wip: put up translated doc

- Make sure that we're calling `getType` instead of deprecated `_getType()`
- Make sure that we're referencing the proper camelCase symbols instead of snake_case symbols
- Make sure that we're using proper links to the API reference
- Make sure to chain calls of StateGraph methods
- Make sure to use Zod state in all snippets

docs/Makefile

@@ -22,7 +22,7 @@ build-prebuilt:
 	uv run python -m _scripts.third_party_page.create_third_party_page stats.yml docs/agents/prebuilt.md --language python
 
 build-docs: build-typedoc build-prebuilt
-	uv run python -m mkdocs build --clean -f mkdocs.yml --strict
+	TARGET_LANGUAGE=js uv run python -m mkdocs build --clean -f mkdocs.yml --strict
 
 llms-text:
 	uv run python -m _scripts.generate_llms_text docs/llms-full.txt

docs/_scripts/add_translation.py

@@ -4,9 +4,11 @@ import argparse
 
 import requests
 from langchain_anthropic import ChatAnthropic
+from textwrap import dedent
+
 
 # Load reference TypeScript snippets
-URL = "https://gist.githubusercontent.com/eyurtsev/e7486731415463a9bc5b4682358859c8/raw/b5a5fda9c7e3387cfcb781f25082814d43675d50/gistfile1.txt"
+URL = "https://gist.githubusercontent.com/dqbd/b35d49e2ceec80e654fe1c5ab61ec477/raw/f4768aeedb67628190a4e06d063a938afc8e7672/snippets.md"
 response = requests.get(URL)
 response.raise_for_status()
 reference_snippets = response.text
@@ -14,6 +16,80 @@ reference_snippets = response.text
 # Initialize model
 model = ChatAnthropic(model="claude-sonnet-4-0", max_tokens=64_000)
 
+
+FLUENT_INTERFACE_PROMPT = (
+    "CRITICAL: Always use method chaining (fluent interface) for StateGraph operations in TypeScript. "
+    "Never create separate variables for the graph builder or call methods individually. "
+    "The fluent interface provides better type safety and is the preferred pattern.\n\n"
+    "CORRECT examples with fluent interface:\n"
+    + dedent(
+        """
+        ```typescript
+        const graph = new StateGraph(MyState)
+          .addNode('node1', node1)
+          .addNode('node2', node2)
+          .addEdge(START, 'node1')
+          .addEdge('node1', 'node2')
+          .addEdge('node2', END)
+          .compile()
+        ```
+        
+        ```typescript
+        const graph = new StateGraph(MyState)
+          .addNode('chatbot', chatbot)
+          .addEdge(START, 'chatbot')
+          .addEdge('chatbot', END)
+          .compile()
+        ```
+        
+        ```typescript
+        const graph = new StateGraph(MyState)
+          .addNode('chatbot', chatbot)
+          .addEdge(START, 'chatbot')
+          .addEdge('chatbot', END)
+          .compile()
+        ```
+        """
+    )
+    + "\n"
+    + "INCORRECT examples to avoid:\n"
+    + dedent(
+        """
+        ```typescript
+        // WRONG: Creating separate builder variable
+        const graphBuilder = new StateGraph(MyState)
+        graphBuilder.addNode('node1', node1)
+        graphBuilder.addEdge(START, 'node1')
+        const graph = graphBuilder.compile()
+        ```
+        
+        ```typescript
+        // WRONG: Using Python-style method names
+        const workflow = new StateGraph(MyState)
+        workflow.add_node('node1', node1)
+        workflow.add_edge(START, 'node1')
+        const graph = workflow.compile()
+        ```
+        
+        ```typescript
+        // WRONG: Calling methods individually
+        const graphBuilder = new StateGraph(MyState)
+        graphBuilder.addNode('chatbot', chatbot)
+        graphBuilder.addEdge(START, 'chatbot')
+        graphBuilder.addEdge('chatbot', END)
+        const graph = graphBuilder.compile()
+        ```
+        """
+    )
+    + "\n"
+    + "Key rules:\n"
+    + "- Always chain methods directly on the StateGraph constructor\n"
+    + "- Use camelCase method names (addNode, addEdge, not add_node, add_edge)\n"
+    + "- Always end with .compile()\n"
+    + "- Never store the builder in a separate variable\n"
+)
+
+
 TRANSLATION_PROMPT = (
     "You are a helpful assistant that translates Python-based technical "
     "documentation written in Markdown to equivalent TypeScript-based documentation. "
@@ -32,6 +108,12 @@ TRANSLATION_PROMPT = (
     "the translation. "
     "Use the reference TypeScript snippets as guidance whenever possible to "
     "maintain alignment with existing conventions.\n\n"
+    "IMPORTANT REQUIREMENTS:\n"
+    "- Use Zod for state definition for StateGraph. Avoid using Annotation since it will be deprecated in the future.\n"
+    "- ALWAYS use fluent interface (method chaining) for StateGraph operations - this is CRITICAL\n"
+    "- Never create separate variables for graph builders\n"
+    "- Always chain methods directly on the StateGraph constructor and end with .compile()\n\n"
+    f"{FLUENT_INTERFACE_PROMPT}\n\n"
     f"Here are the reference TypeScript snippets:\n\n{reference_snippets}\n\n"
 )
 

docs/_scripts/compact.mjs

@@ -0,0 +1,51 @@
+import * as path from "node:path";
+import * as fs from "node:fs/promises";
+import * as url from "node:url";
+
+const mdPath = url.fileURLToPath(
+  new URL("./add_translation_js_ref_updated.md", import.meta.url)
+);
+
+const extractedDir = url.fileURLToPath(
+  new URL(
+    "../../../oap-langgraphjs-tools-agent/src/add_transaction_js",
+    import.meta.url
+  )
+);
+
+const files = (await fs.readdir(extractedDir, { withFileTypes: true })).sort(
+  (a, b) => {
+    const aInt = Number.parseInt(a.name.split(".")[0], 10);
+    const bInt = Number.parseInt(b.name.split(".")[0], 10);
+    return aInt - bInt;
+  }
+);
+
+let count = 0;
+
+let lines = [];
+
+for (let file of files) {
+  if (file.isDirectory() || !file.name.endsWith(".mts")) continue;
+  count += 1;
+
+  const content = await fs.readFile(path.resolve(extractedDir, file.name), {
+    encoding: "utf-8",
+  });
+
+  lines = lines.concat(
+    content
+      .split("\n")
+      .reduce((acc, line) => {
+        if (line.trimStart().startsWith("// ```")) acc.push([]);
+        acc.at(-1)?.push(line);
+        return acc;
+      }, [])
+      .map((i) => {
+        const tag = i[0].trimStart().slice("// ```".length);
+        return ["```" + tag, ...i.slice(1), "```"].join("\n");
+      })
+  );
+}
+
+await fs.writeFile(mdPath, lines.join("\n\n"));

docs/_scripts/extract.mjs

@@ -0,0 +1,46 @@
+import * as fs from "node:fs/promises";
+import * as path from "node:path";
+import * as url from "node:url";
+
+const mdPath = url.fileURLToPath(
+  new URL("./add_translation_js_ref.md", import.meta.url)
+);
+
+const extractedDir = url.fileURLToPath(
+  new URL(
+    "../../../oap-langgraphjs-tools-agent/src/add_transaction_js",
+    import.meta.url
+  )
+);
+
+await fs.mkdir(extractedDir, { recursive: true });
+
+const md = (await fs.readFile(mdPath, { encoding: "utf-8" })).split("\n");
+
+const chunks = [];
+let current = [];
+
+for (let line of md) {
+  if (line.trimStart().startsWith("```")) {
+    if (current.length > 0) {
+      chunks.push(current.join("\n"));
+      current = [];
+    } else {
+      current.push("// " + line.trimStart());
+    }
+  } else if (current.length > 0) {
+    current.push(line);
+  }
+}
+
+if (current.length > 0) {
+  chunks.push(current.join("\n"));
+}
+
+for (let i = 0; i < chunks.length; i += 1) {
+  await fs.writeFile(path.resolve(extractedDir, `${i}.mts`), chunks[i], {
+    encoding: "utf-8",
+  });
+}
+
+console.log("finished");

docs/_scripts/notebook_hooks.py

@@ -15,8 +15,8 @@ from mkdocs.structure.files import Files, File
 from mkdocs.structure.pages import Page
 
 from _scripts.generate_api_reference_links import update_markdown_with_imports
-from _scripts.notebook_convert import convert_notebook
 from _scripts.link_map import JS_LINK_MAP
+from _scripts.notebook_convert import convert_notebook
 
 logger = logging.getLogger(__name__)
 logging.basicConfig()
@@ -100,9 +100,9 @@ REDIRECT_MAP = {
     "how-tos/create-react-agent-system-prompt.ipynb": "agents/context.md#prompts",
     "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",
+    "how-tos/human_in_the_loop/edit-graph-state.ipynb": "how-tos/human_in_the_loop/time-travel.md",
     # breakpoints
-    "how-tos/human_in_the_loop/dynamic_breakpoints.ipynb": "how-tos/human_in_the_loop/breakpoints.ipynb",
+    "how-tos/human_in_the_loop/dynamic_breakpoints.ipynb": "how-tos/human_in_the_loop/breakpoints.md",
     # misc
     "prebuilt.md": "agents/prebuilt.md",
     "reference/prebuilt.md": "reference/agents.md",
@@ -306,6 +306,12 @@ def _highlight_code_blocks(markdown: str) -> str:
     return markdown
 
 
+TARGET_LANGUAGE = os.environ.get("TARGET_LANGUAGE", "python")
+
+if TARGET_LANGUAGE not in {"python", "js"}:
+    raise ValueError(f"TARGET_LANGUAGE must be 'python' or 'js', got {TARGET_LANGUAGE}")
+
+
 def _on_page_markdown_with_config(
     markdown: str,
     page: Page,
@@ -328,16 +334,15 @@ def _on_page_markdown_with_config(
     markdown = _highlight_code_blocks(markdown)
 
     # Apply conditional rendering for code blocks
-    target_language = kwargs.get("target_language", "python")
-    markdown = _apply_conditional_rendering(markdown, target_language)
-    if target_language == "js":
+    markdown = _apply_conditional_rendering(markdown, TARGET_LANGUAGE)
+    if TARGET_LANGUAGE == "js":
         markdown = _resolve_cross_references(markdown, JS_LINK_MAP)
-    elif target_language == "python":
+    elif TARGET_LANGUAGE == "python":
         # Via a dedicated plugin
         pass
     else:
         raise ValueError(
-            f"Unsupported target language: {target_language}. "
+            f"Unsupported target language: {TARGET_LANGUAGE}. "
             "Supported languages are 'python' and 'js'."
         )
 

docs/docs/agents/context.md

@@ -1,17 +1,8 @@
----
-search:
-  boost: 2
-tags:
-  - agent
-hide:
-  - tags
----
-
 # Context
 
-Agents often require more than a list of messages to function effectively. They need **context**.
+**Context engineering** is the practice of building dynamic systems that provide the right information and tools, in the right format, so that a language model can plausibly accomplish a task.
 
-Context includes *any* data outside the message list that can shape agent behavior or tool execution. This can be:
+Context includes *any* data outside the message list that can shape behavior. This can be:
 
 - Information passed at runtime, like a `user_id` or API credentials.
 - Internal state updated during a multi-step reasoning process.
@@ -22,18 +13,10 @@ LangGraph provides **three** primary ways to supply context:
 | Type                                                                         | Description                                   | Mutable? | Lifetime                |
 |------------------------------------------------------------------------------|-----------------------------------------------|----------|-------------------------|
 | [**Config**](#config-static-context)                                         | data passed at the start of a run             | ❌        | per run                 |
-| [**State**](#state-mutable-context)                                          | dynamic data that can change during execution | ✅        | per run or conversation |
-| [**Long-term Memory (Store)**](#long-term-memory-cross-conversation-context) | data that can be shared between conversations | ✅        | across conversations    |
+| [**Short-term memory (State)**](#short-term-memory-mutable-context)          | dynamic data that can change during execution | ✅        | per run or conversation |
+| [**Long-term memory (Store)**](#long-term-memory-cross-conversation-context) | data that can be shared between conversations | ✅        | across conversations    |
 
-You can use context to:
-
-- Adjust the system prompt the model sees
-- Feed tools with necessary inputs
-- Track facts during an ongoing conversation
-
-## Providing Runtime Context
-
-Use this when you need to inject data into an agent at runtime.
+## Provide runtime context
 
 ### Config (static context)
 
@@ -44,88 +27,83 @@ Specify configuration using a key called **"configurable"** which is reserved
 for this purpose:
 
 ```python
-agent.invoke(
-    {"messages": [{"role": "user", "content": "hi!"}]},
+graph.invoke( # (1)!
+    {"messages": [{"role": "user", "content": "hi!"}]}, # (2)!
     # highlight-next-line
-    config={"configurable": {"user_id": "user_123"}}
+    config={"configurable": {"user_id": "user_123"}} # (3)!
 )
 ```
 
-### State (mutable context)
+1. This is the invocation of the agent or graph. The `invoke` method runs the underlying graph with the provided input.
+2. This example uses messages as an input, which is common, but your application may use different input structures.
+3. This is where you pass the configuration data. The `config` parameter allows you to provide additional context that the agent can use during its execution.
 
-State acts as short-term memory during a run. It holds dynamic data that can evolve during execution, such as values derived from tools or LLM outputs.
-
-```python
-class CustomState(AgentState):
-    # highlight-next-line
-    user_name: str
-
-agent = create_react_agent(
-    # Other agent parameters...
-    # highlight-next-line
-    state_schema=CustomState,
-)
-
-agent.invoke({
-    "messages": "hi!",
-    "user_name": "Jane"
-})
-```
-
-!!! tip "Turning on memory"
-
-    Please see the [memory guide](../how-tos/memory/add-memory.md) for more details on how to enable memory. This is a powerful feature that allows you to persist the agent's state across multiple invocations.
-    Otherwise, the state is scoped only to a single agent run.
-
-
-
-### Long-Term Memory (cross-conversation context)
-
-For context that spans *across* conversations or sessions, LangGraph allows access to **long-term memory** via a `store`. This can be used to read or update persistent facts (e.g., user profiles, preferences, prior interactions). For more, see the [Memory guide](../how-tos/memory/add-memory.md).
-
-## Customizing Prompts with Context { #prompts }
-
-Prompts define how the agent behaves. To incorporate runtime context, you can dynamically generate prompts based on the agent's state or config.
-
-Common use cases:
-
-- Personalization
-- Role or goal customization
-- Conditional behavior (e.g., user is admin)
-
-=== "Using config"
+=== "Agent prompt"
 
     ```python
     from langchain_core.messages import AnyMessage
     from langchain_core.runnables import RunnableConfig
-    from langgraph.prebuilt import create_react_agent
     from langgraph.prebuilt.chat_agent_executor import AgentState
+    from langgraph.prebuilt import create_react_agent
 
-    def prompt(
-        state: AgentState,
-        # highlight-next-line
-        config: RunnableConfig,
-    ) -> list[AnyMessage]:
-        # highlight-next-line
+    # highlight-next-line
+    def prompt(state: AgentState, config: RunnableConfig) -> list[AnyMessage]:
         user_name = config["configurable"].get("user_name")
-        system_msg = f"You are a helpful assistant. User's name is {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 = create_react_agent(
         model="anthropic:claude-3-7-sonnet-latest",
         tools=[get_weather],
-        # highlight-next-line
         prompt=prompt
     )
 
     agent.invoke(
-        ...,
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
         # highlight-next-line
         config={"configurable": {"user_name": "John Smith"}}
     )
     ```
 
-=== "Using state"
+    * See [Agents](../agents/agents.md) for details.
+
+=== "Workflow node"
+
+    ```python
+    from langchain_core.runnables import RunnableConfig
+
+    # highlight-next-line
+    def node(state: State, config: RunnableConfig):
+        user_name = config["configurable"].get("user_name")
+        ...
+    ```
+
+    * See [the Graph API](https://langchain-ai.github.io/langgraph/how-tos/graph-api/#add-runtime-configuration) for details.
+
+=== "In a tool"
+
+    ```python
+    from langchain_core.runnables import RunnableConfig
+
+    @tool
+    # highlight-next-line
+    def get_user_info(config: RunnableConfig) -> str:
+        """Retrieve user information based on user ID."""
+        user_id = config["configurable"].get("user_id")
+        return "User is John Smith" if user_id == "user_123" else "Unknown user"
+    ```
+
+    See the [tool calling guide](../how-tos/tool-calling.md#configuration) for details.
+
+### Short-term memory (mutable context)
+
+State acts as [short-term memory](../concepts/memory.md) during a run. It holds dynamic data that can evolve during execution, such as values derived from tools or LLM outputs.
+
+=== "In an agent"
+
+    Example shows how to incorporate state into an agent **prompt**.
+
+    State can also be accessed by the agent's **tools**, which can read or update the state as needed. See [tool calling guide](../how-tos/tool-calling.md#short-term-memory) for details.
 
     ```python
     from langchain_core.messages import AnyMessage
@@ -133,15 +111,14 @@ Common use cases:
     from langgraph.prebuilt import create_react_agent
     from langgraph.prebuilt.chat_agent_executor import AgentState
 
-    class CustomState(AgentState):
-        # highlight-next-line
+    # highlight-next-line
+    class CustomState(AgentState): # (1)!
         user_name: str
 
     def prompt(
         # highlight-next-line
         state: CustomState
     ) -> list[AnyMessage]:
-        # highlight-next-line
         user_name = state["user_name"]
         system_msg = f"You are a helpful assistant. User's name is {user_name}"
         return [{"role": "system", "content": system_msg}] + state["messages"]
@@ -150,87 +127,58 @@ Common use cases:
         model="anthropic:claude-3-7-sonnet-latest",
         tools=[...],
         # highlight-next-line
-        state_schema=CustomState,
-        # highlight-next-line
+        state_schema=CustomState, # (2)!
         prompt=prompt
     )
 
     agent.invoke({
         "messages": "hi!",
-        # highlight-next-line
         "user_name": "John Smith"
     })
     ```
 
-## Accessing Context in Tools { #tools }
-
-Tools can access context through special parameter **annotations**.
-
-* Use `RunnableConfig` for config access
-* Use `Annotated[StateSchema, InjectedState]` for agent state
+    1. Define a custom state schema that extends `AgentState` or `MessagesState`.
+    2. Pass the custom state schema to the agent. This allows the agent to access and modify the state during execution.
 
 
-!!! tip 
-
-    These annotations prevent LLMs from attempting to fill in the values. These parameters will be **hidden** from the LLM.
-
-=== "Using config"
+=== "In a workflow"
 
     ```python
-    def get_user_info(
-        # highlight-next-line
-        config: RunnableConfig,
-    ) -> str:
-        """Look up user info."""
-        # highlight-next-line
-        user_id = config["configurable"].get("user_id")
-        return "User is John Smith" if user_id == "user_123" else "Unknown user"
+    from typing_extensions import TypedDict
+    from langchain_core.messages import AnyMessage
+    from langgraph.graph import StateGraph
 
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_user_info],
-    )
+    # highlight-next-line
+    class CustomState(TypedDict): # (1)!
+        messages: list[AnyMessage]
+        extra_field: int
 
-    agent.invoke(
-        {"messages": [{"role": "user", "content": "look up user information"}]},
-        # highlight-next-line
-        config={"configurable": {"user_id": "user_123"}}
-    )
+    # highlight-next-line
+    def node(state: CustomState): # (2)!
+        messages = state["messages"]
+        ...
+        return { # (3)!
+            # highlight-next-line
+            "extra_field": state["extra_field"] + 1
+        }
+
+    builder = StateGraph(State)
+    builder.add_node(node)
+    builder.set_entry_point("node")
+    graph = builder.compile()
     ```
+    
+    1. Define a custom state
+    2. Access the state in any node or tool
+    3. The Graph API is designed to work as easily as possible with state. The return value of a node represents a requested update to the state.
 
-=== "Using State"
 
-    ```python
-    from typing import Annotated
-    from langgraph.prebuilt import InjectedState
+!!! tip "Turning on memory"
 
-    class CustomState(AgentState):
-        # highlight-next-line
-        user_id: str
+    Please see the [memory guide](../how-tos/memory/add-memory.md) for more details on how to enable memory. This is a powerful feature that allows you to persist the agent's state across multiple invocations. Otherwise, the state is scoped only to a single run.
 
-    def get_user_info(
-        # highlight-next-line
-        state: Annotated[CustomState, InjectedState]
-    ) -> str:
-        """Look up user info."""
-        # highlight-next-line
-        user_id = state["user_id"]
-        return "User is John Smith" if user_id == "user_123" else "Unknown user"
+### Long-term memory (cross-conversation context)
 
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_user_info],
-        # highlight-next-line
-        state_schema=CustomState,
-    )
+For context that spans *across* conversations or sessions, LangGraph allows access to **long-term memory** via a `store`. This can be used to read or update persistent facts (e.g., user profiles, preferences, prior interactions). 
 
-    agent.invoke({
-        "messages": "look up user information",
-        # highlight-next-line
-        "user_id": "user_123"
-    })
-    ```
-
-### Update Context from Tools
-
-Tools can update agent's context (state and long-term memory) during execution. This is useful for persisting intermediate results or making information accessible to subsequent tools or prompts. See [Memory](../how-tos/memory/add-memory.md#read-short-term) guide for more information.
+For more information, see the [Memory guide](../how-tos/memory/add-memory.md).

docs/docs/cloud/how-tos/add-human-in-the-loop.md

@@ -1,4 +1,4 @@
-# Human-in-the-loop in LangGraph Server
+# Human-in-the-loop using Server API
 
 To review, edit, and approve tool calls in an agent or workflow, use LangGraph's [human-in-the-loop](../../concepts/human_in_the_loop.md) features.
 

docs/docs/cloud/how-tos/human_in_the_loop_breakpoint.md

@@ -1,10 +1,16 @@
-# Breakpoints
+# Set breakpoints using Server API
 
 [Breakpoints](../../concepts/breakpoints.md) pause graph execution at defined points and let you step through each stage. They use LangGraph's [**persistence layer**](../../concepts/persistence.md), which saves the graph state after each step.
 
-With breakpoints, you can inspect the graph's state and node inputs at any point. Execution pauses **indefinitely** until you resume, as the checkpointer preserves the state.
+With breakpoints, you can inspect the graph's state and node inputs at any point. Execution pauses indefinitely until you resume, as the checkpointer preserves the state.
 
-## Set breakpoints
+!!! tip
+
+    For conceptual information on breakpoints, see [Breakpoints](../../concepts/breakpoints.md).
+
+## Set static breakpoints
+
+Static breakpoints are triggered either before or after a node executes. You can set static breakpoints by specifying `interrupt_before` and `interrupt_after` at compile time or run time.
 
 === "Compile time"
 
@@ -78,10 +84,9 @@ With breakpoints, you can inspect the graph's state and node inputs at any point
         }"
         ```
 
-!!! tip
-
-    This example shows how to add **static** breakpoints. See [this guide](../../how-tos/human_in_the_loop/breakpoints.ipynb) for more options for how to add breakpoints.
+## Example
 
+This example shows how to add **static** breakpoints. See [Use breakpoints](../../how-tos/human_in_the_loop/breakpoints.md) for more options on adding breakpoints.
 
