mirror of
https://github.com/langchain-ai/langgraph.git
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fixing links
This commit is contained in:
Sydney Runkle
@@ -194,7 +194,7 @@ agent.invoke(
|
||||
)
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||||
```
|
||||
|
||||
1. The `InMemoryStore` is a store that stores data in memory. In a production setting, you would typically use a database or other persistent storage. Please review the [store documentation](../reference/stores.md) for more options. If you're deploying with **LangGraph Platform**, the platform will provide a production-ready store for you.
|
||||
1. The `InMemoryStore` is a store that stores data in memory. In a production setting, you would typically use a database or other persistent storage. Please review the [store documentation](../reference/store.md) for more options. If you're deploying with **LangGraph Platform**, the platform will provide a production-ready store for you.
|
||||
2. For this example, we write some sample data to the store using the `put` method. Please see the [BaseStore.put][langgraph.store.base.BaseStore.put] API reference for more details.
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||||
3. The first argument is the namespace. This is used to group related data together. In this case, we are using the `users` namespace to group user data.
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||||
4. A key within the namespace. This example uses a user ID for the key.
|
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@@ -245,7 +245,7 @@ agent.invoke(
|
||||
store.get(("users",), "user_123").value
|
||||
```
|
||||
|
||||
1. The `InMemoryStore` is a store that stores data in memory. In a production setting, you would typically use a database or other persistent storage. Please review the [store documentation](../reference/stores.md) for more options. If you're deploying with **LangGraph Platform**, the platform will provide a production-ready store for you.
|
||||
1. The `InMemoryStore` is a store that stores data in memory. In a production setting, you would typically use a database or other persistent storage. Please review the [store documentation](../reference/store.md) for more options. If you're deploying with **LangGraph Platform**, the platform will provide a production-ready store for you.
|
||||
2. The `UserInfo` class is a `TypedDict` that defines the structure of the user information. The LLM will use this to format the response according to the schema.
|
||||
3. The `save_user_info` function is a tool that allows an agent to update user information. This could be useful for a chat application where the user wants to update their profile information.
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4. The `get_store` function is used to access the store. You can call it from anywhere in your code, including tools and prompts. This function returns the store that was passed to the agent when it was created.
|
||||
|
||||
@@ -1,7 +1,44 @@
|
||||
[//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
|
||||
# Community Agents
|
||||
|
||||
If you’re looking for other prebuilt libraries, explore the community-built options
|
||||
below. These libraries can extend LangGraph's functionality in various ways.
|
||||
|
||||
## 📚 Available Libraries
|
||||
|
||||
[//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
|
||||
| Name | GitHub URL | Description | Weekly Downloads | Stars |
|
||||
| --- | --- | --- | --- | --- |
|
||||
| **trustcall** | [hinthornw/trustcall](https://github.com/hinthornw/trustcall) | Tenacious tool calling built on LangGraph. | -12345 | 
|
||||
| **breeze-agent** | [andrestorres123/breeze-agent](https://github.com/andrestorres123/breeze-agent) | A streamlined research system built inspired on STORM and built on LangGraph. | -12345 | 
|
||||
| **langgraph-supervisor** | [langchain-ai/langgraph-supervisor-py](https://github.com/langchain-ai/langgraph-supervisor-py) | Build supervisor multi-agent systems with LangGraph. | -12345 | 
|
||||
| **langmem** | [langchain-ai/langmem](https://github.com/langchain-ai/langmem) | Build agents that learn and adapt from interactions over time. | -12345 | 
|
||||
| **langchain-mcp-adapters** | [langchain-ai/langchain-mcp-adapters](https://github.com/langchain-ai/langchain-mcp-adapters) | Make Anthropic Model Context Protocol (MCP) tools compatible with LangGraph agents. | -12345 | 
|
||||
| **open-deep-research** | [langchain-ai/open_deep_research](https://github.com/langchain-ai/open_deep_research) | Open source assistant for iterative web research and report writing. | -12345 | 
|
||||
| **langgraph-swarm** | [langchain-ai/langgraph-swarm-py](https://github.com/langchain-ai/langgraph-swarm-py) | Build swarm-style multi-agent systems using LangGraph. | -12345 | 
|
||||
| **delve-taxonomy-generator** | [andrestorres123/delve](https://github.com/andrestorres123/delve) | A taxonomy generator for unstructured data | -12345 | 
|
||||
