mirror of
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[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>
This commit is contained in:
Lauren Hirata Singh
co-authored by
Tat Dat Duong
Sydney Runkle
William Fu-Hinthorn
Vadym Barda
Eugene Yurtsev
ccurme
Andrew Nguonly
David Asamu
infra
Arjun Natarajan
Tat Dat Duong
Sydney Runkle
William Fu-Hinthorn
Vadym Barda
Eugene Yurtsev
ccurme
Andrew Nguonly
David Asamu
infra
Arjun Natarajan
parent
909a4591a8
commit
3055c4b9cc
@@ -1,11 +1,11 @@
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# How to Deploy to Cloud SaaS (Beta)
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# How to Deploy to Cloud SaaS
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Before deploying, review the [conceptual guide for the Cloud SaaS](../../concepts/langgraph_cloud.md) deployment option.
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## Prerequisites
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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.
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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.
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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.
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## Create New Deployment
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# How to Deploy Self-Hosted Control Plane (Beta)
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# How to Deploy Self-Hosted Control Plane
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Before deploying, review the [conceptual guide for the Self-Hosted Control Plane](../../concepts/langgraph_self_hosted_control_plane.md) deployment option.
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@@ -6,7 +6,7 @@ Before deploying, review the [conceptual guide for the Self-Hosted Control Plane
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1. You are using Kubernetes.
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1. You have self-hosted LangSmith deployed.
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1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to [test your application locally](./test_locally.md).
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1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to [test your application locally](../../tutorials/langgraph-platform/local-server.md).
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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.
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1. `KEDA` is installed on your cluster.
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@@ -46,4 +46,4 @@ Before deploying, review the [conceptual guide for the Self-Hosted Control Plane
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1. In your `values.yaml` file, configure the `hostBackendImage` and `operatorImage` options (if you need to mirror images)
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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).
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1. You create a deployment from the [Control Plane UI](../../concepts/langgraph_control_plane.md#control-plane-ui).
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1. You create a deployment from the [control plane UI](../../concepts/langgraph_control_plane.md#control-plane-ui).
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@@ -1,10 +1,10 @@
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# How to Deploy Self-Hosted Data Plane (Beta)
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# How to Deploy Self-Hosted Data Plane
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Before deploying, review the [conceptual guide for the Self-Hosted Data Plane](../../concepts/langgraph_self_hosted_data_plane.md) deployment option.
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## Prerequisites
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1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to [test your application locally](./test_locally.md).
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1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to [test your application locally](../../tutorials/langgraph-platform/local-server.md).
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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.
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## Kubernetes
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@@ -46,7 +46,7 @@ Before deploying, review the [conceptual guide for the Self-Hosted Data Plane](.
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langgraph-dataplane-listener-7fccd788-wn2dx 0/1 Running 0 9s
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langgraph-dataplane-redis-0 0/1 ContainerCreating 0 9s
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1. You create a deployment from the [Control Plane UI](../../concepts/langgraph_control_plane.md#control-plane-ui).
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1. You create a deployment from the [control plane UI](../../concepts/langgraph_control_plane.md#control-plane-ui).
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## Amazon ECS
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@@ -1,6 +1,6 @@
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# How to Set Up a LangGraph Application for Deployment
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# How to Set Up a LangGraph Application with requirements.txt
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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.
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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.
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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.
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@@ -8,9 +8,9 @@ This walkthrough is based on [this repository](https://github.com/langchain-ai/l
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If you prefer using poetry for dependency management, check out [this how-to guide](./setup_pyproject.md) on using `pyproject.toml` for LangGraph Cloud.
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!!! tip "Setup with a Monorepo"
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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.
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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.
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The final repo structure will look something like this:
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The final repository structure will look something like this:
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```bash
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my-app/
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@@ -31,17 +31,17 @@ After each step, an example file directory is provided to demonstrate how code c
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## Specify Dependencies
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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).
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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).
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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:
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```
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langgraph>=0.2.56,<0.4.0
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langgraph-sdk>=0.1.53
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langgraph-checkpoint>=2.0.15,<3.0
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langchain-core>=0.2.38,<0.4.0
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langgraph>=0.3.27
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langgraph-sdk>=0.1.66
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langgraph-checkpoint>=2.0.23
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langchain-core>=0.2.38
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langsmith>=0.1.63
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orjson>=3.9.7
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orjson>=3.9.7,<3.10.17
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httpx>=0.25.0
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tenacity>=8.0.0
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uvicorn>=0.26.0
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@@ -49,7 +49,8 @@ sse-starlette>=2.1.0,<2.2.0
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uvloop>=0.18.0
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httptools>=0.5.0
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jsonschema-rs>=0.20.0
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structlog>=23.1.0
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structlog>=24.1.0
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cloudpickle>=3.0.0
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```
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Example `requirements.txt` file:
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@@ -94,9 +95,9 @@ my-app/
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## Define Graphs
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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).