 === "Python"
 
@@ -177,8 +182,4 @@ With breakpoints, you can inspect the graph's state and node inputs at any point
     --data "{
       \"assistant_id\": \"agent\"
     }"
-    ```
-
-## Learn more
-
-- [**LangGraph breakpoints guide**](../../how-tos/human_in_the_loop/breakpoints.ipynb): learn more about adding breakpoints in LangGraph.
+    ```

docs/docs/cloud/how-tos/human_in_the_loop_time_travel.md

@@ -1,10 +1,8 @@
-# Time travel
+# Time travel using Server API
 
-LangGraph provides [**time travel**](../../concepts/time-travel.md) functionality to **resume execution from a prior checkpoint** — either replaying the same state or modifying it to explore alternatives. In all cases, resuming past execution produces a **new fork** in the history.
+LangGraph provides the [**time travel**](../../concepts/time-travel.md) functionality to resume execution from a prior checkpoint, either replaying the same state or modifying it to explore alternatives. In all cases, resuming past execution produces a new fork in the history.
 
-## Use time travel
-
-To use time-travel in LangGraph:
+To time travel using the LangGraph Server API (via the LangGraph SDK):
 
 1. **Run the graph** with initial inputs using [LangGraph SDK](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/)'s [`client.runs.wait`][langgraph_sdk.client.RunsClient.wait] or [`client.runs.stream`][langgraph_sdk.client.RunsClient.stream] APIs.
 2. **Identify a checkpoint in an existing thread**: Use [`client.threads.get_history`][langgraph_sdk.client.ThreadsClient.get_history] method to retrieve the execution history for a specific `thread_id` and locate the desired `checkpoint_id`.
@@ -12,7 +10,7 @@ To use time-travel in LangGraph:
 3. **(Optional) modify the graph state**: Use the [`client.threads.update_state`][langgraph_sdk.client.ThreadsClient.update_state] method to modify the graph’s state at the checkpoint and resume execution from alternative state.
 4. **Resume execution from the checkpoint**: Use the [`client.runs.wait`][langgraph_sdk.client.RunsClient.wait] or [`client.runs.stream`][langgraph_sdk.client.RunsClient.stream] APIs with an input of `None` and the appropriate `thread_id` and `checkpoint_id`.
 
-## Example
+## Use time travel in a workflow
 
 ??? example "Example graph"
 
@@ -237,4 +235,4 @@ To use time-travel in LangGraph:
 
 ## Learn more
 
-- [**LangGraph time travel guide**](../../how-tos/human_in_the_loop/time-travel.ipynb): learn more about using time travel in LangGraph.
+- [**LangGraph time travel guide**](../../how-tos/human_in_the_loop/time-travel.md): learn more about using time travel in LangGraph.

docs/docs/concepts/breakpoints.md

@@ -5,10 +5,14 @@ search:
 
 # Breakpoints
 
-Breakpoints pause graph execution at defined points and let you step through each stage. They use LangGraph's [**persistence layer**](./persistence.md), which saves the graph state after each step.
+[Breakpoints](../how-tos/human_in_the_loop/breakpoints.md) pause graph execution at defined points and let you step through each stage. They use LangGraph's [**persistence layer**](./persistence.md), which saves the graph state after each step.
 
 With breakpoints, you can inspect the graph's state and node inputs at any point. Execution pauses **indefinitely** until you resume, as the checkpointer preserves the state.
 
 <figure markdown="1">
 ![image](img/breakpoints.png){: style="max-height:400px"}
 <figcaption>An example graph consisting of 3 sequential steps with a breakpoint before step_3. </figcaption> </figure>
+
+!!! tip
+
+    For information on how to use breakpoints, see [Set breakpoints](../how-tos/human_in_the_loop/breakpoints.md) and [Set breakpoints using Server API](../cloud/how-tos/human_in_the_loop_breakpoint.md).

docs/docs/concepts/human_in_the_loop.md

@@ -17,6 +17,10 @@ To review, edit, and approve tool calls in an agent or workflow, [use LangGraph'
 ![image](../concepts/img/human_in_the_loop/tool-call-review.png){: style="max-height:400px"}
 </figure>
 
+!!! tip
+
+    For information on how to use human-in-the-loop, see [Enable human intervention](../how-tos/human_in_the_loop/add-human-in-the-loop.md) and [Human-in-the-loop using Server API](../cloud/how-tos/add-human-in-the-loop.md).
+
 ## Key capabilities
 
 * **Persistent execution state**: LangGraph allows you to pause execution **indefinitely** — for minutes, hours, or even days—until human input is received. This is possible because LangGraph checkpoints the graph state after each step, which allows the system to persist execution context and later resume the workflow, continuing from where it left off. This supports asynchronous human review or input without time constraints.

docs/docs/concepts/langgraph_standalone_container.md

@@ -17,6 +17,10 @@ The Standalone Container deployment option is the least restrictive model for de
 | **Where is it hosted?** | n/a | Your cloud |
 | **Who provisions and manages it?** | n/a | You |
 
+!!! warning
+
+      LangGraph Platform should not be deployed in serverless environments.
+
 ## Architecture
 
 ![Standalone Container](./img/langgraph_platform_deployment_architecture.png)

docs/docs/concepts/low_level.md

@@ -9,6 +9,7 @@ search:
 
 At its core, LangGraph models agent workflows as graphs. You define the behavior of your agents using three key components:
 
+:::python
 1. [`State`](#state): A shared data structure that represents the current snapshot of your application. It can be any Python type, but is typically a `TypedDict` or Pydantic `BaseModel`.
 
 2. [`Nodes`](#nodes): Python functions that encode the logic of your agents. They receive the current `State` as input, perform some computation or side-effect, and return an updated `State`.
@@ -16,6 +17,17 @@ At its core, LangGraph models agent workflows as graphs. You define the behavior
 3. [`Edges`](#edges): Python functions that determine which `Node` to execute next based on the current `State`. They can be conditional branches or fixed transitions.
 
 By composing `Nodes` and `Edges`, you can create complex, looping workflows that evolve the `State` over time. The real power, though, comes from how LangGraph manages that `State`. To emphasize: `Nodes` and `Edges` are nothing more than Python functions - they can contain an LLM or just good ol' Python code.
+:::
+
+:::js
+1. [`State`](#state): A shared data structure that represents the current snapshot of your application. It can be any TypeScript type, but is typically a Zod schema or TypeScript interface.
+
+2. [`Nodes`](#nodes): TypeScript functions that encode the logic of your agents. They receive the current `State` as input, perform some computation or side-effect, and return an updated `State`.
+
+3. [`Edges`](#edges): TypeScript functions that determine which `Node` to execute next based on the current `State`. They can be conditional branches or fixed transitions.
+
+By composing `Nodes` and `Edges`, you can create complex, looping workflows that evolve the `State` over time. The real power, though, comes from how LangGraph manages that `State`. To emphasize: `Nodes` and `Edges` are nothing more than TypeScript functions - they can contain an LLM or just good ol' TypeScript code.
+:::
 
 In short: _nodes do the work, edges tell what to do next_.
 
@@ -31,21 +43,51 @@ The `StateGraph` class is the main graph class to use. This is parameterized by
 
 To build your graph, you first define the [state](#state), you then add [nodes](#nodes) and [edges](#edges), and then you compile it. What exactly is compiling your graph and why is it needed?
 
-Compiling is a pretty simple step. It provides a few basic checks on the structure of your graph (no orphaned nodes, etc). It is also where you can specify runtime args like [checkpointers](./persistence.md) and breakpoints. You compile your graph by just calling the `.compile` method:
+Compiling is a pretty simple step. It provides a few basic checks on the structure of your graph (no orphaned nodes, etc). It is also where you can specify runtime args like [checkpointers](./persistence.md) and breakpoints.
+
+:::python
+You compile your graph by just calling the `.compile` method:
 
 ```python
 graph = graph_builder.compile(...)
 ```
+:::
+
+:::js
+You compile your graph by just calling the `.compile()` method:
+
+```typescript
+const graph = new StateGraph(State)
+  .addNode("node1", node1)
+  .addNode("node2", node2)
+  .addEdge(START, "node1")
+  .addEdge("node1", "node2")
+  .addEdge("node2", END)
+  .compile();
+```
+:::
 
 You **MUST** compile your graph before you can use it.
 
 ## State
 
+:::python
 The first thing you do when you define a graph is define the `State` of the graph. The `State` consists of the [schema of the graph](#schema) as well as [`reducer` functions](#reducers) which specify how to apply updates to the state. The schema of the `State` will be the input schema to all `Nodes` and `Edges` in the graph, and can be either a `TypedDict` or a `Pydantic` model. All `Nodes` will emit updates to the `State` which are then applied using the specified `reducer` function.
+:::
+
+:::js
+The first thing you do when you define a graph is define the `State` of the graph. The `State` consists of the [schema of the graph](#schema) as well as [`reducer` functions](#reducers) which specify how to apply updates to the state. The schema of the `State` will be the input schema to all `Nodes` and `Edges` in the graph, and can be either a Zod schema or a TypeScript interface. All `Nodes` will emit updates to the `State` which are then applied using the specified `reducer` function.
+:::
 
 ### Schema
 
+:::python
 The main documented way to specify the schema of a graph is by using `TypedDict`. However, we also support [using a Pydantic BaseModel](../how-tos/graph-api.ipynb#use-pydantic-models-for-graph-state) as your graph state to add **default values** and additional data validation.
+:::
+
+:::js
+The main documented way to specify the schema of a graph is by using Zod schemas. However, we also support [using TypeScript interfaces](../how-tos/graph-api.ipynb#use-typescript-interfaces-for-graph-state) as your graph state to add **default values** and additional data validation.
+:::
 
 By default, the graph will have the same input and output schemas. If you want to change this, you can also specify explicit input and output schemas directly. This is useful when you have a lot of keys, and some are explicitly for input and others for output. See the [guide here](../how-tos/graph-api.ipynb#define-input-and-output-schemas) for how to use.
 
@@ -62,6 +104,7 @@ It is also possible to define explicit input and output schemas for a graph. In
 
 Let's look at an example:
 
+:::python
 ```python
 class InputState(TypedDict):
     user_input: str
@@ -102,12 +145,77 @@ graph = builder.compile()
 graph.invoke({"user_input":"My"})
 {'graph_output': 'My name is Lance'}
 ```
+:::
+
+:::js
+```typescript
+import { z } from "zod";
+
+const InputState = z.object({
+  userInput: z.string(),
+});
+
+const OutputState = z.object({
+  graphOutput: z.string(),
+});
+
+const OverallState = z.object({
+  foo: z.string(),
+  userInput: z.string(),
+  graphOutput: z.string(),
+});
+
+const PrivateState = z.object({
+  bar: z.string(),
+});
+
+const node1 = (state: z.infer<typeof InputState>): Partial<z.infer<typeof OverallState>> => {
+  // Write to OverallState
+  return { foo: state.userInput + " name" };
+};
+
+const node2 = (state: z.infer<typeof OverallState>): Partial<z.infer<typeof PrivateState>> => {
+  // Read from OverallState, write to PrivateState
+  return { bar: state.foo + " is" };
+};
+
+const node3 = (state: z.infer<typeof PrivateState>): Partial<z.infer<typeof OutputState>> => {
+  // Read from PrivateState, write to OutputState
+  return { graphOutput: state.bar + " Lance" };
+};
+
+const graph = new StateGraph({
+  state: OverallState,
+  input: InputState,
+  output: OutputState,
+})
+  .addNode("node1", node1)
+  .addNode("node2", node2)
+  .addNode("node3", node3)
+  .addEdge(START, "node1")
+  .addEdge("node1", "node2")
+  .addEdge("node2", "node3")
+  .addEdge("node3", END)
+  .compile();
+
+await graph.invoke({ userInput: "My" });
+// { graphOutput: 'My name is Lance' }
+```
+:::
 
 There are two subtle and important points to note here:
 
+:::python
 1. We pass `state: InputState` as the input schema to `node_1`. But, we write out to `foo`, a channel in `OverallState`. How can we write out to a state channel that is not included in the input schema? This is because a node _can write to any state channel in the graph state._ The graph state is the union of the state channels defined at initialization, which includes `OverallState` and the filters `InputState` and `OutputState`.
 
 2. We initialize the graph with `StateGraph(OverallState,input_schema=InputState,output_schema=OutputState)`. So, how can we write to `PrivateState` in `node_2`? How does the graph gain access to this schema if it was not passed in the `StateGraph` initialization? We can do this because _nodes can also declare additional state channels_ as long as the state schema definition exists. In this case, the `PrivateState` schema is defined, so we can add `bar` as a new state channel in the graph and write to it.
+:::
+
+:::js
+1. We pass `state: z.infer<typeof InputState>` as the input schema to `node1`. But, we write out to `foo`, a channel in `OverallState`. How can we write out to a state channel that is not included in the input schema? This is because a node _can write to any state channel in the graph state._ The graph state is the union of the state channels defined at initialization, which includes `OverallState` and the filters `InputState` and `OutputState`.
+
+2. We initialize the graph with `new StateGraph({ state: OverallState, input: InputState, output: OutputState })`. So, how can we write to `PrivateState` in `node2`? How does the graph gain access to this schema if it was not passed in the `StateGraph` initialization? We can do this because _nodes can also declare additional state channels_ as long as the state schema definition exists. In this case, the `PrivateState` schema is defined, so we can add `bar` as a new state channel in the graph and write to it.
+:::
 
 ### Reducers
 
@@ -119,6 +227,7 @@ These two examples show how to use the default reducer:
 
 **Example A:**
 
+:::python
 ```python
 from typing_extensions import TypedDict
 
@@ -128,9 +237,24 @@ class State(TypedDict):
 ```
 
 In this example, no reducer functions are specified for any key. Let's assume the input to the graph is `{"foo": 1, "bar": ["hi"]}`. Let's then assume the first `Node` returns `{"foo": 2}`. This is treated as an update to the state. Notice that the `Node` does not need to return the whole `State` schema - just an update. After applying this update, the `State` would then be `{"foo": 2, "bar": ["hi"]}`. If the second node returns `{"bar": ["bye"]}` then the `State` would then be `{"foo": 2, "bar": ["bye"]}`
+:::
+
+:::js
+```typescript
+import { z } from "zod";
+
+const State = z.object({
+  foo: z.number(),
+  bar: z.array(z.string()),
+});
+```
+
+In this example, no reducer functions are specified for any key. Let's assume the input to the graph is `{ foo: 1, bar: ["hi"] }`. Let's then assume the first `Node` returns `{ foo: 2 }`. This is treated as an update to the state. Notice that the `Node` does not need to return the whole `State` schema - just an update. After applying this update, the `State` would then be `{ foo: 2, bar: ["hi"] }`. If the second node returns `{ bar: ["bye"] }` then the `State` would then be `{ foo: 2, bar: ["bye"] }`
+:::
 
 **Example B:**
 
+:::python
 ```python
 from typing import Annotated
 from typing_extensions import TypedDict
@@ -142,21 +266,51 @@ class State(TypedDict):
 ```
 
 In this example, we've used the `Annotated` type to specify a reducer function (`operator.add`) for the second key (`bar`). Note that the first key remains unchanged. Let's assume the input to the graph is `{"foo": 1, "bar": ["hi"]}`. Let's then assume the first `Node` returns `{"foo": 2}`. This is treated as an update to the state. Notice that the `Node` does not need to return the whole `State` schema - just an update. After applying this update, the `State` would then be `{"foo": 2, "bar": ["hi"]}`. If the second node returns `{"bar": ["bye"]}` then the `State` would then be `{"foo": 2, "bar": ["hi", "bye"]}`. Notice here that the `bar` key is updated by adding the two lists together.
+:::
+
+:::js
+```typescript
+import { z } from "zod";
+import "@langchain/langgraph/zod";
+
+const State = z.object({
+  foo: z.number(),
+  bar: z.array(z.string()).langgraph.reducer((x, y) => x.concat(y)),
+});
+```
+
+In this example, we've used the `.langgraph.reducer()` method to specify a reducer function for the second key (`bar`). Note that the first key remains unchanged. Let's assume the input to the graph is `{ foo: 1, bar: ["hi"] }`. Let's then assume the first `Node` returns `{ foo: 2 }`. This is treated as an update to the state. Notice that the `Node` does not need to return the whole `State` schema - just an update. After applying this update, the `State` would then be `{ foo: 2, bar: ["hi"] }`. If the second node returns `{ bar: ["bye"] }` then the `State` would then be `{ foo: 2, bar: ["hi", "bye"] }`. Notice here that the `bar` key is updated by concatenating the two arrays together.
+:::
 
 ### Working with Messages in Graph State
 
 #### Why use messages?
 
+:::python
 Most modern LLM providers have a chat model interface that accepts a list of messages as input. LangChain's [`ChatModel`](https://python.langchain.com/docs/concepts/#chat-models) in particular accepts a list of `Message` objects as inputs. These messages come in a variety of forms such as `HumanMessage` (user input) or `AIMessage` (LLM response). To read more about what message objects are, please refer to [this](https://python.langchain.com/docs/concepts/#messages) conceptual guide.
+:::
+
+:::js
+Most modern LLM providers have a chat model interface that accepts a list of messages as input. LangChain's [`ChatModel`](https://js.langchain.com/docs/concepts/#chat-models) in particular accepts a list of `Message` objects as inputs. These messages come in a variety of forms such as `HumanMessage` (user input) or `AIMessage` (LLM response). To read more about what message objects are, please refer to [this](https://js.langchain.com/docs/concepts/#messages) conceptual guide.
+:::
 
 #### Using Messages in your Graph
 
+:::python
 In many cases, it is helpful to store prior conversation history as a list of messages in your graph state. To do so, we can add a key (channel) to the graph state that stores a list of `Message` objects and annotate it with a reducer function (see `messages` key in the example below). The reducer function is vital to telling the graph how to update the list of `Message` objects in the state with each state update (for example, when a node sends an update). If you don't specify a reducer, every state update will overwrite the list of messages with the most recently provided value. If you wanted to simply append messages to the existing list, you could use `operator.add` as a reducer.
 
 However, you might also want to manually update messages in your graph state (e.g. human-in-the-loop). If you were to use `operator.add`, the manual state updates you send to the graph would be appended to the existing list of messages, instead of updating existing messages. To avoid that, you need a reducer that can keep track of message IDs and overwrite existing messages, if updated. To achieve this, you can use the prebuilt `add_messages` function. For brand new messages, it will simply append to existing list, but it will also handle the updates for existing messages correctly.
+:::
+
+:::js
+In many cases, it is helpful to store prior conversation history as a list of messages in your graph state. To do so, we can add a key (channel) to the graph state that stores a list of `Message` objects and annotate it with a reducer function (see `messages` key in the example below). The reducer function is vital to telling the graph how to update the list of `Message` objects in the state with each state update (for example, when a node sends an update). If you don't specify a reducer, every state update will overwrite the list of messages with the most recently provided value. If you wanted to simply append messages to the existing list, you could use `(x, y) => x.concat(y)` as a reducer.
+
+However, you might also want to manually update messages in your graph state (e.g. human-in-the-loop). If you were to use `(x, y) => x.concat(y)`, the manual state updates you send to the graph would be appended to the existing list of messages, instead of updating existing messages. To avoid that, you need a reducer that can keep track of message IDs and overwrite existing messages, if updated. To achieve this, you can use the prebuilt `messagesStateReducer` function. For brand new messages, it will simply append to existing list, but it will also handle the updates for existing messages correctly.
+:::
 
 #### Serialization
 
+:::python
 In addition to keeping track of message IDs, the `add_messages` function will also try to deserialize messages into LangChain `Message` objects whenever a state update is received on the `messages` channel. See more information on LangChain serialization/deserialization [here](https://python.langchain.com/docs/how_to/serialization/). This allows sending graph inputs / state updates in the following format:
 
 ```python
@@ -178,9 +332,36 @@ from typing_extensions import TypedDict
 class GraphState(TypedDict):
     messages: Annotated[list[AnyMessage], add_messages]
 ```
+:::
+
+:::js
+In addition to keeping track of message IDs, the `messagesStateReducer` function will also try to deserialize messages into LangChain `Message` objects whenever a state update is received on the `messages` channel. See more information on LangChain serialization/deserialization [here](https://js.langchain.com/docs/how_to/serialization/). This allows sending graph inputs / state updates in the following format:
+
+```typescript
+// this is supported
+{ messages: [new HumanMessage("message")] }
+
+// and this is also supported
+{ messages: [{ role: "human", content: "message" }] }
+```
+
+Since the state updates are always deserialized into LangChain `Messages` when using `messagesStateReducer`, you should use dot notation to access message attributes, like `state.messages[state.messages.length - 1].content`. Below is an example of a graph that uses `messagesStateReducer` as its reducer function.
+
+```typescript
+import { BaseMessage } from "@langchain/core/messages";
+import { messagesStateReducer } from "@langchain/langgraph";
+import { z } from "zod";
+import "@langchain/langgraph/zod";
+
+const GraphState = z.object({
+  messages: z.array(z.any()).langgraph.reducer(messagesStateReducer),
+});
+```
+:::
 
 #### MessagesState
 
+:::python
 Since having a list of messages in your state is so common, there exists a prebuilt state called `MessagesState` which makes it easy to use messages. `MessagesState` is defined with a single `messages` key which is a list of `AnyMessage` objects and uses the `add_messages` reducer. Typically, there is more state to track than just messages, so we see people subclass this state and add more fields, like:
 
 ```python
@@ -189,9 +370,25 @@ from langgraph.graph import MessagesState
 class State(MessagesState):
     documents: list[str]
 ```
+:::
+
+:::js
+Since having a list of messages in your state is so common, there exists a prebuilt state called `MessagesZodState` which makes it easy to use messages. `MessagesZodState` is defined with a single `messages` key which is a list of `BaseMessage` objects and uses the `messagesStateReducer` reducer. Typically, there is more state to track than just messages, so we see people merge this state with other schemas, like:
+
+```typescript
+import { MessagesZodState } from "@langchain/langgraph";
+import { z } from "zod";
+
+const State = z.object({
+  messages: MessagesZodState.shape.messages,
+  documents: z.array(z.string()),
+});
+```
+:::
 
 ## Nodes
 
+:::python
 In LangGraph, nodes are typically python functions (sync or async) where the **first** positional argument is the [state](#state), and (optionally), the **second** positional argument is a "config", containing optional [configurable parameters](#configuration) (such as a `thread_id`).
 
 Similar to `NetworkX`, you add these nodes to a graph using the [add_node][langgraph.graph.StateGraph.add_node] method:
@@ -232,26 +429,88 @@ If you add a node to a graph without specifying a name, it will be given a defau
 builder.add_node(my_node)
 # You can then create edges to/from this node by referencing it as `"my_node"`
 ```
+:::
+
+:::js
+In LangGraph, nodes are typically TypeScript functions (sync or async) where the **first** positional argument is the [state](#state), and (optionally), the **second** positional argument is a "config", containing optional [configurable parameters](#configuration) (such as a `thread_id`).
+
+Similar to `NetworkX`, you add these nodes to a graph using the `addNode` method:
+
+```typescript
+import { z } from "zod";
+import { RunnableConfig } from "@langchain/core/runnables";
+import { StateGraph } from "@langchain/langgraph";
+
+const State = z.object({
+  input: z.string(),
+  results: z.string(),
+});
+
+const myNode = (state: z.infer<typeof State>, config: RunnableConfig) => {
+  console.log("In node: ", config?.configurable?.user_id);
+  return { results: `Hello, ${state.input}!` };
+};
+
+// The second argument is optional
+const myOtherNode = (state: z.infer<typeof State>) => {
+  return state;
+};
+
+const graph = new StateGraph(State)
+  .addNode("myNode", myNode)
+  .addNode("otherNode", myOtherNode)
+  // ...
+  .compile();
+```
+
+Behind the scenes, functions are converted to [RunnableLambda](https://js.langchain.com/docs/concepts/#runnable-lambda)s, which add batch and async support to your function, along with native tracing and debugging.
+:::
 
 ### `START` Node
 
 The `START` Node is a special node that represents the node that sends user input to the graph. The main purpose for referencing this node is to determine which nodes should be called first.
 
+:::python
 ```python
 from langgraph.graph import START
 
 graph.add_edge(START, "node_a")
 ```
+:::
+
+:::js
+```typescript
+import { START } from "@langchain/langgraph";
+
+const graph = new StateGraph(State)
+  .addNode("nodeA", nodeA)
+  .addEdge(START, "nodeA")
+  .compile();
+```
+:::
 
 ### `END` Node
 
 The `END` Node is a special node that represents a terminal node. This node is referenced when you want to denote which edges have no actions after they are done.
 
+:::python
 ```
 from langgraph.graph import END
 
 graph.add_edge("node_a", END)
 ```
+:::
+
+:::js
+```typescript
+import { END } from "@langchain/langgraph";
+
+const graph = new StateGraph(State)
+  .addNode("nodeA", nodeA)
+  .addEdge("nodeA", END)
+  .compile();
+```
+:::
 
 ### Node Caching
 
@@ -264,6 +523,7 @@ LangGraph supports caching of tasks/nodes based on the input to the node. To use
 
 For example:
 
+:::python
 ```py
 import time
 from typing_extensions import TypedDict
@@ -300,6 +560,40 @@ print(graph.invoke({"x": 5}, stream_mode='updates'))  # (2)!
 
