format

docs/_scripts/generate_api_reference_links.py

@@ -22,12 +22,6 @@ MANUAL_API_REFERENCES_LANGGRAPH = [
         "create_react_agent",
         "prebuilt",
     ),
-    (
-        [],
-        "langgraph.prebuilt.chat_agent_executor",
-        "AgentState",
-        "prebuilt",
-    ),
     (["langgraph.prebuilt"], "langgraph.prebuilt.tool_node", "ToolNode", "prebuilt"),
     (
         ["langgraph.prebuilt"],
@@ -69,18 +63,6 @@ MANUAL_API_REFERENCES_LANGGRAPH = [
     ([], "langgraph.checkpoint.sqlite", "SqliteSaver", "checkpoints"),
     ([], "langgraph.checkpoint.postgres.aio", "AsyncPostgresSaver", "checkpoints"),
     ([], "langgraph.checkpoint.postgres", "PostgresSaver", "checkpoints"),
-    # other prebuilts
-    (["langgraph_supervisor"], "langgraph_supervisor.supervisor", "create_supervisor", "supervisor"),
-    (["langgraph_supervisor"], "langgraph_supervisor.handoff", "create_handoff_tool", "supervisor"),
-    ([], "langgraph_supervisor.handoff", "create_forward_message_tool", "supervisor"),
-    (["langgraph_swarm"], "langgraph_swarm.swarm", "create_swarm", "swarm"),
-    (["langgraph_swarm"], "langgraph_swarm.swarm", "add_active_agent_router", "swarm"),
-    (["langgraph_swarm"], "langgraph_swarm.swarm", "SwarmState", "swarm"),
-    (["langgraph_swarm"], "langgraph_swarm.handoff", "create_handoff_tool", "swarm"),
-    ([], "langchain_mcp_adapters.client", "MultiServerMCPClient", "mcp"),
-    ([], "langchain_mcp_adapters.tools", "load_mcp_tools", "mcp"),
-    ([], "langchain_mcp_adapters.prompts", "load_mcp_prompt", "mcp"),
-    ([], "langchain_mcp_adapters.resources", "load_mcp_resources", "mcp"),
 ]
 
 WELL_KNOWN_LANGGRAPH_OBJECTS = {
@@ -162,9 +144,7 @@ def get_imports(code: str, path: str) -> List[ImportInformation]:
     for found_import in found_imports:
         module = found_import["source"]
 
-        if module.startswith("langchain_mcp_adapters"):
-            package_ecosystem = "langgraph"
-        elif module.startswith("langchain"):
+        if module.startswith("langchain"):
             # Handles things like `langchain` or `langchain_anthropic`
             package_ecosystem = "langchain"
         elif module.startswith("langgraph"):

docs/_scripts/notebook_convert.py

@@ -1,6 +1,7 @@
 import ast
 import os
 import re
+from pathlib import Path
 from typing import Literal
 
 import nbformat
@@ -25,7 +26,7 @@ def _uses_input(source: str) -> bool:
 
 
 def _rewrite_cell_magic(code: str) -> str:
-    """Process a code block that uses cell magic.
+    """Process a code block that uses cell magic.:w
 
     - Lines starting with "%%capture" are ignored.
     - Lines starting with "%pip" are rewritten by removing the leading "%" character.
@@ -51,14 +52,10 @@ def _rewrite_cell_magic(code: str) -> str:
         if stripped.startswith("%%capture"):
             continue
         # Rewrite %pip lines by dropping the '%'
-        elif stripped.startswith("%") or stripped.startswith("!"):
-            # Drop the leading '%' character and then drop all leading whitespace
-            stripped = stripped.lstrip("%! \t")
-            # Check if the line starts with "pip"
-            if stripped.startswith("pip"):
-                rewritten_lines.append(stripped)
-            else:
-                raise NotImplementedError(f"Unhandled line: {line}")
+        elif stripped.startswith("%pip"):
+            # Drop the leading '%' character
+            rewritten_lines.append(stripped[1:])
+        # Anything else is not supported
         else:
             raise NotImplementedError(f"Unhandled line: {line}")
 
@@ -250,10 +247,13 @@ class EscapePreprocessor(Preprocessor):
             )
             cell.metadata["exec"] = is_exec
 
-            # For markdown exec migration we'll re-write cell magic as bash commands
-            if source.startswith("%%"):
-                cell.source = _rewrite_cell_magic(source)
-                cell.metadata["language"] = "shell"
+            if self.markdown_exec_migration:
+                # For markdown exec migration we'll re-write cell magic as bash commands
+                if source.startswith("%%"):
+                    cell.source = _rewrite_cell_magic(source)
+                    cell.metadata["language"] = "shell"
+
+                cell.metadata["has_output"] = _has_output(source)
 
             # Remove noqa comments
             cell.source = re.sub(r"#\s*noqa.*$", "", cell.source, flags=re.MULTILINE)
@@ -352,7 +352,7 @@ exporter = MarkdownExporter(
 
 
 def convert_notebook(
-    notebook_path: str,
+    notebook_path: Path,
     mode: Literal["markdown", "exec"] = "markdown",
 ) -> str:
     with open(notebook_path) as f:

docs/_scripts/notebook_convert_templates/mdoutput/index.md.j2

@@ -1,18 +1,5 @@
 {% extends 'markdown/index.md.j2' %}
 
-{% block input %}{# cell.metadata.language is an addition of our docs pipeline. #}
-```{%- if 'language' in cell.metadata -%}
-{{ cell.metadata.language }}
-{%- elif 'magics_language' in cell.metadata -%}
-{{ cell.metadata.magics_language }}
-{%- elif 'name' in nb.metadata.get('language_info', {}) -%}
-{{ nb.metadata.language_info.name }}
-{%- endif %}
-{{ cell.source }}
-```
-{% endblock input %}
-
-
 {%- block traceback_line -%}
 ```output
 {{ line.rstrip() | strip_ansi }}
@@ -21,13 +8,13 @@
 
 {%- block stream -%}
 ```output
-{{ output.text.rstrip() | strip_ansi }}
+{{ output.text.rstrip() }}
 ```
 {%- endblock stream -%}
 
 {%- block data_text scoped -%}
 ```output
-{{ output.data['text/plain'].rstrip() | strip_ansi }}
+{{ output.data['text/plain'].rstrip() }}
 ```
 {%- endblock data_text -%}
 

docs/_scripts/notebook_hooks.py

@@ -32,8 +32,7 @@ REDIRECT_MAP = {
     "cloud/concepts/cloud.md": "concepts/langgraph_cloud.md",
     "cloud/faq/studio.md": "concepts/langgraph_studio.md#studio-faqs",
     # misc
-    "prebuilt.md": "agents/prebuilt.md",
-    "reference/prebuilt.md": "reference/agents.md"
+    "prebuilt.md": "agents/prebuilt.md"
 }
 
 

docs/docs/agents/agents.md

@@ -1,12 +1,3 @@
----
-search:
-  boost: 2
-tags:
-  - agent
-hide:
-  - tags
----
-
 # Agents
 
 ## What is an agent?
@@ -112,7 +103,7 @@ from langgraph.prebuilt import create_react_agent
 
 # highlight-next-line
 def prompt(state: AgentState, config: RunnableConfig) -> list[AnyMessage]:  # (1)!
-    user_name = config["configurable"].get("user_name")
+    user_name = config.get("configurable", {}).get("user_name")
     system_msg = f"You are a helpful assistant. Address the user as {user_name}."
     return [{"role": "system", "content": system_msg}] + state["messages"]
 

docs/docs/agents/context.md

@@ -1,12 +1,3 @@
----
-search:
-  boost: 2
-tags:
-  - agent
-hide:
-  - tags
----
-
 # Context
 
 Agents often require more than a list of messages to function effectively. They need **context**.
@@ -107,7 +98,7 @@ Common use cases:
         config: RunnableConfig,
     ) -> list[AnyMessage]:
         # highlight-next-line
-        user_name = config["configurable"].get("user_name")
+        user_name = config.get("configurable", {}).get("user_name")
         system_msg = f"You are a helpful assistant. User's name is {user_name}"
         return [{"role": "system", "content": system_msg}] + state["messages"]
 
@@ -162,7 +153,7 @@ Common use cases:
     })
     ```
 
-## Accessing Context in Tools
+## Tools
 
 Tools can access context through special parameter **annotations**.
 
@@ -183,7 +174,7 @@ Tools can access context through special parameter **annotations**.
     ) -> str:
         """Look up user info."""
         # highlight-next-line
-        user_id = config["configurable"].get("user_id")
+        user_id = config.get("configurable", {}).get("user_id")
         return "User is John Smith" if user_id == "user_123" else "Unknown user"
 
     agent = create_react_agent(
@@ -231,6 +222,66 @@ Tools can access context through special parameter **annotations**.
     })
     ```
 
-### 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](./memory.md#read-short-term) guide for more information.
+## Update context from tools
+
+Tools can modify the agent's state during execution. This is useful for persisting intermediate results or making information accessible to subsequent tools or prompts.
+
+```python
+from typing import Annotated
+from langchain_core.tools import InjectedToolCallId
+from langchain_core.messages import ToolMessage
+from langgraph.prebuilt import InjectedState
+from langgraph.types import Command
+
+class CustomState(AgentState):
+    # highlight-next-line
+    user_name: str
+
+def get_user_info(
+    # highlight-next-line
+    tool_call_id: Annotated[str, InjectedToolCallId],
+    # highlight-next-line
+    config: RunnableConfig
+) -> Command:
+    """Look up user info."""
+    # highlight-next-line
+    user_id = config.get("configurable", {}).get("user_id")
+    name = "John Smith" if user_id == "user_123" else "Unknown user"
+    return Command(update={
+        # highlight-next-line
+        "user_name": name,
+        # update the message history
+        # highlight-next-line
+        "messages": [
+            ToolMessage(
+                "Successfully looked up user information",
+                # highlight-next-line
+                tool_call_id=tool_call_id
+            )
+        ]
+    })
+
+def greet(
+    # highlight-next-line
+    state: Annotated[CustomState, InjectedState]
+) -> str:
+    """Use this to greet the user once you found their info."""
+    user_name = state["user_name"]
+    return f"Hello {user_name}!"
+
+agent = create_react_agent(
+    model="anthropic:claude-3-7-sonnet-latest",
+    tools=[get_user_info, greet],
+    # highlight-next-line
+    state_schema=CustomState
+)
+
+agent.invoke(
+    {"messages": [{"role": "user", "content": "greet the user"}]},
+    # highlight-next-line
+    config={"configurable": {"user_id": "user_123"}}
+)
+```
+
+For more details, see [how to update state from tools](../how-tos/update-state-from-tools.ipynb).

docs/docs/agents/deployment.md

@@ -1,12 +1,3 @@
----
-search:
-  boost: 2
-tags:
-  - agent
-hide:
-  - tags
----
-
 # Deployment
 
 To deploy your LangGraph agent, create and configure a LangGraph app. This setup supports both local development and production deployments.

docs/docs/agents/evals.md

@@ -1,12 +1,3 @@
----
-search:
-  boost: 2
-tags:
-  - agent
-hide:
-  - tags
----
-
 # Evals
 
 To evaluate your agent's performance you can use `LangSmith` [evaluations](https://docs.smith.langchain.com/evaluation). You would need to first define an evaluator function to judge the results from an agent, such as final outputs or trajectory. Depending on your evaluation technique, this may or may not involve a reference output:

docs/docs/agents/human-in-the-loop.md

@@ -1,17 +1,6 @@
----
-search:
-  boost: 2
-tags:
-  - human-in-the-loop
-  - hil
-  - agent
-hide:
-  - tags
----
-
 # Human-in-the-loop
 
-To review, edit and approve tool calls in an agent you can use LangGraph's built-in [Human-In-the-Loop (HIL)](../concepts/human_in_the_loop.md) features, specifically the [`interrupt()`][langgraph.types.interrupt] primitive.
+To review, edit and approve tool calls in an agent you can use LangGraph's built-in [human-in-the-loop](../concepts/human_in_the_loop.md) features, specifically the [`interrupt()`][langgraph.types.interrupt] primitive.
 
 LangGraph allows you to pause execution **indefinitely** — for minutes, hours, or even days—until human input is received.
 

docs/docs/agents/mcp.md

@@ -1,12 +1,3 @@
----
-search:
-  boost: 2
-tags:
-  - agent
-hide:
-  - tags
----
-
 # MCP Integration
 
 [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) is an open protocol that standardizes how applications provide tools and context to language models. LangGraph agents can use tools defined on MCP servers through the `langchain-mcp-adapters` library.

docs/docs/agents/memory.md

@@ -1,12 +1,3 @@
----
-search:
-  boost: 2
-tags:
-  - agent
-hide:
-  - tags
----
-
 # Memory
 
 LangGraph supports two types of memory essential for building conversational agents:
@@ -149,104 +140,6 @@ agent = create_react_agent(
 
 To learn more about using `pre_model_hook` for managing message history, see this [how-to guide](../how-tos/create-react-agent-manage-message-history.ipynb)
 
-### Read in tools { #read-short-term }
-
-LangGraph allows agent to access its short-term memory (state) inside the tools.
-
-```python
-from typing import Annotated
-from langgraph.prebuilt import InjectedState, create_react_agent
-
-class CustomState(AgentState):
-    # highlight-next-line
-    user_id: str
-
-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"
-
-agent = create_react_agent(
-    model="anthropic:claude-3-7-sonnet-latest",
-    tools=[get_user_info],
-    # highlight-next-line
-    state_schema=CustomState,
-)
-
-agent.invoke({
-    "messages": "look up user information",
-    # highlight-next-line
-    "user_id": "user_123"
-})
-```
-
-See the [Context](./context.md#__tabbed_2_2) guide for more information.
-
-### Write from tools { #write-short-term }
-
-To modify the agent's short-term memory (state) during execution, you can return state updates directly from the tools. This is useful for persisting intermediate results or making information accessible to subsequent tools or prompts.
-
-```python
-from typing import Annotated
-from langchain_core.tools import InjectedToolCallId
-from langchain_core.runnables import RunnableConfig
-from langchain_core.messages import ToolMessage
-from langgraph.prebuilt import InjectedState, create_react_agent
-from langgraph.prebuilt.chat_agent_executor import AgentState
-from langgraph.types import Command
-
-class CustomState(AgentState):
-    # highlight-next-line
-    user_name: str
-
-def update_user_info(
-    tool_call_id: Annotated[str, InjectedToolCallId],
-    config: RunnableConfig
-) -> Command:
-    """Look up and update user info."""
-    user_id = config["configurable"].get("user_id")
-    name = "John Smith" if user_id == "user_123" else "Unknown user"
-    # highlight-next-line
-    return Command(update={
-        # highlight-next-line
-        "user_name": name,
-        # update the message history
-        "messages": [
-            ToolMessage(
-                "Successfully looked up user information",
-                tool_call_id=tool_call_id
-            )
-        ]
-    })
-
-def greet(
-    # highlight-next-line
-    state: Annotated[CustomState, InjectedState]
-) -> str:
-    """Use this to greet the user once you found their info."""
-    user_name = state["user_name"]
-    return f"Hello {user_name}!"
-
-agent = create_react_agent(
-    model="anthropic:claude-3-7-sonnet-latest",
-    tools=[get_user_info, greet],
-    # highlight-next-line
-    state_schema=CustomState
-)
-
-agent.invoke(
-    {"messages": [{"role": "user", "content": "greet the user"}]},
-    # highlight-next-line
-    config={"configurable": {"user_id": "user_123"}}
-)
-```
-
-For more details, see [how to update state from tools](../how-tos/update-state-from-tools.ipynb).
-
 ## Long-term memory
 
 Use long-term memory to store user-specific or application-specific data across conversations. This is useful for applications like chatbots, where you want to remember user preferences or other information.
@@ -256,10 +149,9 @@ To use long-term memory, you need to:
 1. [Configure a store](../how-tos/cross-thread-persistence.ipynb) to persist data across invocations.
 2. Use the [`get_store`][langgraph.config.get_store] function to access the store from within tools or prompts.
 
-### Read { #read-long-term }
+### Reading
 
 ```python title="A tool the agent can use to look up user information"
-from langchain_core.runnables import RunnableConfig
 from langgraph.config import get_store
 from langgraph.prebuilt import create_react_agent
 from langgraph.store.memory import InMemoryStore
@@ -282,7 +174,7 @@ def get_user_info(config: RunnableConfig) -> str:
     # Same as that provided to `create_react_agent`
     # highlight-next-line
     store = get_store() # (6)!
-    user_id = config["configurable"].get("user_id")
+    user_id = config.get("configurable", {}).get("user_id")
     # highlight-next-line
     user_info = store.get(("users",), user_id) # (7)!
     return str(user_info.value) if user_info else "Unknown user"
@@ -311,7 +203,7 @@ agent.invoke(
 7. The `get` method is used to retrieve data from the store. The first argument is the namespace, and the second argument is the key. This will return a `StoreValue` object, which contains the value and metadata about the value.
 8. The `store` is passed to the agent. This enables the agent to access the store when running tools. You can also use the `get_store` function to access the store from anywhere in your code.
 