| **nodeology** | [xyin-anl/Nodeology](https://github.com/xyin-anl/Nodeology) | Enable researcher to build scientific workflows easily with simplified interface. | -12345 | 
|
||||
| **langgraph-bigtool** | [langchain-ai/langgraph-bigtool](https://github.com/langchain-ai/langgraph-bigtool) | Build LangGraph agents with large numbers of tools. | -12345 | 
|
||||
| **ai-data-science-team** | [business-science/ai-data-science-team](https://github.com/business-science/ai-data-science-team) | An AI-powered data science team of agents to help you perform common data science tasks 10X faster. | -12345 | 
|
||||
| **langgraph-reflection** | [langchain-ai/langgraph-reflection](https://github.com/langchain-ai/langgraph-reflection) | LangGraph agent that runs a reflection step. | -12345 | 
|
||||
| **langgraph-codeact** | [langchain-ai/langgraph-codeact](https://github.com/langchain-ai/langgraph-codeact) | LangGraph implementation of CodeAct agent that generates and executes code instead of tool calling. | -12345 | 
|
||||
|
||||
## ✨ Contributing Your Library
|
||||
|
||||
Have you built an awesome open-source library using LangGraph? We'd love to feature
|
||||
your project on the official LangGraph documentation pages! 🏆
|
||||
|
||||
To share your project, simply open a Pull Request adding an entry for your package in our [packages.yml](https://github.com/langchain-ai/langgraph/blob/main/docs/_scripts/third_party_page/packages.yml) file.
|
||||
|
||||
[//]: # (This file is stub. Do not edit this file directly!)
|
||||
[//]: # (1. Update the `packages.yml` file in the `docs/_scripts/third_party_page` directory.)
|
||||
[//]: # (2. From the /docs directory, run `make build-prebuilt` to generate an updated version of this file for testing locally.)
|
||||
**Guidelines**
|
||||
|
||||
- Your repo must be distributed as an installable package (e.g., PyPI for Python, npm
|
||||
for JavaScript/TypeScript, etc.) 📦
|
||||
- The repo should either use the Graph API (exposing a `StateGraph` instance) or
|
||||
the Functional API (exposing an `entrypoint`).
|
||||
- The package must include documentation (e.g., a `README.md` or docs site)
|
||||
explaining how to use it.
|
||||
|
||||
We'll review your contribution and merge it in!
|
||||
|
||||
Thanks for contributing! 🚀
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||||
|
||||
@@ -1,7 +1,7 @@
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# Running agents
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||||
|
||||
|
||||
Agents support both synchronous and asynchronous execution using either `.invoke()` / `await .invoke()` for full responses, or `.stream()` / `.astream()` for **incremental** [streaming](#streaming) output. This section explains how to provide input, interpret output, enable streaming, and control execution limits.
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||||
Agents support both synchronous and asynchronous execution using either `.invoke()` / `await .invoke()` for full responses, or `.stream()` / `.astream()` for **incremental** [streaming](streaming.md) output. This section explains how to provide input, interpret output, enable streaming, and control execution limits.
|
||||
|
||||
|
||||
## Basic usage
|
||||
|
||||
@@ -22,7 +22,7 @@ To support this, LangGraph Studio, in combination with LangSmith, allows you to
|
||||
|
||||
First navigate to the LangSmith trace. Here you should see a button to "Run in Studio".
|
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|
||||
{width=1200}
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{width=1200}
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This will prompt you to enter the url that your locally running agent is accessible at. Once provided, select "Clone thread locally". If you have multiple graphs in your agent, you will also be prompted to select a graph to clone this thread under.
|
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|
||||
|
||||
@@ -119,11 +119,11 @@ With this set up, running your graph and viewing in LangGraph Studio will result
|
||||
|
||||
**Note the configuration icon in the top right corner of the `call_model` node**:
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|
||||
{width=1200}
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{width=1200}
|
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|
||||
Clicking this icon will open a modal where you can edit the configuration for all of the fields associated with the `call_model` node. From here, you can save your changes and apply them to the graph. Note that these values reflect the currently active assistant, and saving will update the assistant with the new values.
|
||||
|
||||
{width=1200}
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||||
{width=1200}
|
||||
|
||||
### Playground
|
||||
|
||||
@@ -133,7 +133,7 @@ LangGraph Studio also supports prompt engineering through an integration with th
|
||||
2. Within the thread log, any nodes that have made an LLM call will have a "View LLM Runs" button. Clicking this will open a popover with the LLM runs for that node.
