[Docs] Replace Absolute External Links (#949)

2026-08-25 09:02:25 +02:00 · 2024-07-08 13:02:53 -07:00
parent 64a17599c1
commit 165e82eb33
12 changed files with 42 additions and 42 deletions
@@ -18,7 +18,7 @@ A thread contains the accumulated state of a group of runs. If a run is executed

 The state of a thread at a particular point in time is called a checkpoint.

-For more on threads and checkpoints, see this section of the [LangGraph conceptual guide](https://langchain-ai.github.io/langgraph/concepts/low_level/#checkpointer).
+For more on threads and checkpoints, see this section of the [LangGraph conceptual guide](../../concepts/low_level.md#checkpointer).

 The LangGraph Cloud API provides several endpoints for creating and managing threads and thread state. See the <a href="../reference/api/api_ref.html#tag/threadscreate" target="_blank">API reference</a> for more details.

@@ -92,7 +92,7 @@ See the [how-to guide](../how-tos/cloud_examples/stateless_runs.ipynb) for creat

 For all types of runs, langgraph cloud supports completion webhooks. When you create the run you can pass a webhook URL to be called when the completes (successfully or not). This is especially useful for background runs and cron jobs, as the webhook can give you an indication the run has completed and you can perform further actions for your appilcation.

-See this [how-to guide](https://langchain-ai.github.io/langgraph/cloud/how-tos/cloud_examples/webhooks/) to learn about how to use webhooks with LangGraph Cloud.
+See this [how-to guide](../how-tos/cloud_examples/webhooks.ipynb) to learn about how to use webhooks with LangGraph Cloud.

 ## Deployment

@@ -1,6 +1,6 @@
 ## Enqueue

-This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide](https://langchain-ai.github.io/langgraph/cloud/concepts/#double-texting).
+This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide](../concepts/index.md#double-texting).

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

@@ -12,7 +12,7 @@ You can then resume from that spot to continue.

 ### Code for your graph

-In this how-to we use a simple ReAct style hosted graph (you can see the full code for defining it [here](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/breakpoints/)). The important thing is that there are two nodes (one named `agent` that calls the LLM, and one named `action` that calls the tool), and a routing function from `agent` that determines whether to call `action` next or just end the graph run (the `action` node always calls the `agent` node after execution).
+In this how-to we use a simple ReAct style hosted graph (you can see the full code for defining it [here](../../how-tos/human_in_the_loop/breakpoints.ipynb)). The important thing is that there are two nodes (one named `agent` that calls the LLM, and one named `action` that calls the tool), and a routing function from `agent` that determines whether to call `action` next or just end the graph run (the `action` node always calls the `agent` node after execution).

 ### SDK Initialization

@@ -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](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/edit-graph-state/#build-the-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/edit-graph-state.ipynb#build-the-agent) if you want to. Once this graph is hosted, we are ready to invoke it and wait for user input. 

 ### SDK initialization

@@ -4,7 +4,7 @@ With Langgraph Cloud you have the ability to return to any of your prior states

 ## Setup

-We are not going to show the full code for the graph we are hosting, but you can see it [here](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/time-travel/#build-the-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#build-the-agent) if you want to. Once this graph is hosted, we are ready to invoke it and wait for user input. 

 ### SDK initialization

@@ -14,7 +14,7 @@ Luckily, LangGraph makes it possible to do similar things in a production way. T

 ## Setup

-We are not going to show the full code for the graph we are hosting, but you can see it [here](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/wait-user-input/#build-the-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/wait-user-input.ipynb#build-the-agent) if you want to. Once this graph is hosted, we are ready to invoke it and wait for user input. 

 ### SDK initialization

@@ -1,6 +1,6 @@
 ## Interrupt

-This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide](https://langchain-ai.github.io/langgraph/cloud/concepts/#double-texting).
+This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide](../concepts/index.md#double-texting).

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

@@ -1,6 +1,6 @@
 ## Reject

-This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide](https://langchain-ai.github.io/langgraph/cloud/concepts/#double-texting).
+This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide][double-texting].