-### Write { #write-long-term }
+### Writing
 
 ```python title="Example of a tool that updates user information"
 from typing_extensions import TypedDict
@@ -330,7 +222,7 @@ def save_user_info(user_info: UserInfo, config: RunnableConfig) -> str: # (3)!
     # Same as that provided to `create_react_agent`
     # highlight-next-line
     store = get_store() # (4)!
-    user_id = config["configurable"].get("user_id")
+    user_id = config.get("configurable", {}).get("user_id")
     # highlight-next-line
     store.put(("users",), user_id, user_info) # (5)!
     return "Successfully saved user info."

docs/docs/agents/models.md

@@ -1,14 +1,3 @@
----
-search:
-  boost: 2
-tags:
-  - anthropic
-  - openai
-  - agent
-hide:
-  - tags
----
-
 # Models
 
 This page describes how to configure the chat model used by an agent.
@@ -74,72 +63,7 @@ agent = create_react_agent(
 
     The example above uses `ChatAnthropic`, which is already supported by `init_chat_model`. This pattern is shown to illustrate how to manually instantiate a model not available through init_chat_model.
 
-## Disable streaming
-
-To disable streaming of the individual LLM tokens, set `disable_streaming=True` when initializing the model:
-
-=== "`init_chat_model`"
-
-    ```python
-    from langchain.chat_models import init_chat_model
-
-    model = init_chat_model(
-        "anthropic:claude-3-7-sonnet-latest",
-        # highlight-next-line
-        disable_streaming=True
-    )
-    ```
-
-=== "`ChatModel`"
-
-    ```python
-    from langchain_anthropic import ChatAnthropic
-
-    model = ChatAnthropic(
-        model="claude-3-7-sonnet-latest",
-        # highlight-next-line
-        disable_streaming=True
-    )
-    ```
-
-Refer to the [API reference](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html#langchain_core.language_models.chat_models.BaseChatModel.disable_streaming) for more information on `disable_streaming`
-
-## Adding model fallbacks
-
-You can add a fallback to a different model or a different LLM provider using `model.with_fallbacks([...])`:
-
-=== "`init_chat_model`"
-
-    ```python
-    from langchain.chat_models import init_chat_model
-
-    model_with_fallbacks = (
-        init_chat_model("anthropic:claude-3-5-haiku-latest")
-        # highlight-next-line
-        .with_fallbacks([
-            init_chat_model("openai:gpt-4.1-mini"),
-        ])
-    )
-    ```
-
-=== "`ChatModel`"
-
-    ```python
-    from langchain_anthropic import ChatAnthropic
-    from langchain_openai import ChatOpenAI
-
-    model_with_fallbacks = (
-        ChatAnthropic(model="claude-3-5-haiku-latest")
-        # highlight-next-line
-        .with_fallbacks([
-            ChatOpenAI(model="gpt-4.1-mini"),
-        ])
-    )
-    ```
-
-See this [guide](https://python.langchain.com/docs/how_to/fallbacks/#fallback-to-better-model) for more information on model fallbacks.
-
 ## Additional resources
 
 - [Model integration directory](https://python.langchain.com/docs/integrations/chat/)
-- [Universal initialization with `init_chat_model`](https://python.langchain.com/docs/how_to/chat_models_universal_init/)
+- [Universal initialization with `init_chat_model`](https://python.langchain.com/docs/how_to/chat_models_universal_init/)

docs/docs/agents/multi-agent.md

@@ -1,12 +1,3 @@
----
-search:
-  boost: 2
-tags:
-  - agent
-hide:
-  - tags
----
-
 # Multi-agent
 
 A single agent might struggle if it needs to specialize in multiple domains or manage many tools. To tackle this, you can break your agent into smaller, independent agents and composing them into a [multi-agent system](../concepts/multi_agent.md).

docs/docs/agents/overview.md

@@ -1,11 +1,5 @@
 ---
 title: Overview
-search:
-  boost: 2
-tags:
-  - agent
-hide:
-  - tags
 ---
 
 # Agent development with LangGraph

docs/docs/agents/prebuilt.md

@@ -1,10 +1,3 @@
----
-tags:
-  - agent
-hide:
-  - tags
----
-
 # Community Agents
 
 To share your project, simply open a Pull Request adding an entry for your package in our [packages.yml](https://github.com/langchain-ai/langgraph/blob/main/docs/_scripts/third_party_page/packages.yml) file.

docs/docs/agents/run_agents.md

@@ -1,12 +1,3 @@
----
-search:
-  boost: 2
-tags:
-  - agent
-hide:
-  - tags
----
-
 # Running agents
 
 

docs/docs/agents/streaming.md

@@ -1,12 +1,3 @@
----
-search:
-  boost: 2
-tags:
-  - agent
-hide:
-  - tags
----
-
 # Streaming
 
 Streaming is key to building responsive applications. There are a few types of data you’ll want to stream:
@@ -212,12 +203,6 @@ You can specify multiple streaming modes by passing stream mode as a list: `stre
         print("\n")
     ```
 
-## Disable streaming
-
-In some applications you might need to disable streaming of individual tokens for a given model. This is useful in [multi-agent](./multi-agent.md) systems to control which agents stream their output.
-
-See the [Models](./models.md#disable-streaming) guide to learn how to disable streaming.
-
 ## Additional resources
 
 * [Streaming in LangGraph](https://langchain-ai.github.io/langgraph/how-tos/streaming)

docs/docs/agents/tools.md

@@ -1,12 +1,3 @@
----
-search:
-  boost: 2
-tags:
-  - agent
-hide:
-  - tags
----
-
 # Tools
 
 [Tools](https://python.langchain.com/docs/concepts/tools/) are a way to encapsulate a function and its input schema in a way that can be passed to a chat model that supports tool calling. This allows the model to request the execution of this function with specific inputs.
@@ -271,13 +262,6 @@ By default, the agent will catch all exceptions raised during tool calls and wil
 
 See [API reference][langgraph.prebuilt.tool_node.ToolNode] for more information on different tool error handling options.
 
-## Working with memory
-
-LangGraph allows access to short-term and long-term memory from tools. See [Memory](./memory.md) guide for more information on:
-
-* how to [read](./memory.md#read-short-term) from and [write](./memory.md#write-short-term) to **short-term** memory
-* how to [read](./memory.md#read-long-term) from and [write](./memory.md#write-long-term) to **long-term** memory
-
 ## Prebuilt tools
 
 LangChain supports a wide range of prebuilt tool integrations for interacting with APIs, databases, file systems, web data, and more. These tools extend the functionality of agents and enable rapid development.

docs/docs/agents/ui.md

@@ -1,12 +1,3 @@
----
-search:
-  boost: 2
-tags:
-  - agent
-hide:
-  - tags
----
-
 # UI
 
 You can use a prebuilt chat UI for interacting with any LangGraph agent through the [Agent Chat UI](https://github.com/langchain-ai/agent-chat-ui). Using the [deployed version](https://agentchat.vercel.app) is the quickest way to get started, and allows you to interact with both local and deployed graphs.

docs/docs/cloud/deployment/cloud.md

@@ -107,13 +107,13 @@ After installing and authorizing LangChain's `hosted-langserve` GitHub app, repo
 All traffic from `LangGraph Platform` deployments created after January 6th 2025 will come through a NAT gateway.
 This NAT gateway will have several static ip addresses depending on the region you are deploying in. Refer to the table below for the list of IP addresses to whitelist:
 
-| US             | EU              |
-|----------------|-----------------|
-| 35.197.29.146  | 34.90.213.236   |
-| 34.145.102.123 | 34.13.244.114   |
-| 34.169.45.153  | 34.32.180.189   |
-| 34.82.222.17   | 34.34.69.108    |
-| 35.227.171.135 | 34.32.145.240   | 
-| 34.169.88.30   | 34.90.157.44    |
-| 34.19.93.202   | 34.141.242.180  |
-| 34.19.34.50    | 34.32.141.108   |
+| US             | EU             |
+|----------------|----------------|
+| 35.197.29.146  | 34.13.192.67   |
+| 34.145.102.123 | 34.147.105.64  |
+| 34.169.45.153  | 34.90.22.166   |
+| 34.82.222.17   | 34.147.36.213  |
+| 35.227.171.135 | 34.32.137.113  | 
+| 34.169.88.30   | 34.91.238.184  |
+| 34.19.93.202   | 35.204.101.241 |
+| 34.19.34.50    | 35.204.48.32   |

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

@@ -17,70 +17,4 @@ Here's how to customize the included and excluded headers:
 }
 ```
 
-
 The `include` and `exclude` lists accept exact header names or patterns using `*` to match any number of characters. For your security, no other regex patterns are supported.
-
-## Using within your graph
-
-You can access the included headers in your graph using the `config` argument of any node.
-
-```python
-def my_node(state, config):
-  organization_id = config["configurable"].get("x-organization-id")
-  ...
-```
-
-Or by fetching from context (useful in tools and or within other nested functions).
-
-```python
-from langgraph.config import get_config
-
-def search_everything(query: str):
-  organization_id = get_config()["configurable"].get("x-organization-id")
-  ...
-```
-
-
-You can even use this to dynamically compile the graph.
-
-```python
-# my_graph.py.
-import contextlib
-
-@contextlib.asynccontextmanager
-async def generate_agent(config):
-  organization_id = config["configurable"].get("x-organization-id")
-  if organization_id == "org1":
-    graph = ...
-    yield graph
-  else:
-    graph = ...
-    yield graph
-
-```
-
-```json
-{
-  "graphs": {"agent": "my_grph.py:generate_agent"}
-}
-```
-
-For more examples on how to use runtime configuration, check out the [configuration how-to](../../how-tos/configuration.ipynb).
-
-### Opt-out of configurable headers
-
-If you'd like to opt-out of configurable headers, you can simply set a wildcard pattern in the `exclude` list:
-
-```json
-{
-  "http": {
-    "configurable_headers": {
-      "exclude": ["*"]
-    }
-  }
-}
-```
-
-This will exclude all headers from being added to your run's configuration.
-
-Note that exclusions take precedence over inclusions.

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

@@ -207,6 +207,18 @@ Behind the scenes, `LoadExternalComponent` will fetch the JS and CSS for the UI
 
 ## How-to guides
 
+### Show loading UI when components are loading
+
+You can provide a fallback UI to be rendered when the components are loading.
+
+```tsx
+<LoadExternalComponent
+  stream={thread}
+  message={ui}
+  fallback={<div>Loading...</div>}
+/>
+```
+
 ### Provide custom components on the client side
 
 If you already have the components loaded in your client application, you can provide a map of such components to be rendered directly without fetching the UI code from LangGraph Platform.
@@ -223,18 +235,6 @@ const clientComponents = {
 />;
 ```
 
-### Show loading UI when components are loading
-
-You can provide a fallback UI to be rendered when the components are loading.
-
-```tsx
-<LoadExternalComponent
-  stream={thread}
-  message={ui}
-  fallback={<div>Loading...</div>}
-/>
-```
-
 ### Customise the namespace of UI components.
 
 By default `LoadExternalComponent` will use the `assistantId` from `useStream()` hook to fetch the code for UI components. You can customise this by providing a `namespace` prop to the `LoadExternalComponent` component.
@@ -316,9 +316,9 @@ const WeatherComponent = (props: { city: string }) => {
 };
 ```
 
-### Streaming UI messages from the server
+### Streaming UI updates before the node execution is finished
 
-You can stream UI messages before the node execution is finished by using the `onCustomEvent` callback of the `useStream()` hook. This is especially useful when updating the UI component as the LLM is generating the response.
+You can stream UI updates before the node execution is finished by using the `onCustomEvent` callback of the `useStream()` hook.
 
 ```tsx
 import { uiMessageReducer } from "@langchain/langgraph-sdk/react-ui";
@@ -335,162 +335,6 @@ const { thread, submit } = useStream({
 });
 ```
 
