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

Main changes made: - Add top level horizontal tabs - Reorganize the sidenav - Build out README/index page - Consolidate how-tos under each section - Remove duplicate content --------- Signed-off-by: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com> Co-authored-by: Tat Dat Duong <david@duong.cz> Co-authored-by: Sydney Runkle <sydneymarierunkle@gmail.com> Co-authored-by: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com> Co-authored-by: Vadym Barda <vadym@langchain.dev> Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com> Co-authored-by: ccurme <chester.curme@gmail.com> Co-authored-by: Andrew Nguonly <andrewnguonly@users.noreply.github.com> Co-authored-by: David Asamu <david.asamu@langchain.dev> Co-authored-by: infra <mukil@langchain.dev> Co-authored-by: Arjun Natarajan <arjun@langchain.dev>
2026-08-17 21:25:46 +02:00 · 2025-05-09 16:09:43 -04:00
parent 909a4591a8
commit 3055c4b9cc
239 changed files with 14663 additions and 27446 deletions
@@ -38,12 +38,13 @@ jobs:
        with:
          filter: "docs/docs/**"

-  run-changed-notebooks:
-    needs: get-changed-files
-    uses: ./.github/workflows/run_notebooks.yml
-    secrets: inherit
-    with:
-      changed-files: ${{ needs.get-changed-files.outputs.changed-files }}
+  # TODO: Uncomment this to run on PRs
+  # run-changed-notebooks:
+  #   needs: get-changed-files
+  #   uses: ./.github/workflows/run_notebooks.yml
+  #   secrets: inherit
+  #   with:
+  #     changed-files: ${{ needs.get-changed-files.outputs.changed-files }}

  deploy:
    # needs: run-changed-notebooks
@@ -98,6 +99,7 @@ jobs:
      - name: Check links in notebooks
        env:
          LANGCHAIN_API_KEY: test
+        if: github.event_name == 'schedule'
        run: |
          if [ "${{ github.event_name }}" == "schedule" ]; then
            echo "Running link check on all HTML files matching notebooks in docs directory..."
@@ -16,48 +16,41 @@
 > [!NOTE]
 > Looking for the JS version? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://langchain-ai.github.io/langgraphjs/).

-LangGraph — used by Replit, Uber, LinkedIn, GitLab and more — is a low-level orchestration framework for building controllable agents. While langchain provides integrations and composable components to streamline LLM application development, the LangGraph library enables agent orchestration — offering customizable architectures, long-term memory, and human-in-the-loop to reliably handle complex tasks.
+LangGraph is a low-level orchestration framework for building controllable agents. While [LangChain](https://python.langchain.com/docs/introduction/) provides integrations and composable components to streamline LLM application development, the LangGraph library enables agent orchestration — offering customizable architectures, long-term memory, and human-in-the-loop to reliably handle complex tasks.