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||||
3. Select the LLM run you want to edit. This will open the LangSmith Playground with the selected LLM run.
|
||||
|
||||
{width=1200}
|
||||
{width=1200}
|
||||
|
||||
From here you can edit the prompt, test different model configurations and re-run just this LLM call without having to re-run the entire graph. When you are happy with your changes, you can copy the updated prompt back into your graph.
|
||||
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
# How to stream events
|
||||
|
||||
!!! info "Prerequisites"
|
||||
* [Streaming](../../concepts/streaming.md#streaming-llm-tokens-and-events-astream_events)
|
||||
* [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.
|
||||
|
||||
|
||||
@@ -476,7 +476,7 @@ The `useStream()` hook provides several callback options to help you respond to
|
||||
- `onError`: Called when an error occurs.
|
||||
- `onFinish`: Called when the stream is finished.
|
||||
- `onUpdateEvent`: Called when an update event is received.
|
||||
- `onCustomEvent`: Called when a custom event is received. See [Custom events](../../concepts/streaming.md#custom) to learn how to stream custom events.
|
||||
- `onCustomEvent`: Called when a custom event is received. See [Custom events](../../how-tos/streaming.ipynb#custom) to learn how to stream custom events.
|
||||
- `onMetadataEvent`: Called when a metadata event is received, which contains the Run ID and Thread ID.
|
||||
|
||||
## Learn More
|
||||
|
||||
@@ -7,19 +7,17 @@
|
||||
|
||||
## Versions
|
||||
|
||||
There are two versions of the self-hosted deployment: [Self-Hosted Enterprise](./deployment_options.md#self-hosted-enterprise) and [Self-Hosted Lite](./deployment_options.md#self-hosted-lite).
|
||||
There are two versions of the self-hosted deployment: [Self-Hosted Data Plane](./deployment_options.md#self-hosted-data-plane) and [Self-Hosted Control Plane](./deployment_options.md#self-hosted-control-plane).
|
||||
|
||||
### Self-Hosted Lite
|
||||
### Self-Hosted Data Plane
|
||||
|
||||
The Self-Hosted Lite version is a limited version of LangGraph Platform that you can run locally or in a self-hosted manner (up to 1 million nodes executed per year).
|
||||
The [Self-Hosted Data Plane](./langgraph_self_hosted_data_plane.md) deployment option is a "hybrid" model for deployment where we manage the [control plane](./langgraph_control_plane.md) in our cloud and you manage the [data plane](./langgraph_data_plane.md) in your cloud. This option provides a way to securely manage your data plane infrastructure, while offloading control plane management to us.
|
||||
|
||||
When using the Self-Hosted Lite version, you authenticate with a [LangSmith](https://smith.langchain.com/) API key.
|
||||
When using the Self-Hosted Data Plane version, you authenticate with a [LangSmith](https://smith.langchain.com/) API key.
|
||||
|
||||
### Self-Hosted Enterprise
|
||||
### Self-Hosted Control Plane
|
||||
|
||||
The Self-Hosted Enterprise version is the full version of LangGraph Platform.
|
||||
|
||||
To use the Self-Hosted Enterprise version, you must acquire a license key that you will need to pass in when running the Docker image. To acquire a license key, please email sales@langchain.dev.
|
||||
The [Self-Hosted Control Plane](./langgraph_self_hosted_control_plane.md) deployment option is a fully self-hosted model for deployment where you manage the [control plane](./langgraph_control_plane.md) and [data plane](./langgraph_data_plane.md) in your cloud. This option give you full control and responsibility of the control plane and data plane infrastructure.
|
||||
|
||||
## Requirements
|
||||
|
||||
|
||||
@@ -39,7 +39,7 @@ You will eventually need to pass in the following environment variables to the L
|
||||
|
||||
`<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**.
|
||||
|
||||
- `LANGSMITH_API_KEY`: (If using [Self-Hosted Lite](../concepts/deployment_options.md#self-hosted-lite)) LangSmith API key. This will be used to authenticate ONCE at server start up.