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

@@ -1,6 +1,6 @@
 ## Rollback

-This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide](https://langchain-ai.github.io/langgraph/cloud/concepts/#double-texting).
+This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide][double-texting].

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

@@ -1,4 +1,5 @@
 # Quick Start
+
 This quick start guide will cover how to build a simple agent that can look up things on the internet. We will then deploy it to LangGraph Cloud, use the LangGraph Studio to visualize and test it out, and use the LangGraph SDK to interact with it.

 ## Set up requirements
@@ -9,10 +10,9 @@ This tutorial will use:
 - Tavily for the search engine - sign up and get an API key [here](https://app.tavily.com/)
 - LangSmith for hosting - sign up and get an API key [here](https://smith.langchain.com/)

-
 ## Set up local files

-1. Create a new application with the following directory and files:
+1.  Create a new application with the following directory and files:

        <my-app>/
        |-- agent.py            # code for your LangGraph agent
@@ -20,48 +20,49 @@ This tutorial will use:
        |-- langgraph.json      # configuration file for LangGraph
        |-- .env                # environment files with API keys

-2. The `agent.py` file should contain Python code for defining your graph. The following code is a simple example, the important thing is that at some point in your file you compile your graph and assign the compiled graph to a variable (in this case the `graph` variable). This example code uses `create_react_agent`, a prebuilt agent, read more about it [here](..//concepts/agentic_concepts.md#react-agent).
+2.  The `agent.py` file should contain Python code for defining your graph. The following code is a simple example, the important thing is that at some point in your file you compile your graph and assign the compiled graph to a variable (in this case the `graph` variable). This example code uses `create_react_agent`, a prebuilt agent, read more about it [here](..//concepts/agentic_concepts.md#react-agent).

    ```python
    from langchain_anthropic import ChatAnthropic
    from langchain_community.tools.tavily_search import TavilySearchResults
    from langgraph.prebuilt import create_react_agent
-   
+
    model = ChatAnthropic(model="claude-3-5-sonnet-20240620")
-   
+
    tools = [TavilySearchResults(max_results=2)]
-   
+
    graph = create_react_agent(model, tools)
    ```

-3. The `requirements.txt` file should contain any dependencies for your graph(s). In this case we only require four packages for our graph to run:
+3.  The `requirements.txt` file should contain any dependencies for your graph(s). In this case we only require four packages for our graph to run:

        langgraph
        langchain_anthropic
        tavily-python
        langchain_community

-4. The [`langgraph.json`][langgraph.json] file is a configuration file that describes what graph(s) you are going to host. In this case we only have one graph to host: the compiled `graph` object from `agent.py`.
+4.  The [`langgraph.json`][langgraph.json] file is a configuration file that describes what graph(s) you are going to host. In this case we only have one graph to host: the compiled `graph` object from `agent.py`.

    ```json
    {
-        "dependencies": ["."],
-        "graphs": {
-            "agent": "./agent.py:graph"
-        },
-        "env": ".env"
+      "dependencies": ["."],
+      "graphs": {
+        "agent": "./agent.py:graph"
+      },
+      "env": ".env"
    }
    ```

    Learn more about the LangGraph CLI configuration file [here](./reference/cli.md#configuration-file).
-5. The `.env` file should have any environment variables needed to run your graph. This will only be used for local testing, so if you are not testing locally you can skip this step. NOTE: if you do add this, you should NOT check this into git. For this graph, we need two environment variables:
+
+5.  The `.env` file should have any environment variables needed to run your graph. This will only be used for local testing, so if you are not testing locally you can skip this step. NOTE: if you do add this, you should NOT check this into git. For this graph, we need two environment variables:

    ```shell
    ANTHROPIC_API_KEY=...
    TAVILY_API_KEY=...
    ```

-Now that we have set everything up on our local file system, we are ready to host our graph. 
+Now that we have set everything up on our local file system, we are ready to host our graph.