-Then you can pushing updates to the UI component by calling `ui.push()` / `push_ui_message()` with the same ID as the UI message you wish to update.
-
-=== "Python"
-
-    ```python
-    from typing import Annotated, Sequence, TypedDict
-
-    from langchain_anthropic import ChatAnthropic
-    from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage
-    from langgraph.graph import StateGraph
-    from langgraph.graph.message import add_messages
-    from langgraph.graph.ui import AnyUIMessage, push_ui_message, ui_message_reducer
-
-
-    class AgentState(TypedDict):  # noqa: D101
-        messages: Annotated[Sequence[BaseMessage], add_messages]
-        ui: Annotated[Sequence[AnyUIMessage], ui_message_reducer]
-
-
-    class CreateTextDocument(TypedDict):
-        """Prepare a document heading for the user."""
-
-        title: str
-
-
-    async def writer_node(state: AgentState):
-        model = ChatAnthropic(model="claude-3-5-sonnet-latest")
-        message: AIMessage = await model.bind_tools(
-            tools=[CreateTextDocument],
-            tool_choice={"type": "tool", "name": "CreateTextDocument"},
-        ).ainvoke(state["messages"])
-
-        tool_call = next(
-            (x["args"] for x in message.tool_calls if x["name"] == "CreateTextDocument"),
-            None,
-        )
-
-        if tool_call:
-            ui_message = push_ui_message("writer", tool_call, message=message)
-            ui_message_id = ui_message["id"]
-
-            # We're already streaming the LLM response to the client through UI messages
-            # so we don't need to stream it again to the `messages` stream mode.
-            content_stream = model.with_config({"tags": ["nostream"]}).astream(
-                f"Create a document with the title: {tool_call['title']}"
-            )
-
-            content: AIMessageChunk | None = None
-            async for chunk in content_stream:
-                content = content + chunk if content else chunk
-
-                push_ui_message(
-                    "writer",
-                    {"content": content.text()},
-                    id=ui_message_id,
-                    message=message,
-                    # Use `merge=rue` to merge props with the existing UI message
-                    merge=True,
-                )
-
-        return {"messages": [message]}
-    ```
-
-=== "JS"
-
-    ```tsx
-    import {
-      Annotation,
-      MessagesAnnotation,
-      type LangGraphRunnableConfig,
-    } from "@langchain/langgraph";
-    import { z } from "zod";
-    import { ChatAnthropic } from "@langchain/anthropic";
-    import {
-      typedUi,
-      uiMessageReducer,
-    } from "@langchain/langgraph-sdk/react-ui/server";
-    import type { AIMessageChunk } from "@langchain/core/messages";
-
-    import type ComponentMap from "./ui";
-
-    const AgentState = Annotation.Root({
-      ...MessagesAnnotation.spec,
-      ui: Annotation({ reducer: uiMessageReducer, default: () => [] }),
-    });
-
-    async function writerNode(
-      state: typeof AgentState.State,
-      config: LangGraphRunnableConfig
-    ): Promise<typeof AgentState.Update> {
-      const ui = typedUi<typeof ComponentMap>(config);
-
-      const model = new ChatAnthropic({ model: "claude-3-5-sonnet-latest" });
-      const message = await model
-        .bindTools(
-          [
-            {
-              name: "create_text_document",
-              description: "Prepare a document heading for the user.",
-              schema: z.object({ title: z.string() }),
-            },
-          ],
-          { tool_choice: { type: "tool", name: "create_text_document" } }
-        )
-        .invoke(state.messages);
-
-      type ToolCall = { name: "create_text_document"; args: { title: string } };
-      const toolCall = message.tool_calls?.find(
-        (tool): tool is ToolCall => tool.name === "create_text_document"
-      );
-
-      if (toolCall) {
-        const { id, name } = ui.push(
-          { name: "writer", props: { title: toolCall.args.title } },
-          { message }
-        );
-
-        const contentStream = await model
-          // We're already streaming the LLM response to the client through UI messages
-          // so we don't need to stream it again to the `messages` stream mode.
-          .withConfig({ tags: ["nostream"] })
-          .stream(`Create a short poem with the topic: ${message.text}`);
-
-        let content: AIMessageChunk | undefined;
-        for await (const chunk of contentStream) {
-          content = content?.concat(chunk) ?? chunk;
-
-          ui.push(
-            { id, name, props: { content: content?.text } },
-            // Use `merge: true` to merge props with the existing UI message
-            { message, merge: true }
-          );
-        }
-      }
-
-      return { messages: [message] };
-    }
-    ```
-
-=== "`ui.tsx`"
-
-    ```tsx
-    function WriterComponent(props: { title: string; content?: string }) {
-      return (
-        <article>
-          <h2>{props.title}</h2>
-          <p style={{ whiteSpace: "pre-wrap" }}>{props.content}</p>
-        </article>
-      );
-    }
-
-    export default {
-      weather: WriterComponent,
-    };
-    ```
-
 ### Remove UI messages from state
 
 Similar to how messages can be removed from the state by appending a RemoveMessage you can remove an UI message from the state by calling `remove_ui_message` / `ui.delete` with the ID of the UI message.

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

@@ -1,4 +1,4 @@
-# How to use the interrupt option
+# Interrupt
 
 This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide](../../concepts/double_texting.md).
 

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

@@ -1,5 +1,4 @@
-# How to use the Rollback option
-
+# Rollback
 
 This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide](../../concepts/double_texting.md).
 

docs/docs/concepts/agentic_concepts.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # Agent architectures
 
 Many LLM applications implement a particular control flow of steps before and / or after LLM calls. As an example, [RAG](https://github.com/langchain-ai/rag-from-scratch) performs retrieval of documents relevant to a user question, and passes those documents to an LLM in order to ground the model's response in the provided document context. 

docs/docs/concepts/application_structure.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # Application Structure
 
 !!! info "Prerequisites"

docs/docs/concepts/assistants.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # Assistants
 
 !!! info "Prerequisites"

docs/docs/concepts/auth.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # Authentication & Access Control
 
 LangGraph Platform provides a flexible authentication and authorization system that can integrate with most authentication schemes.

docs/docs/concepts/breakpoints.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # Breakpoints
 
 Breakpoints pause graph execution at specific points and enable stepping through execution step by step. Breakpoints are powered by LangGraph's [**persistence layer**](./persistence.md), which saves the state after each graph step. Breakpoints can also be used to enable [**human-in-the-loop**](./human_in_the_loop.md) workflows, though we recommend using the [`interrupt` function](./human_in_the_loop.md#interrupt) for this purpose.

docs/docs/concepts/bring_your_own_cloud.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # Bring Your Own Cloud (BYOC)
 
 !!! note Prerequisites

docs/docs/concepts/deployment_options.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # Deployment Options
 
 !!! info "Prerequisites"

docs/docs/concepts/double_texting.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # Double Texting
 
 !!! info "Prerequisites"

docs/docs/concepts/durable_execution.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # Durable Execution
 
 **Durable execution** is a technique in which a process or workflow saves its progress at key points, allowing it to pause and later resume exactly where it left off. This is particularly useful in scenarios that require [human-in-the-loop](./human_in_the_loop.md), where users can inspect, validate, or modify the process before continuing, and in long-running tasks that might encounter interruptions or errors (e.g., calls to an LLM timing out). By preserving completed work, durable execution enables a process to resume without reprocessing previous steps -- even after a significant delay (e.g., a week later). 

docs/docs/concepts/faq.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # FAQ
 
 Common questions and their answers!

docs/docs/concepts/functional_api.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # Functional API
 
 ## Overview

docs/docs/concepts/high_level.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # Why LangGraph?
 
 ## LLM applications

docs/docs/concepts/human_in_the_loop.md

@@ -1,13 +1,3 @@
----
-search:
-  boost: 2
-tags:
-  - human-in-the-loop
-  - hil
-hide:
-  - tags
----
-
 # Human-in-the-loop
 
