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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
@@ -6,11 +6,11 @@
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2. [Resource Authorization](resource_auth.md) - Let users have private conversations
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3. Production Auth (you are here) - Add real user accounts and validate using OAuth2
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In the [Making Conversations Private](resource_auth.md) tutorial, we added [resource authorization](../../concepts/auth.md#resource-authorization) to give users private conversations. However, we were still using hard-coded tokens for authentication, which is not secure. Now we'll replace those tokens with real user accounts using [OAuth2](../../concepts/auth.md#oauth2-authentication).
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In the [Making Conversations Private](resource_auth.md) tutorial, we added [resource authorization](../../tutorials/auth/resource_auth.md) to give users private conversations. However, we were still using hard-coded tokens for authentication, which is not secure. Now we'll replace those tokens with real user accounts using [OAuth2](../auth/getting_started.md).
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We'll keep the same [`Auth`](../../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.Auth) object and [resource-level access control](../../concepts/auth.md#resource-level-access-control), but upgrade our authentication to use Supabase as our identity provider. While we use Supabase in this tutorial, the concepts apply to any OAuth2 provider. You'll learn how to:
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We'll keep the same [`Auth`](../../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.Auth) object and [resource-level access control](../../concepts/auth.md#single-owner-resources), but upgrade our authentication to use Supabase as our identity provider. While we use Supabase in this tutorial, the concepts apply to any OAuth2 provider. You'll learn how to:
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1. Replace test tokens with real [JWT tokens](../../concepts/auth.md#jwt-tokens)
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1. Replace test tokens with real JWT tokens
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2. Integrate with OAuth2 providers for secure user authentication
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3. Handle user sessions and metadata while maintaining our existing authorization logic
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@@ -11,7 +11,7 @@
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This guide assumes basic familiarity with the following concepts:
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* [**Authentication & Access Control**](../../concepts/auth.md)
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* [**LangGraph Platform**](../../concepts/index.md#langgraph-platform)
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* [**LangGraph Platform**](../../concepts/langgraph_platform.md)
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!!! note "Python only"
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@@ -17,7 +17,7 @@ In this tutorial, we will extend our chatbot to give each user their own private
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## Understanding Resource Authorization
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In the last tutorial, we controlled who could access our bot. But right now, any authenticated user can see everyone else's conversations! Let's fix that by adding [resource authorization](../../concepts/auth.md#resource-authorization).
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In the last tutorial, we controlled who could access our bot. But right now, any authenticated user can see everyone else's conversations! Let's fix that by adding [resource authorization](../auth/resource_auth.md).
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First, make sure you have completed the [Basic Authentication](getting_started.md) tutorial and that your secure bot can be run without errors:
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@@ -3,32 +3,20 @@ search:
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boost: 2
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---
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# Deployment
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# Deployment 🚀
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Get started deploying your LangGraph applications locally or on the cloud with
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[LangGraph Platform](../concepts/langgraph_platform.md).
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There are two free options for deploying LangGraph applications via the LangGraph Server:
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## Get Started 🚀 {#quick-start}
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- [Local](./langgraph-platform/local-server.md): Deploy for local testing and development.
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- Self-Hosted Lite: A limited version of Standalone Container for deployments unlikely to see more that 1 million node executions per year and that do not need crons and other enterprise features. Self-Hosted Lite is free with a LangSmith API key.
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- [LangGraph Server Quickstart](../tutorials/langgraph-platform/local-server.md): Launch a LangGraph server locally and interact with it using REST API and LangGraph Studio Web UI.
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- [LangGraph Template Quickstart](../concepts/template_applications.md): Start building with LangGraph Platform using a template application.
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- [Deploy with LangGraph Cloud Quickstart](../cloud/quick_start.md): Deploy a LangGraph app using LangGraph Cloud.
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## Other deployment options
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Additionally, you can deploy to production with [LangGraph Platform](../concepts/langgraph_platform.md):
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## Deployment Options
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- <a href="../../concepts/langgraph_cloud/">Cloud SaaS<sup>(Beta)</sup></a>: Connect to your GitHub repositories and deploy LangGraph Servers to LangChain's cloud. We manage everything.
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- <a href="../../concepts/langgraph_self_hosted_data_plane/">Self-Hosted Data Plane<sup>(Beta)</sup></a>: Create deployments from the [Control Plane UI](../concepts/langgraph_control_plane.md#control-plane-ui) and deploy LangGraph Servers to your cloud. We manage the [control plane](../concepts/langgraph_control_plane.md), you manage the deployments.
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- <a href="../../concepts/langgraph_self_hosted_control_plane/">Self-Hosted Control Plane<sup>(Beta)</sup></a>: Create deployments from a self-hosted [Control Plane UI](../concepts/langgraph_control_plane.md#control-plane-ui) and deploy LangGraph Servers to your cloud. You manage everything.
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- [Cloud SaaS](../concepts/langgraph_cloud.md): Connect your GitHub repositories and deploy LangGraph Servers within LangChain's cloud. *We manage everything.*
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- [Self-Hosted Data Plane<sup>(Beta)</sup>](../concepts/langgraph_self_hosted_data_plane.md): Create deployments from the [Control Plane UI](../concepts/langgraph_control_plane.md#control-plane-ui) and deploy LangGraph Servers to **your** cloud. *We manage the [control plane](../concepts/langgraph_control_plane.md). You manage the deployments.*
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- [Self-Hosted Control Plane<sup>(Beta)</sup>](../concepts/langgraph_self_hosted_control_plane.md): Create deployments from a self-hosted [Control Plane UI](../concepts/langgraph_control_plane.md#control-plane-ui) and deploy LangGraph Servers to **your** cloud. *You manage everything.*
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- [Standalone Container](../concepts/langgraph_standalone_container.md): Deploy LangGraph Server Docker images however you like.
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A quick comparison...
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| | **Cloud SaaS** | **Self-Hosted [Data Plane](../concepts/langgraph_data_plane.md)** | **Self-Hosted [Control Plane](../concepts/langgraph_control_plane.md)** | **Standalone Container** |
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|----------------------|----------------|----------------------------|-------------------------------|--------------------------|
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| **[Control Plane UI/API](../concepts/langgraph_control_plane.md)** | Yes | Yes | Yes | No |
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| **CI/CD** | Managed internally by platform | Managed externally by you | Managed externally by you | Managed externally by you |
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| **Data/Compute Residency** | LangChain’s cloud | Your cloud | Your cloud | Your cloud |
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| **Required Permissions** | None | See details [here](). | See details [here](). | None |
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| **LangSmith Compatibility** | Trace to LangSmith SaaS | Trace to LangSmith SaaS | Trace to Self-Hosted LangSmith | Optional tracing |
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| **[Pricing](https://www.langchain.com/pricing-langgraph-platform)** | Plus | Enterprise | Enterprise | Developer |
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For more information, see [Deployment options](../concepts/deployment_options.md)
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@@ -0,0 +1,204 @@
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# Build a basic chatbot
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In this tutorial, you will build a basic chatbot. This chatbot is the basis for the following series of tutorials where you will progressively add more sophisticated capabilities, and be introduced to key LangGraph concepts along the way. Let’s dive in! 🌟
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## Prerequisites
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Before you start this tutorial, ensure you have access to a LLM that supports
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tool-calling features, such as [OpenAI](https://platform.openai.com/api-keys),
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[Anthropic](https://console.anthropic.com/settings/admin-keys), or
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[Google Gemini](https://ai.google.dev/gemini-api/docs/api-key).
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## 1. Install packages
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Install the required packages:
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```bash
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pip install -U langgraph langsmith
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```
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!!! tip
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Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph. For more information on how to get started, see [LangSmith docs](https://docs.smith.langchain.com).
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## 2. Create a `StateGraph`
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Now you can create a basic chatbot using LangGraph. This chatbot will respond directly to user messages.
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Start by creating a `StateGraph`. A `StateGraph` object defines the structure of our chatbot as a "state machine". We'll add `nodes` to represent the llm and functions our chatbot can call and `edges` to specify how the bot should transition between these functions.
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```python
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from typing import Annotated
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from typing_extensions import TypedDict
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from langgraph.graph import StateGraph, START
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from langgraph.graph.message import add_messages
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class State(TypedDict):
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# Messages have the type "list". The `add_messages` function
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# in the annotation defines how this state key should be updated
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# (in this case, it appends messages to the list, rather than overwriting them)
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messages: Annotated[list, add_messages]
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graph_builder = StateGraph(State)
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```
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Our graph can now handle two key tasks:
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1. Each `node` can receive the current `State` as input and output an update to the state.
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2. Updates to `messages` will be appended to the existing list rather than overwriting it, thanks to the prebuilt [`add_messages`](https://langchain-ai.github.io/langgraph/reference/graphs/?h=add+messages#add_messages) function used with the `Annotated` syntax.
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------
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!!! tip "Concept"
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When defining a graph, the first step is to define its `State`. The `State` includes the graph's schema and [reducer functions](https://langchain-ai.github.io/langgraph/concepts/low_level/#reducers) that handle state updates. In our example, `State` is a `TypedDict` with one key: `messages`. The [`add_messages`](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.message.add_messages) reducer function is used to append new messages to the list instead of overwriting it. Keys without a reducer annotation will overwrite previous values. To learn more about state, reducers, and related concepts, see [LangGraph reference docs](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.message.add_messages).
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## 3. Add a node
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Next, add a "`chatbot`" node. **Nodes** represent units of work and are typically regular Python functions.
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Let's first select a chat model:
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{!snippets/chat_model_tabs.md!}
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<!---
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```python
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from langchain.chat_models import init_chat_model
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llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")
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```
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-->
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We can now incorporate the chat model into a simple node:
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```python
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def chatbot(state: State):
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return {"messages": [llm.invoke(state["messages"])]}
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# The first argument is the unique node name
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# The second argument is the function or object that will be called whenever
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# the node is used.
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graph_builder.add_node("chatbot", chatbot)
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```
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**Notice** how the `chatbot` node function takes the current `State` as input and returns a dictionary containing an updated `messages` list under the key "messages". This is the basic pattern for all LangGraph node functions.
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The `add_messages` function in our `State` will append the LLM's response messages to whatever messages are already in the state.
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## 4. Add an `entry` point
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Add an `entry` point to tell the graph **where to start its work** each time it is run:
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```python
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graph_builder.add_edge(START, "chatbot")
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```
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## 5. Compile the graph
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Before running the graph, we'll need to compile it. We can do so by calling `compile()`
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on the graph builder. This creates a `CompiledGraph` we can invoke on our state.
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```python
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graph = graph_builder.compile()
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```
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## 6. Visualize the graph (optional)
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You can visualize the graph using the `get_graph` method and one of the "draw" methods, like `draw_ascii` or `draw_png`. The `draw` methods each require additional dependencies.
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```python
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from IPython.display import Image, display
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try:
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display(Image(graph.get_graph().draw_mermaid_png()))
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except Exception:
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# This requires some extra dependencies and is optional
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pass
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```
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## 7. Run the chatbot
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Now run the chatbot!
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!!! tip
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You can exit the chat loop at any time by typing `quit`, `exit`, or `q`.
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```python
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def stream_graph_updates(user_input: str):
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for event in graph.stream({"messages": [{"role": "user", "content": user_input}]}):
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for value in event.values():
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print("Assistant:", value["messages"][-1].content)
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while True:
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try:
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user_input = input("User: ")
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if user_input.lower() in ["quit", "exit", "q"]:
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print("Goodbye!")
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break
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stream_graph_updates(user_input)
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except:
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# fallback if input() is not available
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user_input = "What do you know about LangGraph?"
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print("User: " + user_input)
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stream_graph_updates(user_input)
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break
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```
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```
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Assistant: LangGraph is a library designed to help build stateful multi-agent applications using language models. It provides tools for creating workflows and state machines to coordinate multiple AI agents or language model interactions. LangGraph is built on top of LangChain, leveraging its components while adding graph-based coordination capabilities. It's particularly useful for developing more complex, stateful AI applications that go beyond simple query-response interactions.
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Goodbye!
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```
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**Congratulations!** You've built your first chatbot using LangGraph. This bot can engage in basic conversation by taking user input and generating responses using an LLM. You can inspect a [LangSmith Trace](https://smith.langchain.com/public/7527e308-9502-4894-b347-f34385740d5a/r) for the call above.
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Below is the full code for this tutorial:
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```python
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from typing import Annotated
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from langchain.chat_models import init_chat_model
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from typing_extensions import TypedDict
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from langgraph.graph import StateGraph, START
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from langgraph.graph.message import add_messages
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class State(TypedDict):
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messages: Annotated[list, add_messages]
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graph_builder = StateGraph(State)
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llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")
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def chatbot(state: State):
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return {"messages": [llm.invoke(state["messages"])]}
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# The first argument is the unique node name
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# The second argument is the function or object that will be called whenever
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# the node is used.
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graph_builder.add_node("chatbot", chatbot)
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graph_builder.add_edge(START, "chatbot")
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graph = graph_builder.compile()
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```
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## Next steps
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You may have noticed that the bot's knowledge is limited to what's in its training data. In the next part, we'll [add a web search tool](./2-add-tools.md) to expand the bot's knowledge and make it more capable.
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@@ -0,0 +1,330 @@
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# Add tools
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To handle queries you chatbot can't answer "from memory", integrate a web search tool. The chatbot can use this tool to find relevant information and provide better responses.
