cloud/deployment/setup/).
+
+Next, install the [LangGraph CLI](../../../concepts/langgraph_cli.md):
+
+```
+pip install -U "langgraph-cli[inmem]"
+```
+
+and run:
+
+```
+langgraph dev
+```
+
+!!! warning "Browser Compatibility"
+    Safari blocks `localhost` connections to Studio. To work around this, run the above command with `--tunnel` to access Studio via a secure tunnel.
+
+This will start the LangGraph Server locally, running in-memory. The server will run in watch mode, listening for and automatically restarting on code changes. Read this [reference](https://langchain-ai.github.io/langgraph/cloud/reference/cli/#dev) to learn about all the options for starting the API server.
+
+If successful, you will see the following logs:
+
+> Ready!
+>
+> - API: [http://localhost:2024](http://localhost:2024/)
+>
+> - Docs: http://localhost:2024/docs
+>
+> - LangGraph Studio Web UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
+
+Once running, you will automatically be directed to LangGraph Studio. You can manually access Studio by navigating to the following URL: `https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024`. If running your server at a different host or port, simply update the `baseUrl` to match.
+
+### (Optional) Attach a debugger
+
+For step-by-step debugging with breakpoints and variable inspection:
+
+```bash
+# Install debugpy package
+pip install debugpy
+
+# Start server with debugging enabled
+langgraph dev --debug-port 5678
+```
+
+Then attach your preferred debugger:
+
+=== "VS Code"
+Add this configuration to `launch.json`:
+`json
+    {
+      "name": "Attach to LangGraph",
+      "type": "debugpy",
+      "request": "attach",
+      "connect": {
+        "host": "0.0.0.0",
+        "port": 5678
+      }
+    }
+    `
+Specify the port number you chose in the previous step.
+
+=== "PyCharm" 1. Go to Run → Edit Configurations 2. Click + and select "Python Debug Server" 3. Set IDE host name: `localhost` 4. Set port: `5678` (or the port number you chose in the previous step) 5. Click "OK" and start debugging
+
+## Next steps
+
+See the following how-tos for more information on how to use Studio:
+
+- [How to manage Assistants](../invoke_studio.md)
+- [How to manage Threads](../threads_studio.md)
+- [How to create datasets](../datasets_studio.md)
+- [How to prompt engineer](../iterate_graph_studio.md)
+- [How to locally debug remote traces](../clone_traces_studio.md)
@@ -1,16 +0,0 @@
-# Test LangGraph Platform Deployment
-
-The LangGraph Studio UI connects directly to LangGraph Platform deployments.
-
-Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>...
-
-1. In the left-hand navigation panel, select `LangGraph Platform`. The `LangGraph Platform` view contains a list of existing LangGraph Platform deployments.
-1. Select an existing deployment to test with LangGraph Studio.
-1. In the top-right corner, select `Open LangGraph Studio`.
-1. [Invoke an assistant](./invoke_studio.md) or [view an existing thread](./threads_studio.md).
-
-The following video shows these exact steps being carried out:
-
-<video controls allowfullscreen="true" poster="../img/studio_usage_poster.png">
-    <source src="../img/studio_usage.mp4" type="video/mp4">
-</video>
@@ -1,32 +0,0 @@
-# LangGraph Studio With Local Deployment
-
-!!! warning "Browser Compatibility"
-    Safari blocks `localhost` connections to Studio. To work around this, start the server with `--tunnel` and you’ll be able to access Studio from Safari via a secure tunnel.
-
-## 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](https://langchain-ai.github.io/langgraph/cloud/deployment/setup/) 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](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](https://langchain-ai.github.io/langgraph/cloud/reference/cli/#up) 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):
-
-![LangGraph Studio](./img/studio_screenshot.png)
-
-## Use the Studio for Testing
-
-To learn about how to use the studio for testing, read the [LangGraph Studio how-tos](https://langchain-ai.github.io/langgraph/cloud/how-tos/#langgraph-studio).
@@ -121,7 +121,7 @@ curl --request POST \

 ## Webhook Payload

-LangGraph Cloud sends webhook notifications in the format of a [Run](../../concepts/langgraph_server.md/#runs). See the [API Reference](https://langchain-ai.github.io/langgraph/cloud/reference/api/api_ref.html#model/run) for details. The request payload includes run input, configuration, and other metadata in the `kwargs` field.
+LangGraph Cloud sends webhook notifications in the format of a [Run](../../cloud/concepts/runs.md). See the [API Reference](https://langchain-ai.github.io/langgraph/cloud/reference/api/api_ref.html#model/run) for details. The request payload includes run input, configuration, and other metadata in the `kwargs` field.