 !!! tip "This guide uses the new `interrupt` function."
@@ -450,22 +440,6 @@ Upon **resuming** the graph, the counter will be incremented a second time, resu
 The value of counter is: 2
 ```
 
-### Resuming multiple interrupts with one invocation
-
-If you have multiple interrupts in the task queue, you can use `Command.resume` with a dictionary mapping
-of interrupt ids to resume values to resume multiple interrupts with a single `invoke` / `stream` call.
-
-For example, once your graph has been interrupted (multiple times, theoretically) and is stalled:
-
-```python
-resume_map = {
-    i.interrupt_id: f"human input for prompt {i.value}"
-    for i in parent.get_state(thread_config).interrupts
-}
-
-parent_graph.invoke(Command(resume=resume_map), config=thread_config)
-```
-
 ## Common Pitfalls
 
 ### Side-effects

docs/docs/concepts/index.md

@@ -1,8 +1,6 @@
 ---
 title: Concepts
 description: Conceptual Guide for LangGraph
-search:
-  boost: 0.5
 ---
 
 # Conceptual Guide

docs/docs/concepts/langgraph_cli.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # LangGraph CLI
 
 !!! info "Prerequisites"

docs/docs/concepts/langgraph_cloud.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # Cloud SaaS (Beta)
 
 To deploy a [LangGraph Server](../concepts/langgraph_server.md), follow the how-to guide for [how to deploy to Cloud SaaS](../cloud/deployment/cloud.md).

docs/docs/concepts/langgraph_control_plane.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # LangGraph Control Plane
 
 The term "control plane" is used broadly to refer to the Control Plane UI where users create and update [LangGraph Servers](./langgraph_server.md) (deployments) and the Control Plane APIs that support the UI experience.

docs/docs/concepts/langgraph_data_plane.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # LangGraph Data Plane
 
 The term "data plane" is used broadly to refer to [LangGraph Servers](./langgraph_server.md) (deployments), the corresponding infrastructure for each server, and the "listener" application that continuously polls for updates from the [LangGraph Control Plane](./langgraph_control_plane.md).

docs/docs/concepts/langgraph_self_hosted_control_plane.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # Self-Hosted Control Plane (Beta)
 
 To deploy a [LangGraph Server](../concepts/langgraph_server.md), follow the how-to guide for [how to deploy the Self-Hosted Control Plane](../cloud/deployment/self_hosted_control_plane.md).

docs/docs/concepts/langgraph_self_hosted_data_plane.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # Self-Hosted Data Plane (Beta)
 
 To deploy a [LangGraph Server](../concepts/langgraph_server.md), follow the how-to guide for [how to deploy the Self-Hosted Data Plane](../cloud/deployment/self_hosted_data_plane.md).

docs/docs/concepts/langgraph_server.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # LangGraph Server
 
 !!! info "Prerequisites"

docs/docs/concepts/langgraph_standalone_container.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # Standalone Container
 
 To deploy a [LangGraph Server](../concepts/langgraph_server.md), follow the how-to guide for [how to deploy a Standalone Container](../cloud/deployment/standalone_container.md).

docs/docs/concepts/langgraph_studio.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # LangGraph Studio
 
 !!! info "Prerequisites"

docs/docs/concepts/low_level.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # LangGraph Glossary
 
 ## Graphs

docs/docs/concepts/memory.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # Memory
 
 ## What is Memory?

docs/docs/concepts/multi_agent.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # Multi-agent Systems
 
 An [agent](./agentic_concepts.md#agent-architectures) is _a system that uses an LLM to decide the control flow of an application_. As you develop these systems, they might grow more complex over time, making them harder to manage and scale. For example, you might run into the following problems:

docs/docs/concepts/persistence.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # Persistence
 
 LangGraph has a built-in persistence layer, implemented through checkpointers. When you compile graph with a checkpointer, the checkpointer saves a `checkpoint` of the graph state at every super-step. Those checkpoints are saved to a `thread`, which can be accessed after graph execution. Because `threads` allow access to graph's state after execution, several powerful capabilities including human-in-the-loop, memory, time travel, and fault-tolerance are all possible. See [this how-to guide](../how-tos/persistence.ipynb) for an end-to-end example on how to add and use checkpointers with your graph. Below, we'll discuss each of these concepts in more detail. 

docs/docs/concepts/plans.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # LangGraph Platform Plans
 
 

docs/docs/concepts/platform_architecture.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # LangGraph Platform Architecture
 
 ![](img/langgraph_platform_deployment_architecture.png)

docs/docs/concepts/pregel.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # LangGraph's Runtime (Pregel)
 
 [Pregel][langgraph.pregel.Pregel] implements LangGraph's runtime, managing the execution of LangGraph applications.
@@ -27,7 +22,7 @@ Repeat until no **actors** are selected for execution, or a maximum number of st
 
 ## Actors 
 
-An **actor** is a `PregelNode`. It subscribes to channels, reads data from them, and writes data to them. It can be thought of as an **actor** in the Pregel algorithm. `PregelNodes` implement LangChain's Runnable interface.
+An **actor** is a [PregelNode][langgraph.pregel.read.PregelNode]. It subscribes to channels, reads data from them, and writes data to them. It can be thought of as an **actor** in the Pregel algorithm. [PregelNodes][langgraph.pregel.read.PregelNode] implement LangChain's Runnable interface.
 
 ## Channels
 

docs/docs/concepts/scalability_and_resilience.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # LangGraph Platform: Scalability & Resilience
 
 LangGraph Platform is designed to scale horizontally with your workload. Each instance of the service is stateless, and keeps no resources in memory. The service is designed to gracefully handle new instances being added or removed, including hard shutdown cases.

docs/docs/concepts/sdk.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # LangGraph SDK
 
 !!! info "Prerequisites"

docs/docs/concepts/self_hosted.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # Self-Hosted
 
 !!! note Prerequisites

docs/docs/concepts/streaming.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # Streaming
 
 Building a responsive app for end-users? Real-time updates are key to keeping users engaged as your app progresses.

docs/docs/concepts/template_applications.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # Template Applications
 
 Templates are open source reference applications designed to help you get started quickly when building with LangGraph. They provide working examples of common agentic workflows that can be customized to your needs.

docs/docs/concepts/time-travel.md

@@ -1,8 +1,3 @@
----
-search:
-  boost: 2
----
-
 # Time Travel ⏱️
 
 !!! note "Prerequisites"

docs/docs/how-tos/create-react-agent-manage-message-history.ipynb

@@ -691,8 +691,7 @@
     "\n",
     "checkpointer = InMemorySaver()\n",
     "graph = create_react_agent(\n",
-    "    # limit the output size to ensure consistent behavior\n",
-    "    model.bind(max_tokens=256),\n",
+    "    model,\n",
     "    tools,\n",
     "    # highlight-next-line\n",
     "    pre_model_hook=summarization_node,\n",

docs/docs/how-tos/index.md

@@ -1,8 +1,6 @@
 ---
 title: How-to Guides
 description: How to accomplish common tasks in LangGraph
-search:
-  boost: 0.5
 ---
 
 # How-to Guides
@@ -153,7 +151,7 @@ See the below guide for how to integrate with other frameworks using the [Functi
 
 ### Prebuilt ReAct Agent
 
-The LangGraph [prebuilt ReAct agent](../reference/agents.md#langgraph.prebuilt.chat_agent_executor.create_react_agent) is pre-built implementation of a [tool calling agent](../concepts/agentic_concepts.md#tool-calling-agent).
+The LangGraph [prebuilt ReAct agent](../reference/prebuilt.md#langgraph.prebuilt.chat_agent_executor.create_react_agent) is pre-built implementation of a [tool calling agent](../concepts/agentic_concepts.md#tool-calling-agent).
 
 One of the big benefits of LangGraph is that you can easily create your own agent architectures. So while it's fine to start here to build an agent quickly, we would strongly recommend learning how to build your own agent so that you can take full advantage of LangGraph.
 

docs/docs/reference/agents.md

@@ -1,39 +0,0 @@
-# Agents
-
-::: langgraph.prebuilt.chat_agent_executor
-    options:
-      members:
-        - AgentState
-        - create_react_agent
-
-::: langgraph.prebuilt.tool_node.ToolNode
-    options:
-      show_if_no_docstring: true
-      show_root_heading: true
-      show_root_full_path: false
-      inherited_members: false
-      members:
-        - inject_tool_args
-
-::: langgraph.prebuilt.tool_node
-    options:
-      members:
-        - InjectedState
-        - InjectedStore
-        - tools_condition
-
-::: langgraph.prebuilt.tool_validator.ValidationNode
-    options:
-      show_if_no_docstring: true
-      show_root_heading: true
-      show_root_full_path: false
-      inherited_members: false
-      members: false
-
-::: langgraph.prebuilt.interrupt
-    options:
-      members:
-        - HumanInterruptConfig
-        - ActionRequest
-        - HumanInterrupt
-        - HumanResponse

docs/docs/reference/checkpoints.md

@@ -25,11 +25,5 @@
 ::: langgraph.checkpoint.sqlite.aio
 
 ::: langgraph.checkpoint.postgres
-    options:
-      members:
-        - PostgresSaver
 
-::: langgraph.checkpoint.postgres.aio
-    options:
-      members:
-        - AsyncPostgresSaver
+::: langgraph.checkpoint.postgres.aio

docs/docs/reference/graphs.md

@@ -1,75 +1,16 @@
 # Graph Definitions
 
-::: langgraph.graph.state.StateGraph
+::: langgraph.graph.graph
     options:
-      show_if_no_docstring: true
-      show_root_heading: true
-      show_root_full_path: false
       members:
-        - add_node
-        - add_edge
-        - add_conditional_edges
-        - add_sequence
-        - compile
+        - Graph
+        - CompiledGraph
 
-::: langgraph.graph.state.CompiledStateGraph
+::: langgraph.graph.state
     options:
-      show_if_no_docstring: true
-      show_root_heading: true
-      show_root_full_path: false
       members:
-        - stream
-        - astream
-        - invoke
-        - ainvoke
-        - get_state
-        - aget_state
-        - get_state_history
-        - aget_state_history
-        - update_state
-        - aupdate_state
-        - bulk_update_state
-        - abulk_update_state
-        - get_graph
-        - aget_graph
-        - get_subgraphs
-        - aget_subgraphs
-        - with_config
-
-::: langgraph.graph.graph.Graph
-    options:
-      show_if_no_docstring: true
-      show_root_heading: true
-      show_root_full_path: false
-      members:
-        - add_node
-        - add_edge
-        - add_conditional_edges
-        - compile
-
-::: langgraph.graph.graph.CompiledGraph
-    options:
-      show_if_no_docstring: true
-      show_root_heading: true
-      show_root_full_path: false
-      members:
-        - stream
-        - astream
-        - invoke
-        - ainvoke
-        - get_state
-        - aget_state
-        - get_state_history
-        - aget_state_history
-        - update_state
-        - aupdate_state
-        - bulk_update_state
-        - abulk_update_state
-        - get_graph
-        - aget_graph
-        - get_subgraphs
-        - aget_subgraphs
-        - with_config
+        - StateGraph
+        - CompiledStateGraph
 
 ::: langgraph.graph.message
     options:

docs/docs/reference/index.md

@@ -1,8 +1,6 @@
 ---
 title: Reference
 description: API reference for LangGraph
-search:
-  boost: 0.5
 ---
 
 <style>

docs/docs/reference/mcp.md

@@ -1,21 +0,0 @@
-# LangChain Model Context Protocol (MCP) Adapters
-
-::: langchain_mcp_adapters.client
-    options:
-      members:
-        - MultiServerMCPClient
-
-::: langchain_mcp_adapters.tools
-    options:
-      members:
-        - load_mcp_tools
-
-::: langchain_mcp_adapters.prompts
-    options:
-      members:
-        - load_mcp_prompt
-
-::: langchain_mcp_adapters.resources
-    options:
-      members:
-        - load_mcp_resources

docs/docs/reference/prebuilt.md

@@ -0,0 +1,28 @@
+# Prebuilt
+
+::: langgraph.prebuilt.chat_agent_executor
+    options:
+      members:
+        - create_react_agent
+
+::: langgraph.prebuilt.tool_node
+    options:
+      members:
+        - ToolNode
+        - InjectedState
+        - InjectedStore
+        - tools_condition
+
+::: langgraph.prebuilt.tool_validator
+    options:
+      members:
+        - ValidationNode
+
+
+::: langgraph.prebuilt.interrupt
+    options:
+      members:
+        - HumanInterruptConfig
+        - ActionRequest
+        - HumanInterrupt
+        - HumanResponse

docs/docs/reference/pregel.md

@@ -1,25 +1,7 @@
 # Pregel
 
-::: langgraph.pregel.Pregel
+::: langgraph.pregel
     options:
-      show_if_no_docstring: true
-      show_root_heading: true
-      show_root_full_path: false
       members:
-        - stream
-        - astream
-        - invoke
-        - ainvoke
-        - get_state
-        - aget_state
-        - get_state_history
-        - aget_state_history
-        - update_state
-        - aupdate_state
-        - bulk_update_state
-        - abulk_update_state
-        - get_graph
-        - aget_graph
-        - get_subgraphs
-        - aget_subgraphs
-        - with_config
+        - Pregel 
+        - PregelNode 

docs/docs/reference/supervisor.md

@@ -1,12 +0,0 @@
-# LangGraph Supervisor
-
-::: langgraph_supervisor.supervisor
-    options:
-      members:
-        - create_supervisor
-
-::: langgraph_supervisor.handoff
-    options:
-      members:
-        - create_handoff_tool
-        - create_forward_message_tool

docs/docs/reference/swarm.md

@@ -1,13 +0,0 @@
-# LangGraph Swarm
-
-::: langgraph_swarm.swarm
-    options:
-      members:
-        - SwarmState
-        - create_swarm
-        - add_active_agent_router
-
-::: langgraph_swarm.handoff
-    options:
-      members:
-        - create_handoff_tool

docs/docs/reference/types.md

@@ -10,6 +10,7 @@
         - CachePolicy
         - Interrupt
         - PregelTask
+        - PregelExecutableTask
         - StateSnapshot
         - Send
         - Command

docs/docs/troubleshooting/errors/index.md

@@ -1,7 +1,3 @@
----
-search:
-  boost: 0.5
----
 # Error reference
 
 This page contains guides around resolving common errors you may find while building with LangGraph.

docs/docs/troubleshooting/studio.md

@@ -2,17 +2,17 @@
 
 ## :fontawesome-brands-safari:{ .safari } Safari connection error with local dev server
 
-Safari blocks plain‑HTTP traffic on localhost. If you start Studio with a vanilla `langgraph dev`, the page may report a "Failed to load assistants" error and the browser DevTools will show network errors.
+Safari blocks plain‑HTTP traffic on localhost. If you start Studio with a vanilla  
+`langgraph dev`, the page may report a "Failed to load assistants" error (or something similar) and the browser DevTools will show network errors.
 
 #### Quick fix — run Studio through a secure Cloudflare tunnel
 
 === "Python"
 
     ```shell
-    pip install -U langgraph-cli>=0.2.6
+    pip install -U langgraph-cli>=0.2.6            # Python
     langgraph dev --tunnel
     ```
-
 === "JS"
 
     ```shell
@@ -25,18 +25,17 @@ The command prints a URL like:
 ```shell
 https://smith.langchain.com/studio/?baseUrl=https://hamilton-praise-heart-costumes.trycloudflare.com
 ```
-
 where
-
 ```shell
 ?baseUrl=https://hamilton-praise-heart-costumes.trycloudflare.com
 ```
+indicates the endpoint where your agent server is exposed.
 
-indicates the endpoint where your agent server is exposed. Open that URL in Safari and Studio should load immediately.
+Open that URL in Safari and Studio should load immediately.
 
 #### Alternative — use a Chromium‑based browser
 
-Chrome and other Chromium‑based browsers allow HTTP on localhost, so a plain `langgraph dev` should work without extra steps.
+Chrome, Edge, and Brave allow HTTP on localhost, so a plain `langgraph dev` should work without extra steps.
 
 #### If it’s still not loading
 
@@ -44,43 +43,3 @@ Chrome and other Chromium‑based browsers allow HTTP on localhost, so a plain
 2. Confirm your CLI version with `langgraph --version`.
 
 No other configuration, certificates, or CORS tweaks are required.
-
-## :fontawesome-brands-brave:{ .brave } Brave connection error with local dev server
-
-By default, Brave blocks plain‑HTTP traffic on localhost if Brave Shields are enabled. If you start Studio with a vanilla `langgraph dev`, the page may report a "Failed to load assistants" error and the browser DevTools will show network errors.
-
-#### Quick fix — disable Brave Shields for LangSmith
-
-Click the Brave icon next to the URL bar and turn off the Brave Shields in the popover.
-
-![Brave Shields](./img/brave-shields.png)
-
-#### Alternative — run Studio through a secure Cloudflare tunnel
-
-=== "Python"
-
-    ```shell
-    pip install -U langgraph-cli>=0.2.6
-    langgraph dev --tunnel
-    ```
-
-=== "JS"
-
-    ```shell
-    # Requires @langchain/langgraph-cli>=0.0.26
-    npx @langchain/langgraph-cli dev
-    ```
-
-The command prints a URL like:
-
-```shell
-https://smith.langchain.com/studio/?baseUrl=https://hamilton-praise-heart-costumes.trycloudflare.com
-```
-
-where
-
-```shell
-?baseUrl=https://hamilton-praise-heart-costumes.trycloudflare.com
-```
-
-indicates the endpoint where your agent server is exposed. Open that URL in Brave and Studio should load immediately.

docs/docs/tutorials/index.md

@@ -1,7 +1,5 @@
 ---
 title: Tutorials
-search:
-  boost: 0.5
 ---
 
 # Tutorials

docs/docs/tutorials/workflows/index.md

@@ -1,7 +1,3 @@
----
-search:
-    boost: 2 
----
 # Workflows and Agents
 
 This guide reviews common patterns for agentic systems. In describing these systems, it can be useful to make a distinction between "workflows" and "agents". One way to think about this difference is nicely explained in [Anthropic's](https://python.langchain.com/docs/integrations/providers/anthropic/) `Building Effective Agents` blog post:

docs/mkdocs.yml

@@ -56,7 +56,6 @@ plugins:
   - search:
       separator: '[\s\u200b\-,:!=\[\]()"`/]+|\.(?!\d)|&[lg]t;'
   - autorefs
-  - tags
   - mkdocstrings:
       custom_templates: templates
       handlers:
@@ -384,24 +383,20 @@ nav:
     - Resources:
       # NOTE: prebuilt.md is auto-generated by `make build-prebuilt`
       - agents/prebuilt.md
-  - Reference:
+  - API reference:
     - reference/index.md
-    - LangGraph:
+    - Library:
       - Graphs: reference/graphs.md
       - Checkpointing: reference/checkpoints.md
       - Storage: reference/store.md
-      - Types: reference/types.md
-      - Config: reference/config.md
-      - Functional API: reference/func.md
+      - Prebuilt components: reference/prebuilt.md
+      - Channels: reference/channels.md
       - Errors: reference/errors.md
+      - Types: reference/types.md
       - Constants: reference/constants.md
       - Pregel: reference/pregel.md
-      - Channels: reference/channels.md
-    - Prebuilt:
-      - Agents: reference/agents.md
-      - Supervisor: reference/supervisor.md
-      - Swarm: reference/swarm.md
-      - MCP Adapters: reference/mcp.md
+      - Config: reference/config.md
+      - Functional API: reference/func.md
     - LangGraph Platform:
       - Server API: "cloud/reference/api/api_ref.md"
       - CLI: "cloud/reference/cli.md"

docs/pyproject.toml

@@ -1,11 +1,10 @@
 [tool.poetry]
-name = "langgraph-docs"
+name = "langgraph-monorepo"
 version = "0.0.1"
-description = "LangGraph docs"
+description = "LangGraph monorepo"
 authors = []
 license = "MIT"
 readme = "README.md"
-package-mode = false
 
 [tool.poetry.dependencies]
 python = "^3.10"
@@ -21,10 +20,6 @@ langgraph-checkpoint = { path = "../libs/checkpoint/", develop = true }
 langgraph-checkpoint-sqlite = { path = "../libs/checkpoint-sqlite", develop = true }
 langgraph-checkpoint-postgres = { path = "../libs/checkpoint-postgres", develop = true }
 langgraph-sdk = {path = "../libs/sdk-py", develop = true}
-# TODO: switch these to published versions
-langgraph-supervisor = { git = "https://github.com/langchain-ai/langgraph-supervisor-py" }
-langgraph-swarm = { git = "https://github.com/langchain-ai/langgraph-swarm-py" }
-langchain-mcp-adapters = { git = "https://github.com/langchain-ai/langchain-mcp-adapters" }
 langchain-ollama = "^0.2.3"
 mkdocs = "*"
 mkdocs-autorefs = "*"

docs/tests/unit_tests/test_notebook_conversion.py

@@ -1,13 +1,8 @@
-import os
-import tempfile
-
-import nbformat
 import pytest
 
 from _scripts.notebook_convert import (
     _convert_links_in_markdown,
     _has_output,
-    convert_notebook,
 )
 
 
@@ -37,41 +32,3 @@ def test_has_output() -> None:
 def test_link_conversion(source: str, expected: str) -> None:
     """Test logic to convert links in markdown cells."""
     assert _convert_links_in_markdown(source) == expected
-
-
-EXPECTED_OUTPUT = """\
-```shell
-pip install -U langgraph
-```
-
-
-```python
-print('Hello')
-```\
-
-"""
-
-
-def test_converting_cell_magic() -> None:
-    """Test converting cell magic to code blocks."""
-    with tempfile.TemporaryDirectory() as tmpdir:
-        nb_path = os.path.join(tmpdir, "test_notebook.ipynb")
-
-        # Create a minimal notebook object
-        nb = nbformat.v4.new_notebook()
-        nb.cells = [
-            nbformat.v4.new_code_cell(
-                "%%capture --no-stderr\n"
-                "%pip install -U langgraph"
-            ),
-            nbformat.v4.new_code_cell("print('Hello')"),
-        ]
-        nb.metadata["language_info"] = {"name": "python"}
-
-        # Write to file
-        with open(nb_path, "w", encoding="utf-8") as f:
-            nbformat.write(nb, f)
-
-        # Run the conversion
-        converted = convert_notebook(nb_path)
-        assert converted == EXPECTED_OUTPUT

libs/checkpoint-postgres/langgraph/checkpoint/postgres/__init__.py

@@ -27,8 +27,6 @@ Conn = _internal.Conn  # For backward compatibility
 
 
 class PostgresSaver(BasePostgresSaver):
-    """Checkpointer that stores checkpoints in a Postgres database."""
-
     lock: threading.Lock
 
     def __init__(

libs/checkpoint-postgres/langgraph/checkpoint/postgres/aio.py

@@ -27,8 +27,6 @@ Conn = _ainternal.Conn  # For backward compatibility
 
 
 class AsyncPostgresSaver(BasePostgresSaver):
-    """Asynchronous checkpointer that stores checkpoints in a Postgres database."""
-
     lock: asyncio.Lock
 
     def __init__(

libs/checkpoint-postgres/poetry.lock

@@ -425,7 +425,7 @@ typing-extensions = ">=4.7"
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "2.0.25"
+version = "2.0.24"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 optional = false
 python-versions = ">=3.9"

libs/checkpoint-sqlite/poetry.lock

@@ -357,7 +357,7 @@ typing-extensions = ">=4.7"
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "2.0.25"
+version = "2.0.24"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 optional = false
 python-versions = ">=3.9"

libs/checkpoint/langgraph/checkpoint/serde/jsonplus.py

@@ -34,8 +34,6 @@ EMPTY_BYTES = b""
 
 
 class JsonPlusSerializer(SerializerProtocol):
-    """Serializer that uses ormsgpack, with a fallback to extended JSON serializer."""
-
     def __init__(
         self, *, __unpack_ext_hook__: Optional[Callable[[int, bytes], Any]] = None
     ) -> None:

libs/checkpoint/langgraph/store/base/batch.py

@@ -1,5 +1,3 @@
-"""Utilities for batching operations in a background task."""
-
 import asyncio
 import functools
 import weakref

libs/langgraph/langgraph/channels/base.py

@@ -13,8 +13,6 @@ C = TypeVar("C")
 
 
 class BaseChannel(Generic[Value, Update, C], ABC):
-    """Base class for all channels."""
-
     __slots__ = ("key", "typ")
 
     def __init__(self, typ: Any, key: str = "") -> None:

libs/langgraph/langgraph/constants.py

@@ -14,10 +14,8 @@ EMPTY_SEQ: tuple[str, ...] = tuple()
 MISSING = object()
 
 # --- Public constants ---
-TAG_NOSTREAM = sys.intern("nostream")
+TAG_NOSTREAM = sys.intern("langsmith:nostream")
 """Tag to disable streaming for a chat model."""
-TAG_NOSTREAM_ALT = sys.intern("langsmith:nostream")
-"""Tag to disable streaming for a chat model. (Deprecated in favour of "nostream")"""
 TAG_HIDDEN = sys.intern("langsmith:hidden")
 """Tag to hide a node/edge from certain tracing/streaming environments."""
 START = sys.intern("__start__")
@@ -104,8 +102,6 @@ CONF = cast(Literal["configurable"], sys.intern("configurable"))
 # key for the configurable dict in RunnableConfig
 NULL_TASK_ID = sys.intern("00000000-0000-0000-0000-000000000000")
 # the task_id to use for writes that are not associated with a task
-CONFIG_KEY_RESUME_MAP = sys.intern("__pregel_resume_map")
-# holds a mapping of task ns -> resume value for resuming tasks
 
 RESERVED = {
     TAG_HIDDEN,

libs/langgraph/langgraph/graph/graph.py

@@ -76,15 +76,6 @@ class Graph:
         *,
         metadata: Optional[dict[str, Any]] = None,
     ) -> Self:
-        """Add a new node to the graph.
-
-        Args:
-            node (Union[str, RunnableLike]): The function or runnable this node will run.
-                If a string is provided, it will be used as the node name, and action will be used as the function or runnable.
-            action (Optional[RunnableLike]): The action associated with the node. (default: None)
-                Will be used as the node function or runnable if `node` is a string (node name).
-            metadata (Optional[dict[str, Any]]): The metadata associated with the node. (default: None)
-        """
         if isinstance(node, str):
             for character in (NS_SEP, NS_END):
                 if character in node:
@@ -119,12 +110,6 @@ class Graph:
         return self
 
     def add_edge(self, start_key: str, end_key: str) -> Self:
-        """Add a directed edge from the start node to the end node.
-
-        Args:
-            start_key (str): The key of the start node of the edge.
-            end_key (str): The key of the end node of the edge.
-        """
         if self.compiled:
             logger.warning(
                 "Adding an edge to a graph that has already been compiled. This will "
@@ -317,25 +302,6 @@ class Graph:
         debug: bool = False,
         name: Optional[str] = None,
     ) -> "CompiledGraph":
-        """Compiles the graph into a `CompiledGraph` object.
-
-        The compiled graph implements the `Runnable` interface and can be invoked,
-        streamed, batched, and run asynchronously.
-
-        Args:
-            checkpointer (Optional[Union[Checkpointer, Literal[False]]]): A checkpoint saver object or flag.
-                If provided, this Checkpointer serves as a fully versioned "short-term memory" for the graph,
-                allowing it to be paused, resumed, and replayed from any point.
-                If None, it may inherit the parent graph's checkpointer when used as a subgraph.
-                If False, it will not use or inherit any checkpointer.
-            interrupt_before (Optional[Sequence[str]]): An optional list of node names to interrupt before.
-            interrupt_after (Optional[Sequence[str]]): An optional list of node names to interrupt after.
-            debug (bool): A flag indicating whether to enable debug mode.
-            name (Optional[str]): The name to use for the compiled graph.
-
-        Returns:
-            CompiledGraph: The compiled graph.
-        """
         # assign default values
         interrupt_before = interrupt_before or []
         interrupt_after = interrupt_after or []

libs/langgraph/langgraph/graph/schema_utils.py

@@ -1,7 +1,6 @@
 import functools
 import logging
 import weakref
-from dataclasses import is_dataclass
 from inspect import isclass
 from typing import (
     Annotated,
@@ -14,8 +13,8 @@ from typing import (
     get_type_hints,
 )
 
-from pydantic import BaseModel, ConfigDict, TypeAdapter
-from typing_extensions import is_typeddict
+from pydantic import BaseModel
+from pydantic.v1 import BaseModel as BaseModelV1
 
 __all__ = ["SchemaCoercionMapper"]
 
@@ -46,7 +45,7 @@ class SchemaCoercionMapper:
 
     def __init__(
         self,
-        schema: type[BaseModel],
+        schema: type[Any],
         type_hints: Optional[dict[str, Any]] = None,
         *,
         max_depth: int = 12,
@@ -64,12 +63,30 @@ class SchemaCoercionMapper:
             else get_type_hints(schema, localns={schema.__name__: schema})
         )
 
-        if issubclass(schema, BaseModel):
+        if issubclass(schema, BaseModelV1):
+            self._fields = {
+                n: self.type_hints.get(n, f.annotation)
+                for n, f in schema.__fields__.items()
+            }
+            self._construct = schema.construct
+            unhandled_attrs = (
+                "__pre_root_validators__",
+                "__post_root_validators__",
+                "__validators__",
+            )
+            if any(getattr(schema, c, None) for c in unhandled_attrs):
+                self.coerce: Callable[[Any, Any], Union[BaseModelV1, BaseModel]] = (
+                    lambda v, _: schema(**v)
+                )
+            else:
+                self.coerce = self._coerce
+
+        elif issubclass(schema, BaseModel):
             self._fields = {
                 n: self.type_hints.get(n, f.annotation)
                 for n, f in schema.model_fields.items()
             }
-            self._construct: Callable[..., Any] = schema.model_construct
+            self._construct: Callable[..., Any] = schema.model_construct  # type: ignore
             unhandled_attrs = ("validators", "field_validators", "root_validators")
             if (decorators := getattr(schema, "__pydantic_decorators__", None)) and any(
                 getattr(decorators, attr, None) for attr in unhandled_attrs
@@ -77,8 +94,9 @@ class SchemaCoercionMapper:
                 self.coerce = lambda v, _: schema.model_validate(v)
             else:
                 self.coerce = self._coerce
+
         else:
-            raise TypeError("Schema must be a Pydantic V2 model.")
+            raise TypeError("Schema is neither a Pydantic v1 nor v2 model.")
 
         self._field_coercers: Optional[dict[str, Callable[[Any, int], Any]]] = None
 
@@ -120,12 +138,14 @@ class SchemaCoercionMapper:
 
         if isclass(field_type):
             # This is needed bcs. of issubclass issues on older versions of python
+            is_class_ = True
             try:
-                is_bm_subclass = issubclass(field_type, BaseModel)
+                is_bm_v2 = issubclass(field_type, BaseModel)
             except TypeError:
                 # python < 3.11 issue.
-                is_bm_subclass = False
-            if is_bm_subclass:
+                is_class_ = False
+                is_bm_v2 = False
+            if is_bm_v2 or (is_class_ and issubclass(field_type, BaseModelV1)):
                 mapper = SchemaCoercionMapper(field_type, max_depth=depth - 1)
                 return lambda v, d: mapper.coerce(v, d) if isinstance(v, dict) else v
 
@@ -245,18 +265,67 @@ _IDENTITY_TYPES: tuple[type[Any], ...] = (
     type(None),
 )
 
+try:
+    # Pydantic v2.
+    from pydantic import TypeAdapter
 
-@functools.lru_cache(maxsize=2048)
-def _adapter_for(tp: Any) -> Callable[[Any], Any]:  # noqa: D401
     try:
-        config = (
-            None
-            if (issubclass(tp, BaseModel) or is_dataclass(tp) or is_typeddict(tp))
-            else ConfigDict(arbitrary_types_allowed=True)
+        import pydantic.v1.types as v1_types_
+        from pydantic.v1 import parse_obj_as
+
+        v1_types = tuple(
+            v for k, v in vars(v1_types_).items() if k in v1_types_.__all__
         )
-    except TypeError:
-        config = None
-    return TypeAdapter(tp, config=config).validate_python
+    except ImportError:
+        v1_types = ()
+
+        def parse_obj_as(tp: Any, v: Any) -> Any:  # type: ignore
+            return v
+
+    try:
+        from pydantic.v1 import parse_obj_as
+        from pydantic.v1.main import create_model
+    except ImportError:
+        create_model = None  # type: ignore
+
+    def _get_v1_parser(tp: Any) -> Any:
+        if create_model is not None:
+            try:
+                parser = create_model(
+                    f"ParsingModel[{tp}]",
+                    __root__=(tp, ...),
+                )
+                return lambda v: parser(__root__=v).__root__  # type: ignore
+            except RuntimeError:
+                return lambda v: v
+        return lambda v: parse_obj_as(tp, v)
+
+    @functools.lru_cache(maxsize=2048)
+    def _adapter_for(tp: Any) -> Callable[[Any], Any]:  # noqa: D401
+        if tp in v1_types:
+            return _get_v1_parser(tp)
+        try:
+            return TypeAdapter(
+                tp, config={"arbitrary_types_allowed": True}
+            ).validate_python
+        except TypeError:
+            # Delayed classes like ConstrainedList
+            return _get_v1_parser(tp)
+
+except ImportError:
+    # Pydantic V1
+    from pydantic.v1.main import create_model
+
+    @functools.lru_cache(maxsize=2048)
+    def _adapter_for(tp: Any) -> Callable[[Any], Any]:  # noqa: D401
+        try:
+            parser = create_model(
+                f"ParsingModel[{tp}]",
+                __root__=(tp, ...),
+            )
+            return lambda v: parser(__root__=v).__root__  # type: ignore
+        except RuntimeError:
+            return lambda v: v
 
 
 def _get_adapter(tp: Any) -> Callable[[Any], Any]:

libs/langgraph/langgraph/graph/state.py

@@ -23,6 +23,7 @@ from typing import (
 
 from langchain_core.runnables import Runnable, RunnableConfig
 from pydantic import BaseModel
+from pydantic.v1 import BaseModel as BaseModelV1
 from typing_extensions import Self
 
 from langgraph._api.deprecation import LangGraphDeprecationWarning
@@ -122,44 +123,42 @@ class StateGraph(Graph):
         config_schema (Optional[Type[Any]]): The schema class that defines the configuration.
             Use this to expose configurable parameters in your API.
 
-    Example:
-        ```python
-        from langchain_core.runnables import RunnableConfig
-        from typing_extensions import Annotated, TypedDict
-        from langgraph.checkpoint.memory import MemorySaver
-        from langgraph.graph import StateGraph
-
-        def reducer(a: list, b: int | None) -> list:
-            if b is not None:
-                return a + [b]
-            return a
-
-        class State(TypedDict):
-            x: Annotated[list, reducer]
-
-        class ConfigSchema(TypedDict):
-            r: float
-
-        graph = StateGraph(State, config_schema=ConfigSchema)
-
-        def node(state: State, config: RunnableConfig) -> dict:
-            r = config["configurable"].get("r", 1.0)
-            x = state["x"][-1]
-            next_value = x * r * (1 - x)
-            return {"x": next_value}
-
-        graph.add_node("A", node)
-        graph.set_entry_point("A")
-        graph.set_finish_point("A")
-        compiled = graph.compile()
-
-        print(compiled.config_specs)
-        # [ConfigurableFieldSpec(id='r', annotation=<class 'float'>, name=None, description=None, default=None, is_shared=False, dependencies=None)]
-
-        step1 = compiled.invoke({"x": 0.5}, {"configurable": {"r": 3.0}})
-        # {'x': [0.5, 0.75]}
-        ```
-    """
+    Examples:
+        >>> from langchain_core.runnables import RunnableConfig
+        >>> from typing_extensions import Annotated, TypedDict
+        >>> from langgraph.checkpoint.memory import MemorySaver
+        >>> from langgraph.graph import StateGraph
+        >>>
+        >>> def reducer(a: list, b: int | None) -> list:
+        ...     if b is not None:
+        ...         return a + [b]
+        ...     return a
+        >>>
+        >>> class State(TypedDict):
+        ...     x: Annotated[list, reducer]
+        >>>
+        >>> class ConfigSchema(TypedDict):
+        ...     r: float
+        >>>
+        >>> graph = StateGraph(State, config_schema=ConfigSchema)
+        >>>
+        >>> def node(state: State, config: RunnableConfig) -> dict:
+        ...     r = config["configurable"].get("r", 1.0)
+        ...     x = state["x"][-1]
+        ...     next_value = x * r * (1 - x)
+        ...     return {"x": next_value}
+        >>>
+        >>> graph.add_node("A", node)
+        >>> graph.set_entry_point("A")
+        >>> graph.set_finish_point("A")
+        >>> compiled = graph.compile()
+        >>>
+        >>> print(compiled.config_specs)
+        [ConfigurableFieldSpec(id='r', annotation=<class 'float'>, name=None, description=None, default=None, is_shared=False, dependencies=None)]
+        >>>
+        >>> step1 = compiled.invoke({"x": 0.5}, {"configurable": {"r": 3.0}})
+        >>> print(step1)
+        {'x': [0.5, 0.75]}"""
 
     nodes: dict[str, StateNodeSpec]  # type: ignore[assignment]
     channels: dict[str, BaseChannel]
@@ -252,8 +251,17 @@ class StateGraph(Graph):