|
||||
- `LANGSMITH_API_KEY`: (If using [Self-Hosted Data Plane](../concepts/deployment_options.md#self-hosted-data-plane)) LangSmith API key. This will be used to authenticate ONCE at server start up.
|
||||
- `LANGGRAPH_CLOUD_LICENSE_KEY`: (If using [Self-Hosted Enterprise](../concepts/deployment_options.md#self-hosted-enterprise)) LangGraph Platform license key. This will be used to authenticate ONCE at server start up.
|
||||
- `LANGCHAIN_ENDPOINT`: To send traces to a [self-hosted LangSmith](https://docs.smith.langchain.com/self_hosting) instance, set `LANGCHAIN_ENDPOINT` to the hostname of the self-hosted LangSmith instance.
|
||||
|
||||
|
||||
@@ -249,7 +249,7 @@ LangGraph Platform supports multiple types of runs besides streaming runs.
|
||||
- [How to create cron jobs](../cloud/how-tos/cron_jobs.md)
|
||||
- [How to create stateless runs](../cloud/how-tos/stateless_runs.md)
|
||||
|
||||
### Streaming
|
||||
### Streaming {#streaming_1}
|
||||
|
||||
Streaming the results of your LLM application is vital for ensuring a good user experience, especially when your graph may call multiple models and take a long time to fully complete a run. Read about how to stream values from your graph in these how to guides:
|
||||
|
||||
@@ -267,7 +267,7 @@ With LangGraph Platform you can integrate LangGraph agents into your React appli
|
||||
- [How to integrate LangGraph into your React application](../cloud/how-tos/use_stream_react.md)
|
||||
- [How to implement Generative User Interfaces with LangGraph](../cloud/how-tos/generative_ui_react.md)
|
||||
|
||||
### Human-in-the-loop
|
||||
### Human-in-the-loop {#human-in-the-loop-1}
|
||||
|
||||
When designing complex graphs, relying entirely on the LLM for decision-making can be risky, particularly when it involves tools that interact with files, APIs, or databases. These interactions may lead to unintended data access or modifications, depending on the use case. To mitigate these risks, LangGraph allows you to integrate human-in-the-loop behavior, ensuring your LLM applications operate as intended without undesirable outcomes.
|
||||
|
||||
|
||||
@@ -193,7 +193,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Using in `create_react_agent`\n",
|
||||
"## Using in `create_react_agent` {#using-in-create-react-agent}\n",
|
||||
"\n",
|
||||
"Add semantic search to your tool calling agent by injecting the store in the `prompt` function. You can also use the store in a tool to let your agent manually store or search for memories."
|
||||
]
|
||||
|
||||
@@ -139,7 +139,7 @@ langgraph dev --no-browser
|
||||
}
|
||||
```
|
||||
|
||||
Now let's try to chat with our bot. If we've implemented authentication correctly, we should only be able to access the bot if we provide a valid token in the request header. Users will still, however, be able to access each other's resources until we add [resource authorization handlers](../../concepts/auth.md#resource-authorization) in the next section of our tutorial.
|
||||
Now let's try to chat with our bot. If we've implemented authentication correctly, we should only be able to access the bot if we provide a valid token in the request header. Users will still, however, be able to access each other's resources until we add [resource authorization handlers](../../concepts/auth.md#resource-specific-handlers) in the next section of our tutorial.
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -6,7 +6,7 @@
|
||||
2. Resource Authorization (you are here) - Let users have private conversations
|
||||
3. [Production Auth](add_auth_server.md) - Add real user accounts and validate using OAuth2
|
||||
|
||||
In this tutorial, we will extend our chatbot to give each user their own private conversations. We'll add [resource-level access control](../../concepts/auth.md#resource-level-access-control) so users can only see their own threads.
|
||||
In this tutorial, we will extend our chatbot to give each user their own private conversations. We'll add [resource-level access control](../../concepts/auth.md#single-owner-resources) so users can only see their own threads.
|
||||
|
||||
|
||||
|
||||
@@ -35,7 +35,7 @@ langgraph dev --no-browser
|
||||
|
||||
Recall that in the last tutorial, the [`Auth`](../../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.Auth) object let us register an [authentication function](../../concepts/auth.md#authentication), which the LangGraph platform uses to validate the bearer tokens in incoming requests. Now we'll use it to register an **authorization** handler.