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!!! note
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This tutorial builds on [Build a basic chatbot](./1-build-basic-chatbot.md).
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## Prerequisites
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Before you start this tutorial, ensure you have the following:
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- An API key for the [Tavily Search Engine](https://python.langchain.com/docs/integrations/tools/tavily_search/).
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## 1. Install the search engine
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Install the requirements to use the [Tavily Search Engine](https://python.langchain.com/docs/integrations/tools/tavily_search/):
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```bash
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pip install -U langchain-tavily
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```
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## 2. Configure your environment
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Configure your environment with your search engine API key:
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```bash
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_set_env("TAVILY_API_KEY")
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```
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```
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TAVILY_API_KEY: ········
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```
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## 3. Define the tool
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Define the web search tool:
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```python
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from langchain_tavily import TavilySearch
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tool = TavilySearch(max_results=2)
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tools = [tool]
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tool.invoke("What's a 'node' in LangGraph?")
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```
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The results are page summaries our chat bot can use to answer questions:
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```
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{'query': "What's a 'node' in LangGraph?",
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'follow_up_questions': None,
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'answer': None,
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'images': [],
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'results': [{'title': "Introduction to LangGraph: A Beginner's Guide - Medium",
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'url': 'https://medium.com/@cplog/introduction-to-langgraph-a-beginners-guide-14f9be027141',
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'content': 'Stateful Graph: LangGraph revolves around the concept of a stateful graph, where each node in the graph represents a step in your computation, and the graph maintains a state that is passed around and updated as the computation progresses. LangGraph supports conditional edges, allowing you to dynamically determine the next node to execute based on the current state of the graph. We define nodes for classifying the input, handling greetings, and handling search queries. def classify_input_node(state): LangGraph is a versatile tool for building complex, stateful applications with LLMs. By understanding its core concepts and working through simple examples, beginners can start to leverage its power for their projects. Remember to pay attention to state management, conditional edges, and ensuring there are no dead-end nodes in your graph.',
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'score': 0.7065353,
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'raw_content': None},
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{'title': 'LangGraph Tutorial: What Is LangGraph and How to Use It?',
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'url': 'https://www.datacamp.com/tutorial/langgraph-tutorial',
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'content': 'LangGraph is a library within the LangChain ecosystem that provides a framework for defining, coordinating, and executing multiple LLM agents (or chains) in a structured and efficient manner. By managing the flow of data and the sequence of operations, LangGraph allows developers to focus on the high-level logic of their applications rather than the intricacies of agent coordination. Whether you need a chatbot that can handle various types of user requests or a multi-agent system that performs complex tasks, LangGraph provides the tools to build exactly what you need. LangGraph significantly simplifies the development of complex LLM applications by providing a structured framework for managing state and coordinating agent interactions.',
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'score': 0.5008063,
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'raw_content': None}],
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'response_time': 1.38}
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```
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## 4. Define the graph
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|
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For the `StateGraph` you created in the [first tutorial](./1-build-basic-chatbot.md#2-create-a-stategraph), add `bind_tools` on the LLM. This lets the LLM know the correct JSON format to use if it wants to use the search engine.
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Let's first select our LLM:
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|
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{!snippets/chat_model_tabs.md!}
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|
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<!---
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```python
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from langchain.chat_models import init_chat_model
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|
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llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")
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```
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-->
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We can now incorporate it into a `StateGraph`:
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|
||||
```python hl_lines="15"
|
||||
from typing import Annotated
|
||||
|
||||
from typing_extensions import TypedDict
|
||||
|
||||
from langgraph.graph import StateGraph, START, END
|
||||
from langgraph.graph.message import add_messages
|
||||
|
||||
class State(TypedDict):
|
||||
messages: Annotated[list, add_messages]
|
||||
|
||||
graph_builder = StateGraph(State)
|
||||
|
||||
# Modification: tell the LLM which tools it can call
|
||||
# highlight-next-line
|
||||
llm_with_tools = llm.bind_tools(tools)
|
||||
|
||||
def chatbot(state: State):
|
||||
return {"messages": [llm_with_tools.invoke(state["messages"])]}
|
||||
|
||||
graph_builder.add_node("chatbot", chatbot)
|
||||
```
|
||||
|
||||
## 5. Create a function to run the tools
|
||||
|
||||
Now, create a function to run the tools if they are called. Do this by adding the tools to a new node called`BasicToolNode` that checks the most recent message in the state and calls tools if the message contains `tool_calls`. It relies on the LLM's `tool_calling` support, which is available in Anthropic, OpenAI, Google Gemini, and a number of other LLM providers.
|
||||
|
||||
```python
|
||||
import json
|
||||
|
||||
from langchain_core.messages import ToolMessage
|
||||
|
||||
|
||||
class BasicToolNode:
|
||||
"""A node that runs the tools requested in the last AIMessage."""
|
||||
|
||||
def __init__(self, tools: list) -> None:
|
||||
self.tools_by_name = {tool.name: tool for tool in tools}
|
||||
|
||||
def __call__(self, inputs: dict):
|
||||
if messages := inputs.get("messages", []):
|
||||
message = messages[-1]
|
||||
else:
|
||||
raise ValueError("No message found in input")
|
||||
outputs = []
|
||||
for tool_call in message.tool_calls:
|
||||
tool_result = self.tools_by_name[tool_call["name"]].invoke(
|
||||
tool_call["args"]
|
||||
)
|
||||
outputs.append(
|
||||
ToolMessage(
|
||||
content=json.dumps(tool_result),
|
||||
name=tool_call["name"],
|
||||
tool_call_id=tool_call["id"],
|
||||
)
|
||||
)
|
||||
return {"messages": outputs}
|
||||
|
||||
|
||||
tool_node = BasicToolNode(tools=[tool])
|
||||
graph_builder.add_node("tools", tool_node)
|
||||
```
|
||||
|
||||
!!! note
|
||||
|
||||
If you do not want to build this yourself in the future, you can use LangGraph's prebuilt [ToolNode](https://langchain-ai.github.io/langgraph/reference/prebuilt/#toolnode).
|
||||
|
||||
## 6. Define the `conditional_edges`
|
||||
|
||||
With the tool node added, now you can define the `conditional_edges`.
|
||||
|
||||
**Edges** route the control flow from one node to the next. **Conditional edges** start from a single node and usually contain "if" statements to route to different nodes depending on the current graph state. These functions receive the current graph `state` and return a string or list of strings indicating which node(s) to call next.
|
||||
|
||||
Next, define a router function called `route_tools` that checks for `tool_calls` in the chatbot's output. Provide this function to the graph by calling `add_conditional_edges`, which tells the graph that whenever the `chatbot` node completes to check this function to see where to go next.
|
||||
|
||||
The condition will route to `tools` if tool calls are present and `END` if not. Because the condition can return `END`, you do not need to explicitly set a `finish_point` this time.
|
||||
|
||||
```python
|
||||
def route_tools(
|
||||
state: State,
|
||||
):
|
||||
"""
|
||||
Use in the conditional_edge to route to the ToolNode if the last message
|
||||
has tool calls. Otherwise, route to the end.
|
||||
"""
|
||||
if isinstance(state, list):
|
||||
ai_message = state[-1]
|
||||
elif messages := state.get("messages", []):
|
||||
ai_message = messages[-1]
|
||||
else:
|
||||
raise ValueError(f"No messages found in input state to tool_edge: {state}")
|
||||
if hasattr(ai_message, "tool_calls") and len(ai_message.tool_calls) > 0:
|
||||
return "tools"
|
||||
return END
|
||||
|
||||
|
||||
# The `tools_condition` function returns "tools" if the chatbot asks to use a tool, and "END" if
|
||||
# it is fine directly responding. This conditional routing defines the main agent loop.
|
||||
graph_builder.add_conditional_edges(
|
||||
"chatbot",
|
||||
route_tools,
|
||||
# The following dictionary lets you tell the graph to interpret the condition's outputs as a specific node
|
||||
# It defaults to the identity function, but if you
|
||||
# want to use a node named something else apart from "tools",
|
||||
# You can update the value of the dictionary to something else
|
||||
# e.g., "tools": "my_tools"
|
||||
{"tools": "tools", END: END},
|
||||
)
|
||||
# Any time a tool is called, we return to the chatbot to decide the next step
|
||||
graph_builder.add_edge("tools", "chatbot")
|
||||
graph_builder.add_edge(START, "chatbot")
|
||||
graph = graph_builder.compile()
|
||||
```
|
||||
|
||||
!!! note
|
||||
|
||||
You can replace this with the prebuilt [tools_condition](https://langchain-ai.github.io/langgraph/reference/prebuilt/#tools_condition) to be more concise.
|
||||
|
||||
## 7. Visualize the graph (optional)
|
||||
|
||||
You can visualize the graph using the `get_graph` method and one of the "draw" methods, like `draw_ascii` or `draw_png`. The `draw` methods each require additional dependencies.
|
||||
|
||||
```python
|
||||
from IPython.display import Image, display
|
||||
|
||||
try:
|
||||
display(Image(graph.get_graph().draw_mermaid_png()))
|
||||
except Exception:
|
||||
# This requires some extra dependencies and is optional
|
||||
pass
|
||||
```
|
||||
|
||||
|
||||
|
||||
## 8. Ask the bot questions
|
||||
|
||||
Now you can ask the chatbot questions outside its training data:
|
||||
|
||||
```python
|
||||
def stream_graph_updates(user_input: str):
|
||||
for event in graph.stream({"messages": [{"role": "user", "content": user_input}]}):
|
||||
for value in event.values():
|
||||
print("Assistant:", value["messages"][-1].content)
|
||||
|
||||
while True:
|
||||
try:
|
||||
user_input = input("User: ")
|
||||
if user_input.lower() in ["quit", "exit", "q"]:
|
||||
print("Goodbye!")
|
||||
break
|
||||
|
||||
stream_graph_updates(user_input)
|
||||
except:
|
||||
# fallback if input() is not available
|
||||
user_input = "What do you know about LangGraph?"
|
||||
print("User: " + user_input)
|
||||
stream_graph_updates(user_input)
|
||||
break
|
||||
```
|
||||
|
||||
```
|
||||
Assistant: [{'text': "To provide you with accurate and up-to-date information about LangGraph, I'll need to search for the latest details. Let me do that for you.", 'type': 'text'}, {'id': 'toolu_01Q588CszHaSvvP2MxRq9zRD', 'input': {'query': 'LangGraph AI tool information'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
|
||||
Assistant: [{"url": "https://www.langchain.com/langgraph", "content": "LangGraph sets the foundation for how we can build and scale AI workloads \u2014 from conversational agents, complex task automation, to custom LLM-backed experiences that 'just work'. The next chapter in building complex production-ready features with LLMs is agentic, and with LangGraph and LangSmith, LangChain delivers an out-of-the-box solution ..."}, {"url": "https://github.com/langchain-ai/langgraph", "content": "Overview. LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures ..."}]
|
||||
Assistant: Based on the search results, I can provide you with information about LangGraph:
|
||||
|
||||
1. Purpose:
|
||||
LangGraph is a library designed for building stateful, multi-actor applications with Large Language Models (LLMs). It's particularly useful for creating agent and multi-agent workflows.
|
||||
|
||||
2. Developer:
|
||||
LangGraph is developed by LangChain, a company known for its tools and frameworks in the AI and LLM space.
|
||||
|
||||
3. Key Features:
|
||||
- Cycles: LangGraph allows the definition of flows that involve cycles, which is essential for most agentic architectures.
|
||||
- Controllability: It offers enhanced control over the application flow.
|
||||
- Persistence: The library provides ways to maintain state and persistence in LLM-based applications.
|
||||
|
||||
4. Use Cases:
|
||||
LangGraph can be used for various applications, including:
|
||||
- Conversational agents
|
||||
- Complex task automation
|
||||
- Custom LLM-backed experiences
|
||||
|
||||
5. Integration:
|
||||
LangGraph works in conjunction with LangSmith, another tool by LangChain, to provide an out-of-the-box solution for building complex, production-ready features with LLMs.
|
||||
|
||||
6. Significance:
|
||||
...
|
||||
LangGraph is noted to offer unique benefits compared to other LLM frameworks, particularly in its ability to handle cycles, provide controllability, and maintain persistence.
|
||||
|
||||
LangGraph appears to be a significant tool in the evolving landscape of LLM-based application development, offering developers new ways to create more complex, stateful, and interactive AI systems.
|
||||
Goodbye!
|
||||
Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...
|
||||
```
|
||||
|
||||
## 9. Use prebuilts
|
||||
|
||||
For ease of use, adjust your code to replace the following with LangGraph prebuilt components. These have built in functionality like parallel API execution.