 1. First run takes the full second to run (due to mocked expensive computation).
 2. Second run utilizes cache and returns quickly.
+:::
+
+:::js
+```typescript
+import { z } from "zod";
+import { StateGraph } from "@langchain/langgraph";
+import { InMemoryCache } from "@langchain/langgraph";
+
+const State = z.object({
+  x: z.number(),
+  result: z.number(),
+});
+
+const expensiveNode = (state: z.infer<typeof State>) => {
+  // expensive computation
+  return { result: state.x * 2 };
+};
+
+const graph = new StateGraph(State)
+  .addNode("expensiveNode", expensiveNode, { 
+    cachePolicy: { ttl: 3 } 
+  })
+  .addEdge("__start__", "expensiveNode")
+  .compile({ cache: new InMemoryCache() });
+
+console.log(await graph.invoke({ x: 5 }, { streamMode: "updates" })); // (1)!
+// [{ expensiveNode: { result: 10 } }]
+console.log(await graph.invoke({ x: 5 }, { streamMode: "updates" })); // (2)!
+// [{ expensiveNode: { result: 10 }, __metadata__: { cached: true } }]
+```
+
+1. First run takes the full computation time.
+2. Second run utilizes cache and returns quickly.
+:::
 
 ## Edges
 
@@ -314,14 +608,29 @@ A node can have MULTIPLE outgoing edges. If a node has multiple out-going edges,
 
 ### Normal Edges
 
+:::python
 If you **always** want to go from node A to node B, you can use the [add_edge][langgraph.graph.StateGraph.add_edge] method directly.
 
 ```python
 graph.add_edge("node_a", "node_b")
 ```
+:::
+
+:::js
+If you **always** want to go from node A to node B, you can use the `addEdge` method directly.
+
+```typescript
+const graph = new StateGraph(State)
+  .addNode("nodeA", nodeA)
+  .addNode("nodeB", nodeB)
+  .addEdge("nodeA", "nodeB")
+  .compile();
+```
+:::
 
 ### Conditional Edges
 
+:::python
 If you want to **optionally** route to 1 or more edges (or optionally terminate), you can use the [add_conditional_edges][langgraph.graph.StateGraph.add_conditional_edges] method. This method accepts the name of a node and a "routing function" to call after that node is executed:
 
 ```python
@@ -337,12 +646,45 @@ You can optionally provide a dictionary that maps the `routing_function`'s outpu
 ```python
 graph.add_conditional_edges("node_a", routing_function, {True: "node_b", False: "node_c"})
 ```
+:::
+
+:::js
+If you want to **optionally** route to 1 or more edges (or optionally terminate), you can use the `addConditionalEdges` method. This method accepts the name of a node and a "routing function" to call after that node is executed:
+
+```typescript
+const graph = new StateGraph(State)
+  .addNode("nodeA", nodeA)
+  .addNode("nodeB", nodeB)
+  .addNode("nodeC", nodeC)
+  .addConditionalEdges("nodeA", routingFunction)
+  .compile();
+```
+
+Similar to nodes, the `routingFunction` accepts the current `state` of the graph and returns a value.
+
+By default, the return value `routingFunction` is used as the name of the node (or list of nodes) to send the state to next. All those nodes will be run in parallel as a part of the next superstep.
+
+You can optionally provide a dictionary that maps the `routingFunction`'s output to the name of the next node.
+
+```typescript
+const graph = new StateGraph(State)
+  .addNode("nodeA", nodeA)
+  .addNode("nodeB", nodeB)
+  .addNode("nodeC", nodeC)
+  .addConditionalEdges("nodeA", routingFunction, {
+    true: "nodeB",
+    false: "nodeC",
+  })
+  .compile();
+```
+:::
 
 !!! tip
     Use [`Command`](#command) instead of conditional edges if you want to combine state updates and routing in a single function.
 
 ### Entry Point
 
+:::python
 The entry point is the first node(s) that are run when the graph starts. You can use the [`add_edge`][langgraph.graph.StateGraph.add_edge] method from the virtual [`START`][langgraph.constants.START] node to the first node to execute to specify where to enter the graph.
 
 ```python
@@ -350,9 +692,24 @@ from langgraph.graph import START
 
 graph.add_edge(START, "node_a")
 ```
+:::
+
+:::js
+The entry point is the first node(s) that are run when the graph starts. You can use the `addEdge` method from the virtual `START` node to the first node to execute to specify where to enter the graph.
+
+```typescript
+import { START } from "@langchain/langgraph";
+
+const graph = new StateGraph(State)
+  .addNode("nodeA", nodeA)
+  .addEdge(START, "nodeA")
+  .compile();
+```
+:::
 
 ### Conditional Entry Point
 
+:::python
 A conditional entry point lets you start at different nodes depending on custom logic. You can use [`add_conditional_edges`][langgraph.graph.StateGraph.add_conditional_edges] from the virtual [`START`][langgraph.constants.START] node to accomplish this.
 
 ```python
@@ -366,11 +723,42 @@ You can optionally provide a dictionary that maps the `routing_function`'s outpu
 ```python
 graph.add_conditional_edges(START, routing_function, {True: "node_b", False: "node_c"})
 ```
+:::
+
+:::js
+A conditional entry point lets you start at different nodes depending on custom logic. You can use `addConditionalEdges` from the virtual `START` node to accomplish this.
+
+```typescript
+import { START } from "@langchain/langgraph";
+
+const graph = new StateGraph(State)
+  .addNode("nodeB", nodeB)
+  .addNode("nodeC", nodeC)
+  .addConditionalEdges(START, routingFunction)
+  .compile();
+```
+
+You can optionally provide a dictionary that maps the `routingFunction`'s output to the name of the next node.
+
+```typescript
+import { START } from "@langchain/langgraph";
+
+const graph = new StateGraph(State)
+  .addNode("nodeB", nodeB)
+  .addNode("nodeC", nodeC)
+  .addConditionalEdges(START, routingFunction, {
+    true: "nodeB",
+    false: "nodeC",
+  })
+  .compile();
+```
+:::
 
 ## `Send`
 
 By default, `Nodes` and `Edges` are defined ahead of time and operate on the same shared state. However, there can be cases where the exact edges are not known ahead of time and/or you may want different versions of `State` to exist at the same time. A common example of this is with [map-reduce](https://langchain-ai.github.io/langgraph/how-tos/map-reduce/) design patterns. In this design pattern, a first node may generate a list of objects, and you may want to apply some other node to all those objects. The number of objects may be unknown ahead of time (meaning the number of edges may not be known) and the input `State` to the downstream `Node` should be different (one for each generated object).
 
+:::python
 To support this design pattern, LangGraph supports returning [`Send`][langgraph.types.Send] objects from conditional edges. `Send` takes two arguments: first is the name of the node, and second is the state to pass to that node.
 
 ```python
@@ -379,11 +767,31 @@ def continue_to_jokes(state: OverallState):
 
 graph.add_conditional_edges("node_a", continue_to_jokes)
 ```
+:::
+
+:::js
+To support this design pattern, LangGraph supports returning `Send` objects from conditional edges. `Send` takes two arguments: first is the name of the node, and second is the state to pass to that node.
+
+```typescript
+import { Send } from "@langchain/langgraph";
+
+const continueToJokes = (state: OverallState) => {
+  return state.subjects.map((subject) => new Send("generateJoke", { subject }));
+};
+
+const graph = new StateGraph(State)
+  .addNode("nodeA", nodeA)
+  .addNode("generateJoke", generateJoke)
+  .addConditionalEdges("nodeA", continueToJokes)
+  .compile();
+```
+:::
 
 ## `Command`
 
 It can be useful to combine control flow (edges) and state updates (nodes). For example, you might want to BOTH perform state updates AND decide which node to go to next in the SAME node. LangGraph provides a way to do so by returning a [`Command`][langgraph.types.Command] object from node functions:
 
+:::python
 ```python
 def my_node(state: State) -> Command[Literal["my_other_node"]]:
     return Command(
@@ -393,18 +801,69 @@ def my_node(state: State) -> Command[Literal["my_other_node"]]:
         goto="my_other_node"
     )
 ```
+:::
+
+:::js
+```typescript
+import { Command } from "@langchain/langgraph";
+
+const myNode = (state: State): Command => {
+  return new Command({
+    // state update
+    update: { foo: "bar" },
+    // control flow
+    goto: "myOtherNode",
+  });
+};
+```
+:::
 
 With `Command` you can also achieve dynamic control flow behavior (identical to [conditional edges](#conditional-edges)):
 
+:::python
 ```python
 def my_node(state: State) -> Command[Literal["my_other_node"]]:
     if state["foo"] == "bar":
         return Command(update={"foo": "baz"}, goto="my_other_node")
 ```
+:::
+
+:::js
+```typescript
+import { Command } from "@langchain/langgraph";
+
+const myNode = (state: State): Command => {
+  if (state.foo === "bar") {
+    return new Command({
+      update: { foo: "baz" },
+      goto: "myOtherNode",
+    });
+  }
+  return new Command({
+    update: { foo: "qux" },
+    goto: "myThirdNode",
+  });
+};
+```
+:::
 
 !!! important
 
+    :::python
     When returning `Command` in your node functions, you must add return type annotations with the list of node names the node is routing to, e.g. `Command[Literal["my_other_node"]]`. This is necessary for the graph rendering and tells LangGraph that `my_node` can navigate to `my_other_node`.
+    :::
+
+    :::js
+    When returning `Command` in your node functions, you must specify the `ends` option when adding the node to ensure proper graph rendering and tell LangGraph which nodes this node can navigate to.
+
+    ```typescript
+    const graph = new StateGraph(State)
+      .addNode("myNode", myNode, {
+        ends: ["myOtherNode", "myThirdNode"],
+      })
+      .compile();
+    ```
+    :::
 
 Check out this [how-to guide](../how-tos/graph-api.ipynb#combine-control-flow-and-state-updates-with-command) for an end-to-end example of how to use `Command`.
 
@@ -418,6 +877,7 @@ Use [conditional edges](#conditional-edges) to route between nodes conditionally
 
 If you are using [subgraphs](./subgraphs.md), you might want to navigate from a node within a subgraph to a different subgraph (i.e. a different node in the parent graph). To do so, you can specify `graph=Command.PARENT` in `Command`:
 
+:::python
 ```python
 def my_node(state: State) -> Command[Literal["other_subgraph"]]:
     return Command(
@@ -426,6 +886,21 @@ def my_node(state: State) -> Command[Literal["other_subgraph"]]:
         graph=Command.PARENT
     )
 ```
+:::
+
+:::js
+```typescript
+import { Command } from "@langchain/langgraph";
+
+const myNode = (state: State): Command => {
+  return new Command({
+    update: { foo: "bar" },
+    goto: "otherSubgraph", // where `otherSubgraph` is a node in the parent graph
+    graph: Command.PARENT,
+  });
+};
+```
+:::
 
 !!! note
 
@@ -447,7 +922,13 @@ Refer to [this guide](../how-tos/graph-api.ipynb#use-inside-tools) for detail.
 
 ### Human-in-the-loop
 
+:::python
 `Command` is an important part of human-in-the-loop workflows: when using `interrupt()` to collect user input, `Command` is then used to supply the input and resume execution via `Command(resume="User input")`. Check out [this conceptual guide](./human_in_the_loop.md) for more information.
+:::
+
+:::js
+`Command` is an important part of human-in-the-loop workflows: when using `interrupt()` to collect user input, `Command` is then used to supply the input and resume execution via `new Command({ resume: "User input" })`. Check out [this conceptual guide](./human_in_the_loop.md) for more information.
+:::
 
 ## Graph Migrations
 
@@ -463,6 +944,7 @@ LangGraph can easily handle migrations of graph definitions (nodes, edges, and s
 
 When creating a graph, you can also mark that certain parts of the graph are configurable. This is commonly done to enable easily switching between models or system prompts. This allows you to create a single "cognitive architecture" (the graph) but have multiple different instance of it.
 
+:::python
 You can optionally specify a `config_schema` when creating a graph.
 
 ```python
@@ -488,19 +970,69 @@ def node_a(state, config):
     llm = get_llm(llm_type)
     ...
 ```
+:::
+
+:::js
+You can optionally specify a `configSchema` when creating a graph.
+
+```typescript
+import { z } from "zod";
+
+const ConfigSchema = z.object({
+  llm: z.string(),
+});
+
+const graph = new StateGraph(State, ConfigSchema)
+  .addNode("nodeA", nodeA)
+  .compile();
+```
+
+You can then pass this configuration into the graph using the `configurable` config field.
+
+```typescript
+const config = { configurable: { llm: "anthropic" } };
+
+await graph.invoke(inputs, config);
+```
+
+You can then access and use this configuration inside a node or conditional edge:
+
+```typescript
+const nodeA = (state: State, config: RunnableConfig) => {
+  const llmType = config?.configurable?.llm || "openai";
+  const llm = getLlm(llmType);
+  // ...
+};
+```
+:::
 
 See [this guide](../how-tos/graph-api.ipynb#add-runtime-configuration) for a full breakdown on configuration.
 
 ### Recursion Limit
 
-The recursion limit sets the maximum number of [super-steps](#graphs) the graph can execute during a single execution. Once the limit is reached, LangGraph will raise `GraphRecursionError`. By default this value is set to 25 steps. The recursion limit can be set on any graph at runtime, and is passed to `.invoke`/`.stream` via the config dictionary. Importantly, `recursion_limit` is a standalone `config` key and should not be passed inside the `configurable` key as all other user-defined configuration. See the example below:
+The recursion limit sets the maximum number of [super-steps](#graphs) the graph can execute during a single execution. Once the limit is reached, LangGraph will raise `GraphRecursionError`. By default this value is set to 25 steps.
+
+:::python
+The recursion limit can be set on any graph at runtime, and is passed to `.invoke`/`.stream` via the config dictionary. Importantly, `recursion_limit` is a standalone `config` key and should not be passed inside the `configurable` key as all other user-defined configuration. See the example below:
 
 ```python
 graph.invoke(inputs, config={"recursion_limit": 5, "configurable":{"llm": "anthropic"}})
 ```
+:::
+
+:::js
+The recursion limit can be set on any graph at runtime, and is passed to `.invoke`/`.stream` via the config dictionary. Importantly, `recursionLimit` is a standalone `config` key and should not be passed inside the `configurable` key as all other user-defined configuration. See the example below:
+
+```typescript
+await graph.invoke(inputs, {
+  recursionLimit: 5,
+  configurable: { llm: "anthropic" },
+});
+```
+:::
 
 Read [this how-to](https://langchain-ai.github.io/langgraph/how-tos/recursion-limit/) to learn more about how the recursion limit works.
 
 ## Visualization
 
-It's often nice to be able to visualize graphs, especially as they get more complex. LangGraph comes with several built-in ways to visualize graphs. See [this how-to guide](../how-tos/graph-api.ipynb#visualize-your-graph) for more info.
+It's often nice to be able to visualize graphs, especially as they get more complex. LangGraph comes with several built-in ways to visualize graphs. See [this how-to guide](../how-tos/graph-api.ipynb#visualize-your-graph) for more info.

docs/docs/concepts/persistence.md

@@ -33,7 +33,7 @@ The state of a thread at a particular point in time is called a checkpoint. Chec
 - `metadata`: Metadata associated with this checkpoint.
 - `values`: Values of the state channels at this point in time.
 - `next` A tuple of the node names to execute next in the graph.
-- `tasks`: A tuple of `PregelTask` objects that contain information about next tasks to be executed. If the step was previously attempted, it will include error information. If a graph was interrupted [dynamically](../how-tos/human_in_the_loop/breakpoints.ipynb#dynamic-breakpoints) from within a node, tasks will contain additional data associated with interrupts.
+- `tasks`: A tuple of `PregelTask` objects that contain information about next tasks to be executed. If the step was previously attempted, it will include error information. If a graph was interrupted [dynamically](../how-tos/human_in_the_loop/breakpoints.md#dynamic-breakpoints) from within a node, tasks will contain additional data associated with interrupts.
 
 Checkpoints are persisted and can be used to restore the state of a thread at a later time.
 