|
||||
|
||||
Authorization handlers are functions that run **after** authentication succeeds. These handlers can add [metadata](../../concepts/auth.md#resource-metadata) to resources (like who owns them) and filter what each user can see.
|
||||
Authorization handlers are functions that run **after** authentication succeeds. These handlers can add [metadata](../../concepts/auth.md#filter-operations) to resources (like who owns them) and filter what each user can see.
|
||||
|
||||
Let's update our `src/security/auth.py` and add one authorization handler that is run on every request:
|
||||
|
||||
@@ -211,7 +211,7 @@ This means:
|
||||
|
||||
## Adding scoped authorization handlers {#scoped-authorization}
|
||||
|
||||
The broad `@auth.on` handler matches on all [authorization events](../../concepts/auth.md#authorization-events). This is concise, but it means the contents of the `value` dict are not well-scoped, and we apply the same user-level access control to every resource. If we want to be more fine-grained, we can also control specific actions on resources.
|
||||
The broad `@auth.on` handler matches on all [authorization events](../../concepts/auth.md#supported-resources). This is concise, but it means the contents of the `value` dict are not well-scoped, and we apply the same user-level access control to every resource. If we want to be more fine-grained, we can also control specific actions on resources.
|
||||
|
||||
Update `src/security/auth.py` to add handlers for specific resource types:
|
||||
|
||||
@@ -290,7 +290,7 @@ Notice that instead of one global handler, we now have specific handlers for:
|
||||
2. Reading threads
|
||||
3. Accessing assistants
|
||||
|
||||
The first three of these match specific **actions** on each resource (see [resource actions](../../concepts/auth.md#resource-actions)), while the last one (`@auth.on.assistants`) matches _any_ action on the `assistants` resource. For each request, LangGraph will run the most specific handler that matches the resource and action being accessed. This means that the four handlers above will run rather than the broadly scoped "`@auth.on`" handler.
|
||||
The first three of these match specific **actions** on each resource (see [resource actions](../../concepts/auth.md#resource-specific-handlers)), while the last one (`@auth.on.assistants`) matches _any_ action on the `assistants` resource. For each request, LangGraph will run the most specific handler that matches the resource and action being accessed. This means that the four handlers above will run rather than the broadly scoped "`@auth.on`" handler.
|
||||
|
||||
Try adding the following test code to your test file:
|
||||
|
||||
|
||||
@@ -1627,7 +1627,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 83,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -1697,7 +1697,7 @@
|
||||
"from IPython.display import Markdown\n",
|
||||
"\n",
|
||||
"# We will down-header the sections to create less confusion in this notebook\n",
|
||||
"Markdown(article.replace(\"\\n#\", \"\\n##\"))"
|
||||
"Markdown(article.replace(\"\\n#\", \"\\n##\"))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -104,23 +104,19 @@ nav:
|
||||
- How-to Guides:
|
||||
- how-tos/index.md
|
||||
- LangGraph:
|
||||
- LangGraph: how-tos#langgraph
|
||||
- Graph API Basics:
|
||||
- Graph API Basics: how-tos#graph-api-basics
|
||||