|
||||
|
||||
- `BasicToolNode` is replaced with the prebuilt [ToolNode](https://langchain-ai.github.io/langgraph/reference/prebuilt/#toolnode)
|
||||
- `route_tools` is replaced with the prebuilt [tools_condition](https://langchain-ai.github.io/langgraph/reference/prebuilt/#tools_condition)
|
||||
|
||||
{!snippets/chat_model_tabs.md!}
|
||||
|
||||
|
||||
```python hl_lines="25 30"
|
||||
from typing import Annotated
|
||||
|
||||
from langchain_tavily import TavilySearch
|
||||
from langchain_core.messages import BaseMessage
|
||||
from typing_extensions import TypedDict
|
||||
|
||||
from langgraph.graph import StateGraph, START, END
|
||||
from langgraph.graph.message import add_messages
|
||||
from langgraph.prebuilt import ToolNode, tools_condition
|
||||
|
||||
class State(TypedDict):
|
||||
messages: Annotated[list, add_messages]
|
||||
|
||||
graph_builder = StateGraph(State)
|
||||
|
||||
tool = TavilySearch(max_results=2)
|
||||
tools = [tool]
|
||||
llm_with_tools = llm.bind_tools(tools)
|
||||
|
||||
def chatbot(state: State):
|
||||
return {"messages": [llm_with_tools.invoke(state["messages"])]}
|
||||
|
||||
graph_builder.add_node("chatbot", chatbot)
|
||||
|
||||
tool_node = ToolNode(tools=[tool])
|
||||
graph_builder.add_node("tools", tool_node)
|
||||
|
||||
graph_builder.add_conditional_edges(
|
||||
"chatbot",
|
||||
tools_condition,
|
||||
)
|
||||
# Any time a tool is called, we return to the chatbot to decide the next step
|
||||
graph_builder.add_edge("tools", "chatbot")
|
||||
graph_builder.add_edge(START, "chatbot")
|
||||
graph = graph_builder.compile()
|
||||
```
|
||||
|
||||
**Congratulations!** You've created a conversational agent in LangGraph that can use a search engine to retrieve updated information when needed. Now it can handle a wider range of user queries. To inspect all the steps your agent just took, check out this [LangSmith trace](https://smith.langchain.com/public/4fbd7636-25af-4638-9587-5a02fdbb0172/r).
|
||||
|
||||
## Next steps
|
||||
|
||||
The chatbot cannot remember past interactions on its own, which limits its ability to have coherent, multi-turn conversations. In the next part, you will [add **memory**](./3-add-memory.md) to address this.
|
||||
@@ -0,0 +1,209 @@
|
||||
# Add memory
|
||||
|
||||
The chatbot can now [use tools](./2-add-tools.md) to answer user questions, but it does not remember the context of previous interactions. This limits its ability to have coherent, multi-turn conversations.
|
||||
|
||||
LangGraph solves this problem through **persistent checkpointing**. If you provide a `checkpointer` when compiling the graph and a `thread_id` when calling your graph, LangGraph automatically saves the state after each step. When you invoke the graph again using the same `thread_id`, the graph loads its saved state, allowing the chatbot to pick up where it left off.
|
||||
|
||||
We will see later that **checkpointing** is _much_ more powerful than simple chat memory - it lets you save and resume complex state at any time for error recovery, human-in-the-loop workflows, time travel interactions, and more. But first, let's add checkpointing to enable multi-turn conversations.
|
||||
|
||||
!!! note
|
||||
|
||||
This tutorial builds on [Add tools](./2-add-tools.md).
|
||||
|
||||
## 1. Create a `MemorySaver` checkpointer
|
||||
|
||||
Create a `MemorySaver` checkpointer:
|
||||
|
||||
``` python
|
||||
from langgraph.checkpoint.memory import MemorySaver
|
||||
|
||||
memory = MemorySaver()
|
||||
```
|
||||
|
||||
This is in-memory checkpointer, which is convenient for the tutorial. However, in a production application, you would likely change this to use `SqliteSaver` or `PostgresSaver` and connect a database.
|
||||
|
||||
## 2. Compile the graph
|
||||
|
||||
Compile the graph with the provided checkpointer, which will checkpoint the `State` as the graph works through each node:
|
||||
|
||||
``` python
|
||||
graph = graph_builder.compile(checkpointer=memory)
|
||||
```
|
||||
|
||||
``` python
|
||||
from IPython.display import Image, display
|
||||
|
||||
try:
|
||||
display(Image(graph.get_graph().draw_mermaid_png()))
|
||||
except Exception:
|
||||
# This requires some extra dependencies and is optional
|
||||
pass
|
||||
```
|
||||
|
||||
## 3. Interact with your chatbot
|
||||
|
||||
Now you can interact with your bot!
|
||||
|
||||
1. Pick a thread to use as the key for this conversation.
|
||||
|
||||
```python
|
||||
config = {"configurable": {"thread_id": "1"}}
|
||||
```
|
||||
|
||||
2. Call your chatbot:
|
||||
|
||||
```python
|
||||
user_input = "Hi there! My name is Will."
|
||||
|
||||
# The config is the **second positional argument** to stream() or invoke()!
|
||||
events = graph.stream(
|
||||
{"messages": [{"role": "user", "content": user_input}]},
|
||||
config,
|
||||
stream_mode="values",
|
||||
)
|
||||
for event in events:
|
||||
event["messages"][-1].pretty_print()
|
||||
```
|
||||
|
||||
```
|
||||
================================ Human Message =================================
|
||||
|
||||
Hi there! My name is Will.
|
||||
================================== Ai Message ==================================
|
||||
|
||||
Hello Will! It's nice to meet you. How can I assist you today? Is there anything specific you'd like to know or discuss?
|
||||
```
|
||||
|
||||
!!! note
|
||||
|
||||
The config was provided as the **second positional argument** when calling our graph. It importantly is _not_ nested within the graph inputs (`{'messages': []}`).
|
||||
|
||||
## 4. Ask a follow up question
|
||||
|
||||
Ask a follow up question:
|
||||
|
||||
```python
|
||||
user_input = "Remember my name?"
|
||||
|
||||
# The config is the **second positional argument** to stream() or invoke()!
|
||||
events = graph.stream(
|
||||
{"messages": [{"role": "user", "content": user_input}]},
|
||||
config,
|
||||
stream_mode="values",
|
||||
)
|
||||
for event in events:
|
||||
event["messages"][-1].pretty_print()
|
||||
```
|
||||
|
||||
```
|
||||
================================ Human Message =================================
|
||||
|
||||
Remember my name?
|
||||
================================== Ai Message ==================================
|
||||
|
||||
Of course, I remember your name, Will. I always try to pay attention to important details that users share with me. Is there anything else you'd like to talk about or any questions you have? I'm here to help with a wide range of topics or tasks.
|
||||
```
|
||||
|
||||
**Notice** that we aren't using an external list for memory: it's all handled by the checkpointer! You can inspect the full execution in this [LangSmith trace](https://smith.langchain.com/public/29ba22b5-6d40-4fbe-8d27-b369e3329c84/r) to see what's going on.
|
||||
|
||||
Don't believe me? Try this using a different config.
|
||||
|
||||
```python
|
||||
# The only difference is we change the `thread_id` here to "2" instead of "1"
|
||||
events = graph.stream(
|
||||
{"messages": [{"role": "user", "content": user_input}]},
|
||||
# highlight-next-line
|
||||
{"configurable": {"thread_id": "2"}},
|
||||
stream_mode="values",
|
||||
)
|
||||
for event in events:
|
||||
event["messages"][-1].pretty_print()
|
||||
```
|
||||
|
||||
```
|
||||
================================ Human Message =================================
|
||||
|
||||
Remember my name?
|
||||
================================== Ai Message ==================================
|
||||
|
||||
I apologize, but I don't have any previous context or memory of your name. As an AI assistant, I don't retain information from past conversations. Each interaction starts fresh. Could you please tell me your name so I can address you properly in this conversation?
|
||||
```
|
||||
|
||||
**Notice** that the **only** change we've made is to modify the `thread_id` in the config. See this call's [LangSmith trace](https://smith.langchain.com/public/51a62351-2f0a-4058-91cc-9996c5561428/r) for comparison.
|
||||
|
||||
## 5. Inspect the state
|
||||
|
||||
By now, we have made a few checkpoints across two different threads. But what goes into a checkpoint? To inspect a graph's `state` for a given config at any time, call `get_state(config)`.
|
||||
|
||||
```python
|
||||
snapshot = graph.get_state(config)
|
||||
snapshot
|
||||
```
|
||||
|
||||
```
|
||||
StateSnapshot(values={'messages': [HumanMessage(content='Hi there! My name is Will.', additional_kwargs={}, response_metadata={}, id='8c1ca919-c553-4ebf-95d4-b59a2d61e078'), AIMessage(content="Hello Will! It's nice to meet you. How can I assist you today? Is there anything specific you'd like to know or discuss?", additional_kwargs={}, response_metadata={'id': 'msg_01WTQebPhNwmMrmmWojJ9KXJ', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 405, 'output_tokens': 32}}, id='run-58587b77-8c82-41e6-8a90-d62c444a261d-0', usage_metadata={'input_tokens': 405, 'output_tokens': 32, 'total_tokens': 437}), HumanMessage(content='Remember my name?', additional_kwargs={}, response_metadata={}, id='daba7df6-ad75-4d6b-8057-745881cea1ca'), AIMessage(content="Of course, I remember your name, Will. I always try to pay attention to important details that users share with me. Is there anything else you'd like to talk about or any questions you have? I'm here to help with a wide range of topics or tasks.", additional_kwargs={}, response_metadata={'id': 'msg_01E41KitY74HpENRgXx94vag', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 444, 'output_tokens': 58}}, id='run-ffeaae5c-4d2d-4ddb-bd59-5d5cbf2a5af8-0', usage_metadata={'input_tokens': 444, 'output_tokens': 58, 'total_tokens': 502})]}, next=(), config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef7d06e-93e0-6acc-8004-f2ac846575d2'}}, metadata={'source': 'loop', 'writes': {'chatbot': {'messages': [AIMessage(content="Of course, I remember your name, Will. I always try to pay attention to important details that users share with me. Is there anything else you'd like to talk about or any questions you have? I'm here to help with a wide range of topics or tasks.", additional_kwargs={}, response_metadata={'id': 'msg_01E41KitY74HpENRgXx94vag', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 444, 'output_tokens': 58}}, id='run-ffeaae5c-4d2d-4ddb-bd59-5d5cbf2a5af8-0', usage_metadata={'input_tokens': 444, 'output_tokens': 58, 'total_tokens': 502})]}}, 'step': 4, 'parents': {}}, created_at='2024-09-27T19:30:10.820758+00:00', parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef7d06e-859f-6206-8003-e1bd3c264b8f'}}, tasks=())
|
||||
```
|
||||
|
||||
```
|
||||
snapshot.next # (since the graph ended this turn, `next` is empty. If you fetch a state from within a graph invocation, next tells which node will execute next)
|
||||
```
|
||||
|
||||
The snapshot above contains the current state values, corresponding config, and the `next` node to process. In our case, the graph has reached an `END` state, so `next` is empty.
|
||||
|
||||
**Congratulations!** Your chatbot can now maintain conversation state across sessions thanks to LangGraph's checkpointing system. This opens up exciting possibilities for more natural, contextual interactions. LangGraph's checkpointing even handles **arbitrarily complex graph states**, which is much more expressive and powerful than simple chat memory.
|
||||
|
||||
Check out the code snippet below to review the graph from this tutorial:
|
||||
|
||||
{!snippets/chat_model_tabs.md!}
|
||||
|
||||
<!---
|
||||
```python
|
||||
from langchain.chat_models import init_chat_model
|
||||
|
||||
llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")
|
||||
```
|
||||
-->
|
||||
|
||||
```python
|
||||
from typing import Annotated
|
||||
|
||||
from langchain.chat_models import init_chat_model
|
||||
from langchain_tavily import TavilySearch
|
||||
from langchain_core.messages import BaseMessage
|
||||
from typing_extensions import TypedDict
|
||||
|
||||
from langgraph.checkpoint.memory import MemorySaver
|
||||
from langgraph.graph import StateGraph
|
||||
from langgraph.graph.message import add_messages
|
||||
from langgraph.prebuilt import ToolNode, tools_condition
|
||||
|
||||
class State(TypedDict):
|
||||
messages: Annotated[list, add_messages]
|
||||
|
||||
graph_builder = StateGraph(State)
|
||||
|
||||
tool = TavilySearch(max_results=2)
|
||||
tools = [tool]
|
||||
llm_with_tools = llm.bind_tools(tools)
|
||||
|
||||
def chatbot(state: State):
|
||||
return {"messages": [llm_with_tools.invoke(state["messages"])]}
|
||||
|
||||
graph_builder.add_node("chatbot", chatbot)
|
||||
|
||||
tool_node = ToolNode(tools=[tool])
|
||||
graph_builder.add_node("tools", tool_node)
|
||||
|
||||
graph_builder.add_conditional_edges(
|
||||
"chatbot",
|
||||
tools_condition,
|
||||
)
|
||||
graph_builder.add_edge("tools", "chatbot")
|
||||
graph_builder.set_entry_point("chatbot")
|
||||
memory = MemorySaver()
|
||||
graph = graph_builder.compile(checkpointer=memory)
|
||||
```
|
||||
|
||||
## Next steps
|
||||
|
||||
In the next tutorial, you will [add human-in-the-loop to the chatbot](./4-human-in-the-loop.md) to handle situations where it may need guidance or verification before proceeding.
|
||||
@@ -0,0 +1,277 @@
|
||||
# Add human-in-the-loop controls
|
||||
|
||||
Agents can be unreliable and may need human input to successfully accomplish tasks. Similarly, for some actions, you may want to require human approval before running to ensure that everything is running as intended.