         retry: Optional[Union[RetryPolicy, Sequence[RetryPolicy]]] = None,
         destinations: Optional[Union[dict[str, str], tuple[str, ...]]] = None,
     ) -> Self:
-        """Add a new node to the state graph.
+        """Adds a new node to the state graph.
         Will take the name of the function/runnable as the node name.
+
+        Args:
+            node (RunnableLike): The function or runnable this node will run.
+
+        Raises:
+            ValueError: If the key is already being used as a state key.
+
+        Returns:
+            Self: The instance of the state graph, allowing for method chaining.
         """
         ...
 
@@ -268,7 +276,18 @@ class StateGraph(Graph):
         retry: Optional[Union[RetryPolicy, Sequence[RetryPolicy]]] = None,
         destinations: Optional[Union[dict[str, str], tuple[str, ...]]] = None,
     ) -> Self:
-        """Add a new node to the state graph."""
+        """Adds a new node to the state graph.
+
+        Args:
+            node (str): The key of the node.
+            action (RunnableLike): The action associated with the node.
+
+        Raises:
+            ValueError: If the key is already being used as a state key.
+
+        Returns:
+            Self: The instance of the state graph, allowing for method chaining.
+        """
         ...
 
     def add_node(
@@ -281,13 +300,13 @@ class StateGraph(Graph):
         retry: Optional[Union[RetryPolicy, Sequence[RetryPolicy]]] = None,
         destinations: Optional[Union[dict[str, str], tuple[str, ...]]] = None,
     ) -> Self:
-        """Add a new node to the state graph.
+        """Adds a new node to the state graph.
+
+        Will take the name of the function/runnable as the node name.
 
         Args:
             node (Union[str, RunnableLike]): The function or runnable this node will run.
-                If a string is provided, it will be used as the node name, and action will be used as the function or runnable.
             action (Optional[RunnableLike]): The action associated with the node. (default: None)
-                Will be used as the node function or runnable if `node` is a string (node name).
             metadata (Optional[dict[str, Any]]): The metadata associated with the node. (default: None)
             input (Optional[Type[Any]]): The input schema for the node. (default: the graph's input schema)
             retry (Optional[Union[RetryPolicy, Sequence[RetryPolicy]]]): The policy for retrying the node. (default: None)
@@ -300,29 +319,29 @@ class StateGraph(Graph):
         Raises:
             ValueError: If the key is already being used as a state key.
 
-        Example:
-            ```python
-            from langgraph.graph import START, StateGraph
-
-            def my_node(state, config):
-                return {"x": state["x"] + 1}
-
-            builder = StateGraph(dict)
-            builder.add_node(my_node)  # node name will be 'my_node'
-            builder.add_edge(START, "my_node")
-            graph = builder.compile()
-            graph.invoke({"x": 1})
-            # {'x': 2}
+        Examples:
+            ```pycon
+            >>> from langgraph.graph import START, StateGraph
+            ...
+            >>> def my_node(state, config):
+            ...    return {"x": state["x"] + 1}
+            ...
+            >>> builder = StateGraph(dict)
+            >>> builder.add_node(my_node)  # node name will be 'my_node'
+            >>> builder.add_edge(START, "my_node")
+            >>> graph = builder.compile()
+            >>> graph.invoke({"x": 1})
+            {'x': 2}
             ```
+            Customize the name:
 
-        Example: Customize the name:
-            ```python
-            builder = StateGraph(dict)
-            builder.add_node("my_fair_node", my_node)
-            builder.add_edge(START, "my_fair_node")
-            graph = builder.compile()
-            graph.invoke({"x": 1})
-            # {'x': 2}
+            ```pycon
+            >>> builder = StateGraph(dict)
+            >>> builder.add_node("my_fair_node", my_node)
+            >>> builder.add_edge(START, "my_fair_node")
+            >>> graph = builder.compile()
+            >>> graph.invoke({"x": 1})
+            {'x': 2}
             ```
 
         Returns:
@@ -425,7 +444,7 @@ class StateGraph(Graph):
         return self
 
     def add_edge(self, start_key: Union[str, list[str]], end_key: str) -> Self:
-        """Add a directed edge from the start node (or list of start nodes) to the end node.
+        """Adds a directed edge from the start node (or list of start nodes) to the end node.
 
         When a single start node is provided, the graph will wait for that node to complete
         before executing the end node. When multiple start nodes are provided,
@@ -565,7 +584,7 @@ class StateGraph(Graph):
         debug: bool = False,
         name: Optional[str] = None,
     ) -> "CompiledStateGraph":
-        """Compiles the state graph into a `CompiledStateGraph` object.
+        """Compiles the state graph into a `CompiledGraph` object.
 
         The compiled graph implements the `Runnable` interface and can be invoked,
         streamed, batched, and run asynchronously.
@@ -579,7 +598,6 @@ class StateGraph(Graph):
             interrupt_before (Optional[Sequence[str]]): An optional list of node names to interrupt before.
             interrupt_after (Optional[Sequence[str]]): An optional list of node names to interrupt after.
             debug (bool): A flag indicating whether to enable debug mode.
-            name (Optional[str]): The name to use for the compiled graph.
 
         Returns:
             CompiledStateGraph: The compiled state graph.
@@ -624,7 +642,7 @@ class StateGraph(Graph):
                 self.input
                 if len(self.channels) > 1
                 and isclass(self.input)
-                and issubclass(self.input, BaseModel)
+                and issubclass(self.input, (BaseModel, BaseModelV1))
                 else None
             ),
             nodes={},
@@ -829,9 +847,7 @@ class CompiledStateGraph(CompiledGraph):
         ) -> Sequence[Union[ChannelWriteEntry, Send]]:
             writes = [
                 (
-                    ChannelWriteEntry(
-                        p if p == END else CHANNEL_BRANCH_TO.format(p), None
-                    )
+                    ChannelWriteEntry(CHANNEL_BRANCH_TO.format(p), None)
                     if not isinstance(p, Send)
                     else p
                 )
@@ -1010,7 +1026,7 @@ def _pick_mapper(
     if isclass(schema):
         if issubclass(schema, dict):
             return None
-        if issubclass(schema, BaseModel):
+        if issubclass(schema, (BaseModel, BaseModelV1)):
             return SchemaCoercionMapper(schema, type_hints=type_hints)
     return partial(_coerce_state, schema)
 
@@ -1051,14 +1067,9 @@ def _control_static(
     ends: Union[tuple[str, ...], dict[str, str]],
 ) -> Sequence[tuple[str, Any, Optional[str]]]:
     if isinstance(ends, dict):
-        return [
-            (k if k == END else CHANNEL_BRANCH_TO.format(k), None, label)
-            for k, label in ends.items()
-        ]
+        return [(CHANNEL_BRANCH_TO.format(k), None, label) for k, label in ends.items()]
     else:
-        return [
-            (e if e == END else CHANNEL_BRANCH_TO.format(e), None, None) for e in ends
-        ]
+        return [(CHANNEL_BRANCH_TO.format(e), None, None) for e in ends]
 
 
 def _get_root(input: Any) -> Optional[Sequence[tuple[str, Any]]]:
@@ -1193,7 +1204,7 @@ def _get_schema(
     channels: dict,
     name: str,
 ) -> type[BaseModel]:
-    if isclass(typ) and issubclass(typ, BaseModel):
+    if isclass(typ) and issubclass(typ, (BaseModel, BaseModelV1)):
         return typ
     else:
         keys = list(schemas[typ].keys())

libs/langgraph/langgraph/graph/ui.py

@@ -1,4 +1,4 @@
-from typing import Any, Literal, Optional, Union, cast
+from typing import Any, Literal, Optional, Union
 from uuid import uuid4
 
 from langchain_core.messages import AnyMessage
@@ -55,7 +55,6 @@ def push_ui_message(
     metadata: Optional[dict[str, Any]] = None,
     message: Optional[AnyMessage] = None,
     state_key: str = "ui",
-    merge: bool = False,
 ) -> UIMessage:
     """Push a new UI message to update the UI state.
 
@@ -101,10 +100,10 @@ def push_ui_message(
         "name": name,
         "props": props,
         "metadata": {
-            "merge": merge,
-            "run_id": config.get("run_id", None),
+            **(config.get("metadata") or {}),
             "tags": config.get("tags", None),
             "name": config.get("run_name", None),
+            "run_id": config.get("run_id", None),
             **(metadata or {}),
             **({"message_id": message_id} if message_id else {}),
         },
@@ -193,12 +192,6 @@ def ui_message_reducer(
                 ids_to_remove.add(msg_id)
             else:
                 ids_to_remove.discard(msg_id)
-
-                if cast(UIMessage, msg).get("metadata", {}).get("merge", False):
-                    prev_msg = merged[existing_idx]
-                    msg = msg.copy()
-                    msg["props"] = {**prev_msg["props"], **msg["props"]}
-
                 merged[existing_idx] = msg
         else:
             if msg.get("type") == "remove-ui":

libs/langgraph/langgraph/pregel/__init__.py

@@ -230,15 +230,15 @@ class Pregel(PregelProtocol):
 
     ## Actors
 
-    An **actor** is a `PregelNode`.
+    An **actor** is a [PregelNode][langgraph.pregel.read.PregelNode].
     It subscribes to channels, reads data from them, and writes data to them.
     It can be thought of as an **actor** in the Pregel algorithm.
-    `PregelNodes` implement LangChain's
+    [PregelNodes][langgraph.pregel.read.PregelNode] implement LangChain's
     Runnable interface.
 
     ## Channels
 
-    Channels are used to communicate between actors (`PregelNodes`).
+    Channels are used to communicate between actors (PregelNodes).
     Each channel has a value type, an update type, and an update function – which
     takes a sequence of updates and
     modifies the stored value. Channels can be used to send data from one chain to
@@ -560,7 +560,7 @@ class Pregel(PregelProtocol):
     def get_graph(
         self, config: RunnableConfig | None = None, *, xray: int | bool = False
     ) -> Graph:
-        """Return a drawable representation of the computation graph."""
+        """Returns a drawable representation of the computation graph."""
         # gather subgraphs
         if xray:
             subgraphs = {
@@ -588,7 +588,7 @@ class Pregel(PregelProtocol):
     async def aget_graph(
         self, config: RunnableConfig | None = None, *, xray: int | bool = False
     ) -> Graph:
-        """Return a drawable representation of the computation graph."""
+        """Returns a drawable representation of the computation graph."""
 
         # gather subgraphs
         if xray:
@@ -639,7 +639,6 @@ class Pregel(PregelProtocol):
         return self.__class__(**attrs)
 
     def with_config(self, config: RunnableConfig | None = None, **kwargs: Any) -> Self:
-        """Create a copy of the Pregel object with an updated config."""
         return self.copy(
             {"config": merge_configs(self.config, config, cast(RunnableConfig, kwargs))}
         )
@@ -802,16 +801,6 @@ class Pregel(PregelProtocol):
     def get_subgraphs(
         self, *, namespace: str | None = None, recurse: bool = False
     ) -> Iterator[tuple[str, PregelProtocol]]:
-        """Get the subgraphs of the graph.
-
-        Args:
-            namespace (Optional[str]): The namespace to filter the subgraphs by.
-            recurse (bool): Whether to recurse into the subgraphs.
-                If False, only the immediate subgraphs will be returned.
-
-        Returns:
-            Iterator[tuple[str, PregelProtocol]]: An iterator of the (namespace, subgraph) pairs.
-        """
         for name, node in self.nodes.items():
             # filter by prefix
             if namespace is not None:
@@ -841,16 +830,6 @@ class Pregel(PregelProtocol):
     async def aget_subgraphs(
         self, *, namespace: str | None = None, recurse: bool = False
     ) -> AsyncIterator[tuple[str, PregelProtocol]]:
-        """Get the subgraphs of the graph.
-
-        Args:
-            namespace (Optional[str]): The namespace to filter the subgraphs by.
-            recurse (bool): Whether to recurse into the subgraphs.
-                If False, only the immediate subgraphs will be returned.
-
-        Returns:
-            AsyncIterator[tuple[str, PregelProtocol]]: An iterator of the (namespace, subgraph) pairs.
-        """
         for name, node in self.get_subgraphs(namespace=namespace, recurse=recurse):
             yield name, node
 
@@ -874,7 +853,6 @@ class Pregel(PregelProtocol):
                 created_at=None,
                 parent_config=None,
                 tasks=(),
-                interrupts=(),
             )
 
         # migrate checkpoint if needed
@@ -959,12 +937,6 @@ class Pregel(PregelProtocol):
                     next_tasks[tid].writes.append((k, v))
                 if tasks := [t for t in next_tasks.values() if t.writes]:
                     apply_writes(saved.checkpoint, channels, tasks, None)
-            tasks_with_writes = tasks_w_writes(
-                next_tasks.values(),
-                saved.pending_writes,
-                task_states,
-                self.stream_channels_asis,
-            )
             # assemble the state snapshot
             return StateSnapshot(
                 read_channels(channels, self.stream_channels_asis),
@@ -973,8 +945,12 @@ class Pregel(PregelProtocol):
                 saved.metadata,
                 saved.checkpoint["ts"],
                 patch_checkpoint_map(saved.parent_config, saved.metadata),
-                tasks_with_writes,
-                tuple([i for task in tasks_with_writes for i in task.interrupts]),
+                tasks_w_writes(
+                    next_tasks.values(),
+                    saved.pending_writes,
+                    task_states,
+                    self.stream_channels_asis,
+                ),
             )
 
     async def _aprepare_state_snapshot(
@@ -993,7 +969,6 @@ class Pregel(PregelProtocol):
                 created_at=None,
                 parent_config=None,
                 tasks=(),
-                interrupts=(),
             )
 
         # migrate checkpoint if needed
@@ -1081,13 +1056,6 @@ class Pregel(PregelProtocol):
                     next_tasks[tid].writes.append((k, v))
                 if tasks := [t for t in next_tasks.values() if t.writes]:
                     apply_writes(saved.checkpoint, channels, tasks, None)
-
-            tasks_with_writes = tasks_w_writes(
-                next_tasks.values(),
-                saved.pending_writes,
-                task_states,
-                self.stream_channels_asis,
-            )
             # assemble the state snapshot
             return StateSnapshot(
                 read_channels(channels, self.stream_channels_asis),
@@ -1096,8 +1064,12 @@ class Pregel(PregelProtocol):
                 saved.metadata,
                 saved.checkpoint["ts"],
                 patch_checkpoint_map(saved.parent_config, saved.metadata),
-                tasks_with_writes,
-                tuple([i for task in tasks_with_writes for i in task.interrupts]),
+                tasks_w_writes(
+                    next_tasks.values(),
+                    saved.pending_writes,
+                    task_states,
+                    self.stream_channels_asis,
+                ),
             )
 
     def get_state(
@@ -1192,8 +1164,8 @@ class Pregel(PregelProtocol):
         before: RunnableConfig | None = None,
         limit: int | None = None,
     ) -> Iterator[StateSnapshot]:
-        """Get the history of the state of the graph."""
         config = ensure_config(config)
+        """Get the history of the state of the graph."""
         checkpointer: BaseCheckpointSaver | None = ensure_config(config)[CONF].get(
             CONFIG_KEY_CHECKPOINTER, self.checkpointer
         )
@@ -1243,8 +1215,8 @@ class Pregel(PregelProtocol):
         before: RunnableConfig | None = None,
         limit: int | None = None,
     ) -> AsyncIterator[StateSnapshot]:
-        """Asynchronously get the history of the state of the graph."""
         config = ensure_config(config)
+        """Get the history of the state of the graph."""
         checkpointer: BaseCheckpointSaver | None = ensure_config(config)[CONF].get(
             CONFIG_KEY_CHECKPOINTER, self.checkpointer
         )
@@ -1709,7 +1681,7 @@ class Pregel(PregelProtocol):
         config: RunnableConfig,
         supersteps: Sequence[Sequence[StateUpdate]],
     ) -> RunnableConfig:
-        """Asynchronously apply updates to the graph state in bulk. Requires a checkpointer to be set.
+        """Apply updates to the graph state in bulk. Requires a checkpointer to be set.
 
         Args:
             config: The config to apply the updates to.
@@ -2134,7 +2106,7 @@ class Pregel(PregelProtocol):
         values: dict[str, Any] | Any,
         as_node: str | None = None,
     ) -> RunnableConfig:
-        """Asynchronously update the state of the graph with the given values, as if they came from
+        """Update the state of the graph asynchronously with the given values, as if they 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.
         """
@@ -2237,100 +2209,101 @@ class Pregel(PregelProtocol):
         Yields:
             The output of each step in the graph. The output shape depends on the stream_mode.
 
-        Example: Using stream_mode="values":
-            ```python
-            import operator
-            from typing_extensions import Annotated, TypedDict
-            from langgraph.graph import StateGraph, START
+        Examples:
+            Using different stream modes with a graph:
+            ```pycon
+            >>> import operator
+            >>> from typing_extensions import Annotated, TypedDict
+            >>> from langgraph.graph import StateGraph, START
+            ...
+            >>> class State(TypedDict):
+            ...     alist: Annotated[list, operator.add]
+            ...     another_list: Annotated[list, operator.add]
+            ...
+            >>> builder = StateGraph(State)
+            >>> builder.add_node("a", lambda _state: {"another_list": ["hi"]})
+            >>> builder.add_node("b", lambda _state: {"alist": ["there"]})
+            >>> builder.add_edge("a", "b")
+            >>> builder.add_edge(START, "a")
+            >>> graph = builder.compile()
+            ```
+            With stream_mode="values":
 