-```bash
+## Get started
+
+First, install LangGraph:
+
+```
 pip install -U langgraph
 ```

-To learn more about how to use LangGraph, check out [the docs](https://langchain-ai.github.io/langgraph/). We show a simple example below of how to create a ReAct agent.
+There are two ways to get started with LangGraph:

-```python
-# This code depends on pip install langchain[anthropic]
-from langgraph.prebuilt import create_react_agent
+- [Use prebuilt components](https://langchain-ai.github.io/langgraph/agents/agents/): Construct agentic systems quickly and reliably without the need to implement orchestration, memory, or human feedback handling from scratch.
+- [Use LangGraph](https://langchain-ai.github.io/langgraph/tutorials/introduction/): Customize your architectures, use long-term memory, and implement human-in-the-loop to reliably handle complex tasks.

-def search(query: str):
-    """Call to surf the web."""
-    if "sf" in query.lower() or "san francisco" in query.lower():
-        return "It's 60 degrees and foggy."
-    return "It's 90 degrees and sunny."
+Once you have a LangGraph application and are ready to move into production, use [LangGraph Platform](https://langchain-ai.github.io/langgraph/cloud/quick_start/) to test, debug, and deploy your application.

-agent = create_react_agent("anthropic:claude-3-7-sonnet-latest", tools=[search])
-agent.invoke(
-    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
-)
-```
+## What LangGraph provides

-> [!TIP]
-> Check out [this guide](https://langchain-ai.github.io/langgraph/tutorials/workflows/) that walks through implementing common patterns (workflows and agents) in LangGraph. 
+LangGraph provides low-level supporting infrastructure that sits underneath *any* workflow or agent. It does not abstract prompts or architecture, and provides three central benefits:

-## Why use LangGraph?
+### Persistence

-LangGraph is built for developers who want to build powerful, adaptable AI agents. Developers choose LangGraph for:
+LangGraph has a [persistence layer](https://langchain-ai.github.io/langgraph/concepts/persistence/), which offers a number of benefits:

- **Reliability and controllability.** Steer agent actions with moderation checks and human-in-the-loop approvals. LangGraph persists context for long-running workflows, keeping your agents on course.
- **Low-level and extensible.** Build custom agents with fully descriptive, low-level primitives – free from rigid abstractions that limit customization. Design scalable multi-agent systems, with each agent serving a specific role tailored to your use case.
- **First-class streaming support.** With token-by-token streaming and streaming of intermediate steps, LangGraph gives users clear visibility into agent reasoning and actions as they unfold in real time.
+- [Memory](https://langchain-ai.github.io/langgraph/concepts/memory/): LangGraph persists arbitrary aspects of your application's state, supporting memory of conversations and other updates within and across user interactions;
+- [Human-in-the-loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/): Because state is checkpointed, execution can be interrupted and resumed, allowing for decisions, validation, and corrections via human input.

-LangGraph is trusted in production and powering agents for companies like:
+### Streaming

- [Klarna](https://blog.langchain.dev/customers-klarna/): Customer support bot for 85 million active users
- [Elastic](https://www.elastic.co/blog/elastic-security-generative-ai-features): Security AI assistant for threat detection
- [Uber](https://dpe.org/sessions/ty-smith-adam-huda/this-year-in-ubers-ai-driven-developer-productivity-revolution/): Automated unit test generation
- [Replit](https://www.langchain.com/breakoutagents/replit): Code generation
- And many more ([see list here](https://www.langchain.com/built-with-langgraph))
+LangGraph provides support for [streaming](https://langchain-ai.github.io/langgraph/concepts/streaming/) workflow / agent state to the user (or developer) over the course of execution. LangGraph supports streaming of both events ([such as feedback from a tool call](https://langchain-ai.github.io/langgraph/how-tos/streaming.ipynb#updates)) and [tokens from LLM calls](https://langchain-ai.github.io/langgraph/how-tos/streaming-tokens.ipynb) embedded in an application.
+
+### Debugging and deployment
+
+LangGraph provides an easy onramp for testing, debugging, and deploying applications via [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/). This includes [Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/), an IDE that enables visualization, interaction, and debugging of workflows or agents. This also includes numerous [options](https://langchain-ai.github.io/langgraph/tutorials/deployment/) for deployment. 

 ## LangGraph’s ecosystem

@@ -66,24 +59,14 @@ While LangGraph can be used standalone, it also integrates seamlessly with any L
 - [LangSmith](http://www.langchain.com/langsmith) — Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
 - [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).

-## Pairing with LangGraph Platform
-
-While LangGraph is our open-source agent orchestration framework, enterprises that need scalable agent deployment can benefit from [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/).
-
-LangGraph Platform can help engineering teams:
-
- **Accelerate agent development**: Quickly create agent UXs with configurable templates and [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/) for visualizing and debugging agent interactions.
- **Deploy seamlessly**: We handle the complexity of deploying your agent. LangGraph Platform includes robust APIs for memory, threads, and cron jobs plus auto-scaling task queues & servers.
- **Centralize agent management & reusability**: Discover, reuse, and manage agents across the organization. Business users can also modify agents without coding.
-
 ## Additional resources

+- [Guides](https://langchain-ai.github.io/langgraph/how-tos/): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
+- [Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
+- [Examples](https://langchain-ai.github.io/langgraph/tutorials/): Guided examples on getting started with LangGraph.
 - [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course.
- [Tutorials](https://langchain-ai.github.io/langgraph/tutorials/): Simple walkthroughs with guided examples on getting started with LangGraph.
 - [Templates](https://langchain-ai.github.io/langgraph/concepts/template_applications/): Pre-built reference apps for common agentic workflows (e.g. ReAct agent, memory, retrieval etc.) that can be cloned and adapted.
- [How-to Guides](https://langchain-ai.github.io/langgraph/how-tos/): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
- [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
- [Built with LangGraph](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship powerful, production-ready AI applications.
+- [Case studies](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship powerful, production-ready AI applications.

 ## Acknowledgements

@@ -20,15 +20,8 @@ def _make_llms_text(output_file: str) -> str:
        output_file: Path to output the consolidated text file
    """
    # Collect all markdown and notebook files
-    relative_paths = [
-        # Files relative to docs/docs/
-        "tutorials/introduction.ipynb",
-    ]
-    all_files = [os.path.join(SOURCE_DIR, path) for path in relative_paths]
+    all_files = glob.glob(os.path.join(SOURCE_DIR, "how-tos/*.md"), recursive=True)

-    all_files.extend(
-        glob.glob(os.path.join(SOURCE_DIR, "how-tos/*.md"), recursive=True)
-    )
    all_files.extend(
        glob.glob(os.path.join(SOURCE_DIR, "how-tos/*.ipynb"), recursive=True)
    )
@@ -38,9 +38,13 @@
 {%- endblock data_html -%}

 {%- block data_jpg scoped -%}
-![](data:image/jpg;base64,{{ output.data['image/jpeg'] }})
+<p>
+<img src="data:image/jpg;base64,{{ output.data['image/jpeg'] }}" />
+</p>
 {%- endblock data_jpg -%}

 {%- block data_png scoped -%}
-![](data:image/png;base64,{{ output.data['image/png'] }})
+<p>
+<img src="data:image/png;base64,{{ output.data['image/png'] }}" />
+</p>
 {%- endblock data_png -%}
@@ -18,28 +18,84 @@ DISABLED = os.getenv("DISABLE_NOTEBOOK_CONVERT") in ("1", "true", "True")

 REDIRECT_MAP = {
    # lib redirects
-    "how-tos/stream-values.ipynb": "how-tos/streaming.ipynb#values",
-    "how-tos/stream-updates.ipynb": "how-tos/streaming.ipynb#updates",
-    "how-tos/streaming-content.ipynb": "how-tos/streaming.ipynb#custom",
-    "how-tos/stream-multiple.ipynb": "how-tos/streaming.ipynb#multiple",
-    "how-tos/streaming-tokens-without-langchain.ipynb": "how-tos/streaming-tokens.ipynb#example-without-langchain",
+    "how-tos/stream-values.ipynb": "how-tos/streaming.md#stream-graph-state",
+    "how-tos/stream-updates.ipynb": "how-tos/streaming.md#stream-graph-state",
+    "how-tos/streaming-content.ipynb": "how-tos/streaming.md",
+    "how-tos/stream-multiple.ipynb": "how-tos/streaming.md#stream-multiple-nodes",
+    "how-tos/streaming-tokens-without-langchain.ipynb": "how-tos/streaming.md#use-with-any-llm",
    "how-tos/streaming-from-final-node.ipynb": "how-tos/streaming-specific-nodes.ipynb",
    "how-tos/streaming-events-from-within-tools-without-langchain.ipynb": "how-tos/streaming-events-from-within-tools.ipynb#example-without-langchain",
+    # graph-api
+    "how-tos/state-reducers.ipynb": "how-tos/graph-api#define-and-update-state",
+    "how-tos/sequence.ipynb": "how-tos/graph-api#create-a-sequence-of-steps",
+    "how-tos/branching.ipynb": "how-tos/graph-api#create-branches",
+    "how-tos/recursion-limit.ipynb": "how-tos/graph-api#create-and-control-loops",
+    "how-tos/visualization.ipynb": "how-tos/graph-api#visualize-your-graph",
+    "how-tos/input_output_schema.ipynb": "how-tos/graph-api#define-input-and-output-schemas",
+    "how-tos/pass_private_state.ipynb": "how-tos/graph-api#pass-private-state-between-nodes",
+    "how-tos/state-model.ipynb": "how-tos/graph-api#use-pydantic-models-for-graph-state",
+    "how-tos/map-reduce.ipynb": "how-tos/graph-api/#map-reduce-and-the-send-api",
+    "how-tos/command.ipynb": "how-tos/graph-api/#combine-control-flow-and-state-updates-with-command",
+    "how-tos/configuration.ipynb": "how-tos/graph-api/#add-runtime-configuration",
+    "how-tos/node-retries.ipynb": "how-tos/graph-api/#add-retry-policies",
+    "how-tos/return-when-recursion-limit-hits.ipynb": "how-tos/graph-api/#impose-a-recursion-limit",
+    # memory how-tos
+    "how-tos/memory/manage-conversation-history.ipynb": "how-tos/memory.ipynb",
+    "how-tos/memory/delete-messages.ipynb": "how-tos/memory.ipynb#delete-messages",
+    "how-tos/memory/add-summary-conversation-history.ipynb": "how-tos/memory.ipynb#summarize-messages",
+    # subgraph how-tos
+    "how-tos/subgraph-transform-state.ipynb": "how-tos/subgraph.ipynb#different-state-schemas",
+    "how-tos/subgraphs-manage-state.ipynb": "how-tos/subgraph.ipynb#add-persistence",
+    # persistence how-tos
+    "how-tos/persistence_postgres.ipynb": "how-tos/persistence.ipynb#use-in-production",
+    "how-tos/persistence_mongodb.ipynb": "how-tos/persistence.ipynb#use-in-production",
+    "how-tos/persistence_redis.ipynb": "how-tos/persistence.ipynb#use-in-production",
+    "how-tos/subgraph-persistence.ipynb": "how-tos/persistence.ipynb#use-with-subgraphs",
+    "how-tos/cross-thread-persistence.ipynb": "how-tos/persistence.ipynb#add-long-term-memory",
+    # tool calling how-tos
+    "how-tos/tool-calling-errors.ipynb": "how-tos/tool-calling.ipynb#handle-errors",
+    "how-tos/pass-config-to-tools.ipynb": "how-tos/tool-calling.ipynb#access-config",
+    "how-tos/pass-run-time-values-to-tools.ipynb": "how-tos/tool-calling.ipynb#read-state",
+    "how-tos/update-state-from-tools.ipynb": "how-tos/tool-calling.ipynb#update-state",
+    # multi-agent how-tos
+    "how-tos/agent-handoffs.ipynb": "how-tos/multi_agent.ipynb#handoffs",
+    "how-tos/multi-agent-network.ipynb": "how-tos/multi_agent.ipynb#use-in-a-multi-agent-system",
+    "how-tos/multi-agent-multi-turn-convo.ipynb": "how-tos/multi_agent.ipynb#multi-turn-conversation",
    # cloud redirects
-    "cloud/index.md": "concepts/index.md#langgraph-platform",
-    "cloud/how-tos/index.md": "how-tos/index.md#langgraph-platform",
+    "cloud/index.md": "index.md",
+    "cloud/how-tos/index.md": "concepts/langgraph_platform",
    "cloud/concepts/api.md": "concepts/langgraph_server.md",
    "cloud/concepts/cloud.md": "concepts/langgraph_cloud.md",
    "cloud/faq/studio.md": "concepts/langgraph_studio.md#studio-faqs",
+    # cloud streaming redirects
+    "cloud/how-tos/stream_values.md": "cloud/how-tos/streaming.md#stream-graph-state",
+    "cloud/how-tos/stream_updates.md": "cloud/how-tos/streaming.md#stream-graph-state",
+    "cloud/how-tos/stream_messages.md": "cloud/how-tos/streaming.md#messages",
+    "cloud/how-tos/stream_events.md": "cloud/how-tos/streaming.md#stream-events",
+    "cloud/how-tos/stream_debug.md": "cloud/how-tos/streaming.md#debug",
+    "cloud/how-tos/stream_multiple.md": "cloud/how-tos/streaming.md#stream-multiple-modes",
    # prebuit redirects
    "how-tos/create-react-agent.ipynb": "agents/agents.md#basic-configuration",
    "how-tos/create-react-agent-memory.ipynb": "agents/memory.md",
    "how-tos/create-react-agent-system-prompt.ipynb": "agents/context.md#prompts",
    "how-tos/create-react-agent-hitl.ipynb": "agents/human-in-the-loop.md",
    "how-tos/create-react-agent-structured-output.ipynb": "agents/agents.md#structured-output",
+    # Time-travel
+    "how-tos/human_in_the_loop/edit-graph-state.ipynb": "how-tos/human_in_the_loop/time-travel.ipynb",
+    # breakpoints
+    "how-tos/human_in_the_loop/dynamic_breakpoints.ipynb": "how-tos/human_in_the_loop/breakpoints.ipynb",
    # misc
    "prebuilt.md": "agents/prebuilt.md",
-    "reference/prebuilt.md": "reference/agents.md"
+    "reference/prebuilt.md": "reference/agents.md",
+    "concepts/high_level.md": "index.md",
+    "concepts/index.md": "index.md",
+    "concepts/v0-human-in-the-loop.md": "concepts/human-in-the-loop.md",
+    "how-tos/index.md": "index.md",
+    "tutorials/introduction.ipynb": "concepts/why-langgraph.md",
+    # deployment redirects
+    "how-tos/deploy-self-hosted.md": "cloud/deployment/self_hosted_data_plane.md",
+    "concepts/self_hosted.md": "concepts/langgraph_self_hosted_data_plane.md"
+
 }


@@ -1 +0,0 @@
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@@ -1 +0,0 @@
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@@ -1 +0,0 @@
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@@ -1 +0,0 @@
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@@ -1 +0,0 @@
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@@ -1 +0,0 @@
-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
@@ -1,4 +1,4 @@
-# 🦜🕸️ Companies using LangGraph
+# 🦜🕸️ Case studies

 This list of companies using LangGraph and their success stories is compiled from public sources. If your company uses LangGraph, we'd love for you to share your story and add it to the list. You’re also welcome to contribute updates based on publicly available information from other companies, such as blog posts or press releases.

@@ -7,22 +7,31 @@ hide:
  - tags
 ---

-# Agents
+# LangGraph quickstart

-## What is an agent?
+This guide shows you how to set up and use LangGraph's **prebuilt**, **reusable** components, which are designed to help you construct agentic systems quickly and reliably.

-An *agent* consists of three components: a **large language model (LLM)**, a set of **tools** it can use, and a **prompt** that provides instructions.
+## Prerequisites

-The LLM operates in a loop. In each iteration, it selects a tool to invoke, provides input, receives the result (an observation), and uses that observation to inform the next action. The loop continues until a stopping condition is met — typically when the agent has gathered enough information to respond to the user.
+Before you start this tutorial, ensure you have the following:

-<figure markdown="1">
-![image](./assets/agent.png){: style="max-height:400px"}
-<figcaption>Agent loop: the LLM selects tools and uses their outputs to fulfill a user request.</figcaption>
-</figure>
+- An [Anthropic](https://console.anthropic.com/settings/admin-keys) API key 

-## Basic configuration
+## 1. Install dependencies

-Use [`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent] to instantiate an agent:
+If you haven't already, install LangGraph and LangChain:
+
+```
+pip install -U langgraph "langchain[anthropic]"
+```
+
+!!! info 
+
+    LangChain is installed so the agent can call the [model](https://python.langchain.com/docs/integrations/chat/).
+
+## 2. Create an agent
+
+To create an agent, use [`create_react_agent`](langgraph.prebuilt.chat_agent_executor.create_react_agent):