|
||||
|
||||
LangGraph's [persistence](../../concepts/persistence.md) layer supports **human-in-the-loop** workflows, allowing execution to pause and resume based on user feedback. The primary interface to this functionality is the [`interrupt`](../../concepts/human_in_the_loop.md#interrupt) function. Calling `interrupt` inside a node will pause execution. Execution can be resumed, together with new input from a human, by passing in a [Command](../../concepts/human_in_the_loop.md#the-command-primitive). `interrupt` is ergonomically similar to Python's built-in `input()`, [with some caveats](../../concepts/human_in_the_loop.md#interrupt).
|
||||
|
||||
!!! note
|
||||
|
||||
This tutorial builds on [Add memory](./3-add-memory.md).
|
||||
|
||||
## 1. Add the `human_assistance` tool
|
||||
|
||||
Starting with the existing code from the [Add memory to the chatbot](./3-add-memory.md) tutorial, add the `human_assistance` tool to the chatbot. This tool uses `interrupt` to receive information from a human.
|
||||
|
||||
Let's first select a chat model:
|
||||
|
||||
{!snippets/chat_model_tabs.md!}
|
||||
|
||||
<!---
|
||||
```python
|
||||
from langchain.chat_models import init_chat_model
|
||||
|
||||
llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")
|
||||
```
|
||||
-->
|
||||
|
||||
We can now incorporate it into our `StateGraph` with an additional tool:
|
||||
|
||||
``` python hl_lines="12 19 20 21 22 23"
|
||||
from typing import Annotated
|
||||
|
||||
from langchain_tavily import TavilySearch
|
||||
from langchain_core.tools import tool
|
||||
from typing_extensions import TypedDict
|
||||
|
||||
from langgraph.checkpoint.memory import MemorySaver
|
||||
from langgraph.graph import StateGraph, START, END
|
||||
from langgraph.graph.message import add_messages
|
||||
from langgraph.prebuilt import ToolNode, tools_condition
|
||||
|
||||
from langgraph.types import Command, interrupt
|
||||
|
||||
class State(TypedDict):
|
||||
messages: Annotated[list, add_messages]
|
||||
|
||||
graph_builder = StateGraph(State)
|
||||
|
||||
@tool
|
||||
def human_assistance(query: str) -> str:
|
||||
"""Request assistance from a human."""
|
||||
human_response = interrupt({"query": query})
|
||||
return human_response["data"]
|
||||
|
||||
tool = TavilySearch(max_results=2)
|
||||
tools = [tool, human_assistance]
|
||||
llm_with_tools = llm.bind_tools(tools)
|
||||
|
||||
def chatbot(state: State):
|
||||
message = llm_with_tools.invoke(state["messages"])
|
||||
# Because we will be interrupting during tool execution,
|
||||
# we disable parallel tool calling to avoid repeating any
|
||||
# tool invocations when we resume.
|
||||
assert len(message.tool_calls) <= 1
|
||||
return {"messages": [message]}
|
||||
|
||||
graph_builder.add_node("chatbot", chatbot)
|
||||
|
||||
tool_node = ToolNode(tools=tools)
|
||||
graph_builder.add_node("tools", tool_node)
|
||||
|
||||
graph_builder.add_conditional_edges(
|
||||
"chatbot",
|
||||
tools_condition,
|
||||
)
|
||||
graph_builder.add_edge("tools", "chatbot")
|
||||
graph_builder.add_edge(START, "chatbot")
|
||||
```
|
||||
|
||||
!!! tip
|
||||
|
||||
For more information and examples of human-in-the-loop workflows, see [Human-in-the-loop](../../concepts/human_in_the_loop.md). This includes how to [review and edit tool calls](../../how-tos/human_in_the_loop/review-tool-calls.ipynb) before they are executed.
|
||||
|
||||
## 2. Compile the graph
|
||||
|
||||
We compile the graph with a checkpointer, as before:
|
||||
|
||||
```python
|
||||
memory = MemorySaver()
|
||||
|
||||
graph = graph_builder.compile(checkpointer=memory)
|
||||
```
|
||||
|
||||
## 3. Visualize the graph (optional)
|
||||
|
||||
Visualizing the graph, you get the same layout as before – just with the added tool!
|
||||
|
||||
``` python
|
||||
from IPython.display import Image, display
|
||||
|
||||
try:
|
||||
display(Image(graph.get_graph().draw_mermaid_png()))
|
||||
except Exception:
|
||||
# This requires some extra dependencies and is optional
|
||||
pass
|
||||
```
|
||||
|
||||
|
||||
|
||||
## 4. Prompt the chatbot
|
||||
|
||||
Now, prompt the chatbot with a question that will engage the new `human_assistance` tool:
|
||||
|
||||
```python
|
||||
user_input = "I need some expert guidance for building an AI agent. Could you request assistance for me?"
|
||||
config = {"configurable": {"thread_id": "1"}}
|
||||
|
||||
events = graph.stream(
|
||||
{"messages": [{"role": "user", "content": user_input}]},
|
||||
config,
|
||||
stream_mode="values",
|
||||
)
|
||||
for event in events:
|
||||
if "messages" in event:
|
||||
event["messages"][-1].pretty_print()
|
||||
```
|
||||
|
||||
```
|
||||
================================ Human Message =================================
|
||||
|
||||
I need some expert guidance for building an AI agent. Could you request assistance for me?
|
||||
================================== Ai Message ==================================
|
||||
|
||||
[{'text': "Certainly! I'd be happy to request expert assistance for you regarding building an AI agent. To do this, I'll use the human_assistance function to relay your request. Let me do that for you now.", 'type': 'text'}, {'id': 'toolu_01ABUqneqnuHNuo1vhfDFQCW', 'input': {'query': 'A user is requesting expert guidance for building an AI agent. Could you please provide some expert advice or resources on this topic?'}, 'name': 'human_assistance', 'type': 'tool_use'}]
|
||||
Tool Calls:
|
||||
human_assistance (toolu_01ABUqneqnuHNuo1vhfDFQCW)
|
||||
Call ID: toolu_01ABUqneqnuHNuo1vhfDFQCW
|
||||
Args:
|
||||
query: A user is requesting expert guidance for building an AI agent. Could you please provide some expert advice or resources on this topic?
|
||||
```
|
||||
|
||||
The chatbot generated a tool call, but then execution has been interrupted. If you inspect the graph state, you see that it stopped at the tools node:
|
||||
|
||||
```python
|
||||
snapshot = graph.get_state(config)
|
||||
snapshot.next
|
||||
```
|
||||
|
||||
```
|
||||
('tools',)
|
||||
```
|
||||
|
||||
!!! info Additional information
|
||||
|
||||
Take a closer look at the `human_assistance` tool:
|
||||
|
||||
```python
|
||||
@tool
|
||||
def human_assistance(query: str) -> str:
|
||||
"""Request assistance from a human."""
|
||||
human_response = interrupt({"query": query})
|
||||
return human_response["data"]
|
||||
```
|
||||
|
||||
Similar to Python's built-in `input()` function, calling `interrupt` inside the tool will pause execution. Progress is persisted based on the [checkpointer](../../concepts/persistence.md#checkpointer-libraries); so if it is persisting with Postgres, it can resume at any time as long as the database is alive. In this example, it is persisting with the in-memory checkpointer and can resume any time if the Python kernel is running.
|
||||
|
||||
## 5. Resume execution
|
||||
|
||||
To resume execution, pass a [`Command`](../../concepts/human_in_the_loop.md#the-command-primitive) object containing data expected by the tool. The format of this data can be customized based on needs. For this example, use a dict with a key `"data"`:
|
||||
|
||||
``` python
|
||||
human_response = (
|
||||
"We, the experts are here to help! We'd recommend you check out LangGraph to build your agent."
|
||||
" It's much more reliable and extensible than simple autonomous agents."
|
||||
)
|
||||
|
||||
human_command = Command(resume={"data": human_response})
|
||||
|
||||
events = graph.stream(human_command, config, stream_mode="values")
|
||||
for event in events:
|
||||
if "messages" in event:
|
||||
event["messages"][-1].pretty_print()
|
||||
```
|
||||
|
||||
```
|
||||
================================== Ai Message ==================================
|
||||
|
||||
[{'text': "Certainly! I'd be happy to request expert assistance for you regarding building an AI agent. To do this, I'll use the human_assistance function to relay your request. Let me do that for you now.", 'type': 'text'}, {'id': 'toolu_01ABUqneqnuHNuo1vhfDFQCW', 'input': {'query': 'A user is requesting expert guidance for building an AI agent. Could you please provide some expert advice or resources on this topic?'}, 'name': 'human_assistance', 'type': 'tool_use'}]
|
||||
Tool Calls:
|
||||
human_assistance (toolu_01ABUqneqnuHNuo1vhfDFQCW)
|
||||
Call ID: toolu_01ABUqneqnuHNuo1vhfDFQCW
|
||||
Args:
|
||||
query: A user is requesting expert guidance for building an AI agent. Could you please provide some expert advice or resources on this topic?
|
||||
================================= Tool Message =================================
|
||||
Name: human_assistance
|
||||
|
||||
We, the experts are here to help! We'd recommend you check out LangGraph to build your agent. It's much more reliable and extensible than simple autonomous agents.
|
||||
================================== Ai Message ==================================
|
||||
|
||||
Thank you for your patience. I've received some expert advice regarding your request for guidance on building an AI agent. Here's what the experts have suggested:
|
||||
|
||||
The experts recommend that you look into LangGraph for building your AI agent. They mention that LangGraph is a more reliable and extensible option compared to simple autonomous agents.
|
||||
|
||||
LangGraph is likely a framework or library designed specifically for creating AI agents with advanced capabilities. Here are a few points to consider based on this recommendation:
|
||||
|
||||
1. Reliability: The experts emphasize that LangGraph is more reliable than simpler autonomous agent approaches. This could mean it has better stability, error handling, or consistent performance.
|
||||
|
||||
2. Extensibility: LangGraph is described as more extensible, which suggests that it probably offers a flexible architecture that allows you to easily add new features or modify existing ones as your agent's requirements evolve.
|
||||
|
||||
3. Advanced capabilities: Given that it's recommended over "simple autonomous agents," LangGraph likely provides more sophisticated tools and techniques for building complex AI agents.
|
||||
...
|
||||
2. Look for tutorials or guides specifically focused on building AI agents with LangGraph.
|
||||
3. Check if there are any community forums or discussion groups where you can ask questions and get support from other developers using LangGraph.
|
||||
|
||||
If you'd like more specific information about LangGraph or have any questions about this recommendation, please feel free to ask, and I can request further assistance from the experts.
|
||||
Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...
|
||||
```
|
||||
|
||||
The input has been received and processed as a tool message. Review this call's [LangSmith trace](https://smith.langchain.com/public/9f0f87e3-56a7-4dde-9c76-b71675624e91/r) to see the exact work that was done in the above call. Notice that the state is loaded in the first step so that our chatbot can continue where it left off.
|
||||
|
||||
**Congratulations!** You've used an `interrupt` to add human-in-the-loop execution to your chatbot, allowing for human oversight and intervention when needed. This opens up the potential UIs you can create with your AI systems. Since you have already added a **checkpointer**, as long as the underlying persistence layer is running, the graph can be paused **indefinitely** and resumed at any time as if nothing had happened.
|
||||
|
||||
Check out the code snippet below to review the graph from this tutorial:
|
||||
|
||||
{!snippets/chat_model_tabs.md!}
|
||||
|
||||
```python
|
||||
from typing import Annotated
|
||||
|
||||
from langchain_tavily import TavilySearch
|
||||
from langchain_core.tools import tool
|
||||
from typing_extensions import TypedDict
|
||||
|
||||
from langgraph.checkpoint.memory import MemorySaver
|
||||
from langgraph.graph import StateGraph, START, END
|
||||
from langgraph.graph.message import add_messages
|
||||
from langgraph.prebuilt import ToolNode, tools_condition
|
||||
from langgraph.types import Command, interrupt
|
||||
|
||||
class State(TypedDict):
|
||||
messages: Annotated[list, add_messages]
|
||||
|
||||
graph_builder = StateGraph(State)
|
||||
|
||||
@tool
|
||||
def human_assistance(query: str) -> str:
|
||||
"""Request assistance from a human."""
|
||||
human_response = interrupt({"query": query})
|
||||
return human_response["data"]
|
||||
|
||||
tool = TavilySearch(max_results=2)
|
||||
tools = [tool, human_assistance]
|
||||
llm_with_tools = llm.bind_tools(tools)
|
||||
|
||||
def chatbot(state: State):
|
||||
message = llm_with_tools.invoke(state["messages"])
|
||||
assert(len(message.tool_calls) <= 1)
|
||||
return {"messages": [message]}
|
||||
|
||||
graph_builder.add_node("chatbot", chatbot)
|
||||
|
||||
tool_node = ToolNode(tools=tools)
|
||||
graph_builder.add_node("tools", tool_node)
|
||||
|
||||
graph_builder.add_conditional_edges(
|
||||
"chatbot",
|
||||
tools_condition,
|
||||
)
|
||||
graph_builder.add_edge("tools", "chatbot")
|
||||
graph_builder.add_edge(START, "chatbot")
|
||||
|
||||
memory = MemorySaver()
|
||||
graph = graph_builder.compile(checkpointer=memory)
|
||||
```
|
||||
|
||||
## Next steps
|
||||
|
||||
So far, the tutorial examples have relied on a simple state with one entry: a list of messages. You can go far with this simple state, but if you want to define complex behavior without relying on the message list, you can [add additional fields to the state](./5-customize-state.md).