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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).
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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):
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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):
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```python
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# my_agent/agent.py
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@@ -147,9 +148,9 @@ my-app/
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└── .env # environment variables
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```
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## Create LangGraph API Config
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## Create LangGraph Configuration File
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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.
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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.
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Example `langgraph.json` file:
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@@ -165,8 +166,8 @@ Example `langgraph.json` file:
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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>`).
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!!! warning "Configuration Location"
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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.
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!!! warning "Configuration File Location"
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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.
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Example file directory:
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@@ -187,4 +188,4 @@ my-app/
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## Next
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After you setup your project and place it in a github repo, it's time to [deploy your app](./cloud.md).
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After you setup your project and place it in a GitHub repository, it's time to [deploy your app](./cloud.md).
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@@ -1,10 +1,10 @@
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# How to Set Up a LangGraph.js Application for Deployment
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# How to Set Up a LangGraph.js Application
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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.
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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.
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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.
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The final repo structure will look something like this:
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The final repository structure will look something like this:
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```bash
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my-app/
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@@ -23,7 +23,7 @@ After each step, an example file directory is provided to demonstrate how code c
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## Specify Dependencies
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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).
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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).
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Example `package.json` file:
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@@ -78,7 +78,7 @@ my-app/
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## Define Graphs
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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).
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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).
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Here is an example `agent.ts`:
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@@ -176,7 +176,7 @@ my-app/
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## Create LangGraph API Config
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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.
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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.
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Example `langgraph.json` file:
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@@ -196,8 +196,8 @@ Note that the variable name of the `CompiledGraph` appears at the end of the val
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!!! info "Configuration Location"
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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.
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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.
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## Next
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After you setup your project and place it in a github repo, it's time to [deploy your app](./cloud.md).
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After you setup your project and place it in a GitHub repository, it's time to [deploy your app](./cloud.md).
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@@ -1,6 +1,6 @@
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# How to Set Up a LangGraph Application for Deployment
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# How to Set Up a LangGraph Application with pyproject.toml
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|
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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.
|
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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.
|
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|
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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.
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@@ -10,7 +10,7 @@ This walkthrough is based on [this repository](https://github.com/langchain-ai/l
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!!! tip "Setup with a Monorepo"
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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.
|
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|
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The final repo structure will look something like this:
|
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The final repository structure will look something like this:
|
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|
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```bash
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my-app/
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@@ -31,17 +31,17 @@ After each step, an example file directory is provided to demonstrate how code c
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## Specify Dependencies
|
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|
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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).
|
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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).
|
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|
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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:
|
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|
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```
|
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langgraph>=0.2.56,<0.4.0
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langgraph-sdk>=0.1.53
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langgraph-checkpoint>=2.0.15,<3.0
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langchain-core>=0.2.38,<0.4.0
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langgraph>=0.3.27
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langgraph-sdk>=0.1.66
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langgraph-checkpoint>=2.0.23
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langchain-core>=0.2.38
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langsmith>=0.1.63
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orjson>=3.9.7
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orjson>=3.9.7,<3.10.17
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httpx>=0.25.0
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tenacity>=8.0.0
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uvicorn>=0.26.0
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@@ -49,7 +49,8 @@ sse-starlette>=2.1.0,<2.2.0
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uvloop>=0.18.0
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httptools>=0.5.0
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jsonschema-rs>=0.20.0
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structlog>=23.1.0
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structlog>=24.1.0
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cloudpickle>=3.0.0
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```
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Example `pyproject.toml` file:
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@@ -68,7 +69,6 @@ python = ">=3.9"
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langgraph = "^0.2.0"
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langchain-fireworks = "^0.1.3"
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[build-system]
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requires = ["poetry-core"]
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build-backend = "poetry.core.masonry.api"
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@@ -97,15 +97,15 @@ Example file directory:
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```bash
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my-app/
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├── .env # file with environment variables
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├── .env # file with environment variables
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└── pyproject.toml
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```
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## Define Graphs
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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).
|
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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).
|
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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):
|
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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):
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```python
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# my_agent/agent.py
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@@ -156,9 +156,9 @@ my-app/
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└── pyproject.toml
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```
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## Create LangGraph API Config
|
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## Create LangGraph Configuration File
|
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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.
|
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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.
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Example `langgraph.json` file:
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@@ -174,8 +174,8 @@ Example `langgraph.json` file:
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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>`).
|
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|
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!!! warning "Configuration Location"
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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.
|
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!!! 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.
|
||||
Reference in New Issue
Block a user