@@ -174,7 +174,7 @@ config = {"configurable": {"thread_id": "1", "checkpoint_id": "0c62ca34-ac19-445
 graph.invoke(None, config=config)
 ```
 
-Importantly, LangGraph knows whether a particular step has been executed previously. If it has, LangGraph simply *re-plays* that particular step in the graph and does not re-execute the step, but only for the steps _before_ the provided `checkpoint_id`. All of the steps _after_ `checkpoint_id` will be executed (i.e., a new fork), even if they have been executed previously. See this [how to guide on time-travel to learn more about replaying](../how-tos/human_in_the_loop/time-travel.ipynb).
+Importantly, LangGraph knows whether a particular step has been executed previously. If it has, LangGraph simply *re-plays* that particular step in the graph and does not re-execute the step, but only for the steps _before_ the provided `checkpoint_id`. All of the steps _after_ `checkpoint_id` will be executed (i.e., a new fork), even if they have been executed previously. See this [how to guide on time-travel to learn more about replaying](../how-tos/human_in_the_loop/time-travel.md).
 
 ![Replay](img/persistence/re_play.png)
 
@@ -224,7 +224,7 @@ The `foo` key (channel) is completely changed (because there is no reducer speci
 
 #### `as_node`
 
-The final thing you can optionally specify when calling `update_state` is `as_node`. If you provided it, the update will be applied as if it came from node `as_node`. If `as_node` is not provided, it will be set to the last node that updated the state, if not ambiguous. The reason this matters is that the next steps to execute depend on the last node to have given an update, so this can be used to control which node executes next. See this [how to guide on time-travel to learn more about forking state](../how-tos/human_in_the_loop/time-travel.ipynb).
+The final thing you can optionally specify when calling `update_state` is `as_node`. If you provided it, the update will be applied as if it came from node `as_node`. If `as_node` is not provided, it will be set to the last node that updated the state, if not ambiguous. The reason this matters is that the next steps to execute depend on the last node to have given an update, so this can be used to control which node executes next. See this [how to guide on time-travel to learn more about forking state](../how-tos/human_in_the_loop/time-travel.md).
 
 ![Update](img/persistence/checkpoints_full_story.jpg)
 
@@ -525,7 +525,7 @@ When running on LangGraph Platform, encryption is automatically enabled whenever
 
 ### Human-in-the-loop
 
-First, checkpointers facilitate [human-in-the-loop workflows](agentic_concepts.md#human-in-the-loop) workflows by allowing humans to inspect, interrupt, and approve graph steps. Checkpointers are needed for these workflows as the human has to be able to view the state of a graph at any point in time, and the graph has to be to resume execution after the human has made any updates to the state. See [these how-to guides](../how-tos/human_in_the_loop/breakpoints.ipynb) for concrete examples.
+First, checkpointers facilitate [human-in-the-loop workflows](agentic_concepts.md#human-in-the-loop) workflows by allowing humans to inspect, interrupt, and approve graph steps. Checkpointers are needed for these workflows as the human has to be able to view the state of a graph at any point in time, and the graph has to be to resume execution after the human has made any updates to the state. See [these how-to guides](../how-tos/human_in_the_loop/breakpoints.md) for concrete examples.
 
 ### Memory
 

docs/docs/concepts/time-travel.md

@@ -7,9 +7,12 @@ search:
 
 When working with non-deterministic systems that make model-based decisions (e.g., agents powered by LLMs), it can be useful to examine their decision-making process in detail:
 
-1. 🤔 **Understand Reasoning**: Analyze the steps that led to a successful result.
-2. 🐞 **Debug Mistakes**: Identify where and why errors occurred.
-3. 🔍 **Explore Alternatives**: Test different paths to uncover better solutions.
+1. 🤔 **Understand reasoning**: Analyze the steps that led to a successful result.
+2. 🐞 **Debug mistakes**: Identify where and why errors occurred.
+3. 🔍 **Explore alternatives**: Test different paths to uncover better solutions.
 
+LangGraph provides [time travel functionality](../how-tos/human_in_the_loop/time-travel.md) to support these use cases. Specifically, you can resume execution from a prior checkpoint — either replaying the same state or modifying it to explore alternatives. In all cases, resuming past execution produces a new fork in the history.
 
-LangGraph provides **time travel** functionality to support these use cases. Specifically, you can **resume execution from a prior checkpoint** — either replaying the same state or modifying it to explore alternatives. In all cases, resuming past execution produces a **new fork** in the history.
+!!! tip
+
+    For information on how to use time travel, see [Use time travel](../how-tos/human_in_the_loop/time-travel.md) and [Time travel using Server API](../cloud/how-tos/human_in_the_loop_time_travel.md).

docs/docs/how-tos/human_in_the_loop/add-human-in-the-loop.md

@@ -883,7 +883,7 @@ def node_in_parent_graph(state: State):
 
 ### Using multiple interrupts
 
-Using multiple interrupts within a **single** node can be helpful for patterns like [validating human input](../how-tos/human_in_the_loop/add-human-in-the-loop.md#validate-human-input). However, using multiple interrupts in the same node can lead to unexpected behavior if not handled carefully.
+Using multiple interrupts within a **single** node can be helpful for patterns like [validating human input](#validate-human-input). However, using multiple interrupts in the same node can lead to unexpected behavior if not handled carefully.
 
 When a node contains multiple interrupt calls, LangGraph keeps a list of resume values specific to the task executing the node. Whenever execution resumes, it starts at the beginning of the node. For each interrupt encountered, LangGraph checks if a matching value exists in the task's resume list. Matching is **strictly index-based**, so the order of interrupt calls within the node is critical.
 

docs/docs/how-tos/human_in_the_loop/breakpoints.md

@@ -0,0 +1,342 @@
+# Set breakpoints
+
+There are two places where you can set breakpoints:
+
+1. **Before** or **after** a node executes by setting breakpoints at **compile time** or **run time**. We call these [**static breakpoints**](#static-breakpoints).
+2. **Inside** a node using the `NodeInterrupt` exception. We call these [**dynamic breakpoints**](#dynamic-breakpoints).
+
+To use breakpoints, you will need to:
+
+1. [**Specify a checkpointer**](../../concepts/persistence.md#checkpoints) to save the graph state after each step.
+2. **Set breakpoints** to specify where execution should pause.
+3. **Run the graph** with a [**thread ID**](../../concepts/persistence.md#threads) to pause execution at the breakpoint.
+4. **Resume execution** using `invoke`/`ainvoke`/`stream`/`astream` passing a `None` as the argument for the inputs.
+
+!!! tip
+
+    For a conceptual overview of breakpoints, see [Breakpoints](../../concepts/breakpoints.md).
+
+## Static breakpoints
+
+Static breakpoints are triggered either before or after a node executes. You can set static breakpoints by specifying `interrupt_before` and `interrupt_after` at compile time or run time.
+
+Static breakpoints can be especially useful for debugging if you want to step through the graph execution one
+node at a time or if you want to pause the graph execution at specific nodes.
+
+=== "Compile time"
+
+    ```python
+    # highlight-next-line
+    graph = graph_builder.compile( # (1)!
+        # highlight-next-line
+        interrupt_before=["node_a"], # (2)!
+        # highlight-next-line
+        interrupt_after=["node_b", "node_c"], # (3)!
+        checkpointer=checkpointer, # (4)!
+    )
+
+    config = {
+        "configurable": {
+            "thread_id": "some_thread"
+        }
+    }
+
+    # Run the graph until the breakpoint
+    graph.invoke(inputs, config=thread_config) # (5)!
+
+    # Resume the graph
+    graph.invoke(None, config=thread_config) # (6)!
+    ```
+
+    1. The breakpoints are set during `compile` time.
+    2. `interrupt_before` specifies the nodes where execution should pause before the node is executed.
+    3. `interrupt_after` specifies the nodes where execution should pause after the node is executed.
+    4. A checkpointer is required to enable breakpoints.
+    5. The graph is run until the first breakpoint is hit.
+    6. The graph is resumed by passing in `None` for the input. This will run the graph until the next breakpoint is hit.
+
+=== "Run time"
+
+    ```python
+    # highlight-next-line
+    graph.invoke( # (1)!
+        inputs, 
+        # highlight-next-line
+        interrupt_before=["node_a"], # (2)!
+        # highlight-next-line
+        interrupt_after=["node_b", "node_c"] # (3)!
+        config={
+            "configurable": {"thread_id": "some_thread"}
+        }, 
+    )
+
+    config = {
+        "configurable": {
+            "thread_id": "some_thread"
+        }
+    }
+
+    # Run the graph until the breakpoint
+    graph.invoke(inputs, config=config) # (4)!
+
+    # Resume the graph
+    graph.invoke(None, config=config) # (5)!
+    ```
+
+    1. `graph.invoke` is called with the `interrupt_before` and `interrupt_after` parameters. This is a run-time configuration and can be changed for every invocation.
+    2. `interrupt_before` specifies the nodes where execution should pause before the node is executed.
+    3. `interrupt_after` specifies the nodes where execution should pause after the node is executed.
+    4. The graph is run until the first breakpoint is hit.
+    5. The graph is resumed by passing in `None` for the input. This will run the graph until the next breakpoint is hit.
+
+    !!! note
+
+        You cannot set static breakpoints at runtime for **sub-graphs**.
+        If you have a sub-graph, you must set the breakpoints at compilation time.
+
+??? example "Setting static breakpoints"
+
+    ```python
+    from IPython.display import Image, display
+    from typing_extensions import TypedDict
+    
+    from langgraph.checkpoint.memory import InMemorySaver 
+    from langgraph.graph import StateGraph, START, END
+    
+    
+    class State(TypedDict):
+        input: str
+    
+    
+    def step_1(state):
+        print("---Step 1---")
+        pass
+    
+    
+    def step_2(state):
+        print("---Step 2---")
+        pass
+    
+    
+    def step_3(state):
+        print("---Step 3---")
+        pass
+    
+    
+    builder = StateGraph(State)
+    builder.add_node("step_1", step_1)
+    builder.add_node("step_2", step_2)
+    builder.add_node("step_3", step_3)
+    builder.add_edge(START, "step_1")
+    builder.add_edge("step_1", "step_2")
+    builder.add_edge("step_2", "step_3")
+    builder.add_edge("step_3", END)
+    
+    # Set up a checkpointer 
+    checkpointer = InMemorySaver() # (1)!
+    
+    graph = builder.compile(
+        checkpointer=checkpointer, # (2)!
+        interrupt_before=["step_3"] # (3)!
+    )
+    
+    # View
+    display(Image(graph.get_graph().draw_mermaid_png()))
+    
+    
+    # Input
+    initial_input = {"input": "hello world"}
+    
+    # Thread
+    thread = {"configurable": {"thread_id": "1"}}
+    
+    # Run the graph until the first interruption
+    for event in graph.stream(initial_input, thread, stream_mode="values"):
+        print(event)
+        
+    # This will run until the breakpoint
+    # You can get the state of the graph at this point
+    print(graph.get_state(config))
+    
+    # You can continue the graph execution by passing in `None` for the input
+    for event in graph.stream(None, thread, stream_mode="values"):
+        print(event)
+    ```
+
+## Dynamic breakpoints
+
+Use dynamic breakpoints if you need to interrupt the graph from inside a given node based on a condition.
+
+```python
+from langgraph.errors import NodeInterrupt
+
+def step_2(state: State) -> State:
+    # highlight-next-line
+    if len(state["input"]) > 5:
+        # highlight-next-line
+        raise NodeInterrupt( # (1)!
+            f"Received input that is longer than 5 characters: {state['foo']}"
+        )
+    return state
+```
+
+1. raise NodeInterrupt exception based on a some condition. In this example, we create a dynamic breakpoint if the length of the attribute `input` is longer than 5 characters.
+
+<details class="example"><summary>Using dynamic breakpoints</summary>
+
+```python
+from typing_extensions import TypedDict
+from IPython.display import Image, display
+
+from langgraph.graph import StateGraph, START, END
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.errors import NodeInterrupt
+
+
+class State(TypedDict):
+    input: str
+
+
+def step_1(state: State) -> State:
+    print("---Step 1---")
+    return state
+
+
+def step_2(state: State) -> State:
+    # Let's optionally raise a NodeInterrupt
+    # if the length of the input is longer than 5 characters
+    if len(state["input"]) > 5:
+        raise NodeInterrupt(
+            f"Received input that is longer than 5 characters: {state['input']}"
+        )
+    print("---Step 2---")
+    return state
+
+
+def step_3(state: State) -> State:
+    print("---Step 3---")
+    return state
+
+
+builder = StateGraph(State)
+builder.add_node("step_1", step_1)
+builder.add_node("step_2", step_2)
+builder.add_node("step_3", step_3)
+builder.add_edge(START, "step_1")
+builder.add_edge("step_1", "step_2")
+builder.add_edge("step_2", "step_3")
+builder.add_edge("step_3", END)
+
+# Set up memory
+memory = MemorySaver()
+
+# Compile the graph with memory
+graph = builder.compile(checkpointer=memory)
+
+# View
+display(Image(graph.get_graph().draw_mermaid_png()))
+```
+
+First, let's run the graph with an input that <= 5 characters long. This should safely ignore the interrupt condition we defined and return the original input at the end of the graph execution.
+
+```python
+initial_input = {"input": "hello"}
+thread_config = {"configurable": {"thread_id": "1"}}
+
+for event in graph.stream(initial_input, thread_config, stream_mode="values"):
+    print(event)
+```
+
+If we inspect the graph at this point, we can see that there are no more tasks left to run and that the graph indeed finished execution.
+
+```python
+state = graph.get_state(thread_config)
+print(state.next)
+print(state.tasks)
+```
+
+Now, let's run the graph with an input that's longer than 5 characters. This should trigger the dynamic interrupt we defined via raising a `NodeInterrupt` error inside the `step_2` node.
+
+```python
+initial_input = {"input": "hello world"}
+thread_config = {"configurable": {"thread_id": "2"}}
+
+# Run the graph until the first interruption
+for event in graph.stream(initial_input, thread_config, stream_mode="values"):
+    print(event)
+```
+
+We can see that the graph now stopped while executing `step_2`. If we inspect the graph state at this point, we can see the information on what node is set to execute next (`step_2`), as well as what node raised the interrupt (also `step_2`), and additional information about the interrupt.
+
+```python
+state = graph.get_state(thread_config)
+print(state.next)
+print(state.tasks)
+```
+
+If we try to resume the graph from the breakpoint, we will simply interrupt again as our inputs & graph state haven't changed.
+
+```python
+# NOTE: to resume the graph from a dynamic interrupt we use the same syntax as with regular interrupts -- we pass None as the input
+for event in graph.stream(None, thread_config, stream_mode="values"):
+    print(event)
+```
+
+```python
+state = graph.get_state(thread_config)
+print(state.next)
+print(state.tasks)
+```
+
+</details>
+
+## Use with subgraphs
+
+To add breakpoints to subgraph either:
+
+* Define [static breakpoints](#static-breakpoints) by specifying them when **compiling** the subgraph.
+* Define [dynamic breakpoints](#dynamic-breakpoints).
+
+<details class="example"><summary>Add breakpoints to subgraphs</summary>
+
+```python
+from typing_extensions import TypedDict
+
+from langgraph.graph import START, StateGraph
+from langgraph.checkpoint.memory import InMemorySaver
+from langgraph.types import interrupt
+
+
+class State(TypedDict):
+    foo: str
+
+
+def subgraph_node_1(state: State):
+    return {"foo": state["foo"]}
+
+
+subgraph_builder = StateGraph(State)
+subgraph_builder.add_node(subgraph_node_1)
+subgraph_builder.add_edge(START, "subgraph_node_1")
+
+subgraph = subgraph_builder.compile(interrupt_before=["subgraph_node_1"])
+
+builder = StateGraph(State)
+builder.add_node("node_1", subgraph)  # directly include subgraph as a node
+builder.add_edge(START, "node_1")
+
+checkpointer = InMemorySaver()
+graph = builder.compile(checkpointer=checkpointer)
+
+config = {"configurable": {"thread_id": "1"}}
+
+graph.invoke({"foo": ""}, config)
+
+# Fetch state including subgraph state.
+print(graph.get_state(config, subgraphs=True).tasks[0].state)
+
+# resume the subgraph
+graph.invoke(None, config)
+```
+
+</details> 

docs/docs/how-tos/human_in_the_loop/time-travel.md

@@ -0,0 +1,190 @@
+# Use time-travel
+
+To use [time-travel](../../concepts/time-travel.md) in LangGraph:
+
+1. [Run the graph](#1-run-the-graph) with initial inputs using [`invoke`][langgraph.graph.state.CompiledStateGraph.invoke] or [`stream`][langgraph.graph.state.CompiledStateGraph.stream] methods.
+2. [Identify a checkpoint in an existing thread](#2-identify-a-checkpoint): Use the [`get_state_history()`][langgraph.graph.state.CompiledStateGraph.get_state_history] method to retrieve the execution history for a specific `thread_id` and locate the desired `checkpoint_id`.  
+   Alternatively, set a [breakpoint](../../concepts/breakpoints.md) before the node(s) where you want execution to pause. You can then find the most recent checkpoint recorded up to that breakpoint.
+3. [Update the graph state (optional)](#3-update-the-state-optional): Use the [`update_state`][langgraph.graph.state.CompiledStateGraph.update_state] method to modify the graph's state at the checkpoint and resume execution from alternative state.
+4. [Resume execution from the checkpoint](#4-resume-execution-from-the-checkpoint): Use the `invoke` or `stream` methods with an input of `None` and a configuration containing the appropriate `thread_id` and `checkpoint_id`.
+
+!!! tip
+
+    For a conceptual overview of time-travel, see [Time travel](../../concepts/time-travel.md).
+
+## In a workflow
+
+This example builds a simple LangGraph workflow that generates a joke topic and writes a joke using an LLM. It demonstrates how to run the graph, retrieve past execution checkpoints, optionally modify the state, and resume execution from a chosen checkpoint to explore alternate outcomes.
+
+### Setup
+
+First we need to install the packages required
+
+```python
+%%capture --no-stderr
+%pip install --quiet -U langgraph langchain_anthropic
+```
+
+Next, we need to set API keys for Anthropic (the LLM we will use)
+
+```python
+import getpass
+import os
+
+
+def _set_env(var: str):
+    if not os.environ.get(var):
+        os.environ[var] = getpass.getpass(f"{var}: ")
+
+
+_set_env("ANTHROPIC_API_KEY")
+```
+
+<div class="admonition tip">
+    <p class="admonition-title">Set up <a href="https://smith.langchain.com">LangSmith</a> for LangGraph development</p>
+    <p style="padding-top: 5px;">
+        Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href="https://docs.smith.langchain.com">here</a>. 
+    </p>
+</div>
+
+```python
+import uuid
+
+from typing_extensions import TypedDict, NotRequired
+from langgraph.graph import StateGraph, START, END
+from langchain.chat_models import init_chat_model
+from langgraph.checkpoint.memory import InMemorySaver
+
+
+class State(TypedDict):
+    topic: NotRequired[str]
+    joke: NotRequired[str]
+
+
+llm = init_chat_model(
+    "anthropic:claude-3-7-sonnet-latest",
+    temperature=0,
+)
+
+
+def generate_topic(state: State):
+    """LLM call to generate a topic for the joke"""
+    msg = llm.invoke("Give me a funny topic for a joke")
+    return {"topic": msg.content}
+
+
+def write_joke(state: State):
+    """LLM call to write a joke based on the topic"""
+    msg = llm.invoke(f"Write a short joke about {state['topic']}")
+    return {"joke": msg.content}
+
+
+# Build workflow
+workflow = StateGraph(State)
+
+# Add nodes
+workflow.add_node("generate_topic", generate_topic)
+workflow.add_node("write_joke", write_joke)
+
+# Add edges to connect nodes
+workflow.add_edge(START, "generate_topic")
+workflow.add_edge("generate_topic", "write_joke")
+workflow.add_edge("write_joke", END)
+
+# Compile
+checkpointer = InMemorySaver()
+graph = workflow.compile(checkpointer=checkpointer)
+graph
+```
+
+### 1. Run the graph
+
+```python
+config = {
+    "configurable": {
+        "thread_id": uuid.uuid4(),
+    }
+}
+state = graph.invoke({}, config)
+
+print(state["topic"])
+print()
+print(state["joke"])
+```
+
+**Output:**
+```
+How about "The Secret Life of Socks in the Dryer"? You know, exploring the mysterious phenomenon of how socks go into the laundry as pairs but come out as singles. Where do they go? Are they starting new lives elsewhere? Is there a sock paradise we don't know about? There's a lot of comedic potential in the everyday mystery that unites us all!
+
+# The Secret Life of Socks in the Dryer
+
+I finally discovered where all my missing socks go after the dryer. Turns out they're not missing at all—they've just eloped with someone else's socks from the laundromat to start new lives together.
+
+My blue argyle is now living in Bermuda with a red polka dot, posting vacation photos on Sockstagram and sending me lint as alimony.
+```
+
+### 2. Identify a checkpoint
+
+```python
+# The states are returned in reverse chronological order.
+states = list(graph.get_state_history(config))
+
+for state in states:
+    print(state.next)
+    print(state.config["configurable"]["checkpoint_id"])
+    print()
+```
+
+**Output:**
+```
+()
+1f02ac4a-ec9f-6524-8002-8f7b0bbeed0e
+
+('write_joke',)
+1f02ac4a-ce2a-6494-8001-cb2e2d651227
+
+('generate_topic',)
+1f02ac4a-a4e0-630d-8000-b73c254ba748
+
+('__start__',)
+1f02ac4a-a4dd-665e-bfff-e6c8c44315d9
+```
+
+```python
+# This is the state before last (states are listed in chronological order)
+selected_state = states[1]
+print(selected_state.next)
+print(selected_state.values)
+```
+
+**Output:**
+```
+('write_joke',)
+{'topic': 'How about "The Secret Life of Socks in the Dryer"? You know, exploring the mysterious phenomenon of how socks go into the laundry as pairs but come out as singles. Where do they go? Are they starting new lives elsewhere? Is there a sock paradise we don\\'t know about? There\\'s a lot of comedic potential in the everyday mystery that unites us all!'}
+```
+
+### 3. Update the state (optional)
+
+`update_state` will create a new checkpoint. The new checkpoint will be associated with the same thread, but a new checkpoint ID.
+
+```python
+new_config = graph.update_state(selected_state.config, values={"topic": "chickens"})
+print(new_config)
+```
+
+**Output:**
+```
+{'configurable': {'thread_id': 'c62e2e03-c27b-4cb6-8cea-ea9bfedae006', 'checkpoint_ns': '', 'checkpoint_id': '1f02ac4a-ecee-600b-8002-a1d21df32e4c'}}
+```
+
+### 4. Resume execution from the checkpoint
+
+```python
+graph.invoke(None, new_config)
+```
+
+**Output:**
+```python
+{'topic': 'chickens',
+ 'joke': 'Why did the chicken join a band?\n\nBecause it had excellent drumsticks!'}
+``` 

docs/docs/how-tos/tool-calling.md

@@ -507,7 +507,7 @@ def update_user_name(
     new_name: str,
     tool_call_id: Annotated[str, InjectedToolCallId]
 ) -> Command:
-    """Update user name in short-term memory."""
+    """Update user-name in short-term memory."""
     # highlight-next-line
     return Command(update={
         # highlight-next-line

docs/docs/tutorials/get-started/1-build-basic-chatbot.md

@@ -1,6 +1,6 @@
 # Build a basic chatbot
 
-In this tutorial, you will build a basic chatbot. This chatbot is the basis for the following series of tutorials where you will progressively add more sophisticated capabilities, and be introduced to key LangGraph concepts along the way. Let’s dive in! 🌟
+In this tutorial, you will build a basic chatbot. This chatbot is the basis for the following series of tutorials where you will progressively add more sophisticated capabilities, and be introduced to key LangGraph concepts along the way. Let's dive in! 🌟
 
 ## Prerequisites
 
@@ -13,13 +13,44 @@ tool-calling features, such as [OpenAI](https://platform.openai.com/api-keys),
 
 Install the required packages:
 
+:::python
+
 ```bash
 pip install -U langgraph langsmith
 ```
 
+:::
+
+:::js
+=== "npm"
+
+    ```bash
+    npm install @langchain/langgraph @langchain/core zod
+    ```
+
+=== "yarn"
+
+    ```bash
+    yarn add @langchain/langgraph @langchain/core zod
+    ```
+
+=== "pnpm"
+
+    ```bash
+    pnpm add @langchain/langgraph @langchain/core zod
+    ```
+
+=== "bun"
+
+    ```bash
+    bun add @langchain/langgraph @langchain/core zod
+    ```
+
+:::
+
 !!! tip
 
-    Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph. For more information on how to get started, see [LangSmith docs](https://docs.smith.langchain.com). 
+    Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph. For more information on how to get started, see [LangSmith docs](https://docs.smith.langchain.com).
 
 ## 2. Create a `StateGraph`
 
@@ -27,6 +58,8 @@ Now you can create a basic chatbot using LangGraph. This chatbot will respond di
 
 Start by creating a `StateGraph`. A `StateGraph` object defines the structure of our chatbot as a "state machine". We'll add `nodes` to represent the llm and functions our chatbot can call and `edges` to specify how the bot should transition between these functions.
 
+:::python
+
 ```python
 from typing import Annotated
 
@@ -46,23 +79,41 @@ class State(TypedDict):
 graph_builder = StateGraph(State)
 ```
 
+:::
+
+:::js
+
+```typescript
+import { StateGraph, MessagesZodState, START } from "@langchain/langgraph";
+import { z } from "zod";
+
+const State = z.object({ messages: MessagesZodState.shape.messages });
+
+const graph = new StateGraph(State).compile();
+```
+
+:::
+
 Our graph can now handle two key tasks:
 
 1. Each `node` can receive the current `State` as input and output an update to the state.
-2. Updates to `messages` will be appended to the existing list rather than overwriting it, thanks to the prebuilt [`add_messages`](https://langchain-ai.github.io/langgraph/reference/graphs/?h=add+messages#add_messages) function used with the `Annotated` syntax.
+2. Updates to `messages` will be appended to the existing list rather than overwriting it, thanks to the prebuilt reducer function.
 
-------
+---
 
 !!! tip "Concept"
 
-    When defining a graph, the first step is to define its `State`. The `State` includes the graph's schema and [reducer functions](https://langchain-ai.github.io/langgraph/concepts/low_level/#reducers) that handle state updates. In our example, `State` is a `TypedDict` with one key: `messages`. The [`add_messages`](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.message.add_messages) reducer function is used to append new messages to the list instead of overwriting it. Keys without a reducer annotation will overwrite previous values. To learn more about state, reducers, and related concepts, see [LangGraph reference docs](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.message.add_messages).
+    When defining a graph, the first step is to define its `State`. The `State` includes the graph's schema and [reducer functions](https://langchain-ai.github.io/langgraph/concepts/low_level/#reducers) that handle state updates. In our example, `State` is a schema with one key: `messages`. The reducer function is used to append new messages to the list instead of overwriting it. Keys without a reducer annotation will overwrite previous values.
+
+    To learn more about state, reducers, and related concepts, see [LangGraph reference docs](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.message.add_messages).
 
 ## 3. Add a node
 
-Next, add a "`chatbot`" node. **Nodes** represent units of work and are typically regular Python functions.
+Next, add a "`chatbot`" node. **Nodes** represent units of work and are typically regular functions.
 
 Let's first select a chat model:
 
+:::python
 {!snippets/chat_model_tabs.md!}
 
 <!---
@@ -73,9 +124,26 @@ llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")
 ```
 -->
 
+:::
+
+:::js
+
+```typescript
+import { ChatOpenAI } from "@langchain/openai";
+// or import { ChatAnthropic } from "@langchain/anthropic";
+
+const llm = new ChatOpenAI({
+  model: "gpt-4o",
+  temperature: 0,
+});
+```
+
+:::
 
 We can now incorporate the chat model into a simple node:
 
+:::python
+
 ```python
 
 def chatbot(state: State):
@@ -88,38 +156,132 @@ def chatbot(state: State):
 graph_builder.add_node("chatbot", chatbot)
 ```
 
+:::
+
+:::js
+
+```typescript hl_lines="7-9"
+import { StateGraph, MessagesZodState, START } from "@langchain/langgraph";
+import { z } from "zod";
+
+const State = z.object({ messages: MessagesZodState.shape.messages });
+
+const graph = new StateGraph(State)
+  .addNode("chatbot", async (state: z.infer<typeof State>) => {
+    return { messages: [await llm.invoke(state.messages)] };
+  })
+  .compile();
+```
+
+:::
+
 **Notice** how the `chatbot` node function takes the current `State` as input and returns a dictionary containing an updated `messages` list under the key "messages". This is the basic pattern for all LangGraph node functions.
 
+:::python
 The `add_messages` function in our `State` will append the LLM's response messages to whatever messages are already in the state.
+:::
+
+:::js
+The `addMessages` function used within `MessagesZodState` will append the LLM's response messages to whatever messages are already in the state.
+:::
 
 ## 4. Add an `entry` point
 
 Add an `entry` point to tell the graph **where to start its work** each time it is run:
 
+:::python
+
 ```python
 graph_builder.add_edge(START, "chatbot")
 ```
 
+:::
+
+:::js
+
+```typescript hl_lines="10"
+import { StateGraph, MessagesZodState, START } from "@langchain/langgraph";
+import { z } from "zod";
+
+const State = z.object({ messages: MessagesZodState.shape.messages });
+
+const graph = new StateGraph(State)
+  .addNode("chatbot", async (state: z.infer<typeof State>) => {
+    return { messages: [await llm.invoke(state.messages)] };
+  })
+  .addEdge(START, "chatbot")
+  .compile();
+```
+
+:::
+
 ## 5. Add an `exit` point
 
 Add an `exit` point to indicate **where the graph should finish execution**. This is helpful for more complex flows, but even in a simple graph like this, adding an end node improves clarity.
 
+:::python
+
 ```python
 graph_builder.add_edge("chatbot", END)
 ```
+
+:::
+
+:::js
+
+```typescript hl_lines="11"
+import { StateGraph, MessagesZodState, START, END } from "@langchain/langgraph";
+import { z } from "zod";
+
+const State = z.object({ messages: MessagesZodState.shape.messages });
+
+const graph = new StateGraph(State)
+  .addNode("chatbot", async (state: z.infer<typeof State>) => {
+    return { messages: [await llm.invoke(state.messages)] };
+  })
+  .addEdge(START, "chatbot")
+  .addEdge("chatbot", END)
+  .compile();
+```
+
+:::
+
 This tells the graph to terminate after running the chatbot node.
 