|
||||
@@ -0,0 +1,311 @@
|
||||
# Customize state
|
||||
|
||||
In this tutorial, you will add additional fields to the state to define complex behavior without relying on the message list. The chatbot will use its search tool to find specific information and forward them to a human for review.
|
||||
|
||||
!!! note
|
||||
|
||||
This tutorial builds on [Add human-in-the-loop controls](./4-human-in-the-loop.md).
|
||||
|
||||
## 1. Add keys to the state
|
||||
|
||||
Update the chatbot to research the birthday of an entity by adding `name` and `birthday` keys to the state:
|
||||
|
||||
```python
|
||||
from typing import Annotated
|
||||
|
||||
from typing_extensions import TypedDict
|
||||
|
||||
from langgraph.graph.message import add_messages
|
||||
|
||||
|
||||
class State(TypedDict):
|
||||
messages: Annotated[list, add_messages]
|
||||
# highlight-next-line
|
||||
name: str
|
||||
# highlight-next-line
|
||||
birthday: str
|
||||
```
|
||||
|
||||
Adding this information to the state makes it easily accessible by other graph nodes (like a downstream node that stores or processes the information), as well as the graph's persistence layer.
|
||||
|
||||
## 2. Update the state inside the tool
|
||||
|
||||
Now, populate the state keys inside of the `human_assistance` tool. This allows a human to review the information before it is stored in the state. Use [`Command`](../../concepts/low_level.md#using-inside-tools) to issue a state update from inside the tool.
|
||||
|
||||
``` python
|
||||
from langchain_core.messages import ToolMessage
|
||||
from langchain_core.tools import InjectedToolCallId, tool
|
||||
|
||||
from langgraph.types import Command, interrupt
|
||||
|
||||
@tool
|
||||
# Note that because we are generating a ToolMessage for a state update, we
|
||||
# generally require the ID of the corresponding tool call. We can use
|
||||
# LangChain's InjectedToolCallId to signal that this argument should not
|
||||
# be revealed to the model in the tool's schema.
|
||||
def human_assistance(
|
||||
name: str, birthday: str, tool_call_id: Annotated[str, InjectedToolCallId]
|
||||
) -> str:
|
||||
"""Request assistance from a human."""
|
||||
human_response = interrupt(
|
||||
{
|
||||
"question": "Is this correct?",
|
||||
"name": name,
|
||||
"birthday": birthday,
|
||||
},
|
||||
)
|
||||
# If the information is correct, update the state as-is.
|
||||
if human_response.get("correct", "").lower().startswith("y"):
|
||||
verified_name = name
|
||||
verified_birthday = birthday
|
||||
response = "Correct"
|
||||
# Otherwise, receive information from the human reviewer.
|
||||
else:
|
||||
verified_name = human_response.get("name", name)
|
||||
verified_birthday = human_response.get("birthday", birthday)
|
||||
response = f"Made a correction: {human_response}"
|
||||
|
||||
# This time we explicitly update the state with a ToolMessage inside
|
||||
# the tool.
|
||||
state_update = {
|
||||
"name": verified_name,
|
||||
"birthday": verified_birthday,
|
||||
"messages": [ToolMessage(response, tool_call_id=tool_call_id)],
|
||||
}
|
||||
# We return a Command object in the tool to update our state.
|
||||
return Command(update=state_update)
|
||||
```
|
||||
|
||||
The rest of the graph stays the same.
|
||||
|
||||
## 3. Prompt the chatbot
|
||||
|
||||
Prompt the chatbot to look up the "birthday" of the LangGraph library and direct the chatbot to reach out to the `human_assistance` tool once it has the required information. By setting `name` and `birthday` in the arguments for the tool, you force the chatbot to generate proposals for these fields.
|
||||
|
||||
```python
|
||||
user_input = (
|
||||
"Can you look up when LangGraph was released? "
|
||||
"When you have the answer, use the human_assistance tool for review."
|
||||
)
|
||||
config = {"configurable": {"thread_id": "1"}}
|
||||
|
||||
events = graph.stream(
|
||||
{"messages": [{"role": "user", "content": user_input}]},
|
||||
config,
|
||||
stream_mode="values",
|
||||
)
|
||||
for event in events:
|
||||
if "messages" in event:
|
||||
event["messages"][-1].pretty_print()
|
||||
```
|
||||
|
||||
```
|
||||
================================ Human Message =================================
|
||||
|
||||
Can you look up when LangGraph was released? When you have the answer, use the human_assistance tool for review.
|
||||
================================== Ai Message ==================================
|
||||
|
||||
[{'text': "Certainly! I'll start by searching for information about LangGraph's release date using the Tavily search function. Then, I'll use the human_assistance tool for review.", 'type': 'text'}, {'id': 'toolu_01JoXQPgTVJXiuma8xMVwqAi', 'input': {'query': 'LangGraph release date'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
|
||||
Tool Calls:
|
||||
tavily_search_results_json (toolu_01JoXQPgTVJXiuma8xMVwqAi)
|
||||
Call ID: toolu_01JoXQPgTVJXiuma8xMVwqAi
|
||||
Args:
|
||||
query: LangGraph release date
|
||||
================================= Tool Message =================================
|
||||
Name: tavily_search_results_json
|
||||
|
||||
[{"url": "https://blog.langchain.dev/langgraph-cloud/", "content": "We also have a new stable release of LangGraph. By LangChain 6 min read Jun 27, 2024 (Oct '24) Edit: Since the launch of LangGraph Cloud, we now have multiple deployment options alongside LangGraph Studio - which now fall under LangGraph Platform. LangGraph Cloud is synonymous with our Cloud SaaS deployment option."}, {"url": "https://changelog.langchain.com/announcements/langgraph-cloud-deploy-at-scale-monitor-carefully-iterate-boldly", "content": "LangChain - Changelog | ☁ 🚀 LangGraph Cloud: Deploy at scale, monitor LangChain LangSmith LangGraph LangChain LangSmith LangGraph LangChain LangSmith LangGraph LangChain Changelog Sign up for our newsletter to stay up to date DATE: The LangChain Team LangGraph LangGraph Cloud ☁ 🚀 LangGraph Cloud: Deploy at scale, monitor carefully, iterate boldly DATE: June 27, 2024 AUTHOR: The LangChain Team LangGraph Cloud is now in closed beta, offering scalable, fault-tolerant deployment for LangGraph agents. LangGraph Cloud also includes a new playground-like studio for debugging agent failure modes and quick iteration: Join the waitlist today for LangGraph Cloud. And to learn more, read our blog post announcement or check out our docs. Subscribe By clicking subscribe, you accept our privacy policy and terms and conditions."}]
|
||||
================================== Ai Message ==================================
|
||||
|
||||
[{'text': "Based on the search results, it appears that LangGraph was already in existence before June 27, 2024, when LangGraph Cloud was announced. However, the search results don't provide a specific release date for the original LangGraph. \n\nGiven this information, I'll use the human_assistance tool to review and potentially provide more accurate information about LangGraph's initial release date.", 'type': 'text'}, {'id': 'toolu_01JDQAV7nPqMkHHhNs3j3XoN', 'input': {'name': 'Assistant', 'birthday': '2023-01-01'}, 'name': 'human_assistance', 'type': 'tool_use'}]
|
||||
Tool Calls:
|
||||
human_assistance (toolu_01JDQAV7nPqMkHHhNs3j3XoN)
|
||||
Call ID: toolu_01JDQAV7nPqMkHHhNs3j3XoN
|
||||
Args:
|
||||
name: Assistant
|
||||
birthday: 2023-01-01
|
||||
```
|
||||
|
||||
We've hit the `interrupt` in the `human_assistance` tool again.
|
||||
|
||||
## 4. Add human assistance
|
||||
|
||||
The chatbot failed to identify the correct date, so supply it with information:
|
||||
|
||||
```python
|
||||
human_command = Command(
|
||||
resume={
|
||||
"name": "LangGraph",
|
||||
"birthday": "Jan 17, 2024",
|
||||
},
|
||||
)
|
||||
|
||||
events = graph.stream(human_command, config, stream_mode="values")
|
||||
for event in events:
|
||||
if "messages" in event:
|
||||
event["messages"][-1].pretty_print()
|
||||
```
|
||||
|
||||
```
|
||||
================================== Ai Message ==================================
|
||||
|
||||
[{'text': "Based on the search results, it appears that LangGraph was already in existence before June 27, 2024, when LangGraph Cloud was announced. However, the search results don't provide a specific release date for the original LangGraph. \n\nGiven this information, I'll use the human_assistance tool to review and potentially provide more accurate information about LangGraph's initial release date.", 'type': 'text'}, {'id': 'toolu_01JDQAV7nPqMkHHhNs3j3XoN', 'input': {'name': 'Assistant', 'birthday': '2023-01-01'}, 'name': 'human_assistance', 'type': 'tool_use'}]
|
||||
Tool Calls:
|
||||
human_assistance (toolu_01JDQAV7nPqMkHHhNs3j3XoN)
|
||||
Call ID: toolu_01JDQAV7nPqMkHHhNs3j3XoN
|
||||
Args:
|
||||
name: Assistant
|
||||
birthday: 2023-01-01
|
||||
================================= Tool Message =================================
|
||||
Name: human_assistance
|
||||
|
||||
Made a correction: {'name': 'LangGraph', 'birthday': 'Jan 17, 2024'}
|
||||
================================== Ai Message ==================================
|
||||
|
||||
Thank you for the human assistance. I can now provide you with the correct information about LangGraph's release date.
|
||||
|
||||
LangGraph was initially released on January 17, 2024. This information comes from the human assistance correction, which is more accurate than the search results I initially found.
|
||||
|
||||
To summarize:
|
||||
1. LangGraph's original release date: January 17, 2024
|
||||
2. LangGraph Cloud announcement: June 27, 2024
|
||||
|
||||
It's worth noting that LangGraph had been in development and use for some time before the LangGraph Cloud announcement, but the official initial release of LangGraph itself was on January 17, 2024.
|
||||
```
|
||||
|
||||
Note that these fields are now reflected in the state:
|
||||
|
||||
```python
|
||||
snapshot = graph.get_state(config)
|
||||
|
||||
{k: v for k, v in snapshot.values.items() if k in ("name", "birthday")}
|
||||
```
|
||||
|
||||
```
|
||||
{'name': 'LangGraph', 'birthday': 'Jan 17, 2024'}
|
||||
```
|
||||
|
||||
This makes them easily accessible to downstream nodes (e.g., a node that further processes or stores the information).
|
||||
|
||||
## 5. Manually update the state
|
||||
|
||||
LangGraph gives a high degree of control over the application state. For instance, at any point (including when interrupted), you can manually override a key using `graph.update_state`:
|
||||
|
||||
``` python
|
||||
graph.update_state(config, {"name": "LangGraph (library)"})
|
||||
```
|
||||
|
||||
```
|
||||
{'configurable': {'thread_id': '1',
|
||||
'checkpoint_ns': '',
|
||||
'checkpoint_id': '1efd4ec5-cf69-6352-8006-9278f1730162'}}
|
||||
```
|
||||
|
||||
## 6. View the new value
|
||||
|
||||
If you call `graph.get_state`, you can see the new value is reflected:
|
||||
|
||||
``` python
|
||||
snapshot = graph.get_state(config)
|
||||
|
||||
{k: v for k, v in snapshot.values.items() if k in ("name", "birthday")}
|
||||
```
|
||||
|
||||
```
|
||||
{'name': 'LangGraph (library)', 'birthday': 'Jan 17, 2024'}
|
||||
```
|
||||
|
||||
Manual state updates will [generate a trace](https://smith.langchain.com/public/7ebb7827-378d-49fe-9f6c-5df0e90086c8/r) in LangSmith. If desired, they can also be used to [control human-in-the-loop workflows](../../how-tos/human_in_the_loop/edit-graph-state.md). Use of the `interrupt` function is generally recommended instead, as it allows data to be transmitted in a human-in-the-loop interaction independently of state updates.
|
||||
|
||||
**Congratulations!** You've added custom keys to the state to facilitate a more complex workflow, and learned how to generate state updates from inside tools.
|
||||
|
||||
Check out the code snippet below to review the graph from this tutorial:
|
||||
|
||||
{!snippets/chat_model_tabs.md!}
|
||||
|
||||
<!---
|
||||
```python
|
||||
from langchain.chat_models import init_chat_model
|
||||
|
||||
llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")
|
||||
```
|
||||
-->
|
||||
|
||||
```python
|
||||
from typing import Annotated
|
||||
|
||||
from langchain_tavily import TavilySearch
|
||||
from langchain_core.messages import ToolMessage
|
||||
from langchain_core.tools import InjectedToolCallId, tool
|
||||
from typing_extensions import TypedDict
|
||||
|
||||
from langgraph.checkpoint.memory import MemorySaver
|
||||
from langgraph.graph import StateGraph, START, END
|
||||
from langgraph.graph.message import add_messages
|
||||
from langgraph.prebuilt import ToolNode, tools_condition
|
||||
from langgraph.types import Command, interrupt
|
||||
|
||||
class State(TypedDict):
|
||||
messages: Annotated[list, add_messages]
|
||||
name: str
|
||||
birthday: str
|
||||
|
||||
@tool
|
||||
def human_assistance(
|
||||
name: str, birthday: str, tool_call_id: Annotated[str, InjectedToolCallId]
|
||||
) -> str:
|
||||
"""Request assistance from a human."""