 ```python
 from langgraph.prebuilt import create_react_agent
@@ -48,10 +57,9 @@ agent.invoke(
 3. Provide a list of tools for the model to use.
 4. Provide a system prompt (instructions) to the language model used by the agent.

-## LLM configuration
+## 3. Configure an LLM

-Use [init_chat_model](https://python.langchain.com/api_reference/langchain/chat_models/langchain.chat_models.base.init_chat_model.html) to configure an LLM with specific parameters,
-such as temperature:
+To configure an LLM with specific parameters, such as temperature, use [init_chat_model](https://python.langchain.com/api_reference/langchain/chat_models/langchain.chat_models.base.init_chat_model.html):

 ```python
 from langchain.chat_models import init_chat_model
@@ -71,77 +79,77 @@ agent = create_react_agent(
 )
 ```

-See the [models](./models.md) page for more information on how to configure LLMs.
+For more information on how to configure LLMs, see [Models](./models.md).

-## Custom Prompts
+## 4. Add a custom prompt

-Prompts instruct the LLM how to behave. They can be:
+Prompts instruct the LLM how to behave. Add one of the following types of prompts:

-* **Static**: A string is interpreted as a **system message**
-* **Dynamic**: a list of messages generated at **runtime** based on input or configuration
+* **Static**: A string is interpreted as a **system message**.
+* **Dynamic**: A list of messages generated at **runtime**, based on input or configuration.

-### Static prompts
+=== "Static prompt"

-Define a fixed prompt string or list of messages.
+    Define a fixed prompt string or list of messages:

-```python
-from langgraph.prebuilt import create_react_agent
+    ```python
+    from langgraph.prebuilt import create_react_agent
+
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_weather],
+        # A static prompt that never changes
+        # highlight-next-line
+        prompt="Never answer questions about the weather."
+    )
+
+    agent.invoke(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
+    )
+    ```
+
+=== "Dynamic prompt"
+
+    Define a function that returns a message list based on the agent's state and configuration:
+
+    ```python
+    from langchain_core.messages import AnyMessage
+    from langchain_core.runnables import RunnableConfig
+    from langgraph.prebuilt.chat_agent_executor import AgentState
+    from langgraph.prebuilt import create_react_agent

-agent = create_react_agent(
-    model="anthropic:claude-3-7-sonnet-latest",
-    tools=[get_weather],
-    # A static prompt that never changes
    # highlight-next-line
-    prompt="Never answer questions about the weather."
-)
+    def prompt(state: AgentState, config: RunnableConfig) -> list[AnyMessage]:  # (1)!
+        user_name = config["configurable"].get("user_name")
+        system_msg = f"You are a helpful assistant. Address the user as {user_name}."
+        return [{"role": "system", "content": system_msg}] + state["messages"]

-agent.invoke(
-    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
-)
-```
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_weather],
+        # highlight-next-line
+        prompt=prompt
+    )

-### Dynamic prompts
+    agent.invoke(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+        # highlight-next-line
+        config={"configurable": {"user_name": "John Smith"}}
+    )
+    ```

-Define a function that returns a message list based on the agent's state and configuration:
+    1. Dynamic prompts allow including non-message [context](./context.md) when constructing an input to the LLM, such as:

-```python
-from langchain_core.messages import AnyMessage
-from langchain_core.runnables import RunnableConfig
-from langgraph.prebuilt.chat_agent_executor import AgentState
-from langgraph.prebuilt import create_react_agent
+        - Information passed at runtime, like a `user_id` or API credentials (using `config`).
+        - Internal agent state updated during a multi-step reasoning process (using `state`).