 ## Test the graph build locally

@@ -124,13 +125,13 @@ Turn the `<my-app>` directory into a GitHub repo. You can use the GitHub CLI if

 ### Deploy from GitHub with LangGraph Cloud

-Once you have created your github repository with a Python file containing your compiled graph as well as a `langgraph.json` file containing the configuration for hosting your graph, you can head over to LangSmith and click on the 🚀 icon on the left navbar to create a new deployment. Then click the `+ New Deployment` button. 
+Once you have created your github repository with a Python file containing your compiled graph as well as a `langgraph.json` file containing the configuration for hosting your graph, you can head over to LangSmith and click on the 🚀 icon on the left navbar to create a new deployment. Then click the `+ New Deployment` button.

 ![Langsmith Workflow](./img/cloud_deployment.png)

-***If you have not deployed to LangGraph Cloud before:*** there will be a button that shows up saying Import from GitHub. You’ll need to follow that flow to connect LangGraph Cloud to GitHub.
+**_If you have not deployed to LangGraph Cloud before:_** there will be a button that shows up saying Import from GitHub. You’ll need to follow that flow to connect LangGraph Cloud to GitHub.

-***Once you have set up your GitHub connection:*** the new deployment page will look as follows:
+**_Once you have set up your GitHub connection:_** the new deployment page will look as follows:

 ![Deployment before being filled out](./deployment/img/deployment_page.png)

@@ -140,7 +141,7 @@ To deploy your application, you should do the following:
 2. Search for your repo to deploy in the search bar and select it
 3. Choose any name
 4. In the `LangGraph API config file` field, enter the path to your `langgraph.json` file (which in this case is just `langgraph.json`)
-5. For Git Reference, you can select either the git branch for the code you want to deploy, or the exact commit SHA. 
+5. For Git Reference, you can select either the git branch for the code you want to deploy, or the exact commit SHA.
 6. If your chain relies on environment variables, add those in. They will be propagated to the underlying server so your code can access them. In this case, we need `ANTHROPIC_API_KEY` and `TAVILY_API_KEY`.

 Putting this all together, you should have something as follows for your deployment details:
@@ -157,7 +158,7 @@ After your deployment is complete, your deployments page should look as follows:

 ![Deployed page](./deployment/img/deployed_page.png)

-You can see that by default, you get access to the `Trace Count` monitoring chart and `Recent Traces` run view. These are powered by LangSmith. 
+You can see that by default, you get access to the `Trace Count` monitoring chart and `Recent Traces` run view. These are powered by LangSmith.

 You can click on `All Charts` to view all monitoring info for your server, or click on `See tracing project` to get more information on an individual trace.

@@ -181,7 +182,7 @@ On this page you can test out your graph by passing in starting states and click

 ## Use with the SDK

-Once you have tested that your hosted graph works as expected using LangGraph Studio, you can start using your hosted graph all over your organization by using the LangGraph SDK. Let's see how we can access our hosted graph and execute our run from a python file. 
+Once you have tested that your hosted graph works as expected using LangGraph Studio, you can start using your hosted graph all over your organization by using the LangGraph SDK. Let's see how we can access our hosted graph and execute our run from a python file.

 First, make sure you have the SDK installed by calling `pip install langgraph_sdk`.