 ## 6. Compile the graph
 
-Before running the graph, we'll need to compile it. We can do so by calling `compile()`
-on the graph builder. This creates a `CompiledGraph` we can invoke on our state.
+Before running the graph, we'll need to compile it. We can do so by calling `compile()` on the graph builder. This creates a `CompiledGraph` we can invoke on our state.
+
+:::python
 
 ```python
 graph = graph_builder.compile()
 ```
 
+:::
+
+:::js
+
+```typescript hl_lines="12"
+import { StateGraph, MessagesZodState, START, END } from "@langchain/langgraph";
+import { z } from "zod";
+
+const State = z.object({ messages: MessagesZodState.shape.messages });
+
+const graph = new StateGraph(State)
+  .addNode("chatbot", async (state: z.infer<typeof State>) => {
+    return { messages: [await llm.invoke(state.messages)] };
+  })
+  .addEdge(START, "chatbot")
+  .addEdge("chatbot", END)
+  .compile();
+```
+
+:::
+
 ## 7. Visualize the graph (optional)
 
+:::python
 You can visualize the graph using the `get_graph` method and one of the "draw" methods, like `draw_ascii` or `draw_png`. The `draw` methods each require additional dependencies.
 
 ```python
@@ -132,17 +294,35 @@ except Exception:
     pass
 ```
 
-![basic chatbot diagram](basic-chatbot.png)
+:::
 
+:::js
+You can visualize the graph using the `getGraph` method and render the graph with the `drawMermaidPng` method.
+
+```typescript
+import * as fs from "node:fs/promises";
+
+const drawableGraph = await graph.getGraphAsync();
+const image = await drawableGraph.drawMermaidPng();
+const imageBuffer = new Uint8Array(await image.arrayBuffer());
+
+await fs.writeFile("basic-chatbot.png", imageBuffer);
+```
+
+:::
+
+![basic chatbot diagram](basic-chatbot.png)
 
 ## 8. Run the chatbot
 
-Now run the chatbot! 
+Now run the chatbot!
 
 !!! tip
 
     You can exit the chat loop at any time by typing `quit`, `exit`, or `q`.
 
+:::python
+
 ```python
 def stream_graph_updates(user_input: str):
     for event in graph.stream({"messages": [{"role": "user", "content": user_input}]}):
@@ -165,13 +345,66 @@ while True:
         break
 ```
 
+:::
+
+:::js
+
+```typescript
+import * as readline from "node:readline/promises";
+import { StateGraph, MessagesZodState, START, END } from "@langchain/langgraph";
+import { ChatOpenAI } from "@langchain/openai";
+import { z } from "zod";
+
+const llm = new ChatOpenAI({ model: "gpt-4o-mini" });
+
+const State = z.object({ messages: MessagesZodState.shape.messages });
+
+const graph = new StateGraph(State)
+  .addNode("chatbot", async (state: z.infer<typeof State>) => {
+    return { messages: [await llm.invoke(state.messages)] };
+  })
+  .addEdge(START, "chatbot")
+  .addEdge("chatbot", END)
+  .compile();
+
+async function generateText(content: string) {
+  const stream = await graph.stream(
+    { messages: [{ type: "human", content }] },
+    { streamMode: "values" }
+  );
+
+  for await (const event of stream) {
+    const lastMessage = event.messages.at(-1);
+    if (lastMessage?.getType() === "ai") {
+      console.log(`Assistant: ${lastMessage.text}`);
+    }
+  }
+}
+
+const prompt = readline.createInterface({
+  input: process.stdin,
+  output: process.stdout,
+});
+
+while (true) {
+  const human = await prompt.question("User: ");
+  if (["quit", "exit", "q"].includes(human.trim())) break;
+  await generateText(human || "What do you know about LangGraph?");
+}
+
+prompt.close();
+```
+
+:::
+
 ```
 Assistant: LangGraph is a library designed to help build stateful multi-agent applications using language models. It provides tools for creating workflows and state machines to coordinate multiple AI agents or language model interactions. LangGraph is built on top of LangChain, leveraging its components while adding graph-based coordination capabilities. It's particularly useful for developing more complex, stateful AI applications that go beyond simple query-response interactions.
-Goodbye!
 ```
 
 **Congratulations!** You've built your first chatbot using LangGraph. This bot can engage in basic conversation by taking user input and generating responses using an LLM. You can inspect a [LangSmith Trace](https://smith.langchain.com/public/7527e308-9502-4894-b347-f34385740d5a/r) for the call above.
 
+:::python
+
 Below is the full code for this tutorial:
 
 ```python
@@ -207,8 +440,8 @@ graph_builder.add_edge("chatbot", END)
 graph = graph_builder.compile()
 ```
 
+:::
+
 ## Next steps
 
 You may have noticed that the bot's knowledge is limited to what's in its training data. In the next part, we'll [add a web search tool](./2-add-tools.md) to expand the bot's knowledge and make it more capable.
-
-

docs/docs/tutorials/get-started/2-add-tools.md

@@ -10,19 +10,64 @@ To handle queries that your chatbot can't answer "from memory", integrate a web
 
 Before you start this tutorial, ensure you have the following:
 
+:::python
+
 - An API key for the [Tavily Search Engine](https://python.langchain.com/docs/integrations/tools/tavily_search/).
 
+:::
+
+:::js
+
+- An API key for the [Tavily Search Engine](https://js.langchain.com/docs/integrations/tools/tavily_search/).
+
+:::
+
 ## 1. Install the search engine
 
+:::python
 Install the requirements to use the [Tavily Search Engine](https://python.langchain.com/docs/integrations/tools/tavily_search/):
 
 ```bash
 pip install -U langchain-tavily
 ```
+
+:::
+
+:::js
+Install the requirements to use the [Tavily Search Engine](https://docs.tavily.com/):
+
+=== "npm"
+
+    ```bash
+    npm install @langchain/tavily
+    ```
+
+=== "yarn"
+
+    ```bash
+    yarn add @langchain/tavily
+    ```
+
+=== "pnpm"
+
+    ```bash
+    pnpm add @langchain/tavily
+    ```
+
+=== "bun"
+
+    ```bash
+    bun add @langchain/tavily
+    ```
+
+:::
+
 ## 2. Configure your environment
 
 Configure your environment with your search engine API key:
 
+:::python
+
 ```bash
 _set_env("TAVILY_API_KEY")
 ```
@@ -31,10 +76,22 @@ _set_env("TAVILY_API_KEY")
 TAVILY_API_KEY:  ········
 ```
 
+:::
+
+:::js
+
+```typescript
+process.env.TAVILY_API_KEY = "tvly-...";
+```
+
+:::
+
 ## 3. Define the tool
 
 Define the web search tool:
 
+:::python
+
 ```python
 from langchain_tavily import TavilySearch
 
@@ -43,8 +100,25 @@ tools = [tool]
 tool.invoke("What's a 'node' in LangGraph?")
 ```
 
+:::
+
+:::js
+
+```typescript
+import { TavilySearch } from "@langchain/tavily";
+
+const tool = new TavilySearch({ maxResults: 2 });
+const tools = [tool];
+
+await tool.invoke({ query: "What's a 'node' in LangGraph?" });
+```
+
+:::
+
 The results are page summaries our chat bot can use to answer questions:
 
+:::python
+
 ```
 {'query': "What's a 'node' in LangGraph?",
 'follow_up_questions': None,
@@ -63,12 +137,51 @@ The results are page summaries our chat bot can use to answer questions:
 'response_time': 1.38}
 ```
 
+:::
+
+:::js
+
+```json
+{
+  "query": "What's a 'node' in LangGraph?",
+  "follow_up_questions": null,
+  "answer": null,
+  "images": [],
+  "results": [
+    {
+      "url": "https://blog.langchain.dev/langgraph/",
+      "title": "LangGraph - LangChain Blog",
+      "content": "TL;DR: LangGraph is module built on top of LangChain to better enable creation of cyclical graphs, often needed for agent runtimes. This state is updated by nodes in the graph, which return operations to attributes of this state (in the form of a key-value store). After adding nodes, you can then add edges to create the graph. An example of this may be in the basic agent runtime, where we always want the model to be called after we call a tool. The state of this graph by default contains concepts that should be familiar to you if you've used LangChain agents: `input`, `chat_history`, `intermediate_steps` (and `agent_outcome` to represent the most recent agent outcome)",
+      "score": 0.7407191,
+      "raw_content": null
+    },
+    {
+      "url": "https://medium.com/@cplog/introduction-to-langgraph-a-beginners-guide-14f9be027141",
+      "title": "Introduction to LangGraph: A Beginner's Guide - Medium",
+      "content": "*   **Stateful Graph:** LangGraph revolves around the concept of a stateful graph, where each node in the graph represents a step in your computation, and the graph maintains a state that is passed around and updated as the computation progresses. LangGraph supports conditional edges, allowing you to dynamically determine the next node to execute based on the current state of the graph. Image 10: Introduction to AI Agent with LangChain and LangGraph: A Beginner’s Guide Image 18: How to build LLM Agent with LangGraph — StateGraph and Reducer Image 20: Simplest Graphs using LangGraph Framework Image 24: Building a ReAct Agent with Langgraph: A Step-by-Step Guide Image 28: Building an Agentic RAG with LangGraph: A Step-by-Step Guide",
+      "score": 0.65279555,
+      "raw_content": null
+    }
+  ],
+  "response_time": 1.34
+}
+```
+
+:::
+
 ## 4. Define the graph
 
+:::python
 For the `StateGraph` you created in the [first tutorial](./1-build-basic-chatbot.md), add `bind_tools` on the LLM. This lets the LLM know the correct JSON format to use if it wants to use the search engine.
+:::
+
+:::js
+For the `StateGraph` you created in the [first tutorial](./1-build-basic-chatbot.md), add `bindTools` on the LLM. This lets the LLM know the correct JSON format to use if it wants to use the search engine.
+:::
 
 Let's first select our LLM:
 
+:::python
 {!snippets/chat_model_tabs.md!}
 
 <!---
@@ -79,8 +192,22 @@ llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")
 ```
 -->
 
+:::
+
+:::js
+
+```typescript
+import { ChatAnthropic } from "@langchain/anthropic";
+
+const llm = new ChatAnthropic({ model: "claude-3-5-sonnet-latest" });
+```
+
+:::
+
 We can now incorporate it into a `StateGraph`:
 
+:::python
+
 ```python hl_lines="15"
 from typing import Annotated
 
@@ -104,9 +231,31 @@ def chatbot(state: State):
 graph_builder.add_node("chatbot", chatbot)
 ```
 
+:::
+
+:::js
+
+```typescript hl_lines="7-8"
+import { StateGraph, MessagesZodState } from "@langchain/langgraph";
+import { z } from "zod";
+
+const State = z.object({ messages: MessagesZodState.shape.messages });
+
+const chatbot = async (state: z.infer<typeof State>) => {
+  // Modification: tell the LLM which tools it can call
+  const llmWithTools = llm.bindTools(tools);
+
+  return { messages: [await llmWithTools.invoke(state.messages)] };
+};
+```
+
+:::
+
 ## 5. Create a function to run the tools
 
-Now, create a function to run the tools if they are called. Do this by adding the tools to a new node called`BasicToolNode` that checks the most recent message in the state and calls tools if the message contains `tool_calls`. It relies on the LLM's `tool_calling` support, which is available in Anthropic, OpenAI, Google Gemini, and a number of other LLM providers.
+:::python
+
+Now, create a function to run the tools if they are called. Do this by adding the tools to a new node called `BasicToolNode` that checks the most recent message in the state and calls tools if the message contains `tool_calls`. It relies on the LLM's `tool_calling` support, which is available in Anthropic, OpenAI, Google Gemini, and a number of other LLM providers.
 
 ```python
 import json
@@ -148,16 +297,80 @@ graph_builder.add_node("tools", tool_node)
 
     If you do not want to build this yourself in the future, you can use LangGraph's prebuilt [ToolNode](https://langchain-ai.github.io/langgraph/reference/agents/#langgraph.prebuilt.tool_node.ToolNode).
 
+:::
+
+:::js
+
+Now, create a function to run the tools if they are called. Do this by adding the tools to a new node called `"tools"` that checks the most recent message in the state and calls tools if the message contains `tool_calls`. It relies on the LLM's tool calling support, which is available in Anthropic, OpenAI, Google Gemini, and a number of other LLM providers.
+
+```typescript
+import type { StructuredToolInterface } from "@langchain/core/tools";
+import { isAIMessage, ToolMessage } from "@langchain/core/messages";
+
+function createToolNode(tools: StructuredToolInterface[]) {
+  const toolByName: Record<string, StructuredToolInterface> = {};
+  for (const tool of tools) {
+    toolByName[tool.name] = tool;
+  }
+
+  return async (inputs: z.infer<typeof State>) => {
+    const { messages } = inputs;
+    if (!messages || messages.length === 0) {
+      throw new Error("No message found in input");
+    }
+
+    const message = messages.at(-1);
+    if (!message || !isAIMessage(message) || !message.tool_calls) {
+      throw new Error("Last message is not an AI message with tool calls");
+    }
+
+    const outputs: ToolMessage[] = [];
+    for (const toolCall of message.tool_calls) {
+      if (!toolCall.id) throw new Error("Tool call ID is required");
+
+      const tool = toolByName[toolCall.name];
+      if (!tool) throw new Error(`Tool ${toolCall.name} not found`);
+
+      const result = await tool.invoke(toolCall.args);
+
+      outputs.push(
+        new ToolMessage({
+          content: JSON.stringify(result),
+          name: toolCall.name,
+          tool_call_id: toolCall.id,
+        })
+      );
+    }
+
+    return { messages: outputs };
+  };
+}
+```
+
+!!! note
+
+    If you do not want to build this yourself in the future, you can use LangGraph's prebuilt [ToolNode](https://langchain-ai.github.io/langgraphjs/reference/classes/langgraph_prebuilt.ToolNode.html).
+
+:::
+
 ## 6. Define the `conditional_edges`
 
-With the tool node added, now you can define the `conditional_edges`. 
+With the tool node added, now you can define the `conditional_edges`.
 
 **Edges** route the control flow from one node to the next. **Conditional edges** start from a single node and usually contain "if" statements to route to different nodes depending on the current graph state. These functions receive the current graph `state` and return a string or list of strings indicating which node(s) to call next.
 
-Next, define a router function called `route_tools` that checks for `tool_calls` in the chatbot's output. Provide this function to the graph by calling `add_conditional_edges`, which tells the graph that whenever the `chatbot` node completes to check this function to see where to go next. 
+:::python
+Next, define a router function called `route_tools` that checks for `tool_calls` in the chatbot's output. Provide this function to the graph by calling `add_conditional_edges`, which tells the graph that whenever the `chatbot` node completes to check this function to see where to go next.
+:::
+
+:::js
+Next, define a router function called `routeTools` that checks for `tool_calls` in the chatbot's output. Provide this function to the graph by calling `addConditionalEdges`, which tells the graph that whenever the `chatbot` node completes to check this function to see where to go next.
+:::
 
 The condition will route to `tools` if tool calls are present and `END` if not. Because the condition can return `END`, you do not need to explicitly set a `finish_point` this time.
 
+:::python
+
 ```python
 def route_tools(
     state: State,
@@ -197,10 +410,61 @@ graph = graph_builder.compile()
 
 !!! note
 
-    You can replace this with the prebuilt [tools_condition](https://langchain-ai.github.io/langgraph/reference/prebuilt/#tools_condition) to be more concise. 
+    You can replace this with the prebuilt [tools_condition](https://langchain-ai.github.io/langgraph/reference/prebuilt/#tools_condition) to be more concise.
+
+:::
+
+:::js
+
+```typescript
+import { END, START } from "@langchain/langgraph";
+
+const routeTools = (state: z.infer<typeof State>) => {
+  /**
+   * Use as conditional edge to route to the ToolNode if the last message
+   * has tool calls.
+   */
+  const lastMessage = state.messages.at(-1);
+  if (
+    lastMessage &&
+    isAIMessage(lastMessage) &&
+    lastMessage.tool_calls?.length
+  ) {
+    return "tools";
+  }
+
+  /** Otherwise, route to the end. */
+  return END;
+};
+
+const graph = new StateGraph(State)
+  .addNode("chatbot", chatbot)
+
+  // The `routeTools` function returns "tools" if the chatbot asks to use a tool, and "END" if
+  // it is fine directly responding. This conditional routing defines the main agent loop.
+  .addNode("tools", createToolNode(tools))
+
+  // Start the graph with the chatbot
+  .addEdge(START, "chatbot")
+
+  // The `routeTools` function returns "tools" if the chatbot asks to use a tool, and "END" if
+  // it is fine directly responding.
+  .addConditionalEdges("chatbot", routeTools, ["tools", END])
+
+  // Any time a tool is called, we need to return to the chatbot
+  .addEdge("tools", "chatbot")
+  .compile();
+```
+
+!!! note
+
+    You can replace this with the prebuilt [toolsCondition](https://langchain-ai.github.io/langgraphjs/reference/functions/langgraph_prebuilt.toolsCondition.html) to be more concise.
+
+:::
 
 ## 7. Visualize the graph (optional)
 
+:::python
 You can visualize the graph using the `get_graph` method and one of the "draw" methods, like `draw_ascii` or `draw_png`. The `draw` methods each require additional dependencies.
 
 ```python
@@ -213,12 +477,31 @@ except Exception:
     pass
 ```
 
+:::
+
+:::js
+You can visualize the graph using the `getGraph` method and render the graph with the `drawMermaidPng` method.
+
+```typescript
+import * as fs from "node:fs/promises";
+
+const drawableGraph = await graph.getGraphAsync();
+const image = await drawableGraph.drawMermaidPng();
+const imageBuffer = new Uint8Array(await image.arrayBuffer());
+
+await fs.writeFile("chatbot-with-tools.png", imageBuffer);
+```
+
+:::
+
 ![chatbot-with-tools-diagram](chatbot-with-tools.png)
 
 ## 8. Ask the bot questions
 
 Now you can ask the chatbot questions outside its training data:
 
+:::python
+
 ```python
 def stream_graph_updates(user_input: str):
     for event in graph.stream({"messages": [{"role": "user", "content": user_input}]}):
@@ -241,7 +524,7 @@ while True:
         break
 ```
 
-``` 
+```
 Assistant: [{'text': "To provide you with accurate and up-to-date information about LangGraph, I'll need to search for the latest details. Let me do that for you.", 'type': 'text'}, {'id': 'toolu_01Q588CszHaSvvP2MxRq9zRD', 'input': {'query': 'LangGraph AI tool information'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
 Assistant: [{"url": "https://www.langchain.com/langgraph", "content": "LangGraph sets the foundation for how we can build and scale AI workloads \u2014 from conversational agents, complex task automation, to custom LLM-backed experiences that 'just work'. The next chapter in building complex production-ready features with LLMs is agentic, and with LangGraph and LangSmith, LangChain delivers an out-of-the-box solution ..."}, {"url": "https://github.com/langchain-ai/langgraph", "content": "Overview. LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures ..."}]
 Assistant: Based on the search results, I can provide you with information about LangGraph:
@@ -272,18 +555,99 @@ Assistant: Based on the search results, I can provide you with information about
 
 LangGraph appears to be a significant tool in the evolving landscape of LLM-based application development, offering developers new ways to create more complex, stateful, and interactive AI systems.
 Goodbye!
-Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...
 ```
 
+:::
+
+:::js
+
+```typescript
+import readline from "node:readline/promises";
+
+const prompt = readline.createInterface({
+  input: process.stdin,
+  output: process.stdout,
+});
+
+async function generateText(content: string) {
+  const stream = await graph.stream(
+    { messages: [{ type: "human", content }] },
+    { streamMode: "values" }
+  );
+
+  for await (const event of stream) {
+    const lastMessage = event.messages.at(-1);
+
+    if (lastMessage?.getType() === "ai" || lastMessage?.getType() === "tool") {
+      console.log(`Assistant: ${lastMessage?.text}`);
+    }
+  }
+}
+
+while (true) {
+  const human = await prompt.question("User: ");
+  if (["quit", "exit", "q"].includes(human.trim())) break;
+  await generateText(human || "What do you know about LangGraph?");
+}
+
+prompt.close();
+```
+
+```
+User: What do you know about LangGraph?
+Assistant: I'll search for the latest information about LangGraph for you.
+Assistant: [{"title":"Introduction to LangGraph: A Beginner's Guide - Medium","url":"https://medium.com/@cplog/introduction-to-langgraph-a-beginners-guide-14f9be027141","content":"..."}]
+Assistant: Based on the search results, I can provide you with information about LangGraph:
+
+LangGraph is a library within the LangChain ecosystem designed for building stateful, multi-actor applications with Large Language Models (LLMs). Here are the key aspects:
+
+**Core Purpose:**
+- LangGraph is specifically designed for creating agent and multi-agent workflows
+- It provides a framework for defining, coordinating, and executing multiple LLM agents in a structured manner
+
+**Key Features:**
+1. **Stateful Graph Architecture**: LangGraph revolves around a stateful graph where each node represents a step in computation, and the graph maintains state that is passed around and updated as the computation progresses
+
+2. **Conditional Edges**: It supports conditional edges, allowing you to dynamically determine the next node to execute based on the current state of the graph
+
+3. **Cycles**: Unlike other LLM frameworks, LangGraph allows you to define flows that involve cycles, which is essential for most agentic architectures
+
+4. **Controllability**: It offers enhanced control over the application flow
+
+5. **Persistence**: The library provides ways to maintain state and persistence in LLM-based applications
+
+**Use Cases:**
+- Conversational agents
+- Complex task automation
+- Custom LLM-backed experiences
+- Multi-agent systems that perform complex tasks
+
+**Benefits:**
+LangGraph allows developers to focus on the high-level logic of their applications rather than the intricacies of agent coordination, making it easier to build complex, production-ready features with LLMs.
+
+This makes LangGraph a significant tool in the evolving landscape of LLM-based application development.
+```
+
+:::
+
 ## 9. Use prebuilts
 
 For ease of use, adjust your code to replace the following with LangGraph prebuilt components. These have built in functionality like parallel API execution.
 
+:::python
+
 - `BasicToolNode` is replaced with the prebuilt [ToolNode](https://langchain-ai.github.io/langgraph/reference/prebuilt/#toolnode)
 - `route_tools` is replaced with the prebuilt [tools_condition](https://langchain-ai.github.io/langgraph/reference/prebuilt/#tools_condition)
 
 {!snippets/chat_model_tabs.md!}
 
+<!---
+```python
+from langchain.chat_models import init_chat_model
+
+llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")
+```
+-->
 
 ```python hl_lines="25 30"
 from typing import Annotated
@@ -323,7 +687,46 @@ graph_builder.add_edge(START, "chatbot")
 graph = graph_builder.compile()
 ```
 
-**Congratulations!** You've created a conversational agent in LangGraph that can use a search engine to retrieve updated information when needed. Now it can handle a wider range of user queries. To inspect all the steps your agent just took, check out this [LangSmith trace](https://smith.langchain.com/public/4fbd7636-25af-4638-9587-5a02fdbb0172/r).
+:::
+
+:::js
+
+- `createToolNode` is replaced with the prebuilt [ToolNode](https://langchain-ai.github.io/langgraphjs/reference/classes/langgraph_prebuilt.ToolNode.html)
+- `routeTools` is replaced with the prebuilt [toolsCondition](https://langchain-ai.github.io/langgraphjs/reference/functions/langgraph_prebuilt.toolsCondition.html)
+
+```typescript
+import { TavilySearch } from "@langchain/tavily";
+import { ChatOpenAI } from "@langchain/openai";
+import { StateGraph, START, MessagesZodState, END } from "@langchain/langgraph";
+import { ToolNode, toolsCondition } from "@langchain/langgraph/prebuilt";
+import { z } from "zod";
+
+const State = z.object({ messages: MessagesZodState.shape.messages });
+
+const tools = [new TavilySearch({ maxResults: 2 })];
+
+const llm = new ChatOpenAI({ model: "gpt-4o-mini" }).bindTools(tools);
+
+const graph = new StateGraph(State)
+  .addNode("chatbot", async (state) => ({
+    messages: [await llm.invoke(state.messages)],
+  }))
+  .addNode("tools", new ToolNode(tools))
+  .addConditionalEdges("chatbot", toolsCondition, ["tools", END])
+  .addEdge("tools", "chatbot")
+  .addEdge(START, "chatbot")
+  .compile();
+```
+
+:::
+
+**Congratulations!** You've created a conversational agent in LangGraph that can use a search engine to retrieve updated information when needed. Now it can handle a wider range of user queries.
+
+:::python
+
+To inspect all the steps your agent just took, check out this [LangSmith trace](https://smith.langchain.com/public/4fbd7636-25af-4638-9587-5a02fdbb0172/r).
+
+:::
 
 ## Next steps
 

docs/docs/tutorials/get-started/3-add-memory.md

@@ -2,7 +2,7 @@
 
 The chatbot can now [use tools](./2-add-tools.md) to answer user questions, but it does not remember the context of previous interactions. This limits its ability to have coherent, multi-turn conversations.
 
-LangGraph solves this problem through **persistent checkpointing**. If you provide a `checkpointer` when compiling the graph and a `thread_id` when calling your graph, LangGraph automatically saves the state after each step. When you invoke the graph again using the same `thread_id`, the graph loads its saved state, allowing the chatbot to pick up where it left off. 
+LangGraph solves this problem through **persistent checkpointing**. If you provide a `checkpointer` when compiling the graph and a `thread_id` when calling your graph, LangGraph automatically saves the state after each step. When you invoke the graph again using the same `thread_id`, the graph loads its saved state, allowing the chatbot to pick up where it left off.
 
 We will see later that **checkpointing** is _much_ more powerful than simple chat memory - it lets you save and resume complex state at any time for error recovery, human-in-the-loop workflows, time travel interactions, and more. But first, let's add checkpointing to enable multi-turn conversations.
 
@@ -14,43 +14,79 @@ We will see later that **checkpointing** is _much_ more powerful than simple cha
 
 Create a `MemorySaver` checkpointer:
 
-``` python
+:::python
+
+```python
 from langgraph.checkpoint.memory import MemorySaver
 
 memory = MemorySaver()
 ```
 
+:::
+
+:::js
+
+```typescript
+import { MemorySaver } from "@langchain/langgraph";
+
+const memory = new MemorySaver();
+```
+
+:::
+
 This is in-memory checkpointer, which is convenient for the tutorial. However, in a production application, you would likely change this to use `SqliteSaver` or `PostgresSaver` and connect a database.
 