-            class State(TypedDict):
-                alist: Annotated[list, operator.add]
-                another_list: Annotated[list, operator.add]
+            ```pycon
+            >>> for event in graph.stream({"alist": ['Ex for stream_mode="values"']}, stream_mode="values"):
+            ...     print(event)
+            {'alist': ['Ex for stream_mode="values"'], 'another_list': []}
+            {'alist': ['Ex for stream_mode="values"'], 'another_list': ['hi']}
+            {'alist': ['Ex for stream_mode="values"', 'there'], 'another_list': ['hi']}
+            ```
+            With stream_mode="updates":
 
-            builder = StateGraph(State)
-            builder.add_node("a", lambda _state: {"another_list": ["hi"]})
-            builder.add_node("b", lambda _state: {"alist": ["there"]})
-            builder.add_edge("a", "b")
-            builder.add_edge(START, "a")
-            graph = builder.compile()
+            ```pycon
+            >>> for event in graph.stream({"alist": ['Ex for stream_mode="updates"']}, stream_mode="updates"):
+            ...     print(event)
+            {'a': {'another_list': ['hi']}}
+            {'b': {'alist': ['there']}}
+            ```
+            With stream_mode="debug":
 
-            for event in graph.stream({"alist": ['Ex for stream_mode="values"']}, stream_mode="values"):
-                print(event)
-
-            # {'alist': ['Ex for stream_mode="values"'], 'another_list': []}
-            # {'alist': ['Ex for stream_mode="values"'], 'another_list': ['hi']}
-            # {'alist': ['Ex for stream_mode="values"', 'there'], 'another_list': ['hi']}
+            ```pycon
+            >>> for event in graph.stream({"alist": ['Ex for stream_mode="debug"']}, stream_mode="debug"):
+            ...     print(event)
+            {'type': 'task', 'timestamp': '2024-06-23T...+00:00', 'step': 1, 'payload': {'id': '...', 'name': 'a', 'input': {'alist': ['Ex for stream_mode="debug"'], 'another_list': []}, 'triggers': ['start:a']}}
+            {'type': 'task_result', 'timestamp': '2024-06-23T...+00:00', 'step': 1, 'payload': {'id': '...', 'name': 'a', 'result': [('another_list', ['hi'])]}}
+            {'type': 'task', 'timestamp': '2024-06-23T...+00:00', 'step': 2, 'payload': {'id': '...', 'name': 'b', 'input': {'alist': ['Ex for stream_mode="debug"'], 'another_list': ['hi']}, 'triggers': ['a']}}
+            {'type': 'task_result', 'timestamp': '2024-06-23T...+00:00', 'step': 2, 'payload': {'id': '...', 'name': 'b', 'result': [('alist', ['there'])]}}
             ```
 
-        Example: Using stream_mode="updates":
-            ```python
-            for event in graph.stream({"alist": ['Ex for stream_mode="updates"']}, stream_mode="updates"):
-                print(event)
+            With stream_mode="custom":
 
-            # {'a': {'another_list': ['hi']}}
-            # {'b': {'alist': ['there']}}
+            ```pycon
+            >>> from langgraph.types import StreamWriter
+            ...
+            >>> def node_a(state: State, writer: StreamWriter):
+            ...     writer({"custom_data": "foo"})
+            ...     return {"alist": ["hi"]}
+            ...
+            >>> builder = StateGraph(State)
+            >>> builder.add_node("a", node_a)
+            >>> builder.add_edge(START, "a")
+            >>> graph = builder.compile()
+            ...
+            >>> for event in graph.stream({"alist": ['Ex for stream_mode="custom"']}, stream_mode="custom"):
+            ...     print(event)
+            {'custom_data': 'foo'}
             ```
 
-        Example: Using stream_mode="debug":
-            ```python
-            for event in graph.stream({"alist": ['Ex for stream_mode="debug"']}, stream_mode="debug"):
-                print(event)
+            With stream_mode="messages":
 
-            # {'type': 'task', 'timestamp': '2024-06-23T...+00:00', 'step': 1, 'payload': {'id': '...', 'name': 'a', 'input': {'alist': ['Ex for stream_mode="debug"'], 'another_list': []}, 'triggers': ['start:a']}}
-            # {'type': 'task_result', 'timestamp': '2024-06-23T...+00:00', 'step': 1, 'payload': {'id': '...', 'name': 'a', 'result': [('another_list', ['hi'])]}}
-            # {'type': 'task', 'timestamp': '2024-06-23T...+00:00', 'step': 2, 'payload': {'id': '...', 'name': 'b', 'input': {'alist': ['Ex for stream_mode="debug"'], 'another_list': ['hi']}, 'triggers': ['a']}}
-            # {'type': 'task_result', 'timestamp': '2024-06-23T...+00:00', 'step': 2, 'payload': {'id': '...', 'name': 'b', 'result': [('alist', ['there'])]}}
-            ```
+            ```pycon
+            >>> from typing_extensions import Annotated, TypedDict
+            >>> from langgraph.graph import StateGraph, START
+            >>> from langchain_openai import ChatOpenAI
+            ...
+            >>> llm = ChatOpenAI(model="gpt-4o-mini")
+            ...
+            >>> class State(TypedDict):
+            ...     question: str
+            ...     answer: str
+            ...
+            >>> def node_a(state: State):
+            ...     response = llm.invoke(state["question"])
+            ...     return {"answer": response.content}
+            ...
+            >>> builder = StateGraph(State)
+            >>> builder.add_node("a", node_a)
+            >>> builder.add_edge(START, "a")
+            >>> graph = builder.compile()
 
-        Example: Using stream_mode="custom":
-            ```python
-            from langgraph.types import StreamWriter
-
-            def node_a(state: State, writer: StreamWriter):
-                writer({"custom_data": "foo"})
-                return {"alist": ["hi"]}
-
-            builder = StateGraph(State)
-            builder.add_node("a", node_a)
-            builder.add_edge(START, "a")
-            graph = builder.compile()
-
-            for event in graph.stream({"alist": ['Ex for stream_mode="custom"']}, stream_mode="custom"):
-                print(event)
-
-            # {'custom_data': 'foo'}
-            ```
-
-        Example: Using stream_mode="messages":
-            ```python
-            from typing_extensions import Annotated, TypedDict
-            from langgraph.graph import StateGraph, START
-            from langchain_openai import ChatOpenAI
-
-            llm = ChatOpenAI(model="gpt-4o-mini")
-
-            class State(TypedDict):
-                question: str
-                answer: str
-
-            def node_a(state: State):
-                response = llm.invoke(state["question"])
-                return {"answer": response.content}
-
-            builder = StateGraph(State)
-            builder.add_node("a", node_a)
-            builder.add_edge(START, "a")
-            graph = builder.compile()
-
-            for event in graph.stream({"question": "What is the capital of France?"}, stream_mode="messages"):
-                print(event)
-
-            # (AIMessageChunk(content='The', additional_kwargs={}, response_metadata={}, id='...'), {'langgraph_step': 1, 'langgraph_node': 'a', 'langgraph_triggers': ['start:a'], 'langgraph_path': ('__pregel_pull', 'a'), 'langgraph_checkpoint_ns': '...', 'checkpoint_ns': '...', 'ls_provider': 'openai', 'ls_model_name': 'gpt-4o-mini', 'ls_model_type': 'chat', 'ls_temperature': 0.7})
-            # (AIMessageChunk(content=' capital', additional_kwargs={}, response_metadata={}, id='...'), {'langgraph_step': 1, 'langgraph_node': 'a', 'langgraph_triggers': ['start:a'], ...})
-            # (AIMessageChunk(content=' of', additional_kwargs={}, response_metadata={}, id='...'), {...})
-            # (AIMessageChunk(content=' France', additional_kwargs={}, response_metadata={}, id='...'), {...})
-            # (AIMessageChunk(content=' is', additional_kwargs={}, response_metadata={}, id='...'), {...})
-            # (AIMessageChunk(content=' Paris', additional_kwargs={}, response_metadata={}, id='...'), {...})
+            >>> for event in graph.stream({"question": "What is the capital of France?"}, stream_mode="messages"):
+            ...     print(event)
+            (AIMessageChunk(content='The', additional_kwargs={}, response_metadata={}, id='...'), {'langgraph_step': 1, 'langgraph_node': 'a', 'langgraph_triggers': ['start:a'], 'langgraph_path': ('__pregel_pull', 'a'), 'langgraph_checkpoint_ns': '...', 'checkpoint_ns': '...', 'ls_provider': 'openai', 'ls_model_name': 'gpt-4o-mini', 'ls_model_type': 'chat', 'ls_temperature': 0.7})
+            (AIMessageChunk(content=' capital', additional_kwargs={}, response_metadata={}, id='...'), {'langgraph_step': 1, 'langgraph_node': 'a', 'langgraph_triggers': ['start:a'], ...})
+            (AIMessageChunk(content=' of', additional_kwargs={}, response_metadata={}, id='...'), {...})
+            (AIMessageChunk(content=' France', additional_kwargs={}, response_metadata={}, id='...'), {...})
+            (AIMessageChunk(content=' is', additional_kwargs={}, response_metadata={}, id='...'), {...})
+            (AIMessageChunk(content=' Paris', additional_kwargs={}, response_metadata={}, id='...'), {...})
             ```
         """
 
@@ -2498,7 +2471,7 @@ class Pregel(PregelProtocol):
         debug: bool | None = None,
         subgraphs: bool = False,
     ) -> AsyncIterator[dict[str, Any] | Any]:
-        """Asynchronously stream graph steps for a single input.
+        """Stream graph steps for a single input.
 
         Args:
             input: The input to the graph.
@@ -2523,100 +2496,101 @@ class Pregel(PregelProtocol):
         Yields:
             The output of each step in the graph. The output shape depends on the stream_mode.
 
-        Example: Using stream_mode="values":
-            ```python
-            import operator
-            from typing_extensions import Annotated, TypedDict
-            from langgraph.graph import StateGraph, START
+        Examples:
+            Using different stream modes with a graph:
+            ```pycon
+            >>> import operator
+            >>> from typing_extensions import Annotated, TypedDict
+            >>> from langgraph.graph import StateGraph, START
+            ...
+            >>> class State(TypedDict):
+            ...     alist: Annotated[list, operator.add]
+            ...     another_list: Annotated[list, operator.add]
+            ...
+            >>> builder = StateGraph(State)
+            >>> builder.add_node("a", lambda _state: {"another_list": ["hi"]})
+            >>> builder.add_node("b", lambda _state: {"alist": ["there"]})
+            >>> builder.add_edge("a", "b")
+            >>> builder.add_edge(START, "a")
+            >>> graph = builder.compile()
+            ```
+            With stream_mode="values":
 
-            class State(TypedDict):
-                alist: Annotated[list, operator.add]
-                another_list: Annotated[list, operator.add]
+            ```pycon
+            >>> async for event in graph.astream({"alist": ['Ex for stream_mode="values"']}, stream_mode="values"):
+            ...     print(event)
+            {'alist': ['Ex for stream_mode="values"'], 'another_list': []}
+            {'alist': ['Ex for stream_mode="values"'], 'another_list': ['hi']}
+            {'alist': ['Ex for stream_mode="values"', 'there'], 'another_list': ['hi']}
+            ```
+            With stream_mode="updates":
 
-            builder = StateGraph(State)
-            builder.add_node("a", lambda _state: {"another_list": ["hi"]})
-            builder.add_node("b", lambda _state: {"alist": ["there"]})
-            builder.add_edge("a", "b")
-            builder.add_edge(START, "a")
-            graph = builder.compile()
+            ```pycon
+            >>> async for event in graph.astream({"alist": ['Ex for stream_mode="updates"']}, stream_mode="updates"):
+            ...     print(event)
+            {'a': {'another_list': ['hi']}}
+            {'b': {'alist': ['there']}}
+            ```
+            With stream_mode="debug":
 
-            async for event in graph.astream({"alist": ['Ex for stream_mode="values"']}, stream_mode="values"):
-                print(event)
-
-            # {'alist': ['Ex for stream_mode="values"'], 'another_list': []}
-            # {'alist': ['Ex for stream_mode="values"'], 'another_list': ['hi']}
-            # {'alist': ['Ex for stream_mode="values"', 'there'], 'another_list': ['hi']}
+            ```pycon
+            >>> async for event in graph.astream({"alist": ['Ex for stream_mode="debug"']}, stream_mode="debug"):
+            ...     print(event)
+            {'type': 'task', 'timestamp': '2024-06-23T...+00:00', 'step': 1, 'payload': {'id': '...', 'name': 'a', 'input': {'alist': ['Ex for stream_mode="debug"'], 'another_list': []}, 'triggers': ['start:a']}}
+            {'type': 'task_result', 'timestamp': '2024-06-23T...+00:00', 'step': 1, 'payload': {'id': '...', 'name': 'a', 'result': [('another_list', ['hi'])]}}
+            {'type': 'task', 'timestamp': '2024-06-23T...+00:00', 'step': 2, 'payload': {'id': '...', 'name': 'b', 'input': {'alist': ['Ex for stream_mode="debug"'], 'another_list': ['hi']}, 'triggers': ['a']}}
+            {'type': 'task_result', 'timestamp': '2024-06-23T...+00:00', 'step': 2, 'payload': {'id': '...', 'name': 'b', 'result': [('alist', ['there'])]}}
             ```
 
-        Example: Using stream_mode="updates":
-            ```python
-            async for event in graph.astream({"alist": ['Ex for stream_mode="updates"']}, stream_mode="updates"):
-                print(event)
+            With stream_mode="custom":
 
-            # {'a': {'another_list': ['hi']}}
-            # {'b': {'alist': ['there']}}
+            ```pycon
+            >>> from langgraph.types import StreamWriter
+            ...
+            >>> async def node_a(state: State, writer: StreamWriter):
+            ...     writer({"custom_data": "foo"})
+            ...     return {"alist": ["hi"]}
+            ...
+            >>> builder = StateGraph(State)
+            >>> builder.add_node("a", node_a)
+            >>> builder.add_edge(START, "a")
+            >>> graph = builder.compile()
+            ...
+            >>> async for event in graph.astream({"alist": ['Ex for stream_mode="custom"']}, stream_mode="custom"):
+            ...     print(event)
+            {'custom_data': 'foo'}
             ```
 
-        Example: Using stream_mode="debug":
-            ```python
-            async for event in graph.astream({"alist": ['Ex for stream_mode="debug"']}, stream_mode="debug"):
-                print(event)
+            With stream_mode="messages":
 