-# highlight-next-line
-def prompt(state: AgentState, config: RunnableConfig) -> list[AnyMessage]:  # (1)!
-    user_name = config["configurable"].get("user_name")
-    system_msg = f"You are a helpful assistant. Address the user as {user_name}."
-    return [{"role": "system", "content": system_msg}] + state["messages"]
+        Dynamic prompts can be defined as functions that take `state` and `config` and return a list of messages to send to the LLM.

-agent = create_react_agent(
-    model="anthropic:claude-3-7-sonnet-latest",
-    tools=[get_weather],
-    # highlight-next-line
-    prompt=prompt
-)
+For more information, see [Context](./context.md).

-agent.invoke(
-    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
-    # highlight-next-line
-    config={"configurable": {"user_name": "John Smith"}}
-)
-```
+## 5. Add memory

-1. Dynamic prompts allow including non-message [context](./context.md) when constructing an input to the LLM, such as:
-
-    - Information passed at runtime, like a `user_id` or API credentials (using `config`).
-    - Internal agent state updated during a multi-step reasoning process (using `state`).
-
-    Dynamic prompts can be defined as functions that take `state` and `config` and return a list of messages to send to the LLM.
-
-See the [context](./context.md) page for more information.
-
-## Memory
-
-To allow multi-turn conversations with an agent, you need to enable [persistence](../concepts/persistence.md) by providing a `checkpointer` when creating an agent. At runtime you need to provide a config containing `thread_id` — a unique identifier for the conversation (session):
+To allow multi-turn conversations with an agent, you need to enable [persistence](../concepts/persistence.md) by providing a `checkpointer` when creating an agent. At runtime, you need to provide a config containing `thread_id` — a unique identifier for the conversation (session):

 ```python
 from langgraph.prebuilt import create_react_agent
@@ -179,10 +187,9 @@ When you enable the checkpointer, it stores agent state at every step in the pro

 Note that in the above example, when the agent is invoked the second time with the same `thread_id`, the original message history from the first conversation is automatically included, together with the new user input.

-Please see the [memory guide](./memory.md) for more details on how to work with memory.
+For more information, see [Memory](./memory.md).

-
-## Structured output
+## 6. Configure structured output

 To produce structured responses conforming to a schema, use the `response_format` parameter. The schema can be defined with a `Pydantic` model or `TypedDict`. The result will be accessible via the `structured_response` field.

@@ -216,3 +223,8 @@ response["structured_response"]

    Structured output requires an additional call to the LLM to format the response according to the schema.

+## Next steps
+
+- [Deploy your agent locally](../tutorials/langgraph-platform/local-server.md)
+- [Learn more about prebuilt agents](../agents/overview.md)
+- [LangGraph Platform quickstart](../cloud/quick_start.md)
@@ -12,6 +12,17 @@ hide:

 **LangGraph** provides both low-level primitives and high-level prebuilt components for building agent-based applications. This section focuses on the **prebuilt**, **reusable** components designed to help you construct agentic systems quickly and reliably—without the need to implement orchestration, memory, or human feedback handling from scratch.

+## What is an agent?
+
+An *agent* consists of three components: a **large language model (LLM)**, a set of **tools** it can use, and a **prompt** that provides instructions.
+
+The LLM operates in a loop. In each iteration, it selects a tool to invoke, provides input, receives the result (an observation), and uses that observation to inform the next action. The loop continues until a stopping condition is met — typically when the agent has gathered enough information to respond to the user.
+
+<figure markdown="1">
+![image](./assets/agent.png){: style="max-height:400px"}
+<figcaption>Agent loop: the LLM selects tools and uses their outputs to fulfill a user request.</figcaption>
+</figure>
+
 ## Key features

 LangGraph includes several capabilities essential for building robust, production-ready agentic systems:
@@ -1,14 +1,44 @@
---
-tags:
-  - agent
-hide:
-  - tags
---
-
+[//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
 # Community Agents

+If you’re looking for other prebuilt libraries, explore the community-built options 
+below. These libraries can extend LangGraph's functionality in various ways.
+
+## 📚 Available Libraries
+
+[//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
+| Name | GitHub URL | Description | Weekly Downloads | Stars |
+| --- | --- | --- | --- | --- |
+| **trustcall** | [hinthornw/trustcall](https://github.com/hinthornw/trustcall) | Tenacious tool calling built on LangGraph. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/hinthornw/trustcall?style=social)
+| **breeze-agent** | [andrestorres123/breeze-agent](https://github.com/andrestorres123/breeze-agent) | A streamlined research system built inspired on STORM and built on LangGraph. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/andrestorres123/breeze-agent?style=social)
+| **langgraph-supervisor** | [langchain-ai/langgraph-supervisor-py](https://github.com/langchain-ai/langgraph-supervisor-py) | Build supervisor multi-agent systems with LangGraph. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langgraph-supervisor-py?style=social)
+| **langmem** | [langchain-ai/langmem](https://github.com/langchain-ai/langmem) | Build agents that learn and adapt from interactions over time. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langmem?style=social)
+| **langchain-mcp-adapters** | [langchain-ai/langchain-mcp-adapters](https://github.com/langchain-ai/langchain-mcp-adapters) | Make Anthropic Model Context Protocol (MCP) tools compatible with LangGraph agents. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langchain-mcp-adapters?style=social)
+| **open-deep-research** | [langchain-ai/open_deep_research](https://github.com/langchain-ai/open_deep_research) | Open source assistant for iterative web research and report writing. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/open_deep_research?style=social)
+| **langgraph-swarm** | [langchain-ai/langgraph-swarm-py](https://github.com/langchain-ai/langgraph-swarm-py) | Build swarm-style multi-agent systems using LangGraph. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langgraph-swarm-py?style=social)
+| **delve-taxonomy-generator** | [andrestorres123/delve](https://github.com/andrestorres123/delve) | A taxonomy generator for unstructured data | -12345 | ![GitHub stars](https://img.shields.io/github/stars/andrestorres123/delve?style=social)
+| **nodeology** | [xyin-anl/Nodeology](https://github.com/xyin-anl/Nodeology) | Enable researcher to build scientific workflows easily with simplified interface. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/xyin-anl/Nodeology?style=social)
+| **langgraph-bigtool** | [langchain-ai/langgraph-bigtool](https://github.com/langchain-ai/langgraph-bigtool) | Build LangGraph agents with large numbers of tools. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langgraph-bigtool?style=social)
+| **ai-data-science-team** | [business-science/ai-data-science-team](https://github.com/business-science/ai-data-science-team) | An AI-powered data science team of agents to help you perform common data science tasks 10X faster. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/business-science/ai-data-science-team?style=social)
+| **langgraph-reflection** | [langchain-ai/langgraph-reflection](https://github.com/langchain-ai/langgraph-reflection) | LangGraph agent that runs a reflection step. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langgraph-reflection?style=social)
+| **langgraph-codeact** | [langchain-ai/langgraph-codeact](https://github.com/langchain-ai/langgraph-codeact) | LangGraph implementation of CodeAct agent that generates and executes code instead of tool calling. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langgraph-codeact?style=social)
+
+## ✨ Contributing Your Library
+
+Have you built an awesome open-source library using LangGraph? We'd love to feature 
+your project on the official LangGraph documentation pages! 🏆
+
 To share your project, simply open a Pull Request adding an entry for your package in our [packages.yml](https://github.com/langchain-ai/langgraph/blob/main/docs/_scripts/third_party_page/packages.yml) file.

-[//]: # (This file is stub. Do not edit this file directly!)
-[//]: # (1. Update the `packages.yml` file in the `docs/_scripts/third_party_page` directory.)
-[//]: # (2. From the /docs directory, run `make build-prebuilt` to generate an updated version of this file for testing locally.)
+**Guidelines**
+
+- Your repo must be distributed as an installable package (e.g., PyPI for Python, npm 
+  for JavaScript/TypeScript, etc.) 📦
+- The repo should either use the Graph API (exposing a `StateGraph` instance) or 
+  the Functional API (exposing an `entrypoint`).
+- The package must include documentation (e.g., a `README.md` or docs site) 
+  explaining how to use it.
+  
+We'll review your contribution and merge it in!
+
+Thanks for contributing! 🚀
@@ -72,7 +72,7 @@ more about [LangChain messages](https://python.langchain.com/docs/concepts/messa
 Agent output is a dictionary containing:

 - `messages`: A list of all messages exchanged during execution (user input, assistant replies, tool invocations).
- Optionally, `structured_response` if [structured output](./agents.md#structured-output) is configured.
+- Optionally, `structured_response` if [structured output](./agents.md#6-configure-structured-output) is configured.
 - If using a custom `state_schema`, additional keys corresponding to your defined fields may also be present in the output. These can hold updated state values from tool execution or prompt logic.

 See the [context guide](./context.md) for more details on working with custom state schemas and accessing context.
@@ -65,7 +65,7 @@ class MultiplyInputSchema(BaseModel):
 # highlight-next-line
@tool("multiply_tool", args_schema=MultiplyInputSchema)
 def multiply(a: int, b: int) -> int:
-   return a * b
+    return a * b
 ```