 ## 2. Compile the graph
 
 Compile the graph with the provided checkpointer, which will checkpoint the `State` as the graph works through each node:
 
-``` python
+:::python
+
+```python
 graph = graph_builder.compile(checkpointer=memory)
 ```
 
-``` python
-from IPython.display import Image, display
+:::
 
-try:
-    display(Image(graph.get_graph().draw_mermaid_png()))
-except Exception:
-    # This requires some extra dependencies and is optional
-    pass
+:::js
+
+```typescript hl_lines="7"
+const graph = new StateGraph(State)
+  .addNode("chatbot", chatbot)
+  .addNode("tools", new ToolNode(tools))
+  .addConditionalEdges("chatbot", toolsCondition, ["tools", END])
+  .addEdge("tools", "chatbot")
+  .addEdge(START, "chatbot")
+  .compile({ checkpointer: memory });
 ```
 
+:::
+
 ## 3. Interact with your chatbot
 
 Now you can interact with your bot!
 
-1. Pick a thread to use as the key for this conversation.
+1.  Pick a thread to use as the key for this conversation.
+
+    :::python
 
     ```python
     config = {"configurable": {"thread_id": "1"}}
     ```
 
-2. Call your chatbot:
+    :::
+
+    :::js
+
+    ```typescript
+    const config = { configurable: { thread_id: "1" } };
+    ```
+
+    :::
+
+2.  Call your chatbot:
+
+    :::python
 
     ```python
     user_input = "Hi there! My name is Will."
@@ -74,14 +110,45 @@ Now you can interact with your bot!
     Hello Will! It's nice to meet you. How can I assist you today? Is there anything specific you'd like to know or discuss?
     ```
 
-    !!! note 
+    !!! note
 
         The config was provided as the **second positional argument** when calling our graph. It importantly is _not_ nested within the graph inputs (`{'messages': []}`).
 
+    :::
+
+    :::js
+
+    ```typescript
+    const userInput = "Hi there! My name is Will.";
+
+    const events = await graph.stream(
+      { messages: [{ type: "human", content: userInput }] },
+      { configurable: { thread_id: "1" }, streamMode: "values" }
+    );
+
+    for await (const event of events) {
+      const lastMessage = event.messages.at(-1);
+      console.log(`${lastMessage?.getType()}: ${lastMessage?.text}`);
+    }
+    ```
+
+    ```
+    human: Hi there! My name is Will.
+    ai: Hello Will! It's nice to meet you. How can I assist you today? Is there anything specific you'd like to know or discuss?
+    ```
+
+    !!! note
+
+        The config was provided as the **second parameter** when calling our graph. It importantly is _not_ nested within the graph inputs (`{"messages": []}`).
+
+    :::
+
 ## 4. Ask a follow up question
 
 Ask a follow up question:
 
+:::python
+
 ```python
 user_input = "Remember my name?"
 
@@ -104,10 +171,37 @@ Remember my name?
 Of course, I remember your name, Will. I always try to pay attention to important details that users share with me. Is there anything else you'd like to talk about or any questions you have? I'm here to help with a wide range of topics or tasks.
 ```
 
+:::
+
+:::js
+
+```typescript
+const userInput2 = "Remember my name?";
+
+const events2 = await graph.stream(
+  { messages: [{ type: "human", content: userInput2 }] },
+  { configurable: { thread_id: "1" }, streamMode: "values" }
+);
+
+for await (const event of events2) {
+  const lastMessage = event.messages.at(-1);
+  console.log(`${lastMessage?.getType()}: ${lastMessage?.text}`);
+}
+```
+
+```
+human: Remember my name?
+ai: Yes, your name is Will. How can I help you today?
+```
+
+:::
+
 **Notice** that we aren't using an external list for memory: it's all handled by the checkpointer! You can inspect the full execution in this [LangSmith trace](https://smith.langchain.com/public/29ba22b5-6d40-4fbe-8d27-b369e3329c84/r) to see what's going on.
 
 Don't believe me? Try this using a different config.
 
+:::python
+
 ```python
 # The only difference is we change the `thread_id` here to "2" instead of "1"
 events = graph.stream(
@@ -129,10 +223,36 @@ Remember my name?
 I apologize, but I don't have any previous context or memory of your name. As an AI assistant, I don't retain information from past conversations. Each interaction starts fresh. Could you please tell me your name so I can address you properly in this conversation?
 ```
 
+:::
+
+:::js
+
+```typescript hl_lines="3-4"
+const events3 = await graph.stream(
+  { messages: [{ type: "human", content: userInput2 }] },
+  // The only difference is we change the `thread_id` here to "2" instead of "1"
+  { configurable: { thread_id: "2" }, streamMode: "values" }
+);
+
+for await (const event of events3) {
+  const lastMessage = event.messages.at(-1);
+  console.log(`${lastMessage?.getType()}: ${lastMessage?.text}`);
+}
+```
+
+```
+human: Remember my name?
+ai: I don't have the ability to remember personal information about users between interactions. However, I'm here to help you with any questions or topics you want to discuss!
+```
+
+:::
+
 **Notice** that the **only** change we've made is to modify the `thread_id` in the config. See this call's [LangSmith trace](https://smith.langchain.com/public/51a62351-2f0a-4058-91cc-9996c5561428/r) for comparison.
 
 ## 5. Inspect the state
 
+:::python
+
 By now, we have made a few checkpoints across two different threads. But what goes into a checkpoint? To inspect a graph's `state` for a given config at any time, call `get_state(config)`.
 
 ```python
@@ -148,12 +268,94 @@ StateSnapshot(values={'messages': [HumanMessage(content='Hi there! My name is Wi
 snapshot.next  # (since the graph ended this turn, `next` is empty. If you fetch a state from within a graph invocation, next tells which node will execute next)
 ```
 
+:::
+
+:::js
+
+By now, we have made a few checkpoints across two different threads. But what goes into a checkpoint? To inspect a graph's `state` for a given config at any time, call `getState(config)`.
+
+```typescript
+await graph.getState({ configurable: { thread_id: "1" } });
+```
+
+```typescript
+{
+  values: {
+    messages: [
+      HumanMessage {
+        "id": "32fabcef-b3b8-481f-8bcb-fd83399a5f8d",
+        "content": "Hi there! My name is Will.",
+        "additional_kwargs": {},
+        "response_metadata": {}
+      },
+      AIMessage {
+        "id": "chatcmpl-BrPbTsCJbVqBvXWySlYoTJvM75Kv8",
+        "content": "Hello Will! How can I assist you today?",
+        "additional_kwargs": {},
+        "response_metadata": {},
+        "tool_calls": [],
+        "invalid_tool_calls": []
+      },
+      HumanMessage {
+        "id": "561c3aad-f8fc-4fac-94a6-54269a220856",
+        "content": "Remember my name?",
+        "additional_kwargs": {},
+        "response_metadata": {}
+      },
+      AIMessage {
+        "id": "chatcmpl-BrPbU4BhhsUikGbW37hYuF5vvnnE2",
+        "content": "Yes, I remember your name, Will! How can I help you today?",
+        "additional_kwargs": {},
+        "response_metadata": {},
+        "tool_calls": [],
+        "invalid_tool_calls": []
+      }
+    ]
+  },
+  next: [],
+  tasks: [],
+  metadata: {
+    source: 'loop',
+    step: 4,
+    parents: {},
+    thread_id: '1'
+  },
+  config: {
+    configurable: {
+      thread_id: '1',
+      checkpoint_id: '1f05cccc-9bb6-6270-8004-1d2108bcec77',
+      checkpoint_ns: ''
+    }
+  },
+  createdAt: '2025-07-09T13:58:27.607Z',
+  parentConfig: {
+    configurable: {
+      thread_id: '1',
+      checkpoint_ns: '',
+      checkpoint_id: '1f05cccc-78fa-68d0-8003-ffb01a76b599'
+    }
+  }
+}
+```
+
+```typescript
+import * as assert from "node:assert";
+
+// Since the graph ended this turn, `next` is empty.
+// If you fetch a state from within a graph invocation, next tells which node will execute next)
+assert.deepEqual(snapshot.next, []);
+```
+
+:::
+
 The snapshot above contains the current state values, corresponding config, and the `next` node to process. In our case, the graph has reached an `END` state, so `next` is empty.
 
 **Congratulations!** Your chatbot can now maintain conversation state across sessions thanks to LangGraph's checkpointing system. This opens up exciting possibilities for more natural, contextual interactions. LangGraph's checkpointing even handles **arbitrarily complex graph states**, which is much more expressive and powerful than simple chat memory.
-  
+
 Check out the code snippet below to review the graph from this tutorial:
 
+:::python
+
 {!snippets/chat_model_tabs.md!}
 
 <!---
@@ -204,6 +406,43 @@ memory = MemorySaver()
 graph = graph_builder.compile(checkpointer=memory)
 ```
 
+:::
+
+:::js
+
+```typescript hl_lines="16 26"
+import { END, MessagesZodState, START } from "@langchain/langgraph";
+import { ChatOpenAI } from "@langchain/openai";
+import { TavilySearch } from "@langchain/tavily";
+
+import { MemorySaver } from "@langchain/langgraph";
+import { StateGraph } from "@langchain/langgraph";
+import { ToolNode, toolsCondition } from "@langchain/langgraph/prebuilt";
+import { z } from "zod";
+
+const State = z.object({
+  messages: MessagesZodState.shape.messages,
+});
+
+const tools = [new TavilySearch({ maxResults: 2 })];
+const llm = new ChatOpenAI({ model: "gpt-4o-mini" }).bindTools(tools);
+// highlight-next-line
+const memory = new MemorySaver();
+
+const graph = new StateGraph(State)
+  .addNode("chatbot", async (state) => ({
+    messages: [await llm.invoke(state.messages)],
+  }))
+  .addNode("tools", new ToolNode(tools))
+  .addConditionalEdges("chatbot", toolsCondition, ["tools", END])
+  .addEdge("tools", "chatbot")
+  .addEdge(START, "chatbot")
+  // highlight-next-line
+  .compile({ checkpointer: memory });
+```
+
+:::
+
 ## Next steps
 
 In the next tutorial, you will [add human-in-the-loop to the chatbot](./4-human-in-the-loop.md) to handle situations where it may need guidance or verification before proceeding.

docs/docs/tutorials/get-started/4-human-in-the-loop.md

@@ -2,7 +2,15 @@
 
 Agents can be unreliable and may need human input to successfully accomplish tasks. Similarly, for some actions, you may want to require human approval before running to ensure that everything is running as intended.
 
-LangGraph's [persistence](../../concepts/persistence.md) layer supports **human-in-the-loop** workflows, allowing execution to pause and resume based on user feedback. The primary interface to this functionality is the [`interrupt`](../../how-tos/human_in_the_loop/add-human-in-the-loop.md) function. Calling `interrupt` inside a node will pause execution. Execution can be resumed, together with new input from a human, by passing in a [Command](../../concepts/low_level.md#command). `interrupt` is ergonomically similar to Python's built-in `input()`, [with some caveats](../../how-tos/human_in_the_loop/add-human-in-the-loop.md).
+LangGraph's [persistence](../../concepts/persistence.md) layer supports **human-in-the-loop** workflows, allowing execution to pause and resume based on user feedback. The primary interface to this functionality is the [`interrupt`](../../how-tos/human_in_the_loop/add-human-in-the-loop.md) function. Calling `interrupt` inside a node will pause execution. Execution can be resumed, together with new input from a human, by passing in a [Command](../../concepts/low_level.md#command).
+
+:::python
+`interrupt` is ergonomically similar to Python's built-in `input()`, [with some caveats](../../how-tos/human_in_the_loop/add-human-in-the-loop.md).
+:::
+
+:::js
+`interrupt` is ergonomically similar to Node.js's built-in `readline.question()` function, [with some caveats](../../how-tos/human_in_the_loop/add-human-in-the-loop.md).
+:::
 
 !!! note
 
@@ -14,6 +22,7 @@ Starting with the existing code from the [Add memory to the chatbot](./3-add-mem
 
 Let's first select a chat model:
 
+:::python
 {!snippets/chat_model_tabs.md!}
 
 <!---
@@ -24,9 +33,22 @@ llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")
 ```
 -->
 
+:::
+
+:::js
+
+```typescript
+// Add your API key here
+process.env.ANTHROPIC_API_KEY = "YOUR_API_KEY";
+```
+
+:::
+
 We can now incorporate it into our `StateGraph` with an additional tool:
 
-``` python hl_lines="12 19 20 21 22 23"
+:::python
+
+```python hl_lines="12 19 20 21 22 23"
 from typing import Annotated
 
 from langchain_tavily import TavilySearch
@@ -76,6 +98,54 @@ graph_builder.add_edge("tools", "chatbot")
 graph_builder.add_edge(START, "chatbot")
 ```
 
+:::
+
+:::js
+
+```typescript hl_lines="1 7-19"
+import { interrupt, MessagesZodState } from "@langchain/langgraph";
+import { ChatAnthropic } from "@langchain/anthropic";
+import { TavilySearch } from "@langchain/tavily";
+import { tool } from "@langchain/core/tools";
+import { z } from "zod";
+
+const humanAssistance = tool(
+  async ({ query }) => {
+    const humanResponse = interrupt({ query });
+    return humanResponse.data;
+  },
+  {
+    name: "humanAssistance",
+    description: "Request assistance from a human.",
+    schema: z.object({
+      query: z.string().describe("Human readable question for the human"),
+    }),
+  }
+);
+
+const searchTool = new TavilySearch({ maxResults: 2 });
+const tools = [searchTool, humanAssistance];
+
+const llmWithTools = new ChatAnthropic({
+  model: "claude-3-5-sonnet-latest",
+}).bindTools(tools);
+
+async function chatbot(state: z.infer<typeof MessagesZodState>) {
+  const message = await llmWithTools.invoke(state.messages);
+
+  // Because we will be interrupting during tool execution,
+  // we disable parallel tool calling to avoid repeating any
+  // tool invocations when we resume.
+  if (message.tool_calls && message.tool_calls.length > 1) {
+    throw new Error("Multiple tool calls not supported with interrupts");
+  }
+
+  return { messages: message };
+}
+```
+
+:::
+
 !!! tip
 
     For more information and examples of human-in-the-loop workflows, see [Human-in-the-loop](../../concepts/human_in_the_loop.md).
@@ -84,17 +154,41 @@ graph_builder.add_edge(START, "chatbot")
 
 We compile the graph with a checkpointer, as before:
 
+:::python
+
 ```python
 memory = MemorySaver()
 
 graph = graph_builder.compile(checkpointer=memory)
 ```
 
+:::
+
+:::js
+
+```typescript hl_lines="3 11"
+import { StateGraph, MemorySaver, START, END } from "@langchain/langgraph";
+
+const memory = new MemorySaver();
+
+const graph = new StateGraph(MessagesZodState)
+  .addNode("chatbot", chatbot)
+  .addNode("tools", new ToolNode(tools))
+  .addConditionalEdges("chatbot", toolsCondition, ["tools", END])
+  .addEdge("tools", "chatbot")
+  .addEdge(START, "chatbot")
+  .compile({ checkpointer: memory });
+```
+
+:::
+
 ## 3. Visualize the graph (optional)
 
 Visualizing the graph, you get the same layout as before – just with the added tool!
 
-``` python
+:::python
+
+```python
 from IPython.display import Image, display
 
 try:
@@ -104,12 +198,30 @@ except Exception:
     pass
 ```
 
+:::
+
+:::js
+
+```typescript
+import * as fs from "node:fs/promises";
+
+const drawableGraph = await graph.getGraphAsync();
+const image = await drawableGraph.drawMermaidPng();
+const imageBuffer = new Uint8Array(await image.arrayBuffer());
+
+await fs.writeFile("chatbot-with-tools.png", imageBuffer);
+```
+
+:::
+
 ![chatbot-with-tools-diagram](chatbot-with-tools.png)
 
 ## 4. Prompt the chatbot
 
 Now, prompt the chatbot with a question that will engage the new `human_assistance` tool:
 
+:::python
+
 ```python
 user_input = "I need some expert guidance for building an AI agent. Could you request assistance for me?"
 config = {"configurable": {"thread_id": "1"}}
@@ -138,8 +250,58 @@ Tool Calls:
     query: A user is requesting expert guidance for building an AI agent. Could you please provide some expert advice or resources on this topic?
 ```
 
+:::
+
+:::js
+
+```typescript
+import { isAIMessage } from "@langchain/core/messages";
+
+const userInput =
+  "I need some expert guidance for building an AI agent. Could you request assistance for me?";
+
+const events = await graph.stream(
+  { messages: [{ role: "user", content: userInput }] },
+  { configurable: { thread_id: "1" }, streamMode: "values" }
+);
+
+for await (const event of events) {
+  if ("messages" in event) {
+    const lastMessage = event.messages.at(-1);
+    console.log(`[${lastMessage?.getType()}]: ${lastMessage?.text}`);
+
+    if (
+      lastMessage &&
+      isAIMessage(lastMessage) &&
+      lastMessage.tool_calls?.length
+    ) {
+      console.log("Tool calls:", lastMessage.tool_calls);
+    }
+  }
+}
+```
+
+```
+[human]: I need some expert guidance for building an AI agent. Could you request assistance for me?
+[ai]: I'll help you request human assistance for guidance on building an AI agent.
+Tool calls: [
+  {
+    name: 'humanAssistance',
+    args: {
+      query: 'I would like expert guidance on building an AI agent. Could you please provide assistance with this topic?'
+    },
+    id: 'toolu_01Bpxc8rFVMhSaRosS6b85Ts',
+    type: 'tool_call'
+  }
+]
+```
+
+:::
+
 The chatbot generated a tool call, but then execution has been interrupted. If you inspect the graph state, you see that it stopped at the tools node:
 
+:::python
+
 ```python
 snapshot = graph.get_state(config)
 snapshot.next
@@ -149,8 +311,25 @@ snapshot.next
 ('tools',)
 ```
 
+:::
+
+:::js
+
+```typescript
+const snapshot = await graph.getState({ configurable: { thread_id: "1" } });
+snapshot.next;
+```
+
+```json
+["tools"]
+```
+
+:::
+
 !!! info Additional information
 
+    :::python
+
     Take a closer look at the `human_assistance` tool:
 
     ```python
@@ -162,12 +341,40 @@ snapshot.next
     ```
 
     Similar to Python's built-in `input()` function, calling `interrupt` inside the tool will pause execution. Progress is persisted based on the [checkpointer](../../concepts/persistence.md#checkpointer-libraries); so if it is persisting with Postgres, it can resume at any time as long as the database is alive. In this example, it is persisting with the in-memory checkpointer and can resume any time if the Python kernel is running.
+    :::
+
+    :::js
+
+    Take a closer look at the `humanAssistance` tool:
+
+    ```typescript hl_lines="3"
+    const humanAssistance = tool(
+      async ({ query }) => {
+        const humanResponse = interrupt({ query });
+        return humanResponse.data;
+      },
+      {
+        name: "humanAssistance",
+        description: "Request assistance from a human.",
+        schema: z.object({
+          query: z.string().describe("Human readable question for the human"),
+        }),
+      },
+    );
+    ```
+
+    Calling `interrupt` inside the tool will pause execution. Progress is persisted based on the [checkpointer](../../concepts/persistence.md#checkpointer-libraries); so if it is persisting with Postgres, it can resume at any time as long as the database is alive. In this example, it is persisting with the in-memory checkpointer and can resume any time if the JavaScript runtime is running.
+    :::
 
 ## 5. Resume execution
 
-To resume execution, pass a [`Command`](../../concepts/low_level.md#command) object containing data expected by the tool. The format of this data can be customized based on needs. For this example, use a dict with a key `"data"`:
+To resume execution, pass a [`Command`](../../concepts/low_level.md#command) object containing data expected by the tool. The format of this data can be customized based on needs.
 