-            # {'type': 'task', 'timestamp': '2024-06-23T...+00:00', 'step': 1, 'payload': {'id': '...', 'name': 'a', 'input': {'alist': ['Ex for stream_mode="debug"'], 'another_list': []}, 'triggers': ['start:a']}}
-            # {'type': 'task_result', 'timestamp': '2024-06-23T...+00:00', 'step': 1, 'payload': {'id': '...', 'name': 'a', 'result': [('another_list', ['hi'])]}}
-            # {'type': 'task', 'timestamp': '2024-06-23T...+00:00', 'step': 2, 'payload': {'id': '...', 'name': 'b', 'input': {'alist': ['Ex for stream_mode="debug"'], 'another_list': ['hi']}, 'triggers': ['a']}}
-            # {'type': 'task_result', 'timestamp': '2024-06-23T...+00:00', 'step': 2, 'payload': {'id': '...', 'name': 'b', 'result': [('alist', ['there'])]}}
-            ```
+            ```pycon
+            >>> from typing_extensions import Annotated, TypedDict
+            >>> from langgraph.graph import StateGraph, START
+            >>> from langchain_openai import ChatOpenAI
+            ...
+            >>> llm = ChatOpenAI(model="gpt-4o-mini")
+            ...
+            >>> class State(TypedDict):
+            ...     question: str
+            ...     answer: str
+            ...
+            >>> async def node_a(state: State):
+            ...     response = await llm.ainvoke(state["question"])
+            ...     return {"answer": response.content}
+            ...
+            >>> builder = StateGraph(State)
+            >>> builder.add_node("a", node_a)
+            >>> builder.add_edge(START, "a")
+            >>> graph = builder.compile()
 
-        Example: Using stream_mode="custom":
-            ```python
-            from langgraph.types import StreamWriter
-
-            async def node_a(state: State, writer: StreamWriter):
-                writer({"custom_data": "foo"})
-                return {"alist": ["hi"]}
-
-            builder = StateGraph(State)
-            builder.add_node("a", node_a)
-            builder.add_edge(START, "a")
-            graph = builder.compile()
-
-            async for event in graph.astream({"alist": ['Ex for stream_mode="custom"']}, stream_mode="custom"):
-                print(event)
-
-            # {'custom_data': 'foo'}
-            ```
-
-        Example: Using stream_mode="messages":
-            ```python
-            from typing_extensions import Annotated, TypedDict
-            from langgraph.graph import StateGraph, START
-            from langchain_openai import ChatOpenAI
-
-            llm = ChatOpenAI(model="gpt-4o-mini")
-
-            class State(TypedDict):
-                question: str
-                answer: str
-
-            async def node_a(state: State):
-                response = await llm.ainvoke(state["question"])
-                return {"answer": response.content}
-
-            builder = StateGraph(State)
-            builder.add_node("a", node_a)
-            builder.add_edge(START, "a")
-            graph = builder.compile()
-
-            async for event in graph.astream({"question": "What is the capital of France?"}, stream_mode="messages"):
-                print(event)
-
-            # (AIMessageChunk(content='The', additional_kwargs={}, response_metadata={}, id='...'), {'langgraph_step': 1, 'langgraph_node': 'a', 'langgraph_triggers': ['start:a'], 'langgraph_path': ('__pregel_pull', 'a'), 'langgraph_checkpoint_ns': '...', 'checkpoint_ns': '...', 'ls_provider': 'openai', 'ls_model_name': 'gpt-4o-mini', 'ls_model_type': 'chat', 'ls_temperature': 0.7})
-            # (AIMessageChunk(content=' capital', additional_kwargs={}, response_metadata={}, id='...'), {'langgraph_step': 1, 'langgraph_node': 'a', 'langgraph_triggers': ['start:a'], ...})
-            # (AIMessageChunk(content=' of', additional_kwargs={}, response_metadata={}, id='...'), {...})
-            # (AIMessageChunk(content=' France', additional_kwargs={}, response_metadata={}, id='...'), {...})
-            # (AIMessageChunk(content=' is', additional_kwargs={}, response_metadata={}, id='...'), {...})
-            # (AIMessageChunk(content=' Paris', additional_kwargs={}, response_metadata={}, id='...'), {...})
+            >>> for event in graph.stream({"question": "What is the capital of France?"}, stream_mode="messages"):
+            ...     print(event)
+            (AIMessageChunk(content='The', additional_kwargs={}, response_metadata={}, id='...'), {'langgraph_step': 1, 'langgraph_node': 'a', 'langgraph_triggers': ['start:a'], 'langgraph_path': ('__pregel_pull', 'a'), 'langgraph_checkpoint_ns': '...', 'checkpoint_ns': '...', 'ls_provider': 'openai', 'ls_model_name': 'gpt-4o-mini', 'ls_model_type': 'chat', 'ls_temperature': 0.7})
+            (AIMessageChunk(content=' capital', additional_kwargs={}, response_metadata={}, id='...'), {'langgraph_step': 1, 'langgraph_node': 'a', 'langgraph_triggers': ['start:a'], ...})
+            (AIMessageChunk(content=' of', additional_kwargs={}, response_metadata={}, id='...'), {...})
+            (AIMessageChunk(content=' France', additional_kwargs={}, response_metadata={}, id='...'), {...})
+            (AIMessageChunk(content=' is', additional_kwargs={}, response_metadata={}, id='...'), {...})
+            (AIMessageChunk(content=' Paris', additional_kwargs={}, response_metadata={}, id='...'), {...})
             ```
         """
 

libs/langgraph/langgraph/pregel/algo.py

@@ -40,7 +40,6 @@ from langgraph.constants import (
     CONFIG_KEY_CHECKPOINTER,
     CONFIG_KEY_PREVIOUS,
     CONFIG_KEY_READ,
-    CONFIG_KEY_RESUME_MAP,
     CONFIG_KEY_SCRATCHPAD,
     CONFIG_KEY_SEND,
     CONFIG_KEY_STORE,
@@ -595,8 +594,6 @@ def prepare_single_task(
                             config[CONF].get(CONFIG_KEY_SCRATCHPAD),
                             pending_writes,
                             task_id,
-                            xxh3_128_hexdigest(task_checkpoint_ns.encode()),
-                            config[CONF].get(CONFIG_KEY_RESUME_MAP),
                         ),
                     },
                 ),
@@ -707,8 +704,6 @@ def prepare_single_task(
                                 config[CONF].get(CONFIG_KEY_SCRATCHPAD),
                                 pending_writes,
                                 task_id,
-                                xxh3_128_hexdigest(task_checkpoint_ns.encode()),
-                                config[CONF].get(CONFIG_KEY_RESUME_MAP),
                             ),
                             CONFIG_KEY_PREVIOUS: checkpoint["channel_values"].get(
                                 PREVIOUS, None
@@ -835,8 +830,6 @@ def prepare_single_task(
                                     config[CONF].get(CONFIG_KEY_SCRATCHPAD),
                                     pending_writes,
                                     task_id,
-                                    xxh3_128_hexdigest(task_checkpoint_ns.encode()),
-                                    config[CONF].get(CONFIG_KEY_RESUME_MAP),
                                 ),
                                 CONFIG_KEY_PREVIOUS: checkpoint["channel_values"].get(
                                     PREVIOUS, None
@@ -888,8 +881,6 @@ def _scratchpad(
     parent_scratchpad: Optional[PregelScratchpad],
     pending_writes: list[PendingWrite],
     task_id: str,
-    namespace_hash: str,
-    resume_map: Optional[dict[str, Any]],
 ) -> PregelScratchpad:
     if len(pending_writes) > 0:
         # find global resume value
@@ -901,7 +892,6 @@ def _scratchpad(
             # None cannot be used as a resume value, because it would be difficult to
             # distinguish from missing when used over http
             null_resume_write = None
-
         # find task-specific resume value
         for w in pending_writes:
             if w[0] == task_id and w[1] == RESUME:
@@ -911,13 +901,8 @@ def _scratchpad(
                 break
         else:
             task_resume_write = []
+        # clear var
         del w
-
-        # find namespace and task-specific resume value
-        if resume_map and namespace_hash in resume_map:
-            mapped_resume_write = resume_map[namespace_hash]
-            task_resume_write.append(mapped_resume_write)
-
     else:
         null_resume_write = None
         task_resume_write = []

libs/langgraph/langgraph/pregel/io.py

@@ -1,10 +1,12 @@
 from collections import Counter
 from collections.abc import Iterator, Mapping, Sequence
 from typing import Any, Literal, Optional, TypeVar, Union
+from uuid import UUID
 
 from langchain_core.runnables.utils import AddableDict
 
 from langgraph.channels.base import BaseChannel, EmptyChannelError
+from langgraph.checkpoint.base import PendingWrite
 from langgraph.constants import (
     EMPTY_SEQ,
     ERROR,
@@ -22,6 +24,15 @@ from langgraph.pregel.log import logger
 from langgraph.types import Command, PregelExecutableTask, Send
 
 
+def is_task_id(task_id: str) -> bool:
+    """Check if a string is a valid task id."""
+    try:
+        UUID(task_id)
+    except Exception:
+        return False
+    return True
+
+
 def read_channel(
     channels: Mapping[str, BaseChannel],
     chan: str,
@@ -55,7 +66,9 @@ def read_channels(
         return values
 
 
-def map_command(cmd: Command) -> Iterator[tuple[str, str, Any]]:
+def map_command(
+    cmd: Command, pending_writes: list[PendingWrite]
+) -> Iterator[tuple[str, str, Any]]:
     """Map input chunk to a sequence of pending writes in the form (channel, value)."""
     if cmd.graph == Command.PARENT:
         raise InvalidUpdateError("There is no parent graph")
@@ -74,7 +87,15 @@ def map_command(cmd: Command) -> Iterator[tuple[str, str, Any]]:
                     f"In Command.goto, expected Send/str, got {type(send).__name__}"
                 )
     if cmd.resume is not None:
-        yield (NULL_TASK_ID, RESUME, cmd.resume)
+        if isinstance(cmd.resume, dict) and all(is_task_id(k) for k in cmd.resume):
+            for tid, resume in cmd.resume.items():
+                existing: list[Any] = next(
+                    (w[2] for w in pending_writes if w[0] == tid and w[1] == RESUME), []
+                )
+                existing.append(resume)
+                yield (tid, RESUME, existing)
+        else:
+            yield (NULL_TASK_ID, RESUME, cmd.resume)
     if cmd.update:
         for k, v in cmd._update_as_tuples():
             yield (NULL_TASK_ID, k, v)

libs/langgraph/langgraph/pregel/loop.py

@@ -47,7 +47,6 @@ from langgraph.constants import (
     CONFIG_KEY_DEDUPE_TASKS,
     CONFIG_KEY_DELEGATE,
     CONFIG_KEY_ENSURE_LATEST,
-    CONFIG_KEY_RESUME_MAP,
     CONFIG_KEY_RESUMING,
     CONFIG_KEY_SCRATCHPAD,
     CONFIG_KEY_STREAM,
@@ -113,7 +112,7 @@ from langgraph.pregel.io import (
 )
 from langgraph.pregel.manager import AsyncChannelsManager, ChannelsManager
 from langgraph.pregel.read import PregelNode
-from langgraph.pregel.utils import get_new_channel_versions, is_xxh3_128_hexdigest
+from langgraph.pregel.utils import get_new_channel_versions
 from langgraph.store.base import BaseStore
 from langgraph.types import (
     All,
@@ -650,22 +649,15 @@ class PregelLoop(LoopProtocol):
 
         # map command to writes
         if isinstance(self.input, Command):
-            if resume_is_map := (
-                (resume := self.input.resume) is not None
-                and isinstance(resume, dict)
-                and all(is_xxh3_128_hexdigest(k) for k in resume)
-            ):
-                self.config[CONF][CONFIG_KEY_RESUME_MAP] = self.input.resume
-            if resume is not None and not self.checkpointer:
+            if self.input.resume is not None and not self.checkpointer:
                 raise RuntimeError(
                     "Cannot use Command(resume=...) without checkpointer"
                 )
             writes: defaultdict[str, list[tuple[str, Any]]] = defaultdict(list)
             # group writes by task ID
-            for tid, c, v in map_command(cmd=self.input):
-                if not (c == RESUME and resume_is_map):
-                    writes[tid].append((c, v))
-            if not writes and not resume_is_map:
+            for tid, c, v in map_command(self.input, self.checkpoint_pending_writes):
+                writes[tid].append((c, v))
+            if not writes:
                 raise EmptyInputError("Received empty Command input")
             # save writes
             for tid, ws in writes.items():

libs/langgraph/langgraph/pregel/messages.py

@@ -13,7 +13,7 @@ from langchain_core.callbacks import BaseCallbackHandler
 from langchain_core.messages import BaseMessage
 from langchain_core.outputs import ChatGenerationChunk, LLMResult
 
-from langgraph.constants import NS_SEP, TAG_HIDDEN, TAG_NOSTREAM, TAG_NOSTREAM_ALT
+from langgraph.constants import NS_SEP, TAG_HIDDEN, TAG_NOSTREAM
 from langgraph.types import Command, StreamChunk
 
 try:
@@ -93,9 +93,7 @@ class StreamMessagesHandler(BaseCallbackHandler, _StreamingCallbackHandler):
         metadata: Optional[dict[str, Any]] = None,
         **kwargs: Any,
     ) -> Any:
-        if metadata and (
-            not tags or (TAG_NOSTREAM not in tags and TAG_NOSTREAM_ALT not in tags)
-        ):
+        if metadata and (not tags or TAG_NOSTREAM not in tags):
             self.metadata[run_id] = (
                 tuple(cast(str, metadata["langgraph_checkpoint_ns"]).split(NS_SEP)),
                 metadata,

libs/langgraph/langgraph/pregel/remote.py

@@ -241,7 +241,7 @@ class RemoteGraph(PregelProtocol):
         )
 
     def _create_state_snapshot(self, state: ThreadState) -> StateSnapshot:
-        tasks: list[PregelTask] = []
+        tasks = []
         for task in state["tasks"]:
             interrupts = []
             for interrupt in task["interrupts"]:
@@ -289,7 +289,6 @@ class RemoteGraph(PregelProtocol):
             if state["parent_checkpoint"]
             else None,
             tasks=tuple(tasks),
-            interrupts=tuple([i for task in tasks for i in task.interrupts]),
         )
 
     def _get_checkpoint(self, config: Optional[RunnableConfig]) -> Optional[Checkpoint]:

libs/langgraph/langgraph/pregel/utils.py

@@ -1,6 +1,5 @@
 import ast
 import inspect
-import re
 import textwrap
 from typing import Any, Callable, Optional
 
@@ -208,8 +207,3 @@ class NonLocals(ast.NodeVisitor):
                         parent = parent.value
                     if isinstance(parent, ast.Name):
                         self.loads.add(parent.id + "." + attr_expr)
-
-
-def is_xxh3_128_hexdigest(value: str) -> bool:
-    """Check if the given string matches the format of xxh3_128_hexdigest."""
-    return bool(re.fullmatch(r"[0-9a-f]{32}", value))