 ## Securing Webhooks

@@ -1,234 +1,164 @@
-# Quickstart: Deploy on LangGraph Cloud
+# Deployment quickstart

-!!! note "Prerequisites"
+This guide shows you how to set up and use LangGraph Platform for a cloud deployment.

-    Before you begin, ensure you have the following:
+## Prerequisites

-    - [GitHub account](https://github.com/)
-    - [LangSmith account](https://smith.langchain.com/)
+Before you begin, ensure you have the following:

-## Create a repository on GitHub
+- A [GitHub account](https://github.com/)
+- A [LangSmith account](https://smith.langchain.com/) – free to sign up

-To deploy a LangGraph application to **LangGraph Cloud**, your application code must reside in a GitHub repository. Both public and private repositories are supported.
+This quickstart uses the [pre-built Python ReAct agent template](https://github.com/langchain-ai/react-agent), which requires the following:

-You can deploy any [LangGraph Application](../concepts/application_structure.md) to LangGraph Cloud.
+- An API key for [Anthropic](https://console.anthropic.com/)
+- An API key for [Tavily](https://app.tavily.com/)

-For this guide, we'll use the pre-built Python [**ReAct Agent**](https://github.com/langchain-ai/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.
+## 1. 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. For this quickstart, use the [pre-built Python ReAct agent template](https://github.com/langchain-ai/react-agent) for your application:

 1. Go to the [ReAct Agent](https://github.com/langchain-ai/react-agent) repository.
-2. Fork the repository to your GitHub account by clicking the `Fork` button in the top right corner.
+1. Click the `Fork` button in the top right corner to fork the repository to your GitHub account.
+1. Click **Create fork**. 

-## Deploy to LangGraph Cloud
+## 2. Deploy to LangGraph Platform

-??? note "1. Log in to [LangSmith](https://smith.langchain.com/)"
+1. Log in to [LangSmith](https://smith.langchain.com/).
+1. In the left sidebar, select **LangGraph Platform**.
+1. Click the **+ New Deployment** button. A modal will open where you can fill in the required fields.
+1. If you are a first time user or adding a private repository that has not been previously connected, click the **Import from GitHub** button and follow the instructions to connect your GitHub account.
+1. Select your ReAct Agent repository.
+1. In the **Environment Variables** section, 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/).
+
+1. Click **Submit** to deploy.
+
+    This may take about 15 minutes to complete. You can check the status in the **Deployment details** view.
+
+## 3. Test your application in LangGraph Studio
+
+Once your application is deployed:
+
+1. Select the deployment you just created to view more details.
+1. Click the **LangGraph Studio** button in the top right corner.
+
+    LangGraph Studio will open to display your graph.

    <figure markdown="1">
-    [![Login to LangSmith](deployment/img/01_login.png){: style="max-height:300px"}](deployment/img/01_login.png)
+    [![image](deployment/img/09_langgraph_studio.png){: style="max-height:400px"}](deployment/img/09_langgraph_studio.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.
+        Sample graph run in LangGraph Studio.
    </figcaption>
    </figure>

+## 4. Get the API URL for your deployment

-??? note "2. Click on <em>LangGraph Platform</em> (the left sidebar)"
+1. In the **Deployment details** view in LangGraph, click the **API URL** to copy it to your clipboard.
+1. Click the `URL` to copy it to the clipboard.

-    <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>
+## 5. Test the API

-??? 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>
-
-
-## LangGraph 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>
-
-<figure markdown="1">
-[![image](deployment/img/09_langgraph_studio.png){: style="max-height:400px"}](deployment/img/09_langgraph_studio.png)
-<figcaption>
-    Sample graph run in LangGraph Studio.
-</figcaption>
-</figure>
-
-## 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.
-
-```shell
-export LANGSMITH_API_KEY=...
-```
+You can now test the API:

 === "Python SDK (Async)"

-    **Install the LangGraph Python SDK**
+    1. Install the LangGraph Python SDK:

-    ```shell
-    pip install langgraph-sdk
-    ```
+        ```shell
+        pip install langgraph-sdk
+        ```

-    **Send a message to the assistant (threadless run)**
+    1. Send a message to the assistant (threadless run):

-    ```python
-    from langgraph_sdk import get_client
+        ```python
+        from langgraph_sdk import get_client

-    client = get_client(url="your-deployment-url", api_key="your-langsmith-api-key")
+        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")
-    ```
+        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**
+    1. Install the LangGraph Python SDK:

-    ```shell
-    pip install langgraph-sdk
-    ```
+        ```shell
+        pip install langgraph-sdk
+        ```

-    **Send a message to the assistant (threadless run)**
+    1. Send a message to the assistant (threadless run):