-``` python
+:::python
+
+For this example, use a dict with a key `"data"`:
+
+```python
 human_response = (
     "We, the experts are here to help! We'd recommend you check out LangGraph to build your agent."
     " It's much more reliable and extensible than simple autonomous agents."
@@ -215,12 +422,60 @@ If you'd like more specific information about LangGraph or have any questions ab
 Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...
 ```
 
+:::
+
+:::js
+For this example, use an object with a key `"data"`:
+
+```typescript
+import { Command } from "@langchain/langgraph";
+
+const humanResponse =
+  "We, the experts are here to help! We'd recommend you check out LangGraph to build your agent." +
+  " It's much more reliable and extensible than simple autonomous agents.";
+
+const humanCommand = new Command({ resume: { data: humanResponse } });
+
+const resumeEvents = await graph.stream(humanCommand, {
+  configurable: { thread_id: "1" },
+  streamMode: "values",
+});
+
+for await (const event of resumeEvents) {
+  if ("messages" in event) {
+    const lastMessage = event.messages.at(-1);
+    console.log(`[${lastMessage?.getType()}]: ${lastMessage?.text}`);
+  }
+}
+```
+
+```
+[tool]: We, the experts are here to help! We'd recommend you check out LangGraph to build your agent. It's much more reliable and extensible than simple autonomous agents.
+[ai]: Thank you for your patience. I've received some expert advice regarding your request for guidance on building an AI agent. Here's what the experts have suggested:
+
+The experts recommend that you look into LangGraph for building your AI agent. They mention that LangGraph is a more reliable and extensible option compared to simple autonomous agents.
+
+LangGraph is likely a framework or library designed specifically for creating AI agents with advanced capabilities. Here are a few points to consider based on this recommendation:
+
+1. Reliability: The experts emphasize that LangGraph is more reliable than simpler autonomous agent approaches. This could mean it has better stability, error handling, or consistent performance.
+
+2. Extensibility: LangGraph is described as more extensible, which suggests that it probably offers a flexible architecture that allows you to easily add new features or modify existing ones as your agent's requirements evolve.
+
+3. Advanced capabilities: Given that it's recommended over "simple autonomous agents," LangGraph likely provides more sophisticated tools and techniques for building complex AI agents.
+
+...
+```
+
+:::
+
 The input has been received and processed as a tool message. Review this call's [LangSmith trace](https://smith.langchain.com/public/9f0f87e3-56a7-4dde-9c76-b71675624e91/r) to see the exact work that was done in the above call. Notice that the state is loaded in the first step so that our chatbot can continue where it left off.
 
 **Congratulations!** You've used an `interrupt` to add human-in-the-loop execution to your chatbot, allowing for human oversight and intervention when needed. This opens up the potential UIs you can create with your AI systems. Since you have already added a **checkpointer**, as long as the underlying persistence layer is running, the graph can be paused **indefinitely** and resumed at any time as if nothing had happened.
 
 Check out the code snippet below to review the graph from this tutorial:
 
+:::python
+
 {!snippets/chat_model_tabs.md!}
 
 ```python
@@ -272,6 +527,73 @@ memory = MemorySaver()
 graph = graph_builder.compile(checkpointer=memory)
 ```
 
+:::
+
+:::js
+
+```typescript
+import {
+  interrupt,
+  MessagesZodState,
+  StateGraph,
+  MemorySaver,
+  START,
+  END,
+} from "@langchain/langgraph";
+import { ToolNode, toolsCondition } from "@langchain/langgraph/prebuilt";
+import { isAIMessage } from "@langchain/core/messages";
+import { ChatAnthropic } from "@langchain/anthropic";
+import { TavilySearch } from "@langchain/tavily";
+import { tool } from "@langchain/core/tools";
+import { z } from "zod";
+
+const humanAssistance = tool(
+  async ({ query }) => {
+    const humanResponse = interrupt({ query });
+    return humanResponse.data;
+  },
+  {
+    name: "humanAssistance",
+    description: "Request assistance from a human.",
+    schema: z.object({
+      query: z.string().describe("Human readable question for the human"),
+    }),
+  }
+);
+
+const searchTool = new TavilySearch({ maxResults: 2 });
+const tools = [searchTool, humanAssistance];
+
+const llmWithTools = new ChatAnthropic({
+  model: "claude-3-5-sonnet-latest",
+}).bindTools(tools);
+
+const chatbot = async (state: z.infer<typeof MessagesZodState>) => {
+  const message = await llmWithTools.invoke(state.messages);
+
+  // Because we will be interrupting during tool execution,
+  // we disable parallel tool calling to avoid repeating any
+  // tool invocations when we resume.
+  if (message.tool_calls && message.tool_calls.length > 1) {
+    throw new Error("Multiple tool calls not supported with interrupts");
+  }
+
+  return { messages: message };
+};
+
+const memory = new MemorySaver();
+
+const graph = new StateGraph(MessagesZodState)
+  .addNode("chatbot", chatbot)
+  .addNode("tools", new ToolNode(tools))
+  .addConditionalEdges("chatbot", toolsCondition, ["tools", END])
+  .addEdge("tools", "chatbot")
+  .addEdge(START, "chatbot")
+  .compile({ checkpointer: memory });
+```
+
+:::
+
 ## Next steps
 
-So far, the tutorial examples have relied on a simple state with one entry: a list of messages. You can go far with this simple state, but if you want to define complex behavior without relying on the message list, you can [add additional fields to the state](./5-customize-state.md).
+So far, the tutorial examples have relied on a simple state with one entry: a list of messages. You can go far with this simple state, but if you want to define complex behavior without relying on the message list, you can [add additional fields to the state](./5-customize-state.md).

docs/docs/tutorials/get-started/5-customize-state.md

@@ -10,6 +10,8 @@ In this tutorial, you will add additional fields to the state to define complex
 
 Update the chatbot to research the birthday of an entity by adding `name` and `birthday` keys to the state:
 
+:::python
+
 ```python
 from typing import Annotated
 
@@ -26,13 +28,34 @@ class State(TypedDict):
     birthday: str
 ```
 
+:::
+
+:::js
+
+```typescript
+import { MessagesZodState } from "@langchain/langgraph";
+import { z } from "zod";
+
+const State = z.object({
+  messages: MessagesZodState.shape.messages,
+  // highlight-next-line
+  name: z.string(),
+  // highlight-next-line
+  birthday: z.string(),
+});
+```
+
+:::
+
 Adding this information to the state makes it easily accessible by other graph nodes (like a downstream node that stores or processes the information), as well as the graph's persistence layer.
 
 ## 2. Update the state inside the tool
 
+:::python
+
 Now, populate the state keys inside of the `human_assistance` tool. This allows a human to review the information before it is stored in the state. Use [`Command`](../../concepts/low_level.md#using-inside-tools) to issue a state update from inside the tool.
 
-``` python
+```python
 from langchain_core.messages import ToolMessage
 from langchain_core.tools import InjectedToolCallId, tool
 
@@ -76,10 +99,78 @@ def human_assistance(
     return Command(update=state_update)
 ```
 
+:::
+
+:::js
+
+Now, populate the state keys inside of the `humanAssistance` tool. This allows a human to review the information before it is stored in the state. Use [`Command`](../../concepts/low_level.md#using-inside-tools) to issue a state update from inside the tool.
+
+```typescript
+import { tool } from "@langchain/core/tools";
+import { ToolMessage } from "@langchain/core/messages";
+import { Command, interrupt } from "@langchain/langgraph";
+
+const humanAssistance = tool(
+  async (input, config) => {
+    // Note that because we are generating a ToolMessage for a state update,
+    // we generally require the ID of the corresponding tool call.
+    // This is available in the tool's config.
+    const toolCallId = config?.toolCall?.id as string | undefined;
+    if (!toolCallId) throw new Error("Tool call ID is required");
+
+    const humanResponse = await interrupt({
+      question: "Is this correct?",
+      name: input.name,
+      birthday: input.birthday,
+    });
+
+    // We explicitly update the state with a ToolMessage inside the tool.
+    const stateUpdate = (() => {
+      // If the information is correct, update the state as-is.
+      if (humanResponse.correct?.toLowerCase().startsWith("y")) {
+        return {
+          name: input.name,
+          birthday: input.birthday,
+          messages: [
+            new ToolMessage({ content: "Correct", tool_call_id: toolCallId }),
+          ],
+        };
+      }
+
+      // Otherwise, receive information from the human reviewer.
+      return {
+        name: humanResponse.name || input.name,
+        birthday: humanResponse.birthday || input.birthday,
+        messages: [
+          new ToolMessage({
+            content: `Made a correction: ${JSON.stringify(humanResponse)}`,
+            tool_call_id: toolCallId,
+          }),
+        ],
+      };
+    })();
+
+    // We return a Command object in the tool to update our state.
+    return new Command({ update: stateUpdate });
+  },
+  {
+    name: "humanAssistance",
+    description: "Request assistance from a human.",
+    schema: z.object({
+      name: z.string().describe("The name of the entity"),
+      birthday: z.string().describe("The birthday/release date of the entity"),
+    }),
+  }
+);
+```
+
+:::
+
 The rest of the graph stays the same.
 
 ## 3. Prompt the chatbot
 
+:::python
 Prompt the chatbot to look up the "birthday" of the LangGraph library and direct the chatbot to reach out to the `human_assistance` tool once it has the required information. By setting `name` and `birthday` in the arguments for the tool, you force the chatbot to generate proposals for these fields.
 
 ```python
@@ -99,6 +190,51 @@ for event in events:
         event["messages"][-1].pretty_print()
 ```
 
+:::
+
+:::js
+Prompt the chatbot to look up the "birthday" of the LangGraph library and direct the chatbot to reach out to the `humanAssistance` tool once it has the required information. By setting `name` and `birthday` in the arguments for the tool, you force the chatbot to generate proposals for these fields.
+
+```typescript
+import { isAIMessage } from "@langchain/core/messages";
+
+const userInput =
+  "Can you look up when LangGraph was released? " +
+  "When you have the answer, use the humanAssistance tool for review.";
+
+const events = await graph.stream(
+  { messages: [{ role: "user", content: userInput }] },
+  { configurable: { thread_id: "1" }, streamMode: "values" }
+);
+
+for await (const event of events) {
+  if ("messages" in event) {
+    const lastMessage = event.messages.at(-1);
+
+    console.log(
+      "=".repeat(32),
+      `${lastMessage?.getType()} Message`,
+      "=".repeat(32)
+    );
+    console.log(lastMessage?.text);
+
+    if (
+      lastMessage &&
+      isAIMessage(lastMessage) &&
+      lastMessage.tool_calls?.length
+    ) {
+      console.log("Tool Calls:");
+      for (const call of lastMessage.tool_calls) {
+        console.log(`  ${call.name} (${call.id})`);
+        console.log(`  Args: ${JSON.stringify(call.args)}`);
+      }
+    }
+  }
+}
+```
+
+:::
+
 ```
 ================================ Human Message =================================
 
@@ -126,12 +262,20 @@ Tool Calls:
     birthday: 2023-01-01
 ```
 
+:::python
 We've hit the `interrupt` in the `human_assistance` tool again.
+:::
+
+:::js
+We've hit the `interrupt` in the `humanAssistance` tool again.
+:::
 
 ## 4. Add human assistance
 
 The chatbot failed to identify the correct date, so supply it with information:
 
+:::python
+
 ```python
 human_command = Command(
     resume={
@@ -146,6 +290,53 @@ for event in events:
         event["messages"][-1].pretty_print()
 ```
 
+:::
+
+:::js
+
+```typescript
+import { Command } from "@langchain/langgraph";
+
+const humanCommand = new Command({
+  resume: {
+    name: "LangGraph",
+    birthday: "Jan 17, 2024",
+  },
+});
+
+const resumeEvents = await graph.stream(humanCommand, {
+  configurable: { thread_id: "1" },
+  streamMode: "values",
+});
+
+for await (const event of resumeEvents) {
+  if ("messages" in event) {
+    const lastMessage = event.messages.at(-1);
+
+    console.log(
+      "=".repeat(32),
+      `${lastMessage?.getType()} Message`,
+      "=".repeat(32)
+    );
+    console.log(lastMessage?.text);
+
+    if (
+      lastMessage &&
+      isAIMessage(lastMessage) &&
+      lastMessage.tool_calls?.length
+    ) {
+      console.log("Tool Calls:");
+      for (const call of lastMessage.tool_calls) {
+        console.log(`  ${call.name} (${call.id})`);
+        console.log(`  Args: ${JSON.stringify(call.args)}`);
+      }
+    }
+  }
+}
+```
+
+:::
+
 ```
 ================================== Ai Message ==================================
 
@@ -175,6 +366,8 @@ It's worth noting that LangGraph had been in development and use for some time b
 
 Note that these fields are now reflected in the state:
 
+:::python
+
 ```python
 snapshot = graph.get_state(config)
 
@@ -185,13 +378,34 @@ snapshot = graph.get_state(config)
 {'name': 'LangGraph', 'birthday': 'Jan 17, 2024'}
 ```
 
+:::
+
+:::js
+
+```typescript
+const snapshot = await graph.getState(config);
+
+const relevantState = Object.fromEntries(
+  Object.entries(snapshot.values).filter(([k]) =>
+    ["name", "birthday"].includes(k)
+  )
+);
+```
+
+```
+{ name: 'LangGraph', birthday: 'Jan 17, 2024' }
+```
+
+:::
+
 This makes them easily accessible to downstream nodes (e.g., a node that further processes or stores the information).
 
 ## 5. Manually update the state
 
+:::python
 LangGraph gives a high degree of control over the application state. For instance, at any point (including when interrupted), you can manually override a key using `graph.update_state`:
 
-``` python
+```python
 graph.update_state(config, {"name": "LangGraph (library)"})
 ```
 
@@ -201,11 +415,36 @@ graph.update_state(config, {"name": "LangGraph (library)"})
   'checkpoint_id': '1efd4ec5-cf69-6352-8006-9278f1730162'}}
 ```
 
+:::
+
+:::js
+LangGraph gives a high degree of control over the application state. For instance, at any point (including when interrupted), you can manually override a key using `graph.updateState`:
+
+```typescript
+await graph.updateState(
+  { configurable: { thread_id: "1" } },
+  { name: "LangGraph (library)" }
+);
+```
+
+```typescript
+{
+  configurable: {
+    thread_id: '1',
+    checkpoint_ns: '',
+    checkpoint_id: '1efd4ec5-cf69-6352-8006-9278f1730162'
+  }
+}
+```
+
+:::
+
 ## 6. View the new value
 
+:::python
 If you call `graph.get_state`, you can see the new value is reflected:
 
-``` python
+```python
 snapshot = graph.get_state(config)
 
 {k: v for k, v in snapshot.values.items() if k in ("name", "birthday")}
@@ -215,12 +454,35 @@ snapshot = graph.get_state(config)
 {'name': 'LangGraph (library)', 'birthday': 'Jan 17, 2024'}
 ```
 
+:::
+
+:::js
+If you call `graph.getState`, you can see the new value is reflected:
+
+```typescript
+const updatedSnapshot = await graph.getState(config);
+
+const updatedRelevantState = Object.fromEntries(
+  Object.entries(updatedSnapshot.values).filter(([k]) =>
+    ["name", "birthday"].includes(k)
+  )
+);
+```
+
+```typescript
+{ name: 'LangGraph (library)', birthday: 'Jan 17, 2024' }
+```
+
+:::
+
 Manual state updates will [generate a trace](https://smith.langchain.com/public/7ebb7827-378d-49fe-9f6c-5df0e90086c8/r) in LangSmith. If desired, they can also be used to [control human-in-the-loop workflows](../../how-tos/human_in_the_loop/add-human-in-the-loop.md). Use of the `interrupt` function is generally recommended instead, as it allows data to be transmitted in a human-in-the-loop interaction independently of state updates.
 
 **Congratulations!** You've added custom keys to the state to facilitate a more complex workflow, and learned how to generate state updates from inside tools.
 
 Check out the code snippet below to review the graph from this tutorial:
 
+:::python
+
 {!snippets/chat_model_tabs.md!}
 
 <!---
@@ -305,7 +567,111 @@ memory = MemorySaver()
 graph = graph_builder.compile(checkpointer=memory)
 ```
 
+:::
+
+:::js
+
+```typescript
+import {
+  Command,
+  interrupt,
+  MessagesZodState,
+  MemorySaver,
+  StateGraph,
+  END,
+  START,
+} from "@langchain/langgraph";
+import { ToolNode, toolsCondition } from "@langchain/langgraph/prebuilt";
+import { ChatAnthropic } from "@langchain/anthropic";
+import { TavilySearch } from "@langchain/tavily";
+import { ToolMessage } from "@langchain/core/messages";
+import { tool } from "@langchain/core/tools";
+import { z } from "zod";
+
+const State = z.object({
+  messages: MessagesZodState.shape.messages,
+  name: z.string(),
+  birthday: z.string(),
+});
+
+const humanAssistance = tool(
+  async (input, config) => {
+    // Note that because we are generating a ToolMessage for a state update, we
+    // generally require the ID of the corresponding tool call. This is available
+    // in the tool's config.
+    const toolCallId = config?.toolCall?.id as string | undefined;
+    if (!toolCallId) throw new Error("Tool call ID is required");
+
+    const humanResponse = await interrupt({
+      question: "Is this correct?",
+      name: input.name,
+      birthday: input.birthday,
+    });
+
+    // We explicitly update the state with a ToolMessage inside the tool.
+    const stateUpdate = (() => {
+      // If the information is correct, update the state as-is.
+      if (humanResponse.correct?.toLowerCase().startsWith("y")) {
+        return {
+          name: input.name,
+          birthday: input.birthday,
+          messages: [
+            new ToolMessage({ content: "Correct", tool_call_id: toolCallId }),
+          ],
+        };
+      }
+
+      // Otherwise, receive information from the human reviewer.
+      return {
+        name: humanResponse.name || input.name,
+        birthday: humanResponse.birthday || input.birthday,
+        messages: [
+          new ToolMessage({
+            content: `Made a correction: ${JSON.stringify(humanResponse)}`,
+            tool_call_id: toolCallId,
+          }),
+        ],
+      };
+    })();
+
+    // We return a Command object in the tool to update our state.
+    return new Command({ update: stateUpdate });
+  },
+  {
+    name: "humanAssistance",
+    description: "Request assistance from a human.",
+    schema: z.object({
+      name: z.string().describe("The name of the entity"),
+      birthday: z.string().describe("The birthday/release date of the entity"),
+    }),
+  }
+);
+
+const searchTool = new TavilySearch({ maxResults: 2 });
+
+const tools = [searchTool, humanAssistance];
+const llmWithTools = new ChatAnthropic({
+  model: "claude-3-5-sonnet-latest",
+}).bindTools(tools);
+
+const memory = new MemorySaver();
+
+const chatbot = async (state: z.infer<typeof State>) => {
+  const message = await llmWithTools.invoke(state.messages);
+  return { messages: message };
+};
+
+const graph = new StateGraph(State)
+  .addNode("chatbot", chatbot)
+  .addNode("tools", new ToolNode(tools))
+  .addConditionalEdges("chatbot", toolsCondition, ["tools", END])
+  .addEdge("tools", "chatbot")
+  .addEdge(START, "chatbot")
+  .compile({ checkpointer: memory });
+```
+
+:::
+
 ## Next steps
 
-There's one more concept to review before finishing the LangGraph basics tutorials: connecting `checkpointing` and `state updates` to [time travel](./6-time-travel.md). 
-
+There's one more concept to review before finishing the LangGraph basics tutorials: connecting `checkpointing` and `state updates` to [time travel](./6-time-travel.md).

docs/docs/tutorials/get-started/6-time-travel.md

@@ -4,7 +4,7 @@ In a typical chatbot workflow, the user interacts with the bot one or more times
 
 What if you want a user to be able to start from a previous response and explore a different outcome? Or what if you want users to be able to rewind your chatbot's work to fix mistakes or try a different strategy, something that is common in applications like autonomous software engineers?
 
-You can create these types of experiences using LangGraph's built-in **time travel** functionality. 
+You can create these types of experiences using LangGraph's built-in **time travel** functionality.
 
 !!! note
 
@@ -12,7 +12,15 @@ You can create these types of experiences using LangGraph's built-in **time trav
 
 ## 1. Rewind your graph
 
+:::python
 Rewind your graph by fetching a checkpoint using the graph's `get_state_history` method. You can then resume execution at this previous point in time.
+:::
+
+:::js
+Rewind your graph by fetching a checkpoint using the graph's `getStateHistory` method. You can then resume execution at this previous point in time.
+:::
+
+:::python
 
 {!snippets/chat_model_tabs.md!}
 
@@ -64,11 +72,49 @@ memory = MemorySaver()
 graph = graph_builder.compile(checkpointer=memory)
 ```
 
+:::
+
+:::js
+
+```typescript
+import {
+  StateGraph,
+  START,
+  END,
+  MessagesZodState,
+  MemorySaver,
+} from "@langchain/langgraph";
+import { ToolNode, toolsCondition } from "@langchain/langgraph/prebuilt";
+import { TavilySearch } from "@langchain/tavily";
+import { ChatOpenAI } from "@langchain/openai";
+import { z } from "zod";
+
+const State = z.object({ messages: MessagesZodState.shape.messages });
+
+const tools = [new TavilySearch({ maxResults: 2 })];
+const llmWithTools = new ChatOpenAI({ model: "gpt-4o-mini" }).bindTools(tools);
+const memory = new MemorySaver();
+
+const graph = new StateGraph(State)
+  .addNode("chatbot", async (state) => ({
+    messages: [await llmWithTools.invoke(state.messages)],
+  }))
+  .addNode("tools", new ToolNode(tools))
+  .addConditionalEdges("chatbot", toolsCondition, ["tools", END])
+  .addEdge("tools", "chatbot")
+  .addEdge(START, "chatbot")
+  .compile({ checkpointer: memory });
+```
+
+:::
+
 ## 2. Add steps
 
 Add steps to your graph. Every step will be checkpointed in its state history:
 
-``` python
+:::python
+
+```python
 config = {"configurable": {"thread_id": "1"}}
 events = graph.stream(
     {
@@ -159,7 +205,7 @@ Tool Calls:
 ================================= Tool Message =================================
 Name: tavily_search_results_json
 
-[{"url": "https://towardsdatascience.com/building-autonomous-multi-tool-agents-with-gemini-2-0-and-langgraph-ad3d7bd5e79d", "content": "Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph | by Youness Mansar | Jan, 2025 | Towards Data Science Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph A practical tutorial with full code examples for building and running multi-tool agents Towards Data Science LLMs are remarkable — they can memorize vast amounts of information, answer general knowledge questions, write code, generate stories, and even fix your grammar. In this tutorial, we are going to build a simple LLM agent that is equipped with four tools that it can use to answer a user’s question. This Agent will have the following specifications: Follow Published in Towards Data Science --------------------------------- Your home for data science and AI. Follow Follow Follow"}, {"url": "https://github.com/anmolaman20/Tools_and_Agents", "content": "GitHub - anmolaman20/Tools_and_Agents: This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository serves as a comprehensive guide for building AI-powered agents using Langchain and Langgraph. It provides hands-on examples, practical tutorials, and resources for developers and AI enthusiasts to master building intelligent systems and workflows. AI Agent Development: Gain insights into creating intelligent systems that think, reason, and adapt in real time. This repository is ideal for AI practitioners, developers exploring language models, or anyone interested in building intelligent systems. This repository provides resources for building AI agents using Langchain and Langgraph."}]
+[{"url": "https://towardsdatascience.com/building-autonomous-multi-tool-agents-with-gemini-2-0-and-langgraph-ad3d7bd5e79d", "content": "Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph | by Youness Mansar | Jan, 2025 | Towards Data Science Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph A practical tutorial with full code examples for building and running multi-tool agents Towards Data Science LLMs are remarkable — they can memorize vast amounts of information, answer general knowledge questions, write code, generate stories, and even fix your grammar. In this tutorial, we are going to build a simple LLM agent that is equipped with four tools that it can use to answer a user's question. This Agent will have the following specifications: Follow Published in Towards Data Science --------------------------------- Your home for data science and AI. Follow Follow Follow"}, {"url": "https://github.com/anmolaman20/Tools_and_Agents", "content": "GitHub - anmolaman20/Tools_and_Agents: This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository serves as a comprehensive guide for building AI-powered agents using Langchain and Langgraph. It provides hands-on examples, practical tutorials, and resources for developers and AI enthusiasts to master building intelligent systems and workflows. AI Agent Development: Gain insights into creating intelligent systems that think, reason, and adapt in real time. This repository is ideal for AI practitioners, developers exploring language models, or anyone interested in building intelligent systems. This repository provides resources for building AI agents using Langchain and Langgraph."}]
 ================================== Ai Message ==================================
 