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

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

 ## Prerequisites

 1. LangGraph Cloud applications are deployed from GitHub repositories. Configure and upload a LangGraph Cloud application to a GitHub repository in order to deploy it to LangGraph Cloud.
-1. [Verify that the LangGraph API runs locally](test_locally.md). If the API does not run successfully (i.e. `langgraph dev`), deploying to LangGraph Cloud will fail as well.
+1. [Verify that the LangGraph API runs locally](../../tutorials/langgraph-platform/local-server.md). If the API does not run successfully (i.e. `langgraph dev`), deploying to LangGraph Cloud will fail as well.

 ## Create New Deployment

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

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

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

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

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

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

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

 ## Prerequisites

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

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

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

 ## Amazon ECS

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

-A LangGraph application must be configured with a [LangGraph API configuration file](../reference/cli.md#configuration-file) in order to be deployed to LangGraph Cloud (or to be self-hosted). This how-to guide discusses the basic steps to setup a LangGraph application for deployment using `requirements.txt` to specify project dependencies.
+A LangGraph application must be configured with a [LangGraph configuration file](../reference/cli.md#configuration-file) in order to be deployed to LangGraph Cloud (or to be self-hosted). This how-to guide discusses the basic steps to setup a LangGraph application for deployment using `requirements.txt` to specify project dependencies.

 This walkthrough is based on [this repository](https://github.com/langchain-ai/langgraph-example), which you can play around with to learn more about how to setup your LangGraph application for deployment.

@@ -8,9 +8,9 @@ This walkthrough is based on [this repository](https://github.com/langchain-ai/l
    If you prefer using poetry for dependency management, check out [this how-to guide](./setup_pyproject.md) on using `pyproject.toml` for LangGraph Cloud.

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

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

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

 ## Specify Dependencies

-Dependencies can optionally be specified in one of the following files: `pyproject.toml`, `setup.py`, or `requirements.txt`. If none of these files is created, then dependencies can be specified later in the [LangGraph API configuration file](#create-langgraph-api-config).
+Dependencies can optionally be specified in one of the following files: `pyproject.toml`, `setup.py`, or `requirements.txt`. If none of these files is created, then dependencies can be specified later in the [LangGraph configuration file](#create-langgraph-api-config).

 The dependencies below will be included in the image, you can also use them in your code, as long as with a compatible version range:

 ```
-langgraph>=0.2.56,<0.4.0
-langgraph-sdk>=0.1.53
-langgraph-checkpoint>=2.0.15,<3.0
-langchain-core>=0.2.38,<0.4.0
+langgraph>=0.3.27
+langgraph-sdk>=0.1.66
+langgraph-checkpoint>=2.0.23
+langchain-core>=0.2.38
 langsmith>=0.1.63
-orjson>=3.9.7
+orjson>=3.9.7,<3.10.17
 httpx>=0.25.0
 tenacity>=8.0.0
 uvicorn>=0.26.0
@@ -49,7 +49,8 @@ sse-starlette>=2.1.0,<2.2.0
 uvloop>=0.18.0
 httptools>=0.5.0
 jsonschema-rs>=0.20.0
-structlog>=23.1.0
+structlog>=24.1.0
+cloudpickle>=3.0.0
 ```

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

 ## Define Graphs

-Implement your graphs! Graphs can be defined in a single file or multiple files. Make note of the variable names of each [CompiledGraph][langgraph.graph.graph.CompiledGraph] to be included in the LangGraph application. The variable names will be used later when creating the [LangGraph API configuration file](../reference/cli.md#configuration-file).
+Implement your graphs! Graphs can be defined in a single file or multiple files. Make note of the variable names of each [CompiledGraph][langgraph.graph.graph.CompiledGraph] to be included in the LangGraph application. The variable names will be used later when creating the [LangGraph configuration file](../reference/cli.md#configuration-file).

-Example `agent.py` file, which shows how to import from other modules you define (code for the modules is not shown here, please see [this repo](https://github.com/langchain-ai/langgraph-example) to see their implementation):
+Example `agent.py` file, which shows how to import from other modules you define (code for the modules is not shown here, please see [this repository](https://github.com/langchain-ai/langgraph-example) to see their implementation):

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

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

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

 Example `langgraph.json` file:

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

 Note that the variable name of the `CompiledGraph` appears at the end of the value of each subkey in the top-level `graphs` key (i.e. `:<variable_name>`).

-!!! warning "Configuration Location"
-    The LangGraph API configuration file must be placed in a directory that is at the same level or higher than the Python files that contain compiled graphs and associated dependencies.
+!!! warning "Configuration File Location"
+    The LangGraph configuration file must be placed in a directory that is at the same level or higher than the Python files that contain compiled graphs and associated dependencies.

 Example file directory:

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

 ## Next

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

-A [LangGraph.js](https://langchain-ai.github.io/langgraphjs/) application must be configured with a [LangGraph API configuration file](../reference/cli.md#configuration-file) in order to be deployed to LangGraph Cloud (or to be self-hosted). This how-to guide discusses the basic steps to setup a LangGraph.js application for deployment using `package.json` to specify project dependencies.
+A [LangGraph.js](https://langchain-ai.github.io/langgraphjs/) application must be configured with a [LangGraph configuration file](../reference/cli.md#configuration-file) in order to be deployed to LangGraph Cloud (or to be self-hosted). This how-to guide discusses the basic steps to setup a LangGraph.js application for deployment using `package.json` to specify project dependencies.