- how-tos/state-reducers.ipynb
|
||||
- how-tos/sequence.ipynb
|
||||
- how-tos/branching.ipynb
|
||||
- how-tos/recursion-limit.ipynb
|
||||
- how-tos/visualization.ipynb
|
||||
- Controllability:
|
||||
- Controllability: how-tos#controllability
|
||||
- how-tos/map-reduce.ipynb
|
||||
- how-tos/command.ipynb
|
||||
- how-tos/configuration.ipynb
|
||||
- how-tos/node-retries.ipynb
|
||||
- how-tos/return-when-recursion-limit-hits.ipynb
|
||||
- Persistence:
|
||||
- Persistence: how-tos#persistence
|
||||
- how-tos/persistence.ipynb
|
||||
- how-tos/subgraph-persistence.ipynb
|
||||
- how-tos/cross-thread-persistence.ipynb
|
||||
@@ -130,13 +126,11 @@ nav:
|
||||
- how-tos/persistence-functional.ipynb
|
||||
- how-tos/cross-thread-persistence-functional.ipynb
|
||||
- Memory:
|
||||
- Memory: how-tos#memory
|
||||
- how-tos/memory/manage-conversation-history.ipynb
|
||||
- how-tos/memory/delete-messages.ipynb
|
||||
- how-tos/memory/add-summary-conversation-history.ipynb
|
||||
- how-tos/memory/semantic-search.ipynb
|
||||
- Human-in-the-loop:
|
||||
- Human-in-the-loop: how-tos#human-in-the-loop
|
||||
- how-tos/human_in_the_loop/breakpoints.ipynb
|
||||
- how-tos/human_in_the_loop/dynamic_breakpoints.ipynb
|
||||
- how-tos/human_in_the_loop/edit-graph-state.ipynb
|
||||
@@ -146,7 +140,6 @@ nav:
|
||||
- how-tos/wait-user-input-functional.ipynb
|
||||
- how-tos/review-tool-calls-functional.ipynb
|
||||
- Streaming:
|
||||
- Streaming: how-tos#streaming
|
||||
- how-tos/streaming.ipynb
|
||||
- how-tos/streaming-tokens.ipynb
|
||||
- how-tos/streaming-specific-nodes.ipynb
|
||||
@@ -154,7 +147,6 @@ nav:
|
||||
- how-tos/streaming-subgraphs.ipynb
|
||||
- how-tos/disable-streaming.ipynb
|
||||
- Tool calling:
|
||||
- Tool calling: how-tos#tool-calling
|
||||
- how-tos/tool-calling.ipynb
|
||||
- how-tos/tool-calling-errors.ipynb
|
||||
- how-tos/pass-run-time-values-to-tools.ipynb
|
||||
@@ -162,31 +154,26 @@ nav:
|
||||
- how-tos/pass-config-to-tools.ipynb
|
||||
- how-tos/many-tools.ipynb
|
||||
- Subgraphs:
|
||||
- Subgraphs: how-tos#subgraphs
|
||||
- how-tos/subgraph.ipynb
|
||||
- how-tos/subgraphs-manage-state.ipynb
|
||||
- how-tos/subgraph-transform-state.ipynb
|
||||
- Multi-agent:
|
||||
- Multi-agent: how-tos#multi-agent
|
||||
- how-tos/agent-handoffs.ipynb
|
||||
- how-tos/multi-agent-network.ipynb
|
||||
- how-tos/multi-agent-multi-turn-convo.ipynb
|
||||
- how-tos/multi-agent-network-functional.ipynb
|
||||
- how-tos/multi-agent-multi-turn-convo-functional.ipynb
|
||||
- State Management:
|
||||
- State Management: how-tos#state-management
|
||||
- how-tos/state-model.ipynb
|
||||
- how-tos/input_output_schema.ipynb
|
||||
- how-tos/pass_private_state.ipynb
|
||||
- Other:
|
||||
- Other: how-tos#other
|
||||
- how-tos/async.ipynb
|
||||
- how-tos/react-agent-structured-output.ipynb
|
||||
- how-tos/run-id-langsmith.ipynb
|
||||
- how-tos/autogen-integration.ipynb
|
||||
- how-tos/autogen-integration-functional.ipynb
|
||||
- Prebuilt ReAct Agent:
|
||||
- Prebuilt ReAct Agent: how-tos#prebuilt-react-agent
|
||||
- how-tos/create-react-agent.ipynb
|
||||
- how-tos/create-react-agent-memory.ipynb
|
||||
- how-tos/create-react-agent-system-prompt.ipynb
|
||||
@@ -196,9 +183,7 @@ nav:
|
||||
- how-tos/react-agent-from-scratch.ipynb
|
||||
- how-tos/react-agent-from-scratch-functional.ipynb
|
||||
- LangGraph Platform:
|
||||
- LangGraph Platform: how-tos#langgraph-platform
|
||||
- Application Structure:
|
||||
- Application Structure: how-tos#application-structure
|
||||
- cloud/deployment/setup.md
|
||||
- cloud/deployment/setup_pyproject.md
|
||||
- cloud/deployment/setup_javascript.md
|
||||
@@ -208,7 +193,6 @@ nav:
|
||||
- cloud/deployment/graph_rebuild.md
|
||||
- how-tos/autogen-langgraph-platform.ipynb
|
||||
- Deployment:
|
||||
- Deployment: how-tos#deployment
|
||||
- cloud/deployment/cloud.md
|
||||
- cloud/deployment/self_hosted_data_plane.md
|
||||
- cloud/deployment/self_hosted_control_plane.md
|
||||
@@ -219,25 +203,20 @@ nav:
|
||||
- Data Management:
|
||||
- how-tos/ttl/configure_ttl.md
|
||||
- Authentication & Access Control:
|
||||
- Authentication & Access Control: how-tos#authentication-access-control
|
||||
- how-tos/auth/custom_auth.md
|
||||
- how-tos/auth/openapi_security.md
|
||||
- Assistants:
|
||||
- Assistants: how-tos#assistants
|
||||
- cloud/how-tos/configuration_cloud.md
|
||||
- cloud/how-tos/assistant_versioning.md
|
||||
- Threads:
|
||||
- Threads: how-tos#threads
|
||||
- cloud/how-tos/copy_threads.md
|
||||
- cloud/how-tos/check_thread_status.md
|
||||
- Runs:
|
||||
- Runs: how-tos#runs
|
||||
- cloud/how-tos/background_run.md
|
||||
- cloud/how-tos/same-thread.md
|
||||
- cloud/how-tos/cron_jobs.md
|
||||
- cloud/how-tos/stateless_runs.md
|
||||
- Streaming:
|
||||
- Streaming: how-tos#streaming_1
|
||||
- cloud/how-tos/stream_values.md
|
||||
- cloud/how-tos/stream_updates.md
|
||||
- cloud/how-tos/stream_messages.md
|
||||
@@ -247,14 +226,12 @@ nav:
|
||||
- cloud/how-tos/use_stream_react.md
|
||||
- cloud/how-tos/generative_ui_react.md
|
||||
- Human-in-the-loop:
|
||||
- Human-in-the-loop: how-tos#human-in-the-loop_1
|
||||
- cloud/how-tos/human_in_the_loop_breakpoint.md
|
||||
- cloud/how-tos/human_in_the_loop_user_input.md
|
||||
- cloud/how-tos/human_in_the_loop_edit_state.md
|
||||
- cloud/how-tos/human_in_the_loop_time_travel.md
|
||||
- cloud/how-tos/human_in_the_loop_review_tool_calls.md
|
||||
- Double-texting:
|
||||
- Double-texting: how-tos#double-texting
|
||||
- cloud/how-tos/interrupt_concurrent.md
|
||||
- cloud/how-tos/rollback_concurrent.md
|
||||
- cloud/how-tos/reject_concurrent.md
|
||||
@@ -264,12 +241,10 @@ nav:
|
||||
- Cron Jobs:
|
||||
- cloud/how-tos/cron_jobs.md
|
||||
- Modifying the API:
|
||||
- Modifying the API: how-tos#modifying-the-api
|
||||
- how-tos/http/custom_lifespan.md
|
||||
- how-tos/http/custom_middleware.md
|
||||
- how-tos/http/custom_routes.md
|
||||
- LangGraph Studio:
|
||||
- LangGraph Studio: how-tos#langgraph-studio
|
||||
- cloud/how-tos/test_deployment.md
|
||||
- cloud/how-tos/test_local_deployment.md
|
||||
- cloud/how-tos/invoke_studio.md
|
||||
@@ -281,7 +256,6 @@ nav:
|
||||
- Concepts:
|
||||
- concepts/index.md
|
||||
- LangGraph:
|
||||
- LangGraph: concepts#langgraph
|
||||
- concepts/high_level.md
|
||||
- concepts/low_level.md
|
||||
- concepts/agentic_concepts.md
|
||||
@@ -297,9 +271,7 @@ nav:
|
||||
- concepts/durable_execution.md
|
||||
- concepts/pregel.md
|
||||
- LangGraph Platform:
|
||||
- LangGraph Platform: concepts#langgraph-platform
|
||||
- High Level:
|
||||
- High Level: concepts#high-level
|
||||
- concepts/langgraph_platform.md
|
||||
- concepts/platform_architecture.md
|
||||
- concepts/scalability_and_resilience.md
|
||||
@@ -308,7 +280,6 @@ nav:
|
||||
- concepts/plans.md
|
||||
- concepts/template_applications.md
|
||||
- Components:
|
||||
- Components: concepts#components
|
||||
- concepts/langgraph_control_plane.md
|
||||
- concepts/langgraph_data_plane.md
|
||||
- concepts/langgraph_server.md
|
||||
@@ -317,13 +288,11 @@ nav:
|
||||
- concepts/sdk.md
|
||||
- how-tos/use-remote-graph.md
|
||||
- LangGraph Server:
|
||||
- LangGraph Server: concepts#langgraph-server
|
||||
- concepts/application_structure.md
|
||||
- concepts/assistants.md
|
||||
- concepts/double_texting.md
|
||||
- concepts/auth.md
|
||||
- Deployment Options:
|
||||
- Deployment Options: concepts#deployment-options
|
||||
- concepts/langgraph_cloud.md
|
||||
- concepts/langgraph_self_hosted_data_plane.md
|
||||
- concepts/langgraph_self_hosted_control_plane.md
|
||||
@@ -332,18 +301,15 @@ nav:
|
||||
- Tutorials:
|
||||
- tutorials/index.md
|
||||
- Quick Start:
|
||||
- Quick Start: tutorials#quick-start
|
||||
- tutorials/introduction.ipynb
|
||||
- tutorials/workflows/index.md
|
||||
- tutorials/langgraph-platform/local-server.md
|
||||
- cloud/quick_start.md
|
||||
- Chatbots:
|
||||
- Chatbots: tutorials#chatbots
|
||||
- tutorials/customer-support/customer-support.ipynb
|
||||
- tutorials/chatbots/information-gather-prompting.ipynb
|
||||
- tutorials/code_assistant/langgraph_code_assistant.ipynb
|
||||
- RAG:
|
||||
- RAG: tutorials#rag
|
||||
- tutorials/rag/langgraph_adaptive_rag.ipynb
|
||||
- tutorials/rag/langgraph_adaptive_rag_local.ipynb
|
||||
- tutorials/rag/langgraph_agentic_rag.ipynb
|
||||
@@ -353,37 +319,30 @@ nav:
|
||||
- tutorials/rag/langgraph_self_rag_local.ipynb
|
||||
- tutorials/sql-agent.ipynb
|
||||
- Agent Architectures:
|
||||
- Agent Architectures: tutorials#agent-architectures
|
||||
- Multi-Agent Systems:
|
||||
- Multi-Agent Systems: tutorials#multi-agent-systems
|
||||
- tutorials/multi_agent/multi-agent-collaboration.ipynb
|
||||
- tutorials/multi_agent/agent_supervisor.ipynb
|
||||
- tutorials/multi_agent/hierarchical_agent_teams.ipynb
|
||||
- Planning Agents:
|
||||
- Planning Agents: tutorials#planning-agents
|
||||
- tutorials/plan-and-execute/plan-and-execute.ipynb
|
||||
- tutorials/rewoo/rewoo.ipynb
|
||||
- tutorials/llm-compiler/LLMCompiler.ipynb
|
||||
- Reflection & Critique:
|
||||
- Reflection & Critique: tutorials#reflection-critique
|
||||
- tutorials/reflection/reflection.ipynb
|
||||
- tutorials/reflexion/reflexion.ipynb
|
||||
- tutorials/tot/tot.ipynb
|
||||
- tutorials/lats/lats.ipynb
|
||||
- tutorials/self-discover/self-discover.ipynb
|
||||
- Evaluation & Analysis:
|
||||
- Evaluation & Analysis: tutorials#evaluation
|
||||
- tutorials/chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb
|
||||
- tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb
|
||||
- Experimental:
|
||||
- Experimental: tutorials#experimental
|
||||
- tutorials/storm/storm.ipynb
|
||||
- tutorials/tnt-llm/tnt-llm.ipynb
|
||||
- tutorials/web-navigation/web_voyager.ipynb
|
||||
- tutorials/usaco/usaco.ipynb
|
||||
- tutorials/extraction/retries.ipynb
|
||||
- LangGraph Platform:
|
||||
- LangGraph Platform: concepts#langgraph-platform
|
||||
- tutorials/auth/getting_started.md
|
||||
- tutorials/auth/resource_auth.md
|
||||
- tutorials/auth/add_auth_server.md
|
||||
|
||||
Reference in New Issue
Block a user