@@ -224,31 +225,30 @@ async for chunk in client.runs.stream(
        input=input,
        stream_mode="updates",
    ):
-    if chunk.data and chunk.event != "metadata": 
+    if chunk.data and chunk.event != "metadata":
        print(chunk.data)
 ```

    {'agent': {'messages': [{'content': "Hi Bagatur! It's nice to meet you. How can I assist you today?", 'additional_kwargs': {}, 'response_metadata': {'finish_reason': 'stop', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_9cb5d38cf7'}, 'type': 'ai', 'name': None, 'id': 'run-c89118b7-1b1e-42b9-a85d-c43fe99881cd', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}

-
 ## What's Next

 Congratulations! If you've worked your way through this tutorial you are well on your way to becoming a LangGraph Cloud expert. Here are some other resources to check out to help you out on the path to expertise:

 ### LangGraph Cloud How-tos

-If you want to learn more about streaming from hosted graphs, check out the Streaming [how-to guides](https://langchain-ai.github.io/langgraph/cloud/how-tos/#streaming).
+If you want to learn more about streaming from hosted graphs, check out the Streaming [how-to guides](how-tos/index.md#streaming).

-To learn more about double-texting and all the ways you can handle it in your application, read up on these [how-to guides](https://langchain-ai.github.io/langgraph/cloud/how-tos/#double-texting).
+To learn more about double-texting and all the ways you can handle it in your application, read up on these [how-to guides](how-tos/index.md#double-texting).

-To learn about how to include different human-in-the-loop behavior in your graph, take a look at [these how-tos](https://langchain-ai.github.io/langgraph/cloud/how-tos/#human-in-the-loop).
+To learn about how to include different human-in-the-loop behavior in your graph, take a look at [these how-tos](how-tos/index.md#human-in-the-loop).

 ### LangGraph Tutorials

 Before hosting, you have to write a graph to host. Here are some tutorials to get you more comfortable with writing LangGraph graphs and give you inspiration for the types of graphs you want to host.

-[This tutorial](https://langchain-ai.github.io/langgraph/tutorials/customer-support/customer-support/) walks you through how to write a customer support bot using LangGraph.
+[This tutorial](../tutorials/customer-support/customer-support.ipynb) walks you through how to write a customer support bot using LangGraph.

-If you are interested in writing a SQL agent, check out [this tutorial](https://langchain-ai.github.io/langgraph/tutorials/sql-agent/).
+If you are interested in writing a SQL agent, check out [this tutorial](../tutorials/sql-agent.ipynb).

-Check out the [LangGraph tutorials](https://langchain-ai.github.io/langgraph/tutorials/) page to read about more exciting use cases.
+Check out the [LangGraph tutorials](../tutorials/index.md) page to read about more exciting use cases.
@@ -1,8 +1,8 @@
 # Conceptual Guides

-In this guide we will explore the concepts behind build agentic and multi-agent systems with LangGraph. We assume you have already learned the basic covered in the [introduction tutorial](https://langchain-ai.github.io/langgraph/tutorials/introduction) and want to deepen your understanding of LangGraph's underlying design and inner workings.
+In this guide we will explore the concepts behind build agentic and multi-agent systems with LangGraph. We assume you have already learned the basic covered in the [introduction tutorial](../tutorials/introduction.ipynb) and want to deepen your understanding of LangGraph's underlying design and inner workings.

-There are three main parts to this concept guide. First, we'll discuss at a very high level what it means to be agentic. Next, we'll look at lower-level concepts in LangGraph that are core for understanding how to build your own agentic systems. Finally, we'll discuss common agentic patterns and how you can achieve those with LangGraph. These will be mostly conceptual guides - for more technical, hands-on guides see our [how-to guides](https://langchain-ai.github.io/langgraph/how-tos/)
+There are three main parts to this concept guide. First, we'll discuss at a very high level what it means to be agentic. Next, we'll look at lower-level concepts in LangGraph that are core for understanding how to build your own agentic systems. Finally, we'll discuss common agentic patterns and how you can achieve those with LangGraph. These will be mostly conceptual guides - for more technical, hands-on guides see our [how-to guides](../how-tos/index.md)


 LangGraph for Agentic Applications
@@ -1,6 +1,6 @@
 # Checkpoints

-You can [compile](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.MessageGraph.compile) any LangGraph workflow with a [CheckPointer](https://langchain-ai.github.io/langgraph/reference/checkpoints/#basecheckpointsaver) to give your agent "memory" by persisting its state. This permits things like:
+You can [compile][langgraph.graph.MessageGraph.compile] any LangGraph workflow with a [CheckPointer][basecheckpointsaver] to give your agent "memory" by persisting its state. This permits things like:

 - Remembering things across multiple interactions
 - Interrupting to wait for user input