 Great idea! Building an autonomous agent with LangGraph is definitely an exciting project. Based on the latest information I've found, here are some insights and tips for building autonomous agents with LangGraph:
@@ -177,11 +223,140 @@ Building an autonomous agent is an iterative process, so be prepared to refine a
 Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...
 ```
 
+:::
+
+:::js
+
+```typescript
+import { randomUUID } from "node:crypto";
+const threadId = randomUUID();
+
+let iter = 0;
+
+for (const userInput of [
+  "I'm learning LangGraph. Could you do some research on it for me?",
+  "Ya that's helpful. Maybe I'll build an autonomous agent with it!",
+]) {
+  iter += 1;
+
+  console.log(`\n--- Conversation Turn ${iter} ---\n`);
+  const events = await graph.stream(
+    { messages: [{ role: "user", content: userInput }] },
+    { configurable: { thread_id: threadId }, streamMode: "values" }
+  );
+
+  for await (const event of events) {
+    if ("messages" in event) {
+      const lastMessage = event.messages.at(-1);
+
+      console.log(
+        "=".repeat(32),
+        `${lastMessage?.getType()} Message`,
+        "=".repeat(32)
+      );
+      console.log(lastMessage?.text);
+    }
+  }
+}
+```
+
+```
+--- Conversation Turn 1 ---
+
+================================ human Message ================================
+I'm learning LangGraph.js. Could you do some research on it for me?
+================================ ai Message ================================
+I'll search for information about LangGraph.js for you.
+================================ tool Message ================================
+{
+  "query": "LangGraph.js framework TypeScript langchain what is it tutorial guide",
+  "follow_up_questions": null,
+  "answer": null,
+  "images": [],
+  "results": [
+    {
+      "url": "https://techcommunity.microsoft.com/blog/educatordeveloperblog/an-absolute-beginners-guide-to-langgraph-js/4212496",
+      "title": "An Absolute Beginner's Guide to LangGraph.js",
+      "content": "(...)",
+      "score": 0.79369855,
+      "raw_content": null
+    },
+    {
+      "url": "https://langchain-ai.github.io/langgraphjs/",
+      "title": "LangGraph.js",
+      "content": "(...)",
+      "score": 0.78154784,
+      "raw_content": null
+    }
+  ],
+  "response_time": 2.37
+}
+================================ ai Message ================================
+Let me provide you with an overview of LangGraph.js based on the search results:
+
+LangGraph.js is a JavaScript/TypeScript library that's part of the LangChain ecosystem, specifically designed for creating and managing complex LLM (Large Language Model) based workflows. Here are the key points about LangGraph.js:
+
+1. Purpose:
+- It's a low-level orchestration framework for building controllable agents
+- Particularly useful for creating agentic workflows where LLMs decide the course of action based on current state
+- Helps model workflows as graphs with nodes and edges
+
+(...)
+
+--- Conversation Turn 2 ---
+
+================================ human Message ================================
+Ya that's helpful. Maybe I'll build an autonomous agent with it!
+================================ ai Message ================================
+Let me search for specific information about building autonomous agents with LangGraph.js.
+================================ tool Message ================================
+{
+  "query": "how to build autonomous agents with LangGraph.js examples tutorial react agent",
+  "follow_up_questions": null,
+  "answer": null,
+  "images": [],
+  "results": [
+    {
+      "url": "https://ai.google.dev/gemini-api/docs/langgraph-example",
+      "title": "ReAct agent from scratch with Gemini 2.5 and LangGraph",
+      "content": "(...)",
+      "score": 0.7602419,
+      "raw_content": null
+    },
+    {
+      "url": "https://www.youtube.com/watch?v=ZfjaIshGkmk",
+      "title": "Build Autonomous AI Agents with ReAct and LangGraph Tools",
+      "content": "(...)",
+      "score": 0.7471924,
+      "raw_content": null
+    }
+  ],
+  "response_time": 1.98
+}
+================================ ai Message ================================
+Based on the search results, I can provide you with a practical overview of how to build an autonomous agent with LangGraph.js. Here's what you need to know:
+
+1. Basic Structure for Building an Agent:
+- LangGraph.js provides a ReAct (Reason + Act) pattern implementation
+- The basic components include:
+  - State management for conversation history
+  - Nodes for different actions
+  - Edges for decision-making flow
+  - Tools for specific functionalities
+
+(...)
+
+```
+
+:::
+
 ## 3. Replay the full state history
 
 Now that you have added steps to the chatbot, you can `replay` the full state history to see everything that occurred.
 
-``` python
+:::python
+
+```python
 to_replay = None
 for state in graph.get_state_history(config):
     print("Num Messages: ", len(state.values["messages"]), "Next: ", state.next)
@@ -214,10 +389,61 @@ Num Messages:  0 Next:  ('__start__',)
 --------------------------------------------------------------------------------
 ```
 
-Checkpoints are saved for every step of the graph. This __spans invocations__ so you can rewind across a full thread's history.
+:::
+
+:::js
+
+```typescript
+import type { StateSnapshot } from "@langchain/langgraph";
+
+let toReplay: StateSnapshot | undefined;
+for await (const state of graph.getStateHistory({
+  configurable: { thread_id: threadId },
+})) {
+  console.log(
+    `Num Messages: ${state.values.messages.length}, Next: ${JSON.stringify(
+      state.next
+    )}`
+  );
+  console.log("-".repeat(80));
+  if (state.values.messages.length === 6) {
+    // We are somewhat arbitrarily selecting a specific state based on the number of chat messages in the state.
+    toReplay = state;
+  }
+}
+```
+
+```
+Num Messages: 8, Next: []
+--------------------------------------------------------------------------------
+Num Messages: 7, Next: ["chatbot"]
+--------------------------------------------------------------------------------
+Num Messages: 6, Next: ["tools"]
+--------------------------------------------------------------------------------
+Num Messages: 5, Next: ["chatbot"]
+--------------------------------------------------------------------------------
+Num Messages: 4, Next: ["__start__"]
+--------------------------------------------------------------------------------
+Num Messages: 4, Next: []
+--------------------------------------------------------------------------------
+Num Messages: 3, Next: ["chatbot"]
+--------------------------------------------------------------------------------
+Num Messages: 2, Next: ["tools"]
+--------------------------------------------------------------------------------
+Num Messages: 1, Next: ["chatbot"]
+--------------------------------------------------------------------------------
+Num Messages: 0, Next: ["__start__"]
+--------------------------------------------------------------------------------
+```
+
+:::
+
+Checkpoints are saved for every step of the graph. This **spans invocations** so you can rewind across a full thread's history.
 
 ## Resume from a checkpoint
 
+:::python
+
 Resume from the `to_replay` state, which is after the `chatbot` node in the second graph invocation. Resuming from this point will call the **action** node next.
 
 ```python
@@ -230,12 +456,37 @@ print(to_replay.config)
 {'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1efd43e3-0c1f-6c4e-8006-891877d65740'}}
 ```
 
+:::
+
+:::js
+
+Resume from the `toReplay` state, which is after the `chatbot` node in one of the graph invocations. Resuming from this point will call the next scheduled node.
+
+```typescript
+console.log(toReplay.next);
+console.log(toReplay.config);
+```
+
+```
+["tools"]
+{
+  configurable: {
+    thread_id: "007708b8-ea9b-4ff7-a7ad-3843364dbf75",
+    checkpoint_ns: "",
+    checkpoint_id: "1efd43e3-0c1f-6c4e-8006-891877d65740"
+  }
+}
+```
+
+:::
+
 ## 4. Load a state from a moment-in-time
 
+:::python
+
 The checkpoint's `to_replay.config` contains a `checkpoint_id` timestamp. Providing this `checkpoint_id` value tells LangGraph's checkpointer to **load** the state from that moment in time.
 
-
-``` python
+```python
 # The `checkpoint_id` in the `to_replay.config` corresponds to a state we've persisted to our checkpointer.
 for event in graph.stream(None, to_replay.config, stream_mode="values"):
     if "messages" in event:
@@ -254,19 +505,16 @@ Tool Calls:
 ================================= Tool Message =================================
 Name: tavily_search_results_json
 
-[{"url": "https://towardsdatascience.com/building-autonomous-multi-tool-agents-with-gemini-2-0-and-langgraph-ad3d7bd5e79d", "content": "Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph | by Youness Mansar | Jan, 2025 | Towards Data Science Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph A practical tutorial with full code examples for building and running multi-tool agents Towards Data Science LLMs are remarkable — they can memorize vast amounts of information, answer general knowledge questions, write code, generate stories, and even fix your grammar. In this tutorial, we are going to build a simple LLM agent that is equipped with four tools that it can use to answer a user’s question. This Agent will have the following specifications: Follow Published in Towards Data Science --------------------------------- Your home for data science and AI. Follow Follow Follow"}, {"url": "https://github.com/anmolaman20/Tools_and_Agents", "content": "GitHub - anmolaman20/Tools_and_Agents: This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository serves as a comprehensive guide for building AI-powered agents using Langchain and Langgraph. It provides hands-on examples, practical tutorials, and resources for developers and AI enthusiasts to master building intelligent systems and workflows. AI Agent Development: Gain insights into creating intelligent systems that think, reason, and adapt in real time. This repository is ideal for AI practitioners, developers exploring language models, or anyone interested in building intelligent systems. This repository provides resources for building AI agents using Langchain and Langgraph."}]
+[{"url": "https://towardsdatascience.com/building-autonomous-multi-tool-agents-with-gemini-2-0-and-langgraph-ad3d7bd5e79d", "content": "Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph | by Youness Mansar | Jan, 2025 | Towards Data Science Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph A practical tutorial with full code examples for building and running multi-tool agents Towards Data Science LLMs are remarkable — they can memorize vast amounts of information, answer general knowledge questions, write code, generate stories, and even fix your grammar. In this tutorial, we are going to build a simple LLM agent that is equipped with four tools that it can use to answer a user's question. This Agent will have the following specifications: Follow Published in Towards Data Science --------------------------------- Your home for data science and AI. Follow Follow Follow"}, {"url": "https://github.com/anmolaman20/Tools_and_Agents", "content": "GitHub - anmolaman20/Tools_and_Agents: This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository serves as a comprehensive guide for building AI-powered agents using Langchain and Langgraph. It provides hands-on examples, practical tutorials, and resources for developers and AI enthusiasts to master building intelligent systems and workflows. AI Agent Development: Gain insights into creating intelligent systems that think, reason, and adapt in real time. This repository is ideal for AI practitioners, developers exploring language models, or anyone interested in building intelligent systems. This repository provides resources for building AI agents using Langchain and Langgraph."}]
 ================================== Ai Message ==================================
 
-Great idea! Building an autonomous agent with LangGraph is indeed an excellent way to apply and deepen your understanding of the technology. Based on the search results, I can provide you with some insights and resources to help you get started:
+Great idea! Building an autonomous agent with LangGraph is definitely an exciting project. Based on the latest information I've found, here are some insights and tips for building autonomous agents with LangGraph:
 
-1. Multi-Tool Agents:
-   LangGraph is well-suited for building autonomous agents that can use multiple tools. This allows your agent to have a variety of capabilities and choose the appropriate tool based on the task at hand.
+1. Multi-Tool Agents: LangGraph is particularly well-suited for creating autonomous agents that can use multiple tools. This allows your agent to have a diverse set of capabilities and choose the right tool for each task.
 
-2. Integration with Large Language Models (LLMs):
-   There's a tutorial that specifically mentions using Gemini 2.0 (Google's LLM) with LangGraph to build autonomous agents. This suggests that LangGraph can be integrated with various LLMs, giving you flexibility in choosing the language model that best fits your needs.
+2. Integration with Large Language Models (LLMs): You can combine LangGraph with powerful LLMs like Gemini 2.0 to create more intelligent and capable agents. The LLM can serve as the "brain" of your agent, making decisions and generating responses.
 
-3. Practical Tutorials:
-   There are tutorials available that provide full code examples for building and running multi-tool agents. These can be invaluable as you start your project, giving you a concrete starting point and demonstrating best practices.
+3. Workflow Management: LangGraph excels at managing complex, multi-step AI workflows. This is crucial for autonomous agents that need to break down tasks into smaller steps and execute them in the right order.
 ...
 
 Remember, building an autonomous agent is an iterative process. Start simple and gradually increase complexity as you become more comfortable with LangGraph and its capabilities.
@@ -275,7 +523,83 @@ Would you like more information on any specific aspect of building your autonomo
 Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...
 ```
 
-The graph resumed execution from the `action` node. You can tell this is the case since the first value printed above is the response from our search engine tool.
+The graph resumed execution from the `tools` node. You can tell this is the case since the first value printed above is the response from our search engine tool.
+:::
+
+:::js
+
+The checkpoint's `toReplay.config` contains a `checkpoint_id` timestamp. Providing this `checkpoint_id` value tells LangGraph's checkpointer to **load** the state from that moment in time.
+
+```typescript
+// The `checkpoint_id` in the `toReplay.config` corresponds to a state we've persisted to our checkpointer.
+for await (const event of await graph.stream(null, {
+  ...toReplay?.config,
+  streamMode: "values",
+})) {
+  if ("messages" in event) {
+    const lastMessage = event.messages.at(-1);
+
+    console.log(
+      "=".repeat(32),
+      `${lastMessage?.getType()} Message`,
+      "=".repeat(32)
+    );
+    console.log(lastMessage?.text);
+  }
+}
+```
+
+```
+================================ ai Message ================================
+Let me search for specific information about building autonomous agents with LangGraph.js.
+================================ tool Message ================================
+{
+  "query": "how to build autonomous agents with LangGraph.js examples tutorial",
+  "follow_up_questions": null,
+  "answer": null,
+  "images": [],
+  "results": [
+    {
+      "url": "https://www.mongodb.com/developer/languages/typescript/build-javascript-ai-agent-langgraphjs-mongodb/",
+      "title": "Build a JavaScript AI Agent With LangGraph.js and MongoDB",
+      "content": "(...)",
+      "score": 0.7672197,
+      "raw_content": null
+    },
+    {
+      "url": "https://medium.com/@lorevanoudenhove/how-to-build-ai-agents-with-langgraph-a-step-by-step-guide-5d84d9c7e832",
+      "title": "How to Build AI Agents with LangGraph: A Step-by-Step Guide",
+      "content": "(...)",
+      "score": 0.7407191,
+      "raw_content": null
+    }
+  ],
+  "response_time": 0.82
+}
+================================ ai Message ================================
+Based on the search results, I can share some practical information about building autonomous agents with LangGraph.js. Here are some concrete examples and approaches:
+
+1. Example HR Assistant Agent:
+- Can handle HR-related queries using employee information
+- Features include:
+  - Starting and continuing conversations
+  - Looking up information using vector search
+  - Persisting conversation state using checkpoints
+  - Managing threaded conversations
+
+2. Energy Savings Calculator Agent:
+- Functions as a lead generation tool for solar panel sales
+- Capabilities include:
+  - Calculating potential energy savings
+  - Handling multi-step conversations
+  - Processing user inputs for personalized estimates
+  - Managing conversation state
+
+(...)
+```
+
+The graph resumed execution from the `tools` node. You can tell this is the case since the first value printed above is the response from our search engine tool.
+:::
 
 **Congratulations!** You've now used time-travel checkpoint traversal in LangGraph. Being able to rewind and explore alternative paths opens up a world of possibilities for debugging, experimentation, and interactive applications.
 
@@ -285,4 +609,4 @@ Take your LangGraph journey further by exploring deployment and advanced feature
 
 - **[LangGraph Server quickstart](../../tutorials/langgraph-platform/local-server.md)**: Launch a LangGraph server locally and interact with it using the REST API and LangGraph Studio Web UI.
 - **[LangGraph Platform quickstart](../../cloud/quick_start.md)**: Deploy your LangGraph app using LangGraph Platform.
-- **[LangGraph Platform concepts](../../concepts/langgraph_platform.md)**: Understand the foundational concepts of the LangGraph Platform.
+- **[LangGraph Platform concepts](../../concepts/langgraph_platform.md)**: Understand the foundational concepts of the LangGraph Platform.

docs/docs/tutorials/rag/langgraph_adaptive_rag.ipynb

@@ -336,7 +336,8 @@
     "rag_chain = prompt | llm | StrOutputParser()\n",
     "\n",
     "# Run\n",
-    "generation = rag_chain.invoke({\"context\": docs, \"question\": question})\n",
+    "docs_txt = format_docs(docs)\n",
+    "generation = rag_chain.invoke({\"context\": docs_txt, \"question\": question})\n",
     "print(generation)"
    ]
   },
@@ -625,7 +626,8 @@
     "    documents = state[\"documents\"]\n",
     "\n",
     "    # RAG generation\n",
-    "    generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n",
+    "    docs_txt = format_docs(documents)\n",
+    "    generation = rag_chain.invoke({\"context\": docs_txt, \"question\": question})\n",
     "    return {\"documents\": documents, \"question\": question, \"generation\": generation}\n",
     "\n",
     "\n",

docs/mkdocs.yml

@@ -93,6 +93,14 @@ nav:
     - index.md
     - Quickstarts:
       - Agent: agents/agents.md
+      - LangGraph basics:
+          -  concepts/why-langgraph.md
+          -  Build a basic chatbot: tutorials/get-started/1-build-basic-chatbot.md
+          -  tutorials/get-started/2-add-tools.md
+          -  tutorials/get-started/3-add-memory.md
+          -  Add human-in-the-loop: tutorials/get-started/4-human-in-the-loop.md
+          -  tutorials/get-started/5-customize-state.md
+          -  tutorials/get-started/6-time-travel.md
       - Local server: tutorials/langgraph-platform/local-server.md
       - Deployment: cloud/quick_start.md
     - General concepts:
@@ -101,7 +109,7 @@ nav:
         - Workflows & agents: tutorials/workflows.md
       - Agent development: agents/overview.md
       - Workflow orchestration:
-        - Graphs: concepts/low_level.md
+        - Graph API: concepts/low_level.md
         - Subgraphs: concepts/subgraphs.md
         - Runtime: concepts/pregel.md
         - Functional API: concepts/functional_api.md
@@ -153,16 +161,17 @@ nav:
       - Stream outputs: how-tos/streaming.md
       - Use Server API: cloud/how-tos/streaming.md
     - Context:
-      - Use in agent: agents/context.md
+      - Add context: agents/context.md
     - Memory:
       - Add memory: how-tos/memory/add-memory.md
     - Human-in-the-loop:
       - how-tos/human_in_the_loop/add-human-in-the-loop.md
       - Use Server API: cloud/how-tos/add-human-in-the-loop.md
     - Time travel:
+      - how-tos/human_in_the_loop/time-travel.md
       - Use Server API: cloud/how-tos/human_in_the_loop_time_travel.md
     - Breakpoints:
-      - Set breakpoints: how-tos/human_in_the_loop/breakpoints.ipynb
+      - Set breakpoints: how-tos/human_in_the_loop/breakpoints.md
       - Use Server API: cloud/how-tos/human_in_the_loop_breakpoint.md
     - Tools:
       - Call tools: how-tos/tool-calling.md
@@ -174,6 +183,8 @@ nav:
     - MCP:
       - Use MCP: agents/mcp.md
       - Server API: concepts/server-mcp.md
+    - Evaluation:
+        - Basic implementation: agents/evals.md
     - Deployment:
       - Basic deployment: agents/deployment.md
       - Set up your application:
@@ -181,13 +192,12 @@ nav:
         - Use pyproject.toml: cloud/deployment/setup_pyproject.md
         - Use JavaScript: cloud/deployment/setup_javascript.md
         - Use custom Docker: cloud/deployment/custom_docker.md
+        - Rebuild graph at runtime: cloud/deployment/graph_rebuild.md
       - Deploy to production:
          - Cloud SaaS: cloud/deployment/cloud.md
          - Self-Hosted Data Plane: cloud/deployment/self_hosted_data_plane.md
          - Self-Hosted Control Plane: cloud/deployment/self_hosted_control_plane.md
          - Standalone Container: cloud/deployment/standalone_container.md
-    - Evaluation:
-      - Basic implementation: agents/evals.md
     - Platform capabilities:
       - LangGraph Studio:
         - Quickstart: cloud/how-tos/studio/quick_start.md
@@ -254,14 +264,6 @@ nav:
 
   - Examples:
     - agents/run_agents.md
-    - LangGraph basics:
-      -  concepts/why-langgraph.md
-      -  Build a basic chatbot: tutorials/get-started/1-build-basic-chatbot.md
-      -  tutorials/get-started/2-add-tools.md
-      -  tutorials/get-started/3-add-memory.md
-      -  Add human-in-the-loop: tutorials/get-started/4-human-in-the-loop.md
-      -  tutorials/get-started/5-customize-state.md
-      -  tutorials/get-started/6-time-travel.md
     - Template applications: concepts/template_applications.md  # TODO: make tutorial
     - Agentic RAG: tutorials/rag/langgraph_agentic_rag.ipynb
     - Agent Supervisor: tutorials/multi_agent/agent_supervisor.ipynb
@@ -273,7 +275,6 @@ nav:
         - tutorials/auth/getting_started.md
         - tutorials/auth/resource_auth.md
         - tutorials/auth/add_auth_server.md
-      - Rebuild graph at runtime: cloud/deployment/graph_rebuild.md
       - Use RemoteGraph: how-tos/use-remote-graph.md
       - Deploy CrewAI, AutoGen, and other frameworks: how-tos/autogen-langgraph-platform.ipynb
         # combine with how-tos/autogen-integration.ipynb

docs/uv.lock

@@ -2590,7 +2590,7 @@ wheels = [
 
 [[package]]
 name = "langgraph"
-version = "0.5.0rc1"
+version = "0.5.0"
 source = { editable = "../libs/langgraph" }
 dependencies = [
     { name = "langchain-core" },
@@ -2894,7 +2894,7 @@ test = [
 
 [[package]]
 name = "langgraph-prebuilt"
-version = "0.5.0rc0"
+version = "0.5.1"
 source = { editable = "../libs/prebuilt" }
 dependencies = [
     { name = "langchain-core" },
@@ -2925,7 +2925,7 @@ dev = [
 
 [[package]]
 name = "langgraph-sdk"
-version = "0.1.70"
+version = "0.1.72"
 source = { editable = "../libs/sdk-py" }
 dependencies = [
     { name = "httpx" },

examples/human_in_the_loop/breakpoints.ipynb

@@ -1,33 +1 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "ac22b8de",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/human_in_the_loop/breakpoints.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+ 

libs/langgraph/uv.lock

@@ -1423,7 +1423,7 @@ inmem = [
 
 [[package]]
 name = "langgraph-prebuilt"
-version = "0.5.0"
+version = "0.5.1"
 source = { editable = "../prebuilt" }
 dependencies = [
     { name = "langchain-core" },

libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py

@@ -448,7 +448,7 @@ def create_react_agent(
 
     if (
         _should_bind_tools(model, tool_classes, num_builtin=len(llm_builtin_tools))
-        and len(tool_classes) > 0
+        and len(tool_classes + llm_builtin_tools) > 0
     ):
         model = cast(BaseChatModel, model).bind_tools(tool_classes + llm_builtin_tools)  # type: ignore[operator]
 

libs/prebuilt/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-prebuilt"
-version = "0.5.0"
+version = "0.5.1"
 description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
 authors = []
 requires-python = ">=3.9"

libs/prebuilt/uv.lock

@@ -464,7 +464,7 @@ dev = [
 
 [[package]]
 name = "langgraph-prebuilt"
-version = "0.5.0"
+version = "0.5.1"
 source = { editable = "." }
 dependencies = [
     { name = "langchain-core" },