-    ```python
-    from langgraph_sdk import get_sync_client
+        ```python
+        from langgraph_sdk import get_sync_client

-    client = get_sync_client(url="your-deployment-url", api_key="your-langsmith-api-key")
+        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")
-    ```
+        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**
+    1. Install the LangGraph JS SDK

-    ```shell
-    npm install @langchain/langgraph-sdk
-    ```
+        ```shell
+        npm install @langchain/langgraph-sdk
+        ```

-    **Send a message to the assistant (threadless run)**
+    1. Send a message to the assistant (threadless run):

-    ```js
-    const { Client } = await import("@langchain/langgraph-sdk");
+        ```js
+        const { Client } = await import("@langchain/langgraph-sdk");

-    const client = new Client({ apiUrl: "your-deployment-url", apiKey: "your-langsmith-api-key" });
+        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",
+        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");
        }
-    );
-
-    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"

@@ -253,20 +183,11 @@ export LANGSMITH_API_KEY=...

 ## 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:
+Congratulations! You have deployed an application using LangGraph Platform.

-### LangGraph Framework
+Here are some other resources to check out:

- **[LangGraph Tutorial](../tutorials/introduction.ipynb)**: Get started with LangGraph framework.
- **[LangGraph Concepts](../concepts/index.md)**: Learn the foundational concepts of LangGraph.
- **[LangGraph How-to Guides](../how-tos/index.md)**: Guides for common tasks with LangGraph.
- 
-### 📚 Learn More about LangGraph Platform
-
-Expand your knowledge with these resources:
-
- **[LangGraph Platform Concepts](../concepts/index.md#langgraph-platform)**: Understand the foundational concepts of the LangGraph Platform.
- **[LangGraph Platform How-to Guides](../how-tos/index.md#langgraph-platform)**: Discover step-by-step guides to build and deploy applications.
- **[Launch Local LangGraph Server](../tutorials/langgraph-platform/local-server.md)**: This quick start guide shows how to start a LangGraph Server locally for the **ReAct Agent** template. The steps are similar for other templates.
+- [LangGraph Platform overview](../concepts/langgraph_platform.md)
+- [Deployment options](../concepts/deployment_options.md)


@@ -18,6 +18,10 @@ A background run can execute for longer than 1 hour, but a client must reconnect

 Defaults to `3600`.

+## `BG_JOB_SHUTDOWN_GRACE_PERIOD_SECS`
+
+Specifies, in seconds, how long the server will wait for background jobs to finish after the queue receives a shutdown signal. After this period, the server will force termination. Defaults to `3600` seconds. Set this to ensure jobs have enough time to complete cleanly during shutdown. Added in `langgraph-api==0.2.16`.
+
 ## `DD_API_KEY`

 Specify `DD_API_KEY` (your [Datadog API Key](https://docs.datadoghq.com/account_management/api-app-keys/)) to automatically enable Datadog tracing for the deployment. Specify other [`DD_*` environment variables](https://ddtrace.readthedocs.io/en/stable/configuration.html) to configure the tracing instrumentation.
@@ -38,11 +42,11 @@ For deployments to LangGraph Cloud, this environment variable is set automatical

 ## `LANGSMITH_RUNS_ENDPOINTS`

-For [Bring Your Own Cloud (BYOC)](../../concepts/bring_your_own_cloud.md) deployments with [self-hosted LangSmith](https://docs.smith.langchain.com/self_hosting) only.
+For deployments with [self-hosted LangSmith](https://docs.smith.langchain.com/self_hosting) only.

-Set this environment variable to have a BYOC deployment send traces to a self-hosted LangSmith instance. The value of `LANGSMITH_RUNS_ENDPOINTS` is a JSON string: `{"<SELF_HOSTED_LANGSMITH_HOSTNAME>":"<LANGSMITH_API_KEY>"}`.
+Set this environment variable to have a deployment send traces to a self-hosted LangSmith instance. The value of `LANGSMITH_RUNS_ENDPOINTS` is a JSON string: `{"<SELF_HOSTED_LANGSMITH_HOSTNAME>":"<LANGSMITH_API_KEY>"}`.

-`SELF_HOSTED_LANGSMITH_HOSTNAME` is the hostname of the self-hosted LangSmith instance. It must be accessible to the BYOC deployment. `LANGSMITH_API_KEY` is a LangSmith API generated from the self-hosted LangSmith instance.
+`SELF_HOSTED_LANGSMITH_HOSTNAME` is the hostname of the self-hosted LangSmith instance. It must be accessible to the deployment. `LANGSMITH_API_KEY` is a LangSmith API generated from the self-hosted LangSmith instance.

 ## `LANGSMITH_TRACING`