 This walkthrough is based on [this repository](https://github.com/langchain-ai/langgraphjs-studio-starter), which you can play around with to learn more about how to setup your LangGraph application for deployment.

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

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

 ## Specify Dependencies

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

 Example `package.json` file:

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

 ## Define Graphs

-Implement your graphs! Graphs can be defined in a single file or multiple files. Make note of the variable names of each compiled graph to be included in the LangGraph application. The variable names will be used later when creating the [LangGraph API configuration file](../reference/cli.md#configuration-file).
+Implement your graphs! Graphs can be defined in a single file or multiple files. Make note of the variable names of each compiled graph to be included in the LangGraph application. The variable names will be used later when creating the [LangGraph configuration file](../reference/cli.md#configuration-file).

 Here is an example `agent.ts`:

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

 ## Create LangGraph API Config

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

 Example `langgraph.json` file:

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

 !!! info "Configuration Location"

-    The LangGraph API configuration file must be placed in a directory that is at the same level or higher than the TypeScript files that contain compiled graphs and associated dependencies.
+    The LangGraph configuration file must be placed in a directory that is at the same level or higher than the TypeScript files that contain compiled graphs and associated dependencies.

 ## Next

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

-A LangGraph application must be configured with a [LangGraph API configuration file](../reference/cli.md#configuration-file) in order to be deployed to LangGraph Cloud (or to be self-hosted). This how-to guide discusses the basic steps to setup a LangGraph application for deployment using `pyproject.toml` to define your package's dependencies.
+A LangGraph application must be configured with a [LangGraph configuration file](../reference/cli.md#configuration-file) in order to be deployed to LangGraph Cloud (or to be self-hosted). This how-to guide discusses the basic steps to setup a LangGraph application for deployment using `pyproject.toml` to define your package's dependencies.

 This walkthrough is based on [this repository](https://github.com/langchain-ai/langgraph-example-pyproject), which you can play around with to learn more about how to setup your LangGraph application for deployment.

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

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

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

 ## Specify Dependencies

-Dependencies can optionally be specified in one of the following files: `pyproject.toml`, `setup.py`, or `requirements.txt`. If none of these files is created, then dependencies can be specified later in the [LangGraph API configuration file](#create-langgraph-api-config).
+Dependencies can optionally be specified in one of the following files: `pyproject.toml`, `setup.py`, or `requirements.txt`. If none of these files is created, then dependencies can be specified later in the [LangGraph configuration file](#create-langgraph-api-config).

 The dependencies below will be included in the image, you can also use them in your code, as long as with a compatible version range:

 ```
-langgraph>=0.2.56,<0.4.0
-langgraph-sdk>=0.1.53
-langgraph-checkpoint>=2.0.15,<3.0
-langchain-core>=0.2.38,<0.4.0
+langgraph>=0.3.27
+langgraph-sdk>=0.1.66
+langgraph-checkpoint>=2.0.23
+langchain-core>=0.2.38
 langsmith>=0.1.63
-orjson>=3.9.7
+orjson>=3.9.7,<3.10.17
 httpx>=0.25.0
 tenacity>=8.0.0
 uvicorn>=0.26.0
@@ -49,7 +49,8 @@ sse-starlette>=2.1.0,<2.2.0
 uvloop>=0.18.0
 httptools>=0.5.0
 jsonschema-rs>=0.20.0
-structlog>=23.1.0
+structlog>=24.1.0
+cloudpickle>=3.0.0
 ```

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

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

 ```bash
 my-app/
-├── .env             # file with environment variables
+├── .env # file with environment variables
 └── pyproject.toml
 ```

 ## Define Graphs

-Implement your graphs! Graphs can be defined in a single file or multiple files. Make note of the variable names of each [CompiledGraph][langgraph.graph.graph.CompiledGraph] to be included in the LangGraph application. The variable names will be used later when creating the [LangGraph API configuration file](../reference/cli.md#configuration-file).
+Implement your graphs! Graphs can be defined in a single file or multiple files. Make note of the variable names of each [CompiledGraph][langgraph.graph.graph.CompiledGraph] to be included in the LangGraph application. The variable names will be used later when creating the [LangGraph configuration file](../reference/cli.md#configuration-file).

-Example `agent.py` file, which shows how to import from other modules you define (code for the modules is not shown here, please see [this repo](https://github.com/langchain-ai/langgraph-example-pyproject) to see their implementation):
+Example `agent.py` file, which shows how to import from other modules you define (code for the modules is not shown here, please see [this repository](https://github.com/langchain-ai/langgraph-example-pyproject) to see their implementation):

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

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

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

 Example `langgraph.json` file:

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

 Note that the variable name of the `CompiledGraph` appears at the end of the value of each subkey in the top-level `graphs` key (i.e. `:<variable_name>`).

-!!! warning "Configuration Location"
-    The LangGraph API configuration file must be placed in a directory that is at the same level or higher than the Python files that contain compiled graphs and associated dependencies.
+!!! warning "Configuration File Location"
+    The LangGraph configuration file must be placed in a directory that is at the same level or higher than the Python files that contain compiled graphs and associated dependencies.

 Example file directory:

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

 ## Next

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

 ## Prerequisites

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

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

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

@@ -1,196 +0,0 @@
-# How to test a LangGraph app locally
-
-This guide assumes you have a LangGraph app correctly set up with a proper configuration file and a corresponding compiled graph, and that you have a proper LangChain API key.
-
-Testing locally ensures that there are no errors or conflicts with Python dependencies and confirms that the configuration file is specified correctly.
-
-## Setup
-
-Install the LangGraph CLI package:
-
-```bash
-pip install -U "langgraph-cli[inmem]"
-```
-
-Ensure you have an API key, which you can create from the [LangSmith UI](https://smith.langchain.com) (Settings > API Keys). This is required to authenticate that you have LangGraph Cloud access. After you have saved the key to a safe place, place the following line in your `.env` file:
-
-```python
-LANGSMITH_API_KEY = *********
-```
-
-## Start the API server
-
-Once you have installed the CLI, you can run the following command to start the API server for local testing:
-
-```shell
-langgraph dev
-```
-
-This will start up the LangGraph API server locally. If this runs successfully, you should see something like:
-
->    Ready!
-> 
->    - API: [http://localhost:2024](http://localhost:2024/)
->     
->    - Docs: http://localhost:2024/docs
->     
->    - LangGraph Studio Web UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
-
-!!! note "In-Memory Mode"
-
-    The `langgraph dev` command starts LangGraph Server in an in-memory mode. This mode is suitable for development and testing purposes. For production use, you should deploy LangGraph Server with access to a persistent storage backend.
-
-    If you want to test your application with a persistent storage backend, you can use the `langgraph up` command instead of `langgraph dev`. You will
-    need to have `docker` installed on your machine to use this command.
-
-
-### Interact with the server
-
-We can now interact with the API server using the LangGraph SDK. First, we need to start our client, select our assistant (in this case a graph we called "agent", make sure to select the proper assistant you wish to test).
-
-You can either initialize by passing authentication or by setting an environment variable.
-
-#### Initialize with authentication
-
-=== "Python"
-
-    ```python
-    from langgraph_sdk import get_client
-
-    # only pass the url argument to get_client() if you changed the default port when calling langgraph dev
-    client = get_client(url=<DEPLOYMENT_URL>,api_key=<LANGSMITH_API_KEY>)
-    # Using the graph deployed with the name "agent"
-    assistant_id = "agent"
-    thread = await client.threads.create()
-    ```
-
-=== "Javascript"
-
-    ```js
-    import { Client } from "@langchain/langgraph-sdk";
-
-    // only set the apiUrl if you changed the default port when calling langgraph dev
-    const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <LANGSMITH_API_KEY> });
-    // Using the graph deployed with the name "agent"
-    const assistantId = "agent";
-    const thread = await client.threads.create();
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request POST \
-      --url <DEPLOYMENT_URL>/threads \
-      --header 'Content-Type: application/json'
-      --header 'x-api-key: <LANGSMITH_API_KEY>'
-    ```
-  
-
-#### Initialize with environment variables
-
-If you have a `LANGSMITH_API_KEY` set in your environment, you do not need to explicitly pass authentication to the client
-
-=== "Python"
-
-    ```python
-    from langgraph_sdk import get_client
-
-    # only pass the url argument to get_client() if you changed the default port when calling langgraph dev
-    client = get_client()
-    # Using the graph deployed with the name "agent"
-    assistant_id = "agent"
-    thread = await client.threads.create()
-    ```
-
-=== "Javascript"
-
-    ```js
-    import { Client } from "@langchain/langgraph-sdk";
-
-    // only set the apiUrl if you changed the default port when calling langgraph dev
-    const client = new Client();
-    // Using the graph deployed with the name "agent"
-    const assistantId = "agent";
-    const thread = await client.threads.create();
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request POST \
-      --url <DEPLOYMENT_URL>/threads \
-      --header 'Content-Type: application/json'
-    ```
-
-Now we can invoke our graph to ensure it is working. Make sure to change the input to match the proper schema for your graph. 
-
-=== "Python"
-
-    ```python
-    input = {"messages": [{"role": "user", "content": "what's the weather in sf"}]}
-    async for chunk in client.runs.stream(
-        thread["thread_id"],
-        assistant_id,
-        input=input,
-        stream_mode="updates",
-    ):
-        print(f"Receiving new event of type: {chunk.event}...")
-        print(chunk.data)
-        print("\n\n")
-    ```
-=== "Javascript"
-
-    ```js
-    const input = { "messages": [{ "role": "user", "content": "what's the weather in sf"}] }
-
-    const streamResponse = client.runs.stream(
-      thread["thread_id"],
-      assistantId,
-      {
-        input: input,
-        streamMode: "updates",
-      }
-    );
-    for await (const chunk of streamResponse) {
-      console.log(`Receiving new event of type: ${chunk.event}...`);
-      console.log(chunk.data);
-      console.log("\n\n");
-    }
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request POST \
-     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
-     --header 'Content-Type: application/json' \
-     --data "{
-       \"assistant_id\": \"agent\",
-       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in sf\"}]},
-       \"stream_mode\": [
-         \"events\"
-       ]
-     }" | \
-     sed 's/\r$//' | \
-     awk '
-     /^event:/ {
-         if (data_content != "") {
-             print data_content "\n"
-         }
-         sub(/^event: /, "Receiving event of type: ", $0)
-         printf "%s...\n", $0
-         data_content = ""
-     }
-     /^data:/ {
-         sub(/^data: /, "", $0)
-         data_content = $0
-     }
-     END {
-         if (data_content != "") {
-             print data_content "\n"
-         }
-     }
-     ' 
-    ```
-
-If your graph works correctly, you should see your graph output displayed in the console. Of course, there are many more ways you might need to test your graph, for a full list of commands you can send with the SDK, see the [Python](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/) and [JS/TS](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/) references.
@@ -1,6 +1,11 @@
-# How to version assistants
+# How to version Assistants

-In this how-to guide we will walk through how you can create and manage different assistant versions. If you haven't already, you can read [this](../../concepts/assistants.md#versioning-assistants) conceptual guide to gain a better understanding of what assistant versioning is. This how-to assumes you have a graph that is configurable, which means you have defined a config schema and passed it to your graph as follows:
+!!! info "Prerequisites"
+
+    - [Assistants Overview](../../concepts/assistants.md)
+    - [How to create an Assistant](./configuration_cloud.md)
+
+In this guide we will show you how to create, manage and use multiple versions of an assistant. If you have not already, please first see [this](./configuration_cloud.md) guide on creating an assistant. For this example, assume you have a graph with the following configuration schema:

 === "Python"

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

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

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

    // the rest of your code

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

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

-First let's set up our client and thread. If you are using the Studio, just open the application to the graph called "agent". If using cURL, you don't need to do anything except copy down your deployment URL and the name of the graph you want to use.
-
-=== "Python"
-
-    ```python
-    from langgraph_sdk import get_client
-
-    client = get_client(url=<DEPLOYMENT_URL>)
-    # Using the graph deployed with the name "agent"
-    graph_name = "agent"
-    ```
-
-=== "Javascript"
-
-    ```js
-    import { Client } from "@langchain/langgraph-sdk";
-
-    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
-    // Using the graph deployed with the name "agent"
-    const graphName = "agent";
-    ```
-
-## Create an assistant
-
-For this example, we will create an assistant by modifying the model name that is used in our graph. We can create a new assistant called "openai_assistant" for this:
-
-=== "Python"
-
-    ```python
-    openai_assistant = await client.assistants.create(graph_name, config={"configurable": {"model_name": "openai"}}, name="openai_assistant")
-    ```
-
-=== "Javascript"
-
-    ```js
-    const openaiAssistant = await client.assistants.create({graphId: graphName, config: { configurable: {"modelName": "openai"}}, name: "openaiAssistant"});
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request POST \
-    --url <DEPOLYMENT_URL>/assistants \
-    --header 'Content-Type: application/json' \
-    --data '{
-    "graph_id": "agent",
-    "config": {"model_name": "openai"},
-    "name": "openai_assistant"
-    }'
-    ```
-
-### Using the studio
-
-To create an assistant using the studio do the following steps:
-
-1. Click on the "Create New Assistant" button:
-
-    ![click create](./img/click_create_assistant.png)
-
-1. Use the create assistant pane to enter info for the assistant you wish to create, and then click create:
-
-    ![create](./img/create_assistant.png)
-
-1. See that your assistant was created and is displayed in the Studio
-
-    ![view create](./img/create_assistant_view.png)
-
-1. Click on the edit button next to the selected assistant to manage your created assistant:
-
-    ![create edit](./img/edit_created_assistant.png)
+    {
+        "assistant_id": "62e209ca-9154-432a-b9e9-2d75c7a9219b",
+        "graph_id": "agent",
+        "name": "Open AI Assistant"
+        "config": {
+            "configurable": {
+                "model_name": "openai",
+                "system_prompt": "You are a helpful assistant."
+            }
+        },
+        "metadata": {}
+        "created_at": "2024-08-31T03:09:10.230718+00:00",
+        "updated_at": "2024-08-31T03:09:10.230718+00:00",
+    }

 ## Create a new version for your assistant

-Let's now say we wanted to add a system prompt to our assistant. We can do this by using the `update` endpoint as follows. Please note that you must pass in the ENTIRE config (and metadata if you are using it). The update endpoint creates new versions completely from scratch and does not rely on previously entered config. In this case, we need to continue telling the assistant to use "openai" as the model.
+### LangGraph SDK

+To edit the assistant, use the `update` method. This will create a new version of the assistant with the provided edits. See the [Python](../reference/sdk/python_sdk_ref.md#langgraph_sdk.client.AssistantsClient.update) and [JS](../reference/sdk/js_ts_sdk_ref.md#update) SDK reference docs for more information.
+
+!!! note "Note"
+    You must pass in the ENTIRE config (and metadata if you are using it). The update endpoint creates new versions completely from scratch and does not rely on previously versions.
+
+For example, to update your assistant's system prompt:
 === "Python"