|
||||
human_response = interrupt(
|
||||
{
|
||||
"question": "Is this correct?",
|
||||
"name": name,
|
||||
"birthday": birthday,
|
||||
},
|
||||
)
|
||||
if human_response.get("correct", "").lower().startswith("y"):
|
||||
verified_name = name
|
||||
verified_birthday = birthday
|
||||
response = "Correct"
|
||||
else:
|
||||
verified_name = human_response.get("name", name)
|
||||
verified_birthday = human_response.get("birthday", birthday)
|
||||
response = f"Made a correction: {human_response}"
|
||||
|
||||
state_update = {
|
||||
"name": verified_name,
|
||||
"birthday": verified_birthday,
|
||||
"messages": [ToolMessage(response, tool_call_id=tool_call_id)],
|
||||
}
|
||||
return Command(update=state_update)
|
||||
|
||||
|
||||
tool = TavilySearch(max_results=2)
|
||||
tools = [tool, human_assistance]
|
||||
llm_with_tools = llm.bind_tools(tools)
|
||||
|
||||
def chatbot(state: State):
|
||||
message = llm_with_tools.invoke(state["messages"])
|
||||
assert(len(message.tool_calls) <= 1)
|
||||
return {"messages": [message]}
|
||||
|
||||
graph_builder = StateGraph(State)
|
||||
graph_builder.add_node("chatbot", chatbot)
|
||||
|
||||
tool_node = ToolNode(tools=tools)
|
||||
graph_builder.add_node("tools", tool_node)
|
||||
|
||||
graph_builder.add_conditional_edges(
|
||||
"chatbot",
|
||||
tools_condition,
|
||||
)
|
||||
graph_builder.add_edge("tools", "chatbot")
|
||||
graph_builder.add_edge(START, "chatbot")
|
||||
|
||||
memory = MemorySaver()
|
||||
graph = graph_builder.compile(checkpointer=memory)
|
||||
```
|
||||
|
||||
## Next steps
|
||||
|
||||
There's one more concept to review before finishing the LangGraph basics tutorials: connecting `checkpointing` and `state updates` to [time travel](./6-time-travel.md).
|
||||
|
||||
@@ -0,0 +1,288 @@
|
||||
# Time travel
|
||||
|
||||
In a typical chatbot workflow, the user interacts with the bot one or more times to accomplish a task. [Memory](./3-add-memory.md) and a [human-in-the-loop](./4-human-in-the-loop.md) enable checkpoints in the graph state and control future responses.
|
||||
|
||||
What if you want a user to be able to start from a previous response and explore a different outcome? Or what if you want users to be able to rewind your chatbot's work to fix mistakes or try a different strategy, something that is common in applications like autonomous software engineers?
|
||||
|
||||
You can create these types of experiences using LangGraph's built-in **time travel** functionality.
|
||||
|
||||
!!! note
|
||||
|
||||
This tutorial builds on [Customize state](./5-customize-state.md).
|
||||
|
||||
## 1. Rewind your graph
|
||||
|
||||
Rewind your graph by fetching a checkpoint using the graph's `get_state_history` method. You can then resume execution at this previous point in time.
|
||||
|
||||
{!snippets/chat_model_tabs.md!}
|
||||
|
||||
<!---
|
||||
```python
|
||||
from langchain.chat_models import init_chat_model
|
||||
|
||||
llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")
|
||||
```
|
||||
-->
|
||||
|
||||
```python
|
||||
from typing import Annotated
|
||||
|
||||
from langchain_tavily import TavilySearch
|
||||
from langchain_core.messages import BaseMessage
|
||||
from typing_extensions import TypedDict
|
||||
|
||||
from langgraph.checkpoint.memory import MemorySaver
|
||||
from langgraph.graph import StateGraph, START, END
|
||||
from langgraph.graph.message import add_messages
|
||||
from langgraph.prebuilt import ToolNode, tools_condition
|
||||
|
||||
class State(TypedDict):
|
||||
messages: Annotated[list, add_messages]
|
||||
|
||||
graph_builder = StateGraph(State)
|
||||
|
||||
tool = TavilySearch(max_results=2)
|
||||
tools = [tool]
|
||||
llm_with_tools = llm.bind_tools(tools)
|
||||
|
||||
def chatbot(state: State):
|
||||
return {"messages": [llm_with_tools.invoke(state["messages"])]}
|
||||
|
||||
graph_builder.add_node("chatbot", chatbot)
|
||||
|
||||
tool_node = ToolNode(tools=[tool])
|
||||
graph_builder.add_node("tools", tool_node)
|
||||
|
||||
graph_builder.add_conditional_edges(
|
||||
"chatbot",
|
||||
tools_condition,
|
||||
)
|
||||
graph_builder.add_edge("tools", "chatbot")
|
||||
graph_builder.add_edge(START, "chatbot")
|
||||
|
||||
memory = MemorySaver()
|
||||
graph = graph_builder.compile(checkpointer=memory)
|
||||
```
|
||||
|
||||
## 2. Add steps
|
||||
|
||||
Add steps to your graph. Every step will be checkpointed in its state history:
|
||||
|
||||
``` python
|
||||
config = {"configurable": {"thread_id": "1"}}
|
||||
events = graph.stream(
|
||||
{
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": (
|
||||
"I'm learning LangGraph. "
|
||||
"Could you do some research on it for me?"
|
||||
),
|
||||
},
|
||||
],
|
||||
},
|
||||
config,
|
||||
stream_mode="values",
|
||||
)
|
||||
for event in events:
|
||||
if "messages" in event:
|
||||
event["messages"][-1].pretty_print()
|
||||
```
|
||||
|
||||
```
|
||||
================================ Human Message =================================
|
||||
|
||||
I'm learning LangGraph. Could you do some research on it for me?
|
||||
================================== Ai Message ==================================
|
||||
|
||||
[{'text': "Certainly! I'd be happy to research LangGraph for you. To get the most up-to-date and accurate information, I'll use the Tavily search engine to look this up. Let me do that for you now.", 'type': 'text'}, {'id': 'toolu_01BscbfJJB9EWJFqGrN6E54e', 'input': {'query': 'LangGraph latest information and features'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
|
||||
Tool Calls:
|
||||
tavily_search_results_json (toolu_01BscbfJJB9EWJFqGrN6E54e)
|
||||
Call ID: toolu_01BscbfJJB9EWJFqGrN6E54e
|
||||
Args:
|
||||
query: LangGraph latest information and features
|
||||
================================= Tool Message =================================
|
||||
Name: tavily_search_results_json
|
||||
|
||||
[{"url": "https://blockchain.news/news/langchain-new-features-upcoming-events-update", "content": "LangChain, a leading platform in the AI development space, has released its latest updates, showcasing new use cases and enhancements across its ecosystem. According to the LangChain Blog, the updates cover advancements in LangGraph Cloud, LangSmith's self-improving evaluators, and revamped documentation for LangGraph."}, {"url": "https://blog.langchain.dev/langgraph-platform-announce/", "content": "With these learnings under our belt, we decided to couple some of our latest offerings under LangGraph Platform. LangGraph Platform today includes LangGraph Server, LangGraph Studio, plus the CLI and SDK. ... we added features in LangGraph Server to deliver on a few key value areas. Below, we'll focus on these aspects of LangGraph Platform."}]
|
||||
================================== Ai Message ==================================
|
||||
|
||||
Thank you for your patience. I've found some recent information about LangGraph for you. Let me summarize the key points:
|
||||
|
||||
1. LangGraph is part of the LangChain ecosystem, which is a leading platform in AI development.
|
||||
|
||||
2. Recent updates and features of LangGraph include:
|
||||
|
||||
a. LangGraph Cloud: This seems to be a cloud-based version of LangGraph, though specific details weren't provided in the search results.
|
||||
...
|
||||
3. Keep an eye on LangGraph Cloud developments, as cloud-based solutions often provide an easier starting point for learners.
|
||||
4. Consider how LangGraph fits into the broader LangChain ecosystem, especially its interaction with tools like LangSmith.
|
||||
|
||||
Is there any specific aspect of LangGraph you'd like to know more about? I'd be happy to do a more focused search on particular features or use cases.
|
||||
Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...
|
||||
```
|
||||
|
||||
```python
|
||||
events = graph.stream(
|
||||
{
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": (
|
||||
"Ya that's helpful. Maybe I'll "
|
||||
"build an autonomous agent with it!"
|
||||
),
|
||||
},
|
||||
],
|
||||
},
|
||||
config,
|
||||
stream_mode="values",
|
||||
)
|
||||
for event in events:
|
||||
if "messages" in event:
|
||||
event["messages"][-1].pretty_print()
|
||||
```
|
||||
|
||||
```
|
||||
================================ Human Message =================================
|
||||
|
||||
Ya that's helpful. Maybe I'll build an autonomous agent with it!
|
||||
================================== Ai Message ==================================
|
||||
|
||||
[{'text': "That's an exciting idea! Building an autonomous agent with LangGraph is indeed a great application of this technology. LangGraph is particularly well-suited for creating complex, multi-step AI workflows, which is perfect for autonomous agents. Let me gather some more specific information about using LangGraph for building autonomous agents.", 'type': 'text'}, {'id': 'toolu_01QWNHhUaeeWcGXvA4eHT7Zo', 'input': {'query': 'Building autonomous agents with LangGraph examples and tutorials'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
|
||||
Tool Calls:
|
||||
tavily_search_results_json (toolu_01QWNHhUaeeWcGXvA4eHT7Zo)
|
||||
Call ID: toolu_01QWNHhUaeeWcGXvA4eHT7Zo
|
||||
Args:
|
||||
query: Building autonomous agents with LangGraph examples and tutorials
|
||||
================================= Tool Message =================================
|
||||
Name: tavily_search_results_json
|
||||
|
||||
[{"url": "https://towardsdatascience.com/building-autonomous-multi-tool-agents-with-gemini-2-0-and-langgraph-ad3d7bd5e79d", "content": "Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph | by Youness Mansar | Jan, 2025 | Towards Data Science Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph A practical tutorial with full code examples for building and running multi-tool agents Towards Data Science LLMs are remarkable — they can memorize vast amounts of information, answer general knowledge questions, write code, generate stories, and even fix your grammar. In this tutorial, we are going to build a simple LLM agent that is equipped with four tools that it can use to answer a user’s question. This Agent will have the following specifications: Follow Published in Towards Data Science --------------------------------- Your home for data science and AI. Follow Follow Follow"}, {"url": "https://github.com/anmolaman20/Tools_and_Agents", "content": "GitHub - anmolaman20/Tools_and_Agents: This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository serves as a comprehensive guide for building AI-powered agents using Langchain and Langgraph. It provides hands-on examples, practical tutorials, and resources for developers and AI enthusiasts to master building intelligent systems and workflows. AI Agent Development: Gain insights into creating intelligent systems that think, reason, and adapt in real time. This repository is ideal for AI practitioners, developers exploring language models, or anyone interested in building intelligent systems. This repository provides resources for building AI agents using Langchain and Langgraph."}]
|
||||
================================== Ai Message ==================================
|
||||
|
||||
Great idea! Building an autonomous agent with LangGraph is definitely an exciting project. Based on the latest information I've found, here are some insights and tips for building autonomous agents with LangGraph:
|
||||
|
||||
1. Multi-Tool Agents: LangGraph is particularly well-suited for creating autonomous agents that can use multiple tools. This allows your agent to have a diverse set of capabilities and choose the right tool for each task.
|
||||
|
||||
2. Integration with Large Language Models (LLMs): You can combine LangGraph with powerful LLMs like Gemini 2.0 to create more intelligent and capable agents. The LLM can serve as the "brain" of your agent, making decisions and generating responses.
|
||||
|
||||
3. Workflow Management: LangGraph excels at managing complex, multi-step AI workflows. This is crucial for autonomous agents that need to break down tasks into smaller steps and execute them in the right order.
|
||||
...
|
||||
6. Pay attention to how you structure the agent's decision-making process and workflow.
|
||||
7. Don't forget to implement proper error handling and safety measures, especially if your agent will be interacting with external systems or making important decisions.
|
||||
|
||||
Building an autonomous agent is an iterative process, so be prepared to refine and improve your agent over time. Good luck with your project! If you need any more specific information as you progress, feel free to ask.
|
||||
Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...
|
||||
```
|
||||
|
||||
## 3. Replay the full state history
|
||||
|
||||
Now that you have added steps to the chatbot, you can `replay` the full state history to see everything that occurred.
|
||||
|
||||
``` python
|
||||
to_replay = None
|
||||
for state in graph.get_state_history(config):
|
||||
print("Num Messages: ", len(state.values["messages"]), "Next: ", state.next)
|
||||
print("-" * 80)
|
||||
if len(state.values["messages"]) == 6:
|
||||
# We are somewhat arbitrarily selecting a specific state based on the number of chat messages in the state.
|
||||
to_replay = state
|
||||
```
|
||||
|
||||
```
|
||||
Num Messages: 8 Next: ()
|
||||
--------------------------------------------------------------------------------
|
||||
Num Messages: 7 Next: ('chatbot',)
|
||||
--------------------------------------------------------------------------------
|
||||
Num Messages: 6 Next: ('tools',)
|
||||
--------------------------------------------------------------------------------
|
||||
Num Messages: 5 Next: ('chatbot',)
|
||||
--------------------------------------------------------------------------------
|
||||
Num Messages: 4 Next: ('__start__',)
|
||||
--------------------------------------------------------------------------------
|
||||
Num Messages: 4 Next: ()
|
||||
--------------------------------------------------------------------------------
|
||||
Num Messages: 3 Next: ('chatbot',)
|
||||
--------------------------------------------------------------------------------
|
||||
Num Messages: 2 Next: ('tools',)
|
||||
--------------------------------------------------------------------------------
|
||||
Num Messages: 1 Next: ('chatbot',)
|
||||
--------------------------------------------------------------------------------
|
||||
Num Messages: 0 Next: ('__start__',)
|
||||
--------------------------------------------------------------------------------
|
||||
```
|
||||
|
||||
Checkpoints are saved for every step of the graph. This __spans invocations__ so you can rewind across a full thread's history.
|
||||
|
||||
## Resume from a checkpoint
|
||||
|
||||
Resume from the `to_replay` state, which is after the `chatbot` node in the second graph invocation. Resuming from this point will call the **action** node next.
|
||||
|
||||
```python
|
||||
print(to_replay.next)
|
||||
print(to_replay.config)
|
||||
```
|
||||
|
||||
```
|
||||
('tools',)
|
||||
{'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1efd43e3-0c1f-6c4e-8006-891877d65740'}}
|
||||
```
|
||||
|
||||
## 4. Load a state from a moment-in-time
|
||||
|
||||
The checkpoint's `to_replay.config` contains a `checkpoint_id` timestamp. Providing this `checkpoint_id` value tells LangGraph's checkpointer to **load** the state from that moment in time.
|
||||
|
||||
|
||||
``` python
|
||||
# The `checkpoint_id` in the `to_replay.config` corresponds to a state we've persisted to our checkpointer.
|
||||
for event in graph.stream(None, to_replay.config, stream_mode="values"):
|
||||
if "messages" in event:
|
||||
event["messages"][-1].pretty_print()
|
||||
```
|
||||
|
||||
```
|
||||
================================== Ai Message ==================================
|
||||
|
||||
[{'text': "That's an exciting idea! Building an autonomous agent with LangGraph is indeed a great application of this technology. LangGraph is particularly well-suited for creating complex, multi-step AI workflows, which is perfect for autonomous agents. Let me gather some more specific information about using LangGraph for building autonomous agents.", 'type': 'text'}, {'id': 'toolu_01QWNHhUaeeWcGXvA4eHT7Zo', 'input': {'query': 'Building autonomous agents with LangGraph examples and tutorials'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
|
||||
Tool Calls:
|
||||
tavily_search_results_json (toolu_01QWNHhUaeeWcGXvA4eHT7Zo)
|
||||
Call ID: toolu_01QWNHhUaeeWcGXvA4eHT7Zo
|
||||
Args:
|
||||
query: Building autonomous agents with LangGraph examples and tutorials
|
||||
================================= Tool Message =================================
|
||||
Name: tavily_search_results_json
|
||||
|
||||
[{"url": "https://towardsdatascience.com/building-autonomous-multi-tool-agents-with-gemini-2-0-and-langgraph-ad3d7bd5e79d", "content": "Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph | by Youness Mansar | Jan, 2025 | Towards Data Science Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph A practical tutorial with full code examples for building and running multi-tool agents Towards Data Science LLMs are remarkable — they can memorize vast amounts of information, answer general knowledge questions, write code, generate stories, and even fix your grammar. In this tutorial, we are going to build a simple LLM agent that is equipped with four tools that it can use to answer a user’s question. This Agent will have the following specifications: Follow Published in Towards Data Science --------------------------------- Your home for data science and AI. Follow Follow Follow"}, {"url": "https://github.com/anmolaman20/Tools_and_Agents", "content": "GitHub - anmolaman20/Tools_and_Agents: This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository serves as a comprehensive guide for building AI-powered agents using Langchain and Langgraph. It provides hands-on examples, practical tutorials, and resources for developers and AI enthusiasts to master building intelligent systems and workflows. AI Agent Development: Gain insights into creating intelligent systems that think, reason, and adapt in real time. This repository is ideal for AI practitioners, developers exploring language models, or anyone interested in building intelligent systems. This repository provides resources for building AI agents using Langchain and Langgraph."}]
|
||||
================================== Ai Message ==================================
|
||||
|
||||
Great idea! Building an autonomous agent with LangGraph is indeed an excellent way to apply and deepen your understanding of the technology. Based on the search results, I can provide you with some insights and resources to help you get started:
|
||||
|
||||
1. Multi-Tool Agents:
|
||||
LangGraph is well-suited for building autonomous agents that can use multiple tools. This allows your agent to have a variety of capabilities and choose the appropriate tool based on the task at hand.
|
||||
|
||||
2. Integration with Large Language Models (LLMs):
|
||||
There's a tutorial that specifically mentions using Gemini 2.0 (Google's LLM) with LangGraph to build autonomous agents. This suggests that LangGraph can be integrated with various LLMs, giving you flexibility in choosing the language model that best fits your needs.
|
||||
|
||||
3. Practical Tutorials:
|
||||
There are tutorials available that provide full code examples for building and running multi-tool agents. These can be invaluable as you start your project, giving you a concrete starting point and demonstrating best practices.
|
||||
...
|
||||
|
||||
Remember, building an autonomous agent is an iterative process. Start simple and gradually increase complexity as you become more comfortable with LangGraph and its capabilities.
|
||||
|
||||
Would you like more information on any specific aspect of building your autonomous agent with LangGraph?
|
||||
Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...
|
||||
```
|
||||
|
||||
The graph resumed execution from the `action` node. You can tell this is the case since the first value printed above is the response from our search engine tool.
|
||||
|
||||
**Congratulations!** You've now used time-travel checkpoint traversal in LangGraph. Being able to rewind and explore alternative paths opens up a world of possibilities for debugging, experimentation, and interactive applications.
|
||||
|
||||
## Learn more
|
||||
|
||||
Take your LangGraph journey further by exploring deployment and advanced features:
|
||||
|
||||
- **[LangGraph Server quickstart](../../tutorials/langgraph-platform/local-server.md)**: Launch a LangGraph server locally and interact with it using the REST API and LangGraph Studio Web UI.
|
||||
- **[LangGraph Cloud quickstart](../../cloud/quick_start.md)**: Deploy your LangGraph app using LangGraph Cloud.
|
||||
- **[LangGraph Platform concepts](../../concepts/langgraph_platform.md)**: Understand the foundational concepts of the LangGraph Platform.
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 6.1 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 26 KiB |
@@ -10,7 +10,7 @@ New to LangGraph or LLM app development? Read this material to get up and runnin
|
||||
|
||||
## Get Started 🚀 {#quick-start}
|
||||
|
||||
- [LangGraph Quickstart](introduction.ipynb): Build a chatbot that can use tools and keep track of conversation history. Add human-in-the-loop capabilities and explore how time-travel works.
|
||||
- [LangGraph basics](get-started/1-build-basic-chatbot.md): Build a chatbot that can use tools and keep track of conversation history. Add human-in-the-loop capabilities and explore how time-travel works.
|
||||
- [Common Workflows](workflows/index.md): Overview of the most common workflows using LLMs implemented with LangGraph.
|
||||
- [LangGraph Server Quickstart](langgraph-platform/local-server.md): Launch a LangGraph server locally and interact with it using REST API and LangGraph Studio Web UI.
|
||||
- [LangGraph Template Quickstart](../concepts/template_applications.md): Start building with LangGraph Platform using a template application.
|
||||
|
||||
File diff suppressed because one or more lines are too long
@@ -1,19 +1,28 @@
|
||||
# Quickstart: Launch Local LangGraph Server
|
||||
# LangGraph Platform quickstart
|
||||
|
||||
This is a quick start guide to help you get a LangGraph app up and running locally.
|
||||
This guide shows you how to run a LangGraph application locally.
|
||||
|
||||
!!! info "Requirements"
|
||||
## Prerequisites
|
||||
|
||||
- Python >= 3.11
|
||||
- [LangGraph CLI](https://langchain-ai.github.io/langgraph/cloud/reference/cli/): Requires langchain-cli[inmem] >= 0.1.58
|
||||
Before you begin, ensure you have the following:
|
||||
|
||||
## Install the LangGraph CLI
|
||||
- An API key for [LangSmith](https://smith.langchain.com/settings) - free to sign up
|
||||
|
||||
This quickstart uses the `react-agent` template and requires the following:
|
||||
|
||||
- An API key for [Anthropic](https://console.anthropic.com/)
|
||||
- An API key for [OpenAI](https://openai.com/)
|
||||
- An API key [Tavily](https://app.tavily.com/)
|
||||
|
||||
## 1. Install the LangGraph CLI
|
||||
|
||||
```bash
|
||||
# Python >= 3.11 is required.
|
||||
|
||||
pip install --upgrade "langgraph-cli[inmem]"
|
||||
```
|
||||
|
||||
## 🌱 Create a LangGraph App
|
||||
## 2. Create a LangGraph app 🌱
|
||||
|
||||
Create a new app from the `react-agent` template. This template is a simple agent that can be flexibly extended to many tools.
|
||||
|
||||
@@ -29,22 +38,31 @@ Create a new app from the `react-agent` template. This template is a simple agen
|
||||
langgraph new path/to/your/app --template react-agent-js
|
||||
```
|
||||
|
||||
!!! tip "Additional Templates"
|
||||
!!! tip "Additional templates"
|
||||
|
||||
If you use `langgraph new` without specifying a template, you will be presented with an interactive menu that will allow you to choose from a list of available templates.
|
||||
|
||||
## Install Dependencies
|
||||
## 3. Install dependencies
|
||||
|
||||
In the root of your new LangGraph app, install the dependencies in `edit` mode so your local changes are used by the server:
|
||||
|
||||
```shell
|
||||
pip install -e .
|
||||
```
|
||||
=== "Python server"
|
||||
|
||||
## Create a `.env` file
|
||||
```shell
|
||||
cd path/to/your/app
|
||||
pip install -e .
|
||||
```
|
||||
|
||||
You will find a `.env.example` in the root of your new LangGraph app. Create
|
||||
a `.env` file in the root of your new LangGraph app and copy the contents of the `.env.example` file into it, filling in the necessary API keys:
|
||||
=== "Node server"
|
||||
|
||||
```shell
|
||||
cd path/to/your/app
|
||||
yarn install
|
||||
```
|
||||
|
||||
## 4. Create a `.env` file
|
||||
|
||||
You will find a `.env.example` in the root of your new LangGraph app. Create a `.env` file in the root of your new LangGraph app and copy the contents of the `.env.example` file into it, filling in the necessary API keys:
|
||||
|
||||
```bash
|
||||
LANGSMITH_API_KEY=lsv2...
|
||||
@@ -53,21 +71,25 @@ ANTHROPIC_API_KEY=sk-
|
||||
OPENAI_API_KEY=sk-...
|
||||
```
|
||||
|
||||
??? note "Get API Keys"
|
||||
## 5. Launch LangGraph Server 🚀
|
||||
|
||||
- **LANGSMITH_API_KEY**: Go to the [LangSmith Settings page](https://smith.langchain.com/settings). Then clck **Create API Key**.
|
||||
- **ANTHROPIC_API_KEY**: Get an API key from [Anthropic](https://console.anthropic.com/).
|
||||
- **OPENAI_API_KEY**: Get an API key from [OpenAI](https://openai.com/).
|
||||
- **TAVILY_API_KEY**: Get an API key on the [Tavily website](https://app.tavily.com/).
|
||||
Start the LangGraph API server locally:
|
||||
|
||||
## 🚀 Launch LangGraph Server
|
||||
=== "Python Server"
|
||||
|
||||
```shell
|
||||
langgraph dev
|
||||
```
|
||||
|
||||
=== "Node Server"
|
||||
|
||||
```shell
|
||||
npx @langchain/langgraph-cli dev
|
||||
```
|
||||
|
||||
Sample output:
|
||||
|
||||
```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/)
|
||||
@@ -75,133 +97,130 @@ This will start up the LangGraph API server locally. If this runs successfully,
|
||||
> - Docs: http://localhost:2024/docs
|
||||
>
|
||||
> - LangGraph Studio Web UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
|
||||
```
|
||||
|
||||
The `langgraph dev` command starts LangGraph Server in an in-memory mode. This mode is suitable for development and testing purposes. For production use, deploy LangGraph Server with access to a persistent storage backend. For more information, see [Deployment options](../../concepts/deployment_options.md).
|
||||
|
||||
!!! note "In-Memory Mode"
|
||||
## 6. Test your application in LangGraph Studio
|
||||
|
||||
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.
|
||||
|
||||
## LangGraph Studio Web UI
|
||||
|
||||
LangGraph Studio Web is a specialized UI that you can connect to LangGraph API server to enable visualization, interaction, and debugging of your application locally. Test your graph in the LangGraph Studio Web UI by visiting the URL provided in the output of the `langgraph dev` command.
|
||||
[LangGraph Studio](../../concepts/langgraph_studio.md) is a specialized UI that you can connect to LangGraph API server to visualize, interact with, and debug your application locally. Test your graph in LangGraph Studio by visiting the URL provided in the output of the `langgraph dev` command:
|
||||
|
||||
```
|
||||
> - LangGraph Studio Web UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
|
||||
```
|
||||
|
||||
!!! info "Connecting to a server with a custom host/port"
|
||||
For a LangGraph Server running on a custom host/port, update the baseURL parameter.
|
||||
|
||||
If you are running the LangGraph API server with a custom host / port, you can point the Studio Web UI at it by changing the `baseUrl` URL param. For example, if you are running your server on port 8000, you can change the above URL to the following:
|
||||
|
||||
```
|
||||
https://smith.langchain.com/studio/baseUrl=http://127.0.0.1:8000
|
||||
```
|
||||
|
||||
|
||||
!!! warning "Safari Compatibility"
|
||||
??? info "Safari compatibility"
|
||||
|
||||
Use the `--tunnel` flag with your command to create a secure tunnel, as Safari has limitations when connecting to localhost servers:
|
||||
|
||||
Currently, LangGraph Studio Web does not support Safari when running a server locally.
|
||||
|
||||
## Test the API
|
||||
|
||||
=== "Python SDK (Async)"
|
||||
|
||||
**Install the LangGraph Python SDK**
|
||||
|
||||
```shell
|
||||
pip install langgraph-sdk
|
||||
langgraph dev --tunnel
|
||||
```
|
||||
|
||||
**Send a message to the assistant (threadless run)**
|
||||
## 7. Test the API
|
||||
|
||||
```python
|
||||
from langgraph_sdk import get_client
|
||||
=== "Python SDK (async)"
|
||||
|
||||
client = get_client(url="http://localhost:2024")
|
||||
1. Install the LangGraph Python SDK:
|
||||
|
||||
async for chunk in client.runs.stream(
|
||||
None, # Threadless run
|
||||
"agent", # Name of assistant. Defined in langgraph.json.
|
||||
input={
|
||||
"messages": [{
|
||||
"role": "human",
|
||||
"content": "What is LangGraph?",
|
||||
}],
|
||||
},
|
||||
stream_mode="updates",
|
||||
):
|
||||
print(f"Receiving new event of type: {chunk.event}...")
|
||||
print(chunk.data)
|
||||
print("\n\n")
|
||||
```
|
||||
```shell
|
||||
pip install langgraph-sdk
|
||||
```
|
||||
|
||||
=== "Python SDK (Sync)"
|
||||
1. Send a message to the assistant (threadless run):
|
||||
|
||||
**Install the LangGraph Python SDK**
|
||||
```python
|
||||
from langgraph_sdk import get_client
|
||||
import asyncio
|
||||
|
||||
```shell
|
||||
pip install langgraph-sdk
|
||||
```
|
||||
client = get_client(url="http://localhost:2024")
|
||||
|
||||
**Send a message to the assistant (threadless run)**
|
||||
async def main():
|
||||
async for chunk in client.runs.stream(
|
||||
None, # Threadless run
|
||||
"agent", # Name of assistant. Defined in langgraph.json.
|
||||
input={
|
||||
"messages": [{
|
||||
"role": "human",
|
||||
"content": "What is LangGraph?",
|
||||
}],
|
||||
},
|
||||
):
|
||||
print(f"Receiving new event of type: {chunk.event}...")
|
||||
print(chunk.data)
|
||||
print("\n\n")
|
||||
|
||||
```python
|
||||
from langgraph_sdk import get_sync_client
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
client = get_sync_client(url="http://localhost:2024")
|
||||
=== "Python SDK (sync)"
|
||||
|
||||
for chunk in client.runs.stream(
|
||||
None, # Threadless run
|
||||
"agent", # Name of assistant. Defined in langgraph.json.
|
||||
input={
|
||||
"messages": [{
|
||||
"role": "human",
|
||||
"content": "What is LangGraph?",
|
||||
}],
|
||||
},
|
||||
stream_mode="updates",
|
||||
):
|
||||
print(f"Receiving new event of type: {chunk.event}...")
|
||||
print(chunk.data)
|
||||
print("\n\n")
|
||||
```
|
||||
1. Install the LangGraph Python SDK:
|
||||
|
||||
```shell
|
||||
pip install langgraph-sdk
|
||||
```
|
||||
|
||||
1. Send a message to the assistant (threadless run):
|
||||
|
||||
```python
|
||||
from langgraph_sdk import get_sync_client
|
||||
|
||||
client = get_sync_client(url="http://localhost:2024")
|
||||
|
||||
for chunk in client.runs.stream(
|
||||
None, # Threadless run
|
||||
"agent", # Name of assistant. Defined in langgraph.json.
|
||||
input={
|
||||
"messages": [{
|
||||
"role": "human",
|
||||
"content": "What is LangGraph?",
|
||||
}],
|
||||
},
|
||||
stream_mode="messages-tuple",
|
||||
):
|
||||
print(f"Receiving new event of type: {chunk.event}...")
|
||||
print(chunk.data)
|
||||
print("\n\n")
|
||||
```
|
||||
|
||||
=== "Javascript SDK"
|
||||
|
||||
**Install the LangGraph JS SDK**
|
||||
1. Install the LangGraph JS SDK:
|
||||
|
||||
```shell
|
||||
npm install @langchain/langgraph-sdk
|
||||
```
|
||||
```shell
|
||||
npm install @langchain/langgraph-sdk
|
||||
```
|
||||
|
||||
**Send a message to the assistant (threadless run)**
|
||||
1. Send a message to the assistant (threadless run):
|
||||
|
||||
```js
|
||||
const { Client } = await import("@langchain/langgraph-sdk");
|
||||
```js
|
||||
const { Client } = await import("@langchain/langgraph-sdk");
|
||||
|
||||
// only set the apiUrl if you changed the default port when calling langgraph dev
|
||||
const client = new Client({ apiUrl: "http://localhost:2024"});
|
||||
// only set the apiUrl if you changed the default port when calling langgraph dev
|
||||
const client = new Client({ apiUrl: "http://localhost:2024"});
|
||||
|
||||
const streamResponse = client.runs.stream(
|
||||
null, // Threadless run
|
||||
"agent", // Assistant ID
|
||||
{
|
||||
input: {
|
||||
"messages": [
|
||||
{ "role": "user", "content": "What is LangGraph?"}
|
||||
]
|
||||
},
|
||||
streamMode: "messages",
|
||||
const streamResponse = client.runs.stream(
|
||||
null, // Threadless run
|
||||
"agent", // Assistant ID
|
||||
{
|
||||
input: {
|
||||
"messages": [
|
||||
{ "role": "user", "content": "What is LangGraph?"}
|
||||
]
|
||||
},
|
||||
streamMode: "messages-tuple",
|
||||
}
|
||||
);
|
||||
|
||||
for await (const chunk of streamResponse) {
|
||||
console.log(`Receiving new event of type: ${chunk.event}...`);
|
||||
console.log(JSON.stringify(chunk.data));
|
||||
console.log("\n\n");
|
||||
}
|
||||
);
|
||||
|
||||
for await (const chunk of streamResponse) {
|
||||
console.log(`Receiving new event of type: ${chunk.event}...`);
|
||||
console.log(JSON.stringify(chunk.data));
|
||||
console.log("\n\n");
|
||||
}
|
||||
```
|
||||
```
|
||||
|
||||
=== "Rest API"
|
||||
|
||||
@@ -219,35 +238,16 @@ LangGraph Studio Web is a specialized UI that you can connect to LangGraph API s
|
||||
}
|
||||
]
|
||||
},
|
||||
\"stream_mode\": \"updates\"
|
||||
\"stream_mode\": \"messages-tuple\"
|
||||
}"
|
||||
```
|
||||
|
||||
!!! tip "Auth"
|
||||
|
||||
If you're connecting to a remote server, you will need to provide a LangSmith
|
||||
API Key for authorization. Please see the API Reference for the clients
|
||||
for more information.
|
||||
|
||||
## Next Steps
|
||||
|
||||
Now that you have a LangGraph app running locally, take your journey further by exploring deployment and advanced features:
|
||||
|
||||
### 🌐 Deploy to LangGraph Cloud
|
||||
|
||||
- **[LangGraph Cloud Quickstart](../../cloud/quick_start.md)**: Deploy your LangGraph app using LangGraph Cloud.
|
||||
|
||||
### 📚 Learn More about LangGraph Platform
|
||||
|
||||
Expand your knowledge with these resources:
|
||||
|
||||
- **[LangGraph Platform Concepts](../../concepts/index.md#langgraph-platform)**: Understand the foundational concepts of the LangGraph Platform.
|
||||
- **[LangGraph Platform How-to Guides](../../how-tos/index.md#langgraph-platform)**: Discover step-by-step guides to build and deploy applications.
|
||||
|
||||
### 🛠️ Developer References
|
||||
|
||||
Access detailed documentation for development and API usage:
|
||||
|
||||
- **[LangGraph Server API Reference](../../cloud/reference/api/api_ref.html)**: Explore the LangGraph Server API documentation.
|
||||
- **[Python SDK Reference](../../cloud/reference/sdk/python_sdk_ref.md)**: Explore the Python SDK API Reference.
|
||||
- **[JS/TS SDK Reference](../../cloud/reference/sdk/js_ts_sdk_ref.md)**: Explore the JS/TS SDK API Reference.
|
||||
- [Deployment quickstart](../../cloud/quick_start.md): Deploy your LangGraph app using LangGraph Platform.
|
||||
- [LangGraph Platform overview](../../concepts/langgraph_platform.md): Learn about foundational LangGraph Platform concepts.
|
||||
- [LangGraph Server API Reference](../../cloud/reference/api/api_ref.html): Explore the LangGraph Server API documentation.
|
||||
- [Python SDK Reference](../../cloud/reference/sdk/python_sdk_ref.md): Explore the Python SDK API Reference.
|
||||
- [JS/TS SDK Reference](../../cloud/reference/sdk/js_ts_sdk_ref.md): Explore the JS/TS SDK API Reference.
|
||||
|
||||
@@ -11,7 +11,7 @@ This guide reviews common patterns for agentic systems. In describing these syst
|
||||
|
||||
Here is a simple way to visualize these differences:
|
||||
|
||||
|
||||
|
||||
|
||||
When building agents and workflows, LangGraph offers a number of benefits including persistence, streaming, and support for debugging as well as deployment.
|
||||
|
||||
@@ -47,7 +47,7 @@ llm = ChatAnthropic(model="claude-3-5-sonnet-latest")
|
||||
|
||||
LLM have augmentations that support building workflows and agents. These include [structured outputs](https://python.langchain.com/docs/concepts/structured_outputs/) and [tool calling](https://python.langchain.com/docs/concepts/tool_calling/), as shown in this image from the Anthropic blog on `Building Effective Agents`:
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
```python
|
||||
@@ -91,7 +91,7 @@ As noted in the Anthropic blog on `Building Effective Agents`:
|
||||
|
||||
> When to use this workflow: This workflow is ideal for situations where the task can be easily and cleanly decomposed into fixed subtasks. The main goal is to trade off latency for higher accuracy, by making each LLM call an easier task.
|
||||
|
||||
|
||||
|
||||
|
||||
=== "Graph API"
|
||||
|
||||
@@ -252,7 +252,7 @@ With parallelization, LLMs work simultaneously on a task:
|
||||
|
||||
> When to use this workflow: Parallelization is effective when the divided subtasks can be parallelized for speed, or when multiple perspectives or attempts are needed for higher confidence results. For complex tasks with multiple considerations, LLMs generally perform better when each consideration is handled by a separate LLM call, allowing focused attention on each specific aspect.
|
||||
|
||||
|
||||
|
||||
|
||||
=== "Graph API"
|
||||
|
||||
@@ -402,7 +402,7 @@ Routing classifies an input and directs it to a followup task. As noted in the A
|
||||
|
||||
> When to use this workflow: Routing works well for complex tasks where there are distinct categories that are better handled separately, and where classification can be handled accurately, either by an LLM or a more traditional classification model/algorithm.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
=== "Graph API"
|
||||
@@ -613,7 +613,7 @@ With orchestrator-worker, an orchestrator breaks down a task and delegates each
|
||||
|
||||
> When to use this workflow: This workflow is well-suited for complex tasks where you can’t predict the subtasks needed (in coding, for example, the number of files that need to be changed and the nature of the change in each file likely depend on the task). Whereas it’s topographically similar, the key difference from parallelization is its flexibility—subtasks aren't pre-defined, but determined by the orchestrator based on the specific input.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
=== "Graph API"
|
||||
@@ -857,7 +857,7 @@ In the evaluator-optimizer workflow, one LLM call generates a response while ano
|
||||
|
||||
> When to use this workflow: This workflow is particularly effective when we have clear evaluation criteria, and when iterative refinement provides measurable value. The two signs of good fit are, first, that LLM responses can be demonstrably improved when a human articulates their feedback; and second, that the LLM can provide such feedback. This is analogous to the iterative writing process a human writer might go through when producing a polished document.
|
||||
|
||||
|
||||
|
||||
|
||||
=== "Graph API"
|
||||
|
||||
@@ -1022,7 +1022,7 @@ Agents are typically implemented as an LLM performing actions (via tool-calling)
|
||||
|
||||
> When to use agents: Agents can be used for open-ended problems where it’s difficult or impossible to predict the required number of steps, and where you can’t hardcode a fixed path. The LLM will potentially operate for many turns, and you must have some level of trust in its decision-making. Agents' autonomy makes them ideal for scaling tasks in trusted environments.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
```python
|
||||
Reference in New Issue
Block a user