    ```python
-    openai_assistant_v2 = await client.assistants.update(openai_assistant['assistant_id'], config={"configurable": {"model_name": "openai", "system_prompt": "You are a helpful assistant!"}})
+    openai_assistant_v2 = await client.assistants.update(
+        openai_assistant["assistant_id"],
+        config={
+            "configurable": {
+                "model_name": "openai",
+                "system_prompt": "You are an unhelpful assistant!",
+            }
+        },
+    )
    ```

 === "Javascript"

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

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

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

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

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

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

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

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

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

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

-After having created multiple versions, we can change the version our assistant points to both by using the SDK and also the Studio. In this case we will be resetting the `openai_assistant` we just created two versions for to point back to the first version. When you create a new version (by using the `update` endpoint) the assistant automatically points to the newly created version, so following the code above our `openai_assistant` is pointing to the second version. Here we will change it to point to the first version:
+You can also change the active version of your assistant. To do so, use the `setLatest` method.
+
+In the example above, to rollback to the first version of the assistant:

 === "Python"

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

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

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

-To change the version, all you have to do is click into the edit pane for an assistant, select the version you want to change to, and then click the "Set As Current Version" button
-
-![set version](./img/select_different_version.png)
-
-## Using your assistant versions
-
-Whether you are a business user iterating without writing code, or a developer using the SDK - assistant versioning allows you to quickly test different agents in a controlled environment, making it easy to iterate fast. You can use any of the assistant versions just how you would a normal assistant, and can read more about how to stream output from these assistants by reading [these guides](https://langchain-ai.github.io/langgraph/cloud/how-tos/#streaming) or [this one](https://langchain-ai.github.io/langgraph/cloud/how-tos/invoke_studio/) if you are using the Studio.
+If using LangGraph Studio, to set the active version of your asssistant, click the "Manage Assistants" button and locate the assistant you would like to use. Select the assistant and the version, and then click the "Active" toggle. This will update the assistant to make the selected version active.

 !!! warning "Deleting Assistants"
-    Deleting as assistant will delete ALL of it's versions, since they all point to the same assistant ID. There is currently no way to just delete a single version, but by pointing your assistant to the correct version you can skip any versions that you don't wish to use.
+    Deleting as assistant will delete ALL of it's versions. There is currently no way to delete a single version, but by pointing your assistant to the correct version you can skip any versions that you don't wish to use.
@@ -1,6 +1,6 @@
 # Configurable Headers

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

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

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

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

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

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

- Define a cognitive architecture once as a LangGraph
- Let that LangGraph be configurable across some attributes (for example, system message or LLM to use)
- Let users create agents with arbitrary configurations, save them, and then use them in the future
+    - [Assistants Overview](../../concepts/assistants.md)
+    - [Configuration](../../concepts/low_level.md#configuration)

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

-If you look at the agent we defined, you can see that inside the `call_model` node we have created the model based on some configuration. That node looks like:
+First, as a brief refresher on the concept of configurations, consider the following simple `call_model` node and configuration schema. Observe that this node tries to read and use the `model_name` as defined by the `config` object's `configurable`.

 === "Python"

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

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

-We are looking inside the config for a `model_name` parameter (which defaults to `anthropic` if none is found). That means that by default we are using Anthropic as our model provider. In this example we will see an example of how to create an example agent that is configured to use OpenAI.
+For more information on configurations, [see here](../../concepts/low_level.md#configuration).

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

 === "Python"

@@ -46,84 +65,9 @@ First let's set up our client and thread:
    from langgraph_sdk import get_client

    client = get_client(url=<DEPLOYMENT_URL>)
-    # Select an assistant that is not configured
-    assistants = await client.assistants.search()
-    assistant = [a for a in assistants if not a["config"]][0]
-    ```
-
-=== "Javascript"
-
-    ```js
-    import { Client } from "@langchain/langgraph-sdk";
-
-    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
-    // Select an assistant that is not configured
-    const assistants = await client.assistants.search();
-    const assistant = assistants.find(a => !a.config);
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request POST \
-        --url <DEPLOYMENT_URL>/assistants/search \
-        --header 'Content-Type: application/json' \
-        --data '{
-            "limit": 10,
-            "offset": 0
-        }' | jq -c 'map(select(.config == null or .config == {})) | .[0]'
-    ```
-
-We can now call `.get_schemas` to get schemas associated with this graph:
-
-=== "Python"
-
-    ```python
-    schemas = await client.assistants.get_schemas(
-        assistant_id=assistant["assistant_id"]
-    )
-    # There are multiple types of schemas
-    # We can get the `config_schema` to look at the configurable parameters
-    print(schemas["config_schema"])
-    ```
-
-=== "Javascript"
-
-    ```js
-    const schemas = await client.assistants.getSchemas(
-      assistant["assistant_id"]
-    );
-    // There are multiple types of schemas
-    // We can get the `config_schema` to look at the configurable parameters
-    console.log(schemas.config_schema);
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request GET \
-        --url <DEPLOYMENT_URL>/assistants/<ASSISTANT_ID>/schemas | jq -r '.config_schema'
-    ```
-
-Output:
-
-    {
-        'model_name': 
-            {
-                'title': 'Model Name',
-                'enum': ['anthropic', 'openai'],
-                'type': 'string'
-            }
-    }
-
-Now we can initialize an assistant with config:
-
-=== "Python"
-
-    ```python
    openai_assistant = await client.assistants.create(
        # "agent" is the name of a graph we deployed
-        "agent", config={"configurable": {"model_name": "openai"}}
+        "agent", config={"configurable": {"model_name": "openai"}}, name="Open AI Assistant"
    )

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

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

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

 Output:
@@ -154,17 +102,32 @@ Output:
    {
        "assistant_id": "62e209ca-9154-432a-b9e9-2d75c7a9219b",
        "graph_id": "agent",
-        "created_at": "2024-08-31T03:09:10.230718+00:00",
-        "updated_at": "2024-08-31T03:09:10.230718+00:00",
+        "name": "Open AI Assistant"
        "config": {
            "configurable": {
-                "model_name": "open_ai"
+                "model_name": "openai"
            }
        },
        "metadata": {}
+        "created_at": "2024-08-31T03:09:10.230718+00:00",
+        "updated_at": "2024-08-31T03:09:10.230718+00:00",
    }

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

 === "Python"

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

    const streamResponse = client.runs.stream(
      thread["thread_id"],
+      // this is where we specify the assistant id to use
      openAIAssistant["assistant_id"],
      {
        input,
@@ -260,5 +225,6 @@ Output:
    Receiving event of type: updates
    {'agent': {'messages': [{'content': 'I was created by OpenAI, a research organization focused on developing and advancing artificial intelligence technology.', 'additional_kwargs': {}, 'response_metadata': {'finish_reason': 'stop', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_157b3831f5'}, 'type': 'ai', 'name': None, 'id': 'run-e1a6b25c-8416-41f2-9981-f9cfe043f414', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}

+### LangGraph Platform UI

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

 ## Setup

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

 ### SDK initialization

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

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

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

 ## Setup

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

 ## Setup

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

 ### SDK initialization

--- a/Show More
+++ b/Show More
				`@@ -1 +0,0 @@`
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				`@@ -1 +0,0 @@`
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				`@@ -1 +0,0 @@`
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				`@@ -1 +0,0 @@`
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				`@@ -1 +0,0 @@`
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				`@@ -1 +0,0 @@`
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				`@@ -1 +0,0 @@`
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				`@@ -1 +0,